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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="allocation optimization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3811</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: allocation optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3811</span> Multi-Objective Optimization of Combined System Reliability and Redundancy Allocation Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijaya%20K.%20Srivastava">Vijaya K. Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Davide%20Spinello"> Davide Spinello</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents established 3<strong><sup>n</sup></strong> enumeration procedure for mixed integer optimization problems for solving multi-objective reliability and redundancy allocation problem subject to design constraints. The formulated problem is to find the optimum level of unit reliability and the number of units for each subsystem. A number of illustrative examples are provided and compared to indicate the application of the superiority of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=integer%20programming" title="integer programming">integer programming</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20programming" title=" mixed integer programming"> mixed integer programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Reliability%20Redundancy%20Allocation" title=" Reliability Redundancy Allocation"> Reliability Redundancy Allocation</a> </p> <a href="https://publications.waset.org/abstracts/107208/multi-objective-optimization-of-combined-system-reliability-and-redundancy-allocation-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">172</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3810</span> A Simulation Modeling Approach for Optimization of Storage Space Allocation in Container Terminal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gamal%20Abd%20El-Nasser%20A.%20Said">Gamal Abd El-Nasser A. Said</a>, <a href="https://publications.waset.org/abstracts/search?q=El-Sayed%20M.%20El-Horbaty"> El-Sayed M. El-Horbaty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Container handling problems at container terminals are NP-hard problems. This paper presents an approach using discrete-event simulation modeling to optimize solution for storage space allocation problem, taking into account all various interrelated container terminal handling activities. The proposed approach is applied on a real case study data of container terminal at Alexandria port. The computational results show the effectiveness of the proposed model for optimization of storage space allocation in container terminal where 54% reduction in containers handling time in port is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=container%20terminal" title="container terminal">container terminal</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-event%20simulation" title=" discrete-event simulation"> discrete-event simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=storage%20space%20allocation" title=" storage space allocation "> storage space allocation </a> </p> <a href="https://publications.waset.org/abstracts/19699/a-simulation-modeling-approach-for-optimization-of-storage-space-allocation-in-container-terminal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19699.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3809</span> Optimisation of B2C Supply Chain Resource Allocation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Firdaous%20Zair">Firdaous Zair</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoubir%20Elfelsoufi"> Zoubir Elfelsoufi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Fourka"> Mohammed Fourka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The allocation of resources is an issue that is needed on the tactical and operational strategic plan. This work considers the allocation of resources in the case of pure players, manufacturers and Click & Mortars that have launched online sales. The aim is to improve the level of customer satisfaction and maintaining the benefits of e-retailer and of its cooperators and reducing costs and risks. Our contribution is a decision support system and tool for improving the allocation of resources in logistics chains e-commerce B2C context. We first modeled the B2C chain with all operations that integrates and possible scenarios since online retailers offer a wide selection of personalized service. The personalized services that online shopping companies offer to the clients can be embodied in many aspects, such as the customizations of payment, the distribution methods, and after-sales service choices. In addition, every aspect of customized service has several modes. At that time, we analyzed the optimization problems of supply chain resource allocation in customized online shopping service mode, which is different from the supply chain resource allocation under traditional manufacturing or service circumstances. Then we realized an optimization model and algorithm for the development based on the analysis of the allocation of the B2C supply chain resources. It is a multi-objective optimization that considers the collaboration of resources in operations, time and costs but also the risks and the quality of services as well as dynamic and uncertain characters related to the request. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title="e-commerce">e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain" title=" supply chain"> supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=B2C" title=" B2C"> B2C</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation" title=" optimisation"> optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a> </p> <a href="https://publications.waset.org/abstracts/42106/optimisation-of-b2c-supply-chain-resource-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3808</span> Multi-Criteria Based Robust Markowitz Model under Box Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pulak%20Swain">Pulak Swain</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Ojha"> A. K. Ojha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of E- constraint method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title="portfolio optimization">portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=%CF%B5%20-%20constraint%20method" title=" ϵ - constraint method"> ϵ - constraint method</a>, <a href="https://publications.waset.org/abstracts/search?q=box%20uncertainty" title=" box uncertainty"> box uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title=" robust optimization"> robust optimization</a> </p> <a href="https://publications.waset.org/abstracts/118411/multi-criteria-based-robust-markowitz-model-under-box-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3807</span> Exploring SSD Suitable Allocation Schemes Incompliance with Workload Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Young%20Park">Jae Young Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Hwansu%20Jung"> Hwansu Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Whether the data has been well parallelized is an important factor in the Solid-State-Drive (SSD) performance. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20allocation" title="dynamic allocation">dynamic allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=NAND%20flash%20based%20SSD" title=" NAND flash based SSD"> NAND flash based SSD</a>, <a href="https://publications.waset.org/abstracts/search?q=SSD%20parallelism" title=" SSD parallelism"> SSD parallelism</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20allocation" title=" static allocation"> static allocation</a> </p> <a href="https://publications.waset.org/abstracts/41931/exploring-ssd-suitable-allocation-schemes-incompliance-with-workload-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41931.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3806</span> Optimal Allocation of Distributed Generation Sources for Loss Reduction and Voltage Profile Improvement by Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Zaheer%20Babar">Muhammad Zaheer Babar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Kashif"> Amer Kashif</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rizwan%20Javed"> Muhammad Rizwan Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distributed%20generation%20%28DG%29" title="Distributed generation (DG)">Distributed generation (DG)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi%20Objective%20Particle%20Swarm%20Optimization%20%28MOPSO%29" title=" Multi Objective Particle Swarm Optimization (MOPSO)"> Multi Objective Particle Swarm Optimization (MOPSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization%20%28PSO%29" title=" particle swarm optimization (PSO)"> particle swarm optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20standard%20Test%20System" title=" IEEE standard Test System"> IEEE standard Test System</a> </p> <a href="https://publications.waset.org/abstracts/42467/optimal-allocation-of-distributed-generation-sources-for-loss-reduction-and-voltage-profile-improvement-by-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3805</span> Optimization of Electric Vehicle (EV) Charging Station Allocation Based on Multiple Data - Taking Nanjing (China) as an Example</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yue%20Huang">Yue Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiheng%20Feng"> Yiheng Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the global pressure on climate and energy, many countries are vigorously promoting electric vehicles and building charging (public) charging facilities. Faced with the supply-demand gap of existing electric vehicle charging stations and unreasonable space usage in China, this paper takes the central city of Nanjing as an example, establishes a site selection model through multivariate data integration, conducts multiple linear regression SPSS analysis, gives quantitative site selection results, and provides optimization models and suggestions for charging station layout planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title="electric vehicle">electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=charging%20station" title=" charging station"> charging station</a>, <a href="https://publications.waset.org/abstracts/search?q=allocation%20optimization" title=" allocation optimization"> allocation optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20mobility" title=" urban mobility"> urban mobility</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20infrastructure" title=" urban infrastructure"> urban infrastructure</a>, <a href="https://publications.waset.org/abstracts/search?q=nanjing" title=" nanjing"> nanjing</a> </p> <a href="https://publications.waset.org/abstracts/162869/optimization-of-electric-vehicle-ev-charging-station-allocation-based-on-multiple-data-taking-nanjing-china-as-an-example" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3804</span> A Fuzzy Multiobjective Model for Bed Allocation Optimized by Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Abdulkareem%20Sultan">Jalal Abdulkareem Sultan</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulhakeem%20Luqman%20Hasan"> Abdulhakeem Luqman Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bed%20allocation%20problem" title="bed allocation problem">bed allocation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/45374/a-fuzzy-multiobjective-model-for-bed-allocation-optimized-by-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3803</span> Cloud Monitoring and Performance Optimization Ensuring High Availability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inayat%20Ur%20Rehman">Inayat Ur Rehman</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgia%20Sakellari"> Georgia Sakellari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud computing has evolved into a vital technology for businesses, offering scalability, flexibility, and cost-effectiveness. However, maintaining high availability and optimal performance in the cloud is crucial for reliable services. This paper explores the significance of cloud monitoring and performance optimization in sustaining the high availability of cloud-based systems. It discusses diverse monitoring tools, techniques, and best practices for continually assessing the health and performance of cloud resources. The paper also delves into performance optimization strategies, including resource allocation, load balancing, and auto-scaling, to ensure efficient resource utilization and responsiveness. Addressing potential challenges in cloud monitoring and optimization, the paper offers insights into data security and privacy considerations. Through this thorough analysis, the paper aims to underscore the importance of cloud monitoring and performance optimization for ensuring a seamless and highly available cloud computing environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20monitoring" title=" cloud monitoring"> cloud monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20optimization" title=" performance optimization"> performance optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20availability" title=" high availability"> high availability</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-scaling" title=" auto-scaling"> auto-scaling</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20security" title=" data security"> data security</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20privacy" title=" data privacy"> data privacy</a> </p> <a href="https://publications.waset.org/abstracts/179118/cloud-monitoring-and-performance-optimization-ensuring-high-availability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179118.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3802</span> Improved Multi–Objective Firefly Algorithms to Find Optimal Golomb Ruler Sequences for Optimal Golomb Ruler Channel Allocation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shonak%20Bansal">Shonak Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Prince%20Jain"> Prince Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Kumar%20Singh"> Arun Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Neena%20Gupta"> Neena Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently nature–inspired algorithms have widespread use throughout the tough and time consuming multi–objective scientific and engineering design optimization problems. In this paper, we present extended forms of firefly algorithm to find optimal Golomb ruler (OGR) sequences. The OGRs have their one of the major application as unequally spaced channel–allocation algorithm in optical wavelength division multiplexing (WDM) systems in order to minimize the adverse four–wave mixing (FWM) crosstalk effect. The simulation results conclude that the proposed optimization algorithm has superior performance compared to the existing conventional computing and nature–inspired optimization algorithms to find OGRs in terms of ruler length, total optical channel bandwidth and computation time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20allocation" title="channel allocation">channel allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20computing" title=" conventional computing"> conventional computing</a>, <a href="https://publications.waset.org/abstracts/search?q=four%E2%80%93wave%20mixing" title=" four–wave mixing"> four–wave mixing</a>, <a href="https://publications.waset.org/abstracts/search?q=nature%E2%80%93inspired%20algorithm" title=" nature–inspired algorithm"> nature–inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20Golomb%20ruler" title=" optimal Golomb ruler"> optimal Golomb ruler</a>, <a href="https://publications.waset.org/abstracts/search?q=l%C3%A9vy%20flight%20distribution" title=" lévy flight distribution"> lévy flight distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20multi%E2%80%93objective%20firefly%20algorithms" title=" improved multi–objective firefly algorithms"> improved multi–objective firefly algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimal" title=" Pareto optimal"> Pareto optimal</a> </p> <a href="https://publications.waset.org/abstracts/46108/improved-multi-objective-firefly-algorithms-to-find-optimal-golomb-ruler-sequences-for-optimal-golomb-ruler-channel-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3801</span> Strategic Asset Allocation Optimization: Enhancing Portfolio Performance Through PCA-Driven Multi-Objective Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghita%20Benayad">Ghita Benayad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asset%20allocation" title="asset allocation">asset allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=principle%20component%20analysis" title=" principle component analysis"> principle component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20modelling" title=" multi-objective modelling"> multi-objective modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20market" title=" financial market"> financial market</a> </p> <a href="https://publications.waset.org/abstracts/183175/strategic-asset-allocation-optimization-enhancing-portfolio-performance-through-pca-driven-multi-objective-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183175.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3800</span> Approaching the Spatial Multi-Objective Land Use Planning Problems at Mountain Areas by a Hybrid Meta-Heuristic Optimization Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Tolidis">Konstantinos Tolidis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mountains are amongst the most fragile environments in the world. The world’s mountain areas cover 24% of the Earth’s land surface and are home to 12% of the global population. A further 14% of the global population is estimated to live in the vicinity of their surrounding areas. As urbanization continues to increase in the world, the mountains are also key centers for recreation and tourism; their attraction is often heightened by their remarkably high levels of biodiversity. Due to the fact that the features in mountain areas vary spatially (development degree, human geography, socio-economic reality, relations of dependency and interaction with other areas-regions), the spatial planning on these areas consists of a crucial process for preserving the natural, cultural and human environment and consists of one of the major processes of an integrated spatial policy. This research has been focused on the spatial decision problem of land use allocation optimization which is an ordinary planning problem on the mountain areas. It is a matter of fact that such decisions must be made not only on what to do, how much to do, but also on where to do, adding a whole extra class of decision variables to the problem when combined with the consideration of spatial optimization. The utility of optimization as a normative tool for spatial problem is widely recognized. However, it is very difficult for planners to quantify the weights of the objectives especially when these are related to mountain areas. Furthermore, the land use allocation optimization problems at mountain areas must be addressed not only by taking into account the general development objectives but also the spatial objectives (e.g. compactness, compatibility and accessibility, etc). Therefore, the main research’s objective was to approach the land use allocation problem by utilizing a hybrid meta-heuristic optimization technique tailored to the mountain areas’ spatial characteristics. The results indicates that the proposed methodological approach is very promising and useful for both generating land use alternatives for further consideration in land use allocation decision-making and supporting spatial management plans at mountain areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20land%20use%20allocation" title="multiobjective land use allocation">multiobjective land use allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=mountain%20areas" title=" mountain areas"> mountain areas</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20planning" title=" spatial planning"> spatial planning</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20decision%20making" title=" spatial decision making"> spatial decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20methods" title=" meta-heuristic methods"> meta-heuristic methods</a> </p> <a href="https://publications.waset.org/abstracts/72240/approaching-the-spatial-multi-objective-land-use-planning-problems-at-mountain-areas-by-a-hybrid-meta-heuristic-optimization-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3799</span> A Cognitive Approach to the Optimization of Power Distribution across an Educational Campus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinmoy%20Majumder">Mrinmoy Majumder</a>, <a href="https://publications.waset.org/abstracts/search?q=Apu%20Kumar%20Saha"> Apu Kumar Saha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ever-increasing human population and its demand for energy is placing stress upon conventional energy sources; and as demand for power continues to outstrip supply, the need to optimize energy distribution and utilization is emerging as an important focus for various stakeholders. The distribution of available energy must be achieved in such a way that the needs of the consumer are satisfied. However, if the availability of resources is not sufficient to satisfy consumer demand, it is necessary to find a method to select consumers based on factors such as their socio-economic or environmental impacts. Weighting consumer types in this way can help separate them based on their relative importance, and cognitive optimization of the allocation process can then be carried out so that, even on days of particularly scarce supply, the socio-economic impacts of not satisfying the needs of consumers can be minimized. In this context, the present study utilized fuzzy logic to assign weightage to different types of consumers based at an educational campus in India, and then established optimal allocation by applying the non-linear mapping capability of neuro-genetic algorithms. The outputs of the algorithms were compared with similar outputs from particle swarm optimization and differential evolution algorithms. The results of the study demonstrate an option for the optimal utilization of available energy based on the socio-economic importance of consumers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20allocation" title="power allocation">power allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20problem" title=" optimization problem"> optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20and%20ecological%20engineering" title=" environmental and ecological engineering"> environmental and ecological engineering</a> </p> <a href="https://publications.waset.org/abstracts/19462/a-cognitive-approach-to-the-optimization-of-power-distribution-across-an-educational-campus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">479</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3798</span> Mobile Agents-Based Framework for Dynamic Resource Allocation in Cloud Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safia%20Rabaaoui">Safia Rabaaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=H%C3%A9la%20Hachicha"> Héla Hachicha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ezzeddine%20Zagrouba"> Ezzeddine Zagrouba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, cloud computing is becoming the more popular technology to various companies and consumers, which benefit from its increased efficiency, cost optimization, data security, unlimited storage capacity, etc. One of the biggest challenges of cloud computing is resource allocation. Its efficiency directly influences the performance of the whole cloud environment. Finding an effective method to address these critical issues and increase cloud performance was necessary. This paper proposes a mobile agents-based framework for dynamic resource allocation in cloud computing to minimize both the cost of using virtual machines and the makespan. Furthermore, its impact on the best response time and power consumption has been studied. The simulation showed that our method gave better results than here. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20resource%20allocation" title=" dynamic resource allocation"> dynamic resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=cost" title=" cost"> cost</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a> </p> <a href="https://publications.waset.org/abstracts/168460/mobile-agents-based-framework-for-dynamic-resource-allocation-in-cloud-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3797</span> Multi-Period Portfolio Optimization Using Predictive Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chyng%20Wen%20Tee"> Chyng Wen Tee</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaofei%20Xu"> Xiaofei Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-period%20portfolio%20optimization" title="multi-period portfolio optimization">multi-period portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=look-ahead%20constrained%20optimization" title=" look-ahead constrained optimization"> look-ahead constrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20decision%20making" title=" sequential decision making"> sequential decision making</a> </p> <a href="https://publications.waset.org/abstracts/186542/multi-period-portfolio-optimization-using-predictive-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186542.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">48</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3796</span> Discretization of Cuckoo Optimization Algorithm for Solving Quadratic Assignment Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Kazemi">Elham Kazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quadratic%20Assignment%20Problem%20%28QAP%29" title="Quadratic Assignment Problem (QAP)">Quadratic Assignment Problem (QAP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Cuckoo%20Optimization%20Algorithm%20%28DCOA%29" title=" Discrete Cuckoo Optimization Algorithm (DCOA)"> Discrete Cuckoo Optimization Algorithm (DCOA)</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20algorithms" title=" meta-heuristic algorithms"> meta-heuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/25249/discretization-of-cuckoo-optimization-algorithm-for-solving-quadratic-assignment-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25249.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3795</span> Loss Allocation in Radial Distribution Networks for Loads of Composite Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Banerjee">Sumit Banerjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandan%20Kumar%20Chanda"> Chandan Kumar Chanda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents allocation of active power losses and energy losses to consumers connected to radial distribution networks in a deregulated environment for loads of composite types. A detailed comparison among four algorithms, namely quadratic loss allocation, proportional loss allocation, pro rata loss allocation and exact loss allocation methods are presented. Quadratic and proportional loss allocations are based on identifying the active and reactive components of current in each branch and the losses are allocated to each consumer, pro rata loss allocation method is based on the load demand of each consumer and exact loss allocation method is based on the actual contribution of active power loss by each consumer. The effectiveness of the proposed comparison among four algorithms for composite load is demonstrated through an example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20type" title="composite type">composite type</a>, <a href="https://publications.waset.org/abstracts/search?q=deregulation" title=" deregulation"> deregulation</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20allocation" title=" loss allocation"> loss allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20distribution%20networks" title=" radial distribution networks"> radial distribution networks</a> </p> <a href="https://publications.waset.org/abstracts/42700/loss-allocation-in-radial-distribution-networks-for-loads-of-composite-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3794</span> Reliability and Cost Focused Optimization Approach for a Communication Satellite Payload Redundancy Allocation Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Nefes">Mehmet Nefes</a>, <a href="https://publications.waset.org/abstracts/search?q=Selman%20Demirel"> Selman Demirel</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20H.%20Ertok"> Hasan H. Ertok</a>, <a href="https://publications.waset.org/abstracts/search?q=Cenk%20Sen"> Cenk Sen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A typical reliability engineering problem regarding communication satellites has been considered to determine redundancy allocation scheme of power amplifiers within payload transponder module, whose dominant function is to amplify power levels of the received signals from the Earth, through maximizing reliability against mass, power, and other technical limitations. Adding each redundant power amplifier component increases not only reliability but also hardware, testing, and launch cost of a satellite. This study investigates a multi-objective approach used in order to solve Redundancy Allocation Problem (RAP) for a communication satellite payload transponder, focusing on design cost due to redundancy and reliability factors. The main purpose is to find the optimum power amplifier redundancy configuration satisfying reliability and capacity thresholds simultaneously instead of analyzing respectively or independently. A mathematical model and calculation approach are instituted including objective function definitions, and then, the problem is solved analytically with different input parameters in MATLAB environment. Example results showed that payload capacity and failure rate of power amplifiers have remarkable effects on the solution and also processing time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20satellite%20payload" title="communication satellite payload">communication satellite payload</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=redundancy%20allocation%20problem" title=" redundancy allocation problem"> redundancy allocation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=transponder" title=" transponder"> transponder</a> </p> <a href="https://publications.waset.org/abstracts/86236/reliability-and-cost-focused-optimization-approach-for-a-communication-satellite-payload-redundancy-allocation-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86236.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3793</span> Synchronized Vehicle Routing for Equitable Resource Allocation in Food Banks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabiatu%20Bonku">Rabiatu Bonku</a>, <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Alkaabneh"> Faisal Alkaabneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inspired by a food banks distribution operation for non-profit organization, we study a variant synchronized vehicle routing problem for equitable resource allocation. This research paper introduces a Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of efficiently distributing vital resources, particularly for food banks serving vulnerable populations in urban areas. Our optimization approach places a strong emphasis on social equity, ensuring a fair allocation of food to partner agencies while minimizing wastage. The primary objective is to enhance operational efficiency while guaranteeing fair distribution and timely deliveries to prevent food spoilage. Furthermore, we assess four distinct models that consider various aspects of sustainability, including social and economic factors. We conduct a comprehensive numerical analysis using real-world data to gain insights into the trade-offs that arise, while also demonstrating the models’ performance in terms of fairness, effectiveness, and the percentage of food waste. This provides valuable managerial insights for food bank managers. We show that our proposed approach makes a significant contribution to the field of logistics optimization and social responsibility, offering valuable insights for improving the operations of food banks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20banks" title="food banks">food banks</a>, <a href="https://publications.waset.org/abstracts/search?q=humanitarian%20logistics" title=" humanitarian logistics"> humanitarian logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=equitable%20resource%20allocation" title=" equitable resource allocation"> equitable resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronized%20vehicle%20routing" title=" synchronized vehicle routing"> synchronized vehicle routing</a> </p> <a href="https://publications.waset.org/abstracts/174101/synchronized-vehicle-routing-for-equitable-resource-allocation-in-food-banks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3792</span> Understanding the Nature of Capital Allocation Problem in Corporate Finance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meltem%20Gurunlu">Meltem Gurunlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the central problems in corporate finance is the allocation of funds. This usually takes two forms: allocation of funds across firms in an economy or allocation of funds across projects or business units within a firm. The first one is typically related to the external markets (the bond market, the stock market, banks and finance companies) whereas the second form of the capital allocation is related to the internal capital markets in which corporate headquarters allocate capital to their business units. (within-group transfers, within-group credit markets, and within-group equity market). The main aim of this study is to investigate the nature of capital allocation dynamics by comparing the relevant studies carried out on external and internal capital markets with paying special significance to the business groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internal%20capital%20markets" title="internal capital markets">internal capital markets</a>, <a href="https://publications.waset.org/abstracts/search?q=external%20capital%20markets" title=" external capital markets"> external capital markets</a>, <a href="https://publications.waset.org/abstracts/search?q=capital%20structure" title=" capital structure"> capital structure</a>, <a href="https://publications.waset.org/abstracts/search?q=capital%20allocation" title=" capital allocation"> capital allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20groups" title=" business groups"> business groups</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20finance" title=" corporate finance"> corporate finance</a> </p> <a href="https://publications.waset.org/abstracts/89423/understanding-the-nature-of-capital-allocation-problem-in-corporate-finance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89423.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3791</span> A New Reliability Allocation Method Based on Fuzzy Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Li">Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuanri%20Li"> Chuanri Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Li"> Tao Li </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reliability allocation is quite important during early design and development stages for a system to apportion its specified reliability goal to subsystems. This paper improves the reliability fuzzy allocation method and gives concrete processes on determining the factor set, the factor weight set, judgment set, and multi-grade fuzzy comprehensive evaluation. To determine the weight of factor set, the modified trapezoidal numbers are proposed to reduce errors caused by subjective factors. To decrease the fuzziness in the fuzzy division, an approximation method based on linear programming is employed. To compute the explicit values of fuzzy numbers, centroid method of defuzzification is considered. An example is provided to illustrate the application of the proposed reliability allocation method based on fuzzy arithmetic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability%20allocation" title="reliability allocation">reliability allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20arithmetic" title=" fuzzy arithmetic"> fuzzy arithmetic</a>, <a href="https://publications.waset.org/abstracts/search?q=allocation%20weight" title=" allocation weight"> allocation weight</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming "> linear programming </a> </p> <a href="https://publications.waset.org/abstracts/27101/a-new-reliability-allocation-method-based-on-fuzzy-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3790</span> Artificial Intelligent-Based Approaches for Task ‎Offloading, ‎Resource ‎Allocation and Service ‎Placement of ‎Internet of Things ‎Applications: State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Z.%20Cherhabil">Fatima Z. Cherhabil</a>, <a href="https://publications.waset.org/abstracts/search?q=Mammar%20Sedrati"> Mammar Sedrati</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonia-Sabrina%20Bendib%E2%80%8E"> Sonia-Sabrina Bendib‎</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20edge%20computing" title="mobile edge computing">mobile edge computing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20%E2%80%8Eintelligence%20%E2%80%8Eapproaches" title=" artificial ‎intelligence ‎approaches"> artificial ‎intelligence ‎approaches</a>, <a href="https://publications.waset.org/abstracts/search?q=task%20offloading" title=" task offloading"> task offloading</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8E%20service%20placement" title="‎ service placement">‎ service placement</a> </p> <a href="https://publications.waset.org/abstracts/150855/artificial-intelligent-based-approaches-for-task-offloading-resource-allocation-and-service-placement-of-internet-of-things-applications-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150855.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">115</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3789</span> A Novel Meta-Heuristic Algorithm Based on Cloud Theory for Redundancy Allocation Problem under Realistic Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Mousavi">H. Mousavi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sharifi"> M. Sharifi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Pourvaziri"> H. Pourvaziri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=redundancy%20allocation%20problem" title="redundancy allocation problem">redundancy allocation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20theory" title=" cloud theory"> cloud theory</a>, <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo%20simulation" title=" monte carlo simulation"> monte carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/33681/a-novel-meta-heuristic-algorithm-based-on-cloud-theory-for-redundancy-allocation-problem-under-realistic-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3788</span> A Location-Allocation-Routing Model for a Home Health Care Supply Chain Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Mohammad%20Fathollahi%20Fard">Amir Mohammad Fathollahi Fard</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Hajiaghaei-Keshteli"> Mostafa Hajiaghaei-Keshteli</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mahdi%20Paydar"> Mohammad Mahdi Paydar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With increasing life expectancy in developed countries, the role of home care services is highlighted by both academia and industrial contributors in Home Health Care Supply Chain (HHCSC) companies. The main decisions in such supply chain systems are the location of pharmacies, the allocation of patients to these pharmacies and also the routing and scheduling decisions of nurses to visit their patients. In this study, for the first time, an integrated model is proposed to consist of all preliminary and necessary decisions in these companies, namely, location-allocation-routing model. This model is a type of NP-hard one. Therefore, an Imperialist Competitive Algorithm (ICA) is utilized to solve the model, especially in large sizes. Results confirm the efficiency of the developed model for HHCSC companies as well as the performance of employed ICA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=home%20health%20care%20supply%20chain" title="home health care supply chain">home health care supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=location-allocation-routing%20problem" title=" location-allocation-routing problem"> location-allocation-routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competitive%20algorithm" title=" imperialist competitive algorithm"> imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/82335/a-location-allocation-routing-model-for-a-home-health-care-supply-chain-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82335.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">397</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3787</span> Spectrum Allocation in Cognitive Radio Using Monarch Butterfly Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Avantika%20Vats">Avantika Vats</a>, <a href="https://publications.waset.org/abstracts/search?q=Kushal%20Thakur"> Kushal Thakur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper displays the point at issue, improvement, and utilization of a Monarch Butterfly Optimization (MBO) rather than a Genetic Algorithm (GA) in cognitive radio for the channel portion. This approach offers a satisfactory approach to get the accessible range of both the users, i.e., primary users (PUs) and secondary users (SUs). The proposed enhancement procedure depends on a nature-inspired metaheuristic algorithm. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. Southern Canada and the northern USA (land 1), and Mexico (Land 2). The positions of the monarch butterflies are modernizing in two ways. At first, the offsprings are generated (position updating) by the migration operator and can be adjusted by the migration ratio. It is trailed by tuning the positions for different butterﬂies by the methods for the butterfly adjusting operator. To keep the population unaltered and minimize fitness evaluations, the aggregate of the recently produced butterﬂies in these two ways stays equivalent to the first population. The outcomes obviously display the capacity of the MBO technique towards ﬁnding the upgraded work values on issues regarding the genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20allocation" title=" channel allocation"> channel allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=monarch%20butterfly%20optimization" title=" monarch butterfly optimization"> monarch butterfly optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary" title=" evolutionary"> evolutionary</a>, <a href="https://publications.waset.org/abstracts/search?q=computation" title=" computation"> computation</a> </p> <a href="https://publications.waset.org/abstracts/181417/spectrum-allocation-in-cognitive-radio-using-monarch-butterfly-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3786</span> An Improved VM Allocation Algorithm by Utilizing Combined Resource Allocation Mechanism and Released Resources in Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Habibul%20Ansary">Md Habibul Ansary</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandan%20Garai"> Chandan Garai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjan%20Dasgupta"> Ranjan Dasgupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Utilization of resources is always a great challenge for any allocation problem, particularly when resource availability is dynamic in nature. In this work VM allocation mechanism has been augmented by providing resources in a combined manner. This approach has some inherent advantages in terms of reduction of wait state for the pending jobs of some users and better utilization of unused resources from the service providers’ point of view. Moreover the algorithm takes care of released resources from the finished jobs as soon as those become available. The proposed algorithm has been explained by suitable example to make the work complete. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bid%20ratio" title="Bid ratio">Bid ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20service" title=" cloud service"> cloud service</a>, <a href="https://publications.waset.org/abstracts/search?q=virtualization" title=" virtualization"> virtualization</a>, <a href="https://publications.waset.org/abstracts/search?q=VM%20allocation%20problem" title=" VM allocation problem"> VM allocation problem</a> </p> <a href="https://publications.waset.org/abstracts/34024/an-improved-vm-allocation-algorithm-by-utilizing-combined-resource-allocation-mechanism-and-released-resources-in-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34024.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3785</span> Cloud Monitoring and Performance Optimization Ensuring High Availability and Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inayat%20Ur%20Rehman">Inayat Ur Rehman</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgia%20Sakellari"> Georgia Sakellari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud computing has evolved into a vital technology for businesses, offering scalability, flexibility, and cost-effectiveness. However, maintaining high availability and optimal performance in the cloud is crucial for reliable services. This paper explores the significance of cloud monitoring and performance optimization in sustaining the high availability of cloud-based systems. It discusses diverse monitoring tools, techniques, and best practices for continually assessing the health and performance of cloud resources. The paper also delves into performance optimization strategies, including resource allocation, load balancing, and auto-scaling, to ensure efficient resource utilization and responsiveness. Addressing potential challenges in cloud monitoring and optimization, the paper offers insights into data security and privacy considerations. Through this thorough analysis, the paper aims to underscore the importance of cloud monitoring and performance optimization for ensuring a seamless and highly available cloud computing environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20monitoring" title=" cloud monitoring"> cloud monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20optimization" title=" performance optimization"> performance optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20availability" title=" high availability"> high availability</a> </p> <a href="https://publications.waset.org/abstracts/182069/cloud-monitoring-and-performance-optimization-ensuring-high-availability-and-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3784</span> A Cloud-Based Spectrum Database Approach for Licensed Shared Spectrum Access</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Abd%20El%20Megeed">Hazem Abd El Megeed</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El-Refaay"> Mohamed El-Refaay</a>, <a href="https://publications.waset.org/abstracts/search?q=Norhan%20Magdi%20Osman"> Norhan Magdi Osman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spectrum scarcity is a challenging obstacle in wireless communications systems. It hinders the introduction of innovative wireless services and technologies that require larger bandwidth comparing to legacy technologies. In addition, the current worldwide allocation of radio spectrum bands is already congested and can not afford additional squeezing or optimization to accommodate new wireless technologies. This challenge is a result of accumulative contributions from different factors that will be discussed later in this paper. One of these factors is the radio spectrum allocation policy governed by national regulatory authorities nowadays. The framework for this policy allocates specified portion of radio spectrum to a particular wireless service provider on exclusive utilization basis. This allocation is executed according to technical specification determined by the standard bodies of each Radio Access Technology (RAT). Dynamic access of spectrum is a framework for flexible utilization of radio spectrum resources. In this framework there is no exclusive allocation of radio spectrum and even the public safety agencies can share their spectrum bands according to a governing policy and service level agreements. In this paper, we explore different methods for accessing the spectrum dynamically and its associated implementation challenges. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=licensed%20shared%20access" title="licensed shared access">licensed shared access</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title=" cognitive radio"> cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sharing" title=" spectrum sharing"> spectrum sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20congestion" title=" spectrum congestion"> spectrum congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20spectrum%20access" title=" dynamic spectrum access"> dynamic spectrum access</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20database" title=" spectrum database"> spectrum database</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20trading" title=" spectrum trading"> spectrum trading</a>, <a href="https://publications.waset.org/abstracts/search?q=reconfigurable%20radio%20systems" title=" reconfigurable radio systems"> reconfigurable radio systems</a>, <a href="https://publications.waset.org/abstracts/search?q=opportunistic%20spectrum%20allocation%20%28OSA%29" title=" opportunistic spectrum allocation (OSA)"> opportunistic spectrum allocation (OSA)</a> </p> <a href="https://publications.waset.org/abstracts/5572/a-cloud-based-spectrum-database-approach-for-licensed-shared-spectrum-access" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5572.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3783</span> A New Method to Winner Determination for Economic Resource Allocation in Cloud Computing Systems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Behrouzian%20Nejad">Ebrahim Behrouzian Nejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rezvan%20Alipoor%20Sabzevari"> Rezvan Alipoor Sabzevari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud computing systems are large-scale distributed systems, so that they focus more on large scale resource sharing, cooperation of several organizations and their use in new applications. One of the main challenges in this realm is resource allocation. There are many different ways to resource allocation in cloud computing. One of the common methods to resource allocation are economic methods. Among these methods, the auction-based method has greater prominence compared with Fixed-Price method. The double combinatorial auction is one of the proper ways of resource allocation in cloud computing. This method includes two phases: winner determination and resource allocation. In this paper a new method has been presented to determine winner in double combinatorial auction-based resource allocation using Imperialist Competitive Algorithm (ICA). The experimental results show that in our new proposed the number of winner users is higher than genetic algorithm. On other hand, in proposed algorithm, the number of winner providers is higher in genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20auction" title=" double auction"> double auction</a>, <a href="https://publications.waset.org/abstracts/search?q=winner%20determination" title=" winner determination "> winner determination </a> </p> <a href="https://publications.waset.org/abstracts/35920/a-new-method-to-winner-determination-for-economic-resource-allocation-in-cloud-computing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35920.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3782</span> A QoE-driven Cross-layer Resource Allocation Scheme for High Traffic Service over Open Wireless Network Downlink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liya%20Shan">Liya Shan</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing%20Liao"> Qing Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinyue%20Hu"> Qinyue Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shantao%20Jiang"> Shantao Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Wang"> Tao Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a Quality of Experience (QoE)-driven cross-layer resource allocation scheme for high traffic service over Open Wireless Network (OWN) downlink is proposed, and the related problem about the users in the whole cell including the users in overlap region of different cells has been solved.A method, in which assess models of the BestEffort service and the no-reference assess algorithm for video service are adopted, to calculate the Mean Opinion Score (MOS) value for high traffic service has been introduced. The cross-layer architecture considers the parameters in application layer, media access control layer and physical layer jointly. Based on this architecture and the MOS value, the Binary Constrained Particle Swarm Optimization (B_CPSO) algorithm is used to solve the cross-layer resource allocation problem. In addition,simulationresults show that the proposed scheme significantly outperforms other schemes in terms of maximizing average users’ MOS value for the whole system as well as maintaining fairness among users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20traffic%20service" title="high traffic service">high traffic service</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-layer%20resource%20allocation" title=" cross-layer resource allocation"> cross-layer resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=QoE" title=" QoE"> QoE</a>, <a href="https://publications.waset.org/abstracts/search?q=B_CPSO" title=" B_CPSO"> B_CPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=OWN" title=" OWN"> OWN</a> </p> <a href="https://publications.waset.org/abstracts/20749/a-qoe-driven-cross-layer-resource-allocation-scheme-for-high-traffic-service-over-open-wireless-network-downlink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=allocation%20optimization&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=allocation%20optimization&page=3">3</a></li> <li class="page-item"><a class="page-link" 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