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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="heuristic search"> <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> 2068</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: heuristic search</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2068</span> Tabu Search Algorithm for Ship Routing and Scheduling Problem with Time Window</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Moh.%20Alhamad">Khaled Moh. Alhamad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a tabu search heuristic for a ship routing and scheduling problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristic" title="heuristic">heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title=" tabu search"> tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation" title=" transportation"> transportation</a> </p> <a href="https://publications.waset.org/abstracts/43484/tabu-search-algorithm-for-ship-routing-and-scheduling-problem-with-time-window" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43484.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">506</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">2067</span> A Hybrid Heuristic for the Team Orienteering Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Bouchakhchoukha">Adel Bouchakhchoukha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hakim%20Akeb"> Hakim Akeb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=team%20orienteering%20problem" title="team orienteering problem">team orienteering problem</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing" title=" vehicle routing"> vehicle routing</a>, <a href="https://publications.waset.org/abstracts/search?q=beam%20search" title=" beam search"> beam search</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a> </p> <a href="https://publications.waset.org/abstracts/12948/a-hybrid-heuristic-for-the-team-orienteering-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12948.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">418</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">2066</span> Penguins Search Optimization Algorithm for Chaotic Synchronization System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofiane%20Bououden">Sofiane Bououden</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilyes%20Boulkaibet"> Ilyes Boulkaibet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of security of the information signal, the meta-heuristic Penguins Search Optimization Algorithm (PeSOA) is applied to synchronize chaotic encryption communications in the case of sensitive dependence on initial conditions in chaotic generator oscillator. The objective of this paper is the use of the PeSOA algorithm to exploring search space with random and iterative processes for synchronization of symmetric keys in both transmission and reception. Simulation results show the effectiveness of the PeSOA algorithm in generating symmetric keys of the encryption process and synchronizing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic" title="meta-heuristic">meta-heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=PeSOA" title=" PeSOA"> PeSOA</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20systems" title=" chaotic systems"> chaotic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronization%20optimization" title=" synchronization optimization"> synchronization optimization</a> </p> <a href="https://publications.waset.org/abstracts/141318/penguins-search-optimization-algorithm-for-chaotic-synchronization-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141318.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">2065</span> Survey Paper on Graph Coloring Problem and Its Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Chharia">Prateek Chharia</a>, <a href="https://publications.waset.org/abstracts/search?q=Biswa%20Bhusan%20Ghosh"> Biswa Bhusan Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph coloring is one of the prominent concepts in graph coloring. It can be defined as a coloring of the various regions of the graph such that all the constraints are fulfilled. In this paper various graphs coloring approaches like greedy coloring, Heuristic search for maximum independent set and graph coloring using edge table is described. Graph coloring can be used in various real time applications like student time tabling generation, Sudoku as a graph coloring problem, GSM phone network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20coloring" title="graph coloring">graph coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20coloring" title=" greedy coloring"> greedy coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20search" title=" heuristic search"> heuristic search</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20table" title=" edge table"> edge table</a>, <a href="https://publications.waset.org/abstracts/search?q=sudoku%20as%20a%20graph%20coloring%20problem" title=" sudoku as a graph coloring problem"> sudoku as a graph coloring problem</a> </p> <a href="https://publications.waset.org/abstracts/19691/survey-paper-on-graph-coloring-problem-and-its-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19691.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">539</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">2064</span> A Coordinate-Based Heuristic Route Search Algorithm for Delivery Truck Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Tarek">Ahmed Tarek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alveed"> Ahmed Alveed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinate-based%20optimal%20routing" title="coordinate-based optimal routing">coordinate-based optimal routing</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamiltonian%20Circuit" title=" Hamiltonian Circuit"> Hamiltonian Circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing%20problem" title=" vehicle routing problem"> vehicle routing problem</a> </p> <a href="https://publications.waset.org/abstracts/136218/a-coordinate-based-heuristic-route-search-algorithm-for-delivery-truck-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136218.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">147</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">2063</span> Heuristic to Generate Random X-Monotone Polygons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamaljit%20Pati">Kamaljit Pati</a>, <a href="https://publications.waset.org/abstracts/search?q=Manas%20Kumar%20Mohanty"> Manas Kumar Mohanty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjib%20Sadhu"> Sanjib Sadhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A heuristic has been designed to generate a random simple monotone polygon from a given set of ‘n’ points lying on a 2-Dimensional plane. Our heuristic generates a random monotone polygon in O(n) time after O(nℓogn) preprocessing time which is improved over the previous work where a random monotone polygon is produced in the same O(n) time but the preprocessing time is O(k) for n < k < n2. However, our heuristic does not generate all possible random polygons with uniform probability. The space complexity of our proposed heuristic is O(n). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sorting" title="sorting">sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=monotone%20polygon" title=" monotone polygon"> monotone polygon</a>, <a href="https://publications.waset.org/abstracts/search?q=visibility" title=" visibility"> visibility</a>, <a href="https://publications.waset.org/abstracts/search?q=chain" title=" chain"> chain</a> </p> <a href="https://publications.waset.org/abstracts/19252/heuristic-to-generate-random-x-monotone-polygons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19252.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">427</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">2062</span> A Combined Meta-Heuristic with Hyper-Heuristic Approach to Single Machine Production Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20E.%20Nugraheni">C. E. Nugraheni</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Abednego"> L. Abednego</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as meta-heuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyper-heuristics" title="hyper-heuristics">hyper-heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20scheduling" title=" production scheduling"> production scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic" title=" meta-heuristic"> meta-heuristic</a> </p> <a href="https://publications.waset.org/abstracts/12732/a-combined-meta-heuristic-with-hyper-heuristic-approach-to-single-machine-production-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12732.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">381</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">2061</span> Efficiency of Robust Heuristic Gradient Based Enumerative and Tunneling Algorithms for Constrained Integer Programming Problems</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 performance of two robust gradient-based heuristic optimization procedures based on 3<sup>n</sup> enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3<sup>n</sup> integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constrained%20integer%20problems" title="constrained integer problems">constrained integer problems</a>, <a href="https://publications.waset.org/abstracts/search?q=enumerative%20search%20algorithm" title=" enumerative search algorithm"> enumerative search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Heuristic%20algorithm" title=" Heuristic algorithm"> Heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Tunneling%20algorithm" title=" Tunneling algorithm"> Tunneling algorithm</a> </p> <a href="https://publications.waset.org/abstracts/58272/efficiency-of-robust-heuristic-gradient-based-enumerative-and-tunneling-algorithms-for-constrained-integer-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58272.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">2060</span> Routing Medical Images with Tabu Search and Simulated Annealing: A Study on Quality of Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mej%C3%ADa%20M.%20Paula">Mejía M. Paula</a>, <a href="https://publications.waset.org/abstracts/search?q=Ram%C3%ADrez%20L.%20Leonardo"> Ramírez L. Leonardo</a>, <a href="https://publications.waset.org/abstracts/search?q=Puerta%20A.%20Gabriel"> Puerta A. Gabriel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In telemedicine, the image repository service is important to increase the accuracy of diagnostic support of medical personnel. This study makes comparison between two routing algorithms regarding the quality of service (QoS), to be able to analyze the optimal performance at the time of loading and/or downloading of medical images. This study focused on comparing the performance of Tabu Search with other heuristic and metaheuristic algorithms that improve QoS in telemedicine services in Colombia. For this, Tabu Search and Simulated Annealing heuristic algorithms are chosen for their high usability in this type of applications; the QoS is measured taking into account the following metrics: Delay, Throughput, Jitter and Latency. In addition, routing tests were carried out on ten images in digital image and communication in medicine (DICOM) format of 40 MB. These tests were carried out for ten minutes with different traffic conditions, reaching a total of 25 tests, from a server of Universidad Militar Nueva Granada (UMNG) in Bogotá-Colombia to a remote user in Universidad de Santiago de Chile (USACH) - Chile. The results show that Tabu search presents a better QoS performance compared to Simulated Annealing, managing to optimize the routing of medical images, a basic requirement to offer diagnostic images services in telemedicine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title="medical image">medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=QoS" title=" QoS"> QoS</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=Tabu%20search" title=" Tabu search"> Tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title=" telemedicine"> telemedicine</a> </p> <a href="https://publications.waset.org/abstracts/99705/routing-medical-images-with-tabu-search-and-simulated-annealing-a-study-on-quality-of-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99705.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">219</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">2059</span> Heuristic Search Algorithm (HSA) for Enhancing the Lifetime of Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tripatjot%20S.%20Panag">Tripatjot S. Panag</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20S.%20Dhillon"> J. S. Dhillon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lifetime of a wireless sensor network can be effectively increased by using scheduling operations. Once the sensors are randomly deployed, the task at hand is to find the largest number of disjoint sets of sensors such that every sensor set provides complete coverage of the target area. At any instant, only one of these disjoint sets is switched on, while all other are switched off. This paper proposes a heuristic search method to find the maximum number of disjoint sets that completely cover the region. A population of randomly initialized members is made to explore the solution space. A set of heuristics has been applied to guide the members to a possible solution in their neighborhood. The heuristics escalate the convergence of the algorithm. The best solution explored by the population is recorded and is continuously updated. The proposed algorithm has been tested for applications which require sensing of multiple target points, referred to as point coverage applications. Results show that the proposed algorithm outclasses the existing algorithms. It always finds the optimum solution, and that too by making fewer number of fitness function evaluations than the existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coverage" title="coverage">coverage</a>, <a href="https://publications.waset.org/abstracts/search?q=disjoint%20sets" title=" disjoint sets"> disjoint sets</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=lifetime" title=" lifetime"> lifetime</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=Wireless%20sensor%20networks" title=" Wireless sensor networks"> Wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN" title=" WSN"> WSN</a> </p> <a href="https://publications.waset.org/abstracts/34165/heuristic-search-algorithm-hsa-for-enhancing-the-lifetime-of-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34165.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">452</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">2058</span> Schedule a New Production Plan by Heuristic Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanife%20Merve%20%C3%96zt%C3%BCrk">Hanife Merve Öztürk</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C4%B1d%C4%B1ka%20Dalgan"> Sıdıka Dalgan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this project, a capacity analysis study is done at TAT A. Ş. Maret Plant. Production capacity of products which generate 80% of sales amount are determined. Obtained data entered the LEKIN Scheduling Program and we get production schedules by using heuristic methods. Besides heuristic methods, as mathematical model, disjunctive programming formulation is adapted to flexible job shop problems by adding a new constraint to find optimal schedule solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scheduling" title="scheduling">scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20problem" title=" flexible job shop problem"> flexible job shop problem</a>, <a href="https://publications.waset.org/abstracts/search?q=shifting%20bottleneck%20heuristic" title=" shifting bottleneck heuristic"> shifting bottleneck heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modelling" title=" mathematical modelling"> mathematical modelling</a> </p> <a href="https://publications.waset.org/abstracts/13135/schedule-a-new-production-plan-by-heuristic-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13135.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">401</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">2057</span> Enunciation on Complexities of Selected Tree Searching Algorithms </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parag%20Bhalchandra">Parag Bhalchandra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Khamitkar"> S. D. Khamitkar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching trees is a most interesting application of Artificial Intelligence. Over the period of time, many innovative methods have been evolved to better search trees with respect to computational complexities. Tree searches are difficult to understand due to the exponential growth of possibilities when increasing the number of nodes or levels in the tree. Usually it is understood when we traverse down in the tree, traverse down to greater depth, in the search of a solution or a goal. However, this does not happen in reality as explicit enumeration is not a very efficient method and there are many algorithmic speedups that will find the optimal solution without the burden of evaluating all possible trees. It was a common question before all researchers where they often wonder what algorithms will yield the best and fastest result The intention of this paper is two folds, one to review selected tree search algorithms and search strategies that can be applied to a problem space and the second objective is to stimulate to implement recent developments in the complexity behavior of search strategies. The algorithms discussed here apply in general to both brute force and heuristic searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trees%20search" title="trees search">trees search</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20complexity" title=" asymptotic complexity"> asymptotic complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=brute%20force" title=" brute force"> brute force</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20algorithms" title=" heuristics algorithms"> heuristics algorithms</a> </p> <a href="https://publications.waset.org/abstracts/13407/enunciation-on-complexities-of-selected-tree-searching-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13407.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">304</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">2056</span> On Exploring Search Heuristics for improving the efficiency in Web Information Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Patricia%20Jim%C3%A9nez">Patricia Jiménez</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20Corchuelo"> Rafael Corchuelo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays the World Wide Web is the most popular source of information that relies on billions of on-line documents. Web mining is used to crawl through these documents, collect the information of interest and process it by applying data mining tools in order to use the gathered information in the best interest of a business, what enables companies to promote theirs. Unfortunately, it is not easy to extract the information a web site provides automatically when it lacks an API that allows to transform the user-friendly data provided in web documents into a structured format that is machine-readable. Rule-based information extractors are the tools intended to extract the information of interest automatically and offer it in a structured format that allow mining tools to process it. However, the performance of an information extractor strongly depends on the search heuristic employed since bad choices regarding how to learn a rule may easily result in loss of effectiveness and/or efficiency. Improving search heuristics regarding efficiency is of uttermost importance in the field of Web Information Extraction since typical datasets are very large. In this paper, we employ an information extractor based on a classical top-down algorithm that uses the so-called Information Gain heuristic introduced by Quinlan and Cameron-Jones. Unfortunately, the Information Gain relies on some well-known problems so we analyse an intuitive alternative, Termini, that is clearly more efficient; we also analyse other proposals in the literature and conclude that none of them outperforms the previous alternative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title="information extraction">information extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20heuristics" title=" search heuristics"> search heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-structured%20documents" title=" semi-structured documents"> semi-structured documents</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20mining." title=" web mining. "> web mining. </a> </p> <a href="https://publications.waset.org/abstracts/25697/on-exploring-search-heuristics-for-improving-the-efficiency-in-web-information-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25697.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">335</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">2055</span> Heuristic Methods for the Capacitated Location- Allocation Problem with Stochastic Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salinee%20Thumronglaohapun">Salinee Thumronglaohapun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location-allocation%20problem" title="location-allocation problem">location-allocation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20demand" title=" stochastic demand"> stochastic demand</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/114723/heuristic-methods-for-the-capacitated-location-allocation-problem-with-stochastic-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114723.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">124</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">2054</span> Implementing Search-Based Activities in Mathematics Instruction, Grounded in Intuitive Reasoning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhanna%20Dedovets">Zhanna Dedovets</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fostering a mathematical style of thinking is crucial for cultivating intellectual personalities capable of thriving in modern society. Intuitive thinking stands as a cornerstone among the components of mathematical cognition, playing a pivotal role in grasping mathematical truths across various disciplines. This article delves into the exploration of leveraging search activities rooted in students' intuitive thinking, particularly when tackling geometric problems. Emphasizing both student engagement with the task and their active involvement in the search process, the study underscores the importance of heuristic procedures and the freedom for students to chart their own problem-solving paths. Spanning several years (2019-2023) at the Physics and Mathematics Lyceum of Dushanbe, the research engaged 17 teachers and 78 high school students. After assessing the initial levels of intuitive thinking in both control and experimental groups, the experimental group underwent training following the authors' methodology. Subsequent analysis revealed a significant advancement in thinking levels among the experimental group students. The methodological approaches and teaching materials developed through this process offer valuable resources for mathematics educators seeking to enhance their students' learning experiences effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=teaching%20of%20mathematics" title="teaching of mathematics">teaching of mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=intuitive%20thinking" title=" intuitive thinking"> intuitive thinking</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20procedures" title=" heuristic procedures"> heuristic procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20problem" title=" geometric problem"> geometric problem</a>, <a href="https://publications.waset.org/abstracts/search?q=students." title=" students."> students.</a> </p> <a href="https://publications.waset.org/abstracts/185467/implementing-search-based-activities-in-mathematics-instruction-grounded-in-intuitive-reasoning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185467.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">46</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">2053</span> Heuristic Evaluation of Children’s Authoring Tool for Game Making</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laili%20Farhana%20Md%20Ibharim">Laili Farhana Md Ibharim</a>, <a href="https://publications.waset.org/abstracts/search?q=Maizatul%20Hayati%20Mohamad%20Yatim"> Maizatul Hayati Mohamad Yatim </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is to evaluate the heuristic inspection of children’s authoring tools to develop games. The researcher has selected 15 authoring tools for making games specifically for educational purposes. Nine students from Diploma of Game Design and Development course and four lecturers from the computing department involved in this evaluation. A set of usability heuristic checklist used as a guideline for the students and lecturers to observe and test the authoring tools selected. The study found that there are just a few authoring tools that fulfill most of the heuristic requirement and suitable to apply to children. In this evaluation, only six out of fifteen authoring tools have passed above than five elements in the heuristic inspection checklist. The researcher identified that in order to develop a usable authoring tool developer has to emphasis children acceptance and interaction of the authoring tool. Furthermore, the authoring tool can be a tool to enhance their mental development especially in creativity and skill. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authoring%20tool" title="authoring tool">authoring tool</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20making" title=" game making"> game making</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/1364/heuristic-evaluation-of-childrens-authoring-tool-for-game-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1364.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">348</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">2052</span> Estimation of Fuel Cost Function Characteristics Using Cuckoo Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Al-Rashidi">M. R. Al-Rashidi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20M.%20El-Naggar"> K. M. El-Naggar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20Al-Hajri"> M. F. Al-Hajri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fuel cost function describes the electric power generation-cost relationship in thermal plants, hence, it sheds light on economical aspects of power industry. Different models have been proposed to describe this relationship with the quadratic function model being the most popular one. Parameters of second order fuel cost function are estimated in this paper using cuckoo search algorithm. It is a new population based meta-heuristic optimization technique that has been used in this study primarily as an accurate estimation tool. Its main features are flexibility, simplicity, and effectiveness when compared to other estimation techniques. The parameter estimation problem is formulated as an optimization one with the goal being minimizing the error associated with the estimated parameters. A case study is considered in this paper to illustrate cuckoo search promising potential as a valuable estimation and optimization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters%20estimation" title=" parameters estimation"> parameters estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20cost%20function" title=" fuel cost function"> fuel cost function</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20dispatch" title=" economic dispatch"> economic dispatch</a> </p> <a href="https://publications.waset.org/abstracts/25377/estimation-of-fuel-cost-function-characteristics-using-cuckoo-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25377.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">581</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">2051</span> BeamGA Median: A Hybrid Heuristic Search Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr">Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Manar%20Hosny"> Manar Hosny</a>, <a href="https://publications.waset.org/abstracts/search?q=Nuha%20Bintayyash"> Nuha Bintayyash</a>, <a href="https://publications.waset.org/abstracts/search?q=Eman%20Albilali"> Eman Albilali</a>, <a href="https://publications.waset.org/abstracts/search?q=Souad%20Larabi%20Marie-Sainte"> Souad Larabi Marie-Sainte</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=median%20problem" title="median problem">median problem</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20tree" title=" phylogenetic tree"> phylogenetic tree</a>, <a href="https://publications.waset.org/abstracts/search?q=permutation" title=" permutation"> permutation</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=beam%20search" title=" beam search"> beam search</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%20rearrangement%20distance" title=" genome rearrangement distance"> genome rearrangement distance</a> </p> <a href="https://publications.waset.org/abstracts/73026/beamga-median-a-hybrid-heuristic-search-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73026.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">265</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">2050</span> Search for APN Permutations in Rings ℤ_2×ℤ_2^k</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Panario">Daniel Panario</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Santana%20de%20Freitas"> Daniel Santana de Freitas</a>, <a href="https://publications.waset.org/abstracts/search?q=Brett%20Stevens"> Brett Stevens</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Almost Perfect Nonlinear (APN) permutations with optimal resistance against differential cryptanalysis can be found in several domains. The permutation used in the standard for symmetric cryptography (the AES), for example, is based on a special kind of inversion in GF(28). Although very close to APN (2-uniform), this permutation still contains one number 4 in its differential spectrum, which means that, rigorously, it must be classified as 4-uniform. This fact motivates the search for fully APN permutations in other domains of definition. The extremely high complexity associated to this kind of problem precludes an exhaustive search for an APN permutation with 256 elements to be performed without the support of a suitable mathematical structure. On the other hand, in principle, there is nothing to indicate which mathematically structured domains can effectively help the search, and it is necessary to test several domains. In this work, the search for APN permutations in rings ℤ2×ℤ2k is investigated. After a full, exhaustive search with k=2 and k=3, all possible APN permutations in those rings were recorded, together with their differential profiles. Some very promising heuristics in these cases were collected so that, when used as a basis to prune backtracking for the same search in ℤ2×ℤ8 (search space with size 16! ≅244), just a few tenths of a second were enough to produce an APN permutation in a single CPU. Those heuristics were empirically extrapolated so that they could be applied to a backtracking search for APNs over ℤ2×ℤ16 (search space with size 32! ≅2117). The best permutations found in this search were further refined through Simulated Annealing, with a definition of neighbors suitable to this domain. The best result produced with this scheme was a 3-uniform permutation over ℤ2×ℤ16 with only 24 values equal to 3 in the differential spectrum (all the other 968 values were less than or equal 2, as it should be the case for an APN permutation). Although far from being fully APN, this result is technically better than a 4-uniform permutation and demanded only a few seconds in a single CPU. This is a strong indication that the use of mathematically structured domains, like the rings described in this work, together with heuristics based on smaller cases, can lead to dramatic cuts in the computational resources involved in the complexity of the search for APN permutations in extremely large domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=APN%20permutations" title="APN permutations">APN permutations</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20searches" title=" heuristic searches"> heuristic searches</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20cryptography" title=" symmetric cryptography"> symmetric cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=S-box%20design" title=" S-box design"> S-box design</a> </p> <a href="https://publications.waset.org/abstracts/145556/search-for-apn-permutations-in-rings-2-2k" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145556.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">159</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">2049</span> Improving the Global Competitiveness of SMEs by Logistics Transportation Management: Case Study Chicken Meat Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Vanichkobchinda">P. Vanichkobchinda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Logistics Transportation techniques, Open Vehicle Routing (OVR) is an approach toward transportation cost reduction, especially for long distance pickup and delivery nodes. The outstanding characteristic of OVR is that the route starting node and ending node are not necessary the same as in typical vehicle routing problems. This advantage enables the routing to flow continuously and the vehicle does not always return to its home base. This research aims to develop a heuristic for the open vehicle routing problem with pickup and delivery under time window and loading capacity constraints to minimize the total distance. The proposed heuristic is developed based on the Insertion method, which is a simple method and suitable for the rapid calculation that allows insertion of the new additional transportation requirements along the original paths. According to the heuristic analysis, cost comparisons between the proposed heuristic and companies are using method, nearest neighbor method show that the insertion heuristic. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing. The research indicates that the improvement of new transport's calculation and the open vehicle routing with "Insertion Heuristic" represent a better outcome with 34.3 percent in average. in cost savings. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20competitiveness" title="business competitiveness">business competitiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20reduction" title=" cost reduction"> cost reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=SMEs" title=" SMEs"> SMEs</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics%20transportation" title=" logistics transportation"> logistics transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=VRP" title=" VRP"> VRP</a> </p> <a href="https://publications.waset.org/abstracts/25842/improving-the-global-competitiveness-of-smes-by-logistics-transportation-management-case-study-chicken-meat-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25842.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">685</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">2048</span> Hybrid Wind Solar Gas Reliability Optimization Using Harmony Search under Performance and Budget Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meziane%20Rachid">Meziane Rachid</a>, <a href="https://publications.waset.org/abstracts/search?q=Boufala%20Seddik"> Boufala Seddik</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamzi%20Amar"> Hamzi Amar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amara%20Mohamed"> Amara Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability%20optimization" title="reliability optimization">reliability optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=harmony%20search%20optimization%20%28HSA%29" title=" harmony search optimization (HSA)"> harmony search optimization (HSA)</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20generating%20function%20%28UMGF%29" title=" universal generating function (UMGF)"> universal generating function (UMGF)</a> </p> <a href="https://publications.waset.org/abstracts/11734/hybrid-wind-solar-gas-reliability-optimization-using-harmony-search-under-performance-and-budget-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11734.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">576</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">2047</span> An Approximation Algorithm for the Non Orthogonal Cutting Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ouafi">R. Ouafi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Ouafi"> F. Ouafi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We study the problem of cutting a rectangular material entity into smaller sub-entities of trapezoidal forms with minimum waste of the material. This problem will be denoted TCP (Trapezoidal Cutting Problem). The TCP has many applications in manufacturing processes of various industries: pipe line design (petro chemistry), the design of airfoil (aeronautical) or cuts of the components of textile products. We introduce an orthogonal build to provide the optimal horizontal and vertical homogeneous strips. In this paper we develop a general heuristic search based upon orthogonal build. By solving two one-dimensional knapsack problems, we combine the horizontal and vertical homogeneous strips to give a non orthogonal cutting pattern. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization" title="combinatorial optimization">combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=cutting%20problem" title=" cutting problem"> cutting problem</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/19497/an-approximation-algorithm-for-the-non-orthogonal-cutting-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19497.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2046</span> Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mafarja%20Majdi">Mafarja Majdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salwani%20Abdullah"> Salwani Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20S.%20Jaddi"> Najmeh S. Jaddi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20set%20theory" title="rough set theory">rough set theory</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20reduction" title=" attribute reduction"> attribute reduction</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=memetic%20algorithms" title=" memetic algorithms"> memetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20to%20record%20algorithm" title=" record to record algorithm"> record to record algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=great%20deluge%20algorithm" title=" great deluge algorithm"> great deluge algorithm</a> </p> <a href="https://publications.waset.org/abstracts/33663/fuzzy-population-based-meta-heuristic-approaches-for-attribute-reduction-in-rough-set-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33663.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">2045</span> Heuristic for Scheduling Correlated Parallel Machine to Minimize Maximum Lateness and Total Weighed Completion Time</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang-Kuei%20Lin">Yang-Kuei Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Xi%20Zhang"> Yun-Xi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research focuses on the bicriteria correlated parallel machine scheduling problem. The two objective functions considered in this problem are to minimize maximum lateness and total weighted completion time. We first present a mixed integer programming (MIP) model that can find the entire efficient frontier for the studied problem. Next, we have proposed a bicriteria heuristic that can find non-dominated solutions for the studied problem. The performance of the proposed bicriteria heuristic is compared with the efficient frontier generated by solving the MIP model. Computational results indicate that the proposed bicriteria heuristic can solve the problem efficiently and find a set of diverse solutions that are uniformly distributed along the efficient frontier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bicriteria" title="bicriteria">bicriteria</a>, <a href="https://publications.waset.org/abstracts/search?q=correlated%20parallel%20machines" title=" correlated parallel machines"> correlated parallel machines</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a> </p> <a href="https://publications.waset.org/abstracts/108546/heuristic-for-scheduling-correlated-parallel-machine-to-minimize-maximum-lateness-and-total-weighed-completion-time" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108546.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">141</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">2044</span> Pattern Recognition Search: An Advancement Over Interpolation Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahpar%20Yilmaz">Shahpar Yilmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasir%20Nadeem"> Yasir Nadeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Mehdi"> Syed A. Mehdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching for a record in a dataset is always a frequent task for any data structure-related application. Hence, a fast and efficient algorithm for the approach has its importance in yielding the quickest results and enhancing the overall productivity of the company. Interpolation search is one such technique used to search through a sorted set of elements. This paper proposes a new algorithm, an advancement over interpolation search for the application of search over a sorted array. Pattern Recognition Search or PR Search (PRS), like interpolation search, is a pattern-based divide and conquer algorithm whose objective is to reduce the sample size in order to quicken the process and it does so by treating the array as a perfect arithmetic progression series and thereby deducing the key element’s position. We look to highlight some of the key drawbacks of interpolation search, which are accounted for in the Pattern Recognition Search. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=array" title="array">array</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=sorting" title=" sorting"> sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=space" title=" space"> space</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/142819/pattern-recognition-search-an-advancement-over-interpolation-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142819.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">243</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">2043</span> SMART: Solution Methods with Ants Running by Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Zufferey">Nicolas Zufferey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant algorithms are well-known metaheuristics which have been widely used since two decades. In most of the literature, an ant is a constructive heuristic able to build a solution from scratch. However, other types of ant algorithms have recently emerged: the discussion is thus not limited by the common framework of the constructive ant algorithms. Generally, at each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (also called the visibility, the short term profit or the heuristic information) and the trail system (central memory which collects historical information of the search process). Usually, all the ants of the population have the same characteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed, namely SMART (for Solution Methods with Ants Running by Types). It relies on the use of different population of ants, where each population has its own personality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20algorithms" title="ant algorithms">ant algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20procedures" title=" evolutionary procedures"> evolutionary procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=population-based%20methods" title=" population-based methods"> population-based methods</a> </p> <a href="https://publications.waset.org/abstracts/36375/smart-solution-methods-with-ants-running-by-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36375.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">365</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">2042</span> Fast Switching Mechanism for Multicasting Failure in OpenFlow Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alaa%20Allakany">Alaa Allakany</a>, <a href="https://publications.waset.org/abstracts/search?q=Koji%20Okamura"> Koji Okamura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multicast technology is an efficient and scalable technology for data distribution in order to optimize network resources. However, in the IP network, the responsibility for management of multicast groups is distributed among network routers, which causes some limitations such as delays in processing group events, high bandwidth consumption and redundant tree calculation. Software Defined Networking (SDN) represented by OpenFlow presented as a solution for many problems, in SDN the control plane and data plane are separated by shifting the control and management to a remote centralized controller, and the routers are used as a forwarder only. In this paper we will proposed fast switching mechanism for solving the problem of link failure in multicast tree based on Tabu Search heuristic algorithm and modifying the functions of OpenFlow switch to fasts switch to the pack up sub tree rather than sending to the controller. In this work we will implement multicasting OpenFlow controller, this centralized controller is a core part in our multicasting approach, which is responsible for 1- constructing the multicast tree, 2- handling the multicast group events and multicast state maintenance. And finally modifying OpenFlow switch functions for fasts switch to pack up paths. Forwarders, forward the multicast packet based on multicast routing entries which were generated by the centralized controller. Tabu search will be used as heuristic algorithm for construction near optimum multicast tree and maintain multicast tree to still near optimum in case of join or leave any members from multicast group (group events). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multicast%20tree" title="multicast tree">multicast tree</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20define%20networks" title=" software define networks"> software define networks</a>, <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title=" tabu search"> tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenFlow" title=" OpenFlow"> OpenFlow</a> </p> <a href="https://publications.waset.org/abstracts/47773/fast-switching-mechanism-for-multicasting-failure-in-openflow-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47773.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">263</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">2041</span> Speedup Breadth-First Search by Graph Ordering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuyi%20Lyu">Qiuyi Lyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Gong"> Bin Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breadth-First Search(BFS) is a core graph algorithm that is widely used for graph analysis. As it is frequently used in many graph applications, improve the BFS performance is essential. In this paper, we present a graph ordering method that could reorder the graph nodes to achieve better data locality, thus, improving the BFS performance. Our method is based on an observation that the sibling relationships will dominate the cache access pattern during the BFS traversal. Therefore, we propose a frequency-based model to construct the graph order. First, we optimize the graph order according to the nodes’ visit frequency. Nodes with high visit frequency will be processed in priority. Second, we try to maximize the child nodes overlap layer by layer. As it is proved to be NP-hard, we propose a heuristic method that could greatly reduce the preprocessing overheads. We conduct extensive experiments on 16 real-world datasets. The result shows that our method could achieve comparable performance with the state-of-the-art methods while the graph ordering overheads are only about 1/15. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breadth-first%20search" title="breadth-first search">breadth-first search</a>, <a href="https://publications.waset.org/abstracts/search?q=BFS" title=" BFS"> BFS</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20ordering" title=" graph ordering"> graph ordering</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a> </p> <a href="https://publications.waset.org/abstracts/136790/speedup-breadth-first-search-by-graph-ordering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136790.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">138</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">2040</span> Vehicle Routing Problem with Mixed Fleet of Conventional and Heterogenous Electric Vehicles and Time Dependent Charging Costs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ons%20Sassi">Ons Sassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wahiba%20Ramdane%20Cherif-Khettaf"> Wahiba Ramdane Cherif-Khettaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Ammar%20Oulamara"> Ammar Oulamara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider a new real-life Heterogenous Electric Vehicle Routing Problem with Time Dependant Charging Costs and a Mixed Fleet (HEVRP-TDMF), in which a set of geographically scattered customers have to be served by a mixed fleet of vehicles composed of a heterogenous fleet of Electric Vehicles (EVs), having different battery capacities and operating costs, and Conventional Vehicles (CVs). We include the possibility of charging EVs in the available charging stations during the routes in order to serve all customers. Each charging station offers charging service with a known technology of chargers and time-dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are not necessarily compatible with all available charging technologies and a partial charging is allowed. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and then minimize the total travel and charging costs. In this study, we present a Mixed Integer Programming Model and develop a Charging Routing Heuristic and a Local Search Heuristic based on the Inject-Eject routine with three different insertion strategies. All heuristics are tested on real data instances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=charging%20problem" title="charging problem">charging problem</a>, <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=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20problem" title=" routing problem"> routing problem</a> </p> <a href="https://publications.waset.org/abstracts/19969/vehicle-routing-problem-with-mixed-fleet-of-conventional-and-heterogenous-electric-vehicles-and-time-dependent-charging-costs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19969.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">463</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">2039</span> A Two Phase VNS Algorithm for the Combined Production Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nejah%20Ben%20Mabrouk">Nejah Ben Mabrouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Bassem%20Jarboui"> Bassem Jarboui</a>, <a href="https://publications.waset.org/abstracts/search?q=Habib%20Chabchoub"> Habib Chabchoub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Production and distribution planning is the most important part in supply chain management. In this paper, a NP-hard production-distribution problem for one product over a multi-period horizon is investigated. The aim is to minimize the sum of costs of three items: production setups, inventories and distribution, while determining, for each period, the amount produced, the inventory levels and the delivery trips. To solve this difficult problem, we propose a bi-phase approach based on a Variable Neighbourhood Search (VNS). This heuristic is tested on 90 randomly generated instances from the literature, with 20 periods and 50, 100, 200 customers. Computational results show that our approach outperforms existing solution procedures available in the literature <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=logistic" title="logistic">logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=production" title=" production"> production</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title="distribution">distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighbourhood%20search" title=" variable neighbourhood search"> variable neighbourhood search</a> </p> <a href="https://publications.waset.org/abstracts/44821/a-two-phase-vns-algorithm-for-the-combined-production-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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