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uncertainty</title> <meta name="description" content="Search results for: demand uncertainty"> <meta name="keywords" content="demand uncertainty"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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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="demand uncertainty"> <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> 4151</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: demand uncertainty</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4151</span> Mind Your Product-Market Strategy on Selecting Marketing Inputs: An Uncertainty Approach in Indian Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Susmita%20Ghosh">Susmita Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhaskar%20Bhowmick"> Bhaskar Bhowmick</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Market is an important factor for start-ups to look into during decision-making in product development and related areas. Emerging country markets are more uncertain in terms of information availability and institutional supports. The literature review of market uncertainty reveals the need for identifying factors representing the market uncertainty. This paper identifies factors for market uncertainty using Exploratory Factor Analysis (EFA) and confirms the number of factor retention using an alternative factor retention criterion, ‘Parallel Analysis’. 500 entrepreneurs, engaged in start-ups from all over India participated in the study. This paper concludes with the factor structure of ‘market uncertainty’ having dimensions of uncertainty in industry orientation, uncertainty in customer orientation and uncertainty in marketing orientation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=market" title=" market"> market</a>, <a href="https://publications.waset.org/abstracts/search?q=orientation" title=" orientation"> orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=competitor" title=" competitor"> competitor</a>, <a href="https://publications.waset.org/abstracts/search?q=demand" title=" demand "> demand </a> </p> <a href="https://publications.waset.org/abstracts/24877/mind-your-product-market-strategy-on-selecting-marketing-inputs-an-uncertainty-approach-in-indian-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24877.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">590</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">4150</span> Product Line Design with Customization in the Presence of Demand Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parisa%20Bagheri%20Tookanlou">Parisa Bagheri Tookanlou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze a product line design problem faced by a manufacturing firm where the product line consists of a customized product in addition to a standard product and is offered in a market in which customers are heterogeneous on aesthetic attributes of the product. The customization level of a product is defined by the fraction of aesthetic attributes of the product that the manufacturer chooses to customize. In contrast to the existing literature on product line design that predominantly assumes deterministic demand, we consider the presence of demand uncertainty and frame the product line design problem in a single period (news vendor) setting. We examine the effect of demand uncertainty on product line decisions. Furthermore, we also examine how product line decisions are influenced by channel structure. While we use the centralized channel as a benchmark, we consider the decentralized dual channel where the customized product is sold through an online channel owned by the manufacturer and the standard product is sold through a retailer. We introduce a supply contract between the manufacturer and the retailer for improving channel efficiency and coordinate the distribution channel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=product%20line%20design" title="product line design">product line design</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20uncertainty" title=" demand uncertainty"> demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=customization%20level" title=" customization level"> customization level</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20channel" title=" distribution channel"> distribution channel</a> </p> <a href="https://publications.waset.org/abstracts/83258/product-line-design-with-customization-in-the-presence-of-demand-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83258.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">186</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">4149</span> Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watcharin%20Sangma">Watcharin Sangma</a>, <a href="https://publications.waset.org/abstracts/search?q=Onsiri%20Chanmuang"> Onsiri Chanmuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow"> Pitsanu Tongkhow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title="forecasting model">forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20demand%20uncertainty" title=" steel demand uncertainty"> steel demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20framework" title=" hierarchical Bayesian framework"> hierarchical Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing%20method" title=" exponential smoothing method"> exponential smoothing method</a> </p> <a href="https://publications.waset.org/abstracts/10196/forecasting-models-for-steel-demand-uncertainty-using-bayesian-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10196.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">350</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">4148</span> Flexible Mixed Model Assembly Line Design: A Strategy to Respond for Demand Uncertainty at Automotive Part Manufacturer in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Yuri">T. Yuri</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zagloel"> M. Zagloel</a>, <a href="https://publications.waset.org/abstracts/search?q=Inaki%20M.%20Hakim"> Inaki M. Hakim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tegu%20Bintang%20Nugraha"> Tegu Bintang Nugraha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an era of customer centricity, automotive parts manufacturer in Indonesia must be able to keep up with the uncertainty and fluctuation of consumer demand. Flexible Manufacturing System (FMS) is a strategy to react to predicted and unpredicted changes of demand in automotive industry. This research is about flexible mixed model assembly line design through Value Stream Mapping (VSM) and Line Balancing in mixed model assembly line prior to simulation. It uses value stream mapping to identify and reduce waste while finding the best position to add or reduce manpower. Line balancing is conducted to minimize or maximize production rate while increasing assembly line productivity and efficiency. Results of this research is a recommendation of standard work combination for specifics demand scenario which can enhance assembly line efficiency and productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automotive%20industry" title="automotive industry">automotive industry</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20uncertainty" title=" demand uncertainty"> demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20assembly%20system" title=" flexible assembly system"> flexible assembly system</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20balancing" title=" line balancing"> line balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20stream%20mapping" title=" value stream mapping"> value stream mapping</a> </p> <a href="https://publications.waset.org/abstracts/57658/flexible-mixed-model-assembly-line-design-a-strategy-to-respond-for-demand-uncertainty-at-automotive-part-manufacturer-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57658.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">330</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">4147</span> Using AI to Advance Factory Planning: A Case Study to Identify Success Factors of Implementing an AI-Based Demand Planning Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ulrike%20Dowie">Ulrike Dowie</a>, <a href="https://publications.waset.org/abstracts/search?q=Ralph%20Grothmann"> Ralph Grothmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20software%20development" title="agile software development">agile software development</a>, <a href="https://publications.waset.org/abstracts/search?q=AI%20project%20success%20factors" title=" AI project success factors"> AI project success factors</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=forecast%20uncertainty" title=" forecast uncertainty"> forecast uncertainty</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=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/131957/using-ai-to-advance-factory-planning-a-case-study-to-identify-success-factors-of-implementing-an-ai-based-demand-planning-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131957.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">189</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">4146</span> A Robust Optimization for Multi-Period Lost-Sales Inventory Control Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shunichi%20Ohmori">Shunichi Ohmori</a>, <a href="https://publications.waset.org/abstracts/search?q=Sirawadee%20Arunyanart"> Sirawadee Arunyanart</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuho%20Yoshimoto"> Kazuho Yoshimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider a periodic review inventory control problem of minimizing production cost, inventory cost, and lost-sales under demand uncertainty, in which product demands are not specified exactly and it is only known to belong to a given uncertainty set, yet the constraints must hold for possible values of the data from the uncertainty set. We propose a robust optimization formulation for obtaining lowest cost possible and guaranteeing the feasibility with respect to range of order quantity and inventory level under demand uncertainty. Our formulation is based on the adaptive robust counterpart, which suppose order quantity is affine function of past demands. We derive certainty equivalent problem via second-order cone programming, which gives 'not too pessimistic' worst-case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title="robust optimization">robust optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20control" title=" inventory control"> inventory control</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20managment" title=" supply chain managment"> supply chain managment</a>, <a href="https://publications.waset.org/abstracts/search?q=second-order%20programming" title=" second-order programming"> second-order programming</a> </p> <a href="https://publications.waset.org/abstracts/42923/a-robust-optimization-for-multi-period-lost-sales-inventory-control-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42923.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">409</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">4145</span> Production and Leftovers Usage Policies to Minimize Food Waste under Uncertain and Correlated Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Esma%20Birisci">Esma Birisci</a>, <a href="https://publications.waset.org/abstracts/search?q=Ronald%20McGarvey"> Ronald McGarvey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the common problems in food service industry is demand uncertainty. This research presents a multi-criteria optimization approach to identify the efficient frontier of points lying between the minimum-waste and minimum-shortfall solutions within uncertain demand environment. It also addresses correlation across demands for items (e.g., hamburgers are often demanded with french fries). Reducing overproduction food waste (and its corresponding environmental impacts) and an aversion to shortfalls (leave some customer hungry) need to consider as two contradictory objectives in an all-you-care-to-eat environment food service operation. We identify optimal production adjustments relative to demand forecasts, demand thresholds for utilization of leftovers, and percentages of demand to be satisfied by leftovers, considering two alternative metrics for overproduction waste: mass; and greenhouse gas emissions. Demand uncertainty and demand correlations are addressed using a kernel density estimation approach. A statistical analysis of the changes in decision variable values across each of the efficient frontiers can then be performed to identify the key variables that could be modified to reduce the amount of wasted food at minimal increase in shortfalls. We illustrate our approach with an application to empirical data from Campus Dining Services operations at the University of Missouri. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20studies" title="environmental studies">environmental studies</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20waste" title=" food waste"> food waste</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20planning" title=" production planning"> production planning</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20and%20correlated%20demand" title=" uncertain and correlated demand"> uncertain and correlated demand</a> </p> <a href="https://publications.waset.org/abstracts/80500/production-and-leftovers-usage-policies-to-minimize-food-waste-under-uncertain-and-correlated-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80500.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">372</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">4144</span> A Reinforcement Learning Based Method for Heating, Ventilation, and Air Conditioning Demand Response Optimization Considering Few-Shot Personalized Thermal Comfort</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaohua%20Zou">Xiaohua Zou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongxin%20Su"> Yongxin Su</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HVAC" title="HVAC">HVAC</a>, <a href="https://publications.waset.org/abstracts/search?q=few-shot%20personalized%20thermal%20comfort" title=" few-shot personalized thermal comfort"> few-shot personalized thermal comfort</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20response" title=" demand response"> demand response</a> </p> <a href="https://publications.waset.org/abstracts/182116/a-reinforcement-learning-based-method-for-heating-ventilation-and-air-conditioning-demand-response-optimization-considering-few-shot-personalized-thermal-comfort" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182116.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">85</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">4143</span> Deep Reinforcement Learning for Optimal Decision-Making in Supply Chains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Singh">Nitin Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Ling"> Meng Ling</a>, <a href="https://publications.waset.org/abstracts/search?q=Talha%20Ahmed"> Talha Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianxia%20Zhao"> Tianxia Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Reinier%20van%20de%20Pol"> Reinier van de Pol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inventory%20management" title="inventory management">inventory management</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20optimization" title=" supply chain optimization"> supply chain optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/155576/deep-reinforcement-learning-for-optimal-decision-making-in-supply-chains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155576.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">107</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">4142</span> Inventory Control for Purchased Part under Long Lead Time and Uncertain Demand: MRP vs Demand-Driven MRP Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Shofa">M. J. Shofa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hidayatno"> A. Hidayatno</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20M.%20Armand"> O. M. Armand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MRP as a production control system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic. Demand-Driven MRP (DDMRP) is a new approach for inventory control system, and it deals with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP work for a long lead time and uncertain demand in terms of on-hand inventory levels. The evaluation is conducted through a discrete event simulation using purchased part data from an automotive company. The result is MRP gives 50,759 pcs / day while DDMRP gives 34,835 pcs / day (reduce 32%), it means DDMRP is more effective inventory control than MRP in terms of on-hand inventory levels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Demand-Driven%20MRP" title="Demand-Driven MRP">Demand-Driven MRP</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20lead%20time" title=" long lead time"> long lead time</a>, <a href="https://publications.waset.org/abstracts/search?q=MRP" title=" MRP"> MRP</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20demand" title=" uncertain demand"> uncertain demand</a> </p> <a href="https://publications.waset.org/abstracts/71415/inventory-control-for-purchased-part-under-long-lead-time-and-uncertain-demand-mrp-vs-demand-driven-mrp-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71415.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">301</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">4141</span> Modelling Mode Choice Behaviour Using Cloud Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leah%20Wright">Leah Wright</a>, <a href="https://publications.waset.org/abstracts/search?q=Trevor%20Townsend"> Trevor Townsend</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand. <p class="card-text"><strong>Keywords:</strong> <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=decision-making" title=" decision-making"> decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=mode%20choice%20models" title=" mode choice models"> mode choice models</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20behaviour" title=" travel behaviour"> travel behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/56507/modelling-mode-choice-behaviour-using-cloud-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56507.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">387</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">4140</span> Production Planning for Animal Food Industry under Demand Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pirom%20Thangchitpianpol">Pirom Thangchitpianpol</a>, <a href="https://publications.waset.org/abstracts/search?q=Suttipong%20Jumroonrut"> Suttipong Jumroonrut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates the distribution of food demand for animal food and the optimum amount of that food production at minimum cost. The data consist of customer purchase orders for the food of laying hens, price of food for laying hens, cost per unit for the food inventory, cost related to food of laying hens in which the food is out of stock, such as fine, overtime, urgent purchase for material. They were collected from January, 1990 to December, 2013 from a factory in Nakhonratchasima province. The collected data are analyzed in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results are used in a stochastic linear programming model for aggregate planning in which the optimum production or minimum cost could be obtained. Programming algorithms in MATLAB and tools in Linprog software are used to get the solution. The distribution of the food demand for laying hens and the random numbers are used in the model. The study shows that the distribution of monthly food demand for laying has a normal distribution, the monthly average amount (unit: 30 kg) of production from January to December. The minimum total cost average for 12 months is Baht 62,329,181.77. Therefore, the production planning can reduce the cost by 14.64% from real cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=animal%20food" title="animal food">animal food</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20linear%20programming" title=" stochastic linear programming"> stochastic linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=aggregate%20planning" title=" aggregate planning"> aggregate planning</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20planning" title=" production planning"> production planning</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20uncertainty" title=" demand uncertainty"> demand uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/10194/production-planning-for-animal-food-industry-under-demand-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10194.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">380</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">4139</span> Meeting the Energy Balancing Needs in a Fully Renewable European Energy System: A Stochastic Portfolio Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iulia%20E.%20Falcan">Iulia E. Falcan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The transition of the European power sector towards a clean, renewable energy (RE) system faces the challenge of meeting power demand in times of low wind speed and low solar radiation, at a reasonable cost. This is likely to be achieved through a combination of 1) energy storage technologies, 2) development of the cross-border power grid, 3) installed overcapacity of RE and 4) dispatchable power sources – such as biomass. This paper uses NASA; derived hourly data on weather patterns of sixteen European countries for the past twenty-five years, and load data from the European Network of Transmission System Operators-Electricity (ENTSO-E), to develop a stochastic optimization model. This model aims to understand the synergies between the four classes of technologies mentioned above and to determine the optimal configuration of the energy technologies portfolio. While this issue has been addressed before, it was done so using deterministic models that extrapolated historic data on weather patterns and power demand, as well as ignoring the risk of an unbalanced grid-risk stemming from both the supply and the demand side. This paper aims to explicitly account for the inherent uncertainty in the energy system transition. It articulates two levels of uncertainty: a) the inherent uncertainty in future weather patterns and b) the uncertainty of fully meeting power demand. The first level of uncertainty is addressed by developing probability distributions for future weather data and thus expected power output from RE technologies, rather than known future power output. The latter level of uncertainty is operationalized by introducing a Conditional Value at Risk (CVaR) constraint in the portfolio optimization problem. By setting the risk threshold at different levels – 1%, 5% and 10%, important insights are revealed regarding the synergies of the different energy technologies, i.e., the circumstances under which they behave as either complements or substitutes to each other. The paper concludes that allowing for uncertainty in expected power output - rather than extrapolating historic data - paints a more realistic picture and reveals important departures from results of deterministic models. In addition, explicitly acknowledging the risk of an unbalanced grid - and assigning it different thresholds - reveals non-linearity in the cost functions of different technology portfolio configurations. This finding has significant implications for the design of the European energy mix. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-border%20grid%20extension" title="cross-border grid extension">cross-border grid extension</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20storage%20technologies" title=" energy storage technologies"> energy storage technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20system%20transition" title=" energy system transition"> energy system transition</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20portfolio%20optimization" title=" stochastic portfolio optimization"> stochastic portfolio optimization</a> </p> <a href="https://publications.waset.org/abstracts/87286/meeting-the-energy-balancing-needs-in-a-fully-renewable-european-energy-system-a-stochastic-portfolio-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87286.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">169</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">4138</span> Consideration of Uncertainty in Engineering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mohammadi">A. Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Moghimi"> M. Moghimi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mohammadi"> S. Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulated" title=" Monte Carlo simulated"> Monte Carlo simulated</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=scenario%20method" title=" scenario method"> scenario method</a> </p> <a href="https://publications.waset.org/abstracts/7164/consideration-of-uncertainty-in-engineering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7164.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">414</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">4137</span> Donoho-Stark’s and Hardy’s Uncertainty Principles for the Short-Time Quaternion Offset Linear Canonical Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Younus%20Bhat">Mohammad Younus Bhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quaternion offset linear canonical transform (QOLCT), which isa time-shifted and frequency-modulated version of the quaternion linear canonical transform (QLCT), provides a more general framework of most existing signal processing tools. For the generalized QOLCT, the classical Heisenberg’s and Lieb’s uncertainty principles have been studied recently. In this paper, we first define the short-time quaternion offset linear canonical transform (ST-QOLCT) and drive its relationship with the quaternion Fourier transform (QFT). The crux of the paper lies in the generalization of several well-known uncertainty principles for the ST-QOLCT, including Donoho-Stark’s uncertainty principle, Hardy’s uncertainty principle, Beurling’s uncertainty principle, and the logarithmic uncertainty principle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20Fourier%20transform" title="Quaternion Fourier transform">Quaternion Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20offset%20linear%20canonical%20transform" title=" Quaternion offset linear canonical transform"> Quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=short-time%20quaternion%20offset%20linear%20canonical%20transform" title=" short-time quaternion offset linear canonical transform"> short-time quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20principle" title=" uncertainty principle"> uncertainty principle</a> </p> <a href="https://publications.waset.org/abstracts/142375/donoho-starks-and-hardys-uncertainty-principles-for-the-short-time-quaternion-offset-linear-canonical-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142375.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">211</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">4136</span> Inter Laboratory Comparison with Coordinate Measuring Machine and Uncertainty Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tugrul%20%20Torun">Tugrul Torun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihsan%20A.%20Yuksel"> Ihsan A. Yuksel</a>, <a href="https://publications.waset.org/abstracts/search?q=Si%CC%87nem%20On%20Aktan"> Si̇nem On Aktan</a>, <a href="https://publications.waset.org/abstracts/search?q=Taha%20K.%20Vezi%CC%87roglu"> Taha K. Vezi̇roglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the quality control processes in some industries, the usage of CMM has increased in recent years. Consequently, the CMMs play important roles in the acceptance or rejection of manufactured parts. For parts, it’s important to be able to make decisions by performing fast measurements. According to related technical drawing and its tolerances, measurement uncertainty should also be considered during assessment. Since uncertainty calculation is difficult and time-consuming, most companies ignore the uncertainty value in their routine inspection method. Although studies on measurement uncertainty have been carried out on CMM’s in recent years, there is still no applicable method for analyzing task-specific measurement uncertainty. There are some standard series for calculating measurement uncertainty (ISO-15530); it is not possible to use it in industrial measurement because it is not a practical method for standard measurement routine. In this study, the inter-laboratory comparison test has been carried out in the ROKETSAN A.Ş. with all dimensional inspection units. The reference part that we used is traceable to the national metrology institute TUBİTAK UME. Each unit has measured reference parts according to related technical drawings, and the task-specific measuring uncertainty has been calculated with related parameters. According to measurement results and uncertainty values, the En values have been calculated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinate%20measurement" title="coordinate measurement">coordinate measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=CMM" title=" CMM"> CMM</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/136496/inter-laboratory-comparison-with-coordinate-measuring-machine-and-uncertainty-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136496.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">211</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">4135</span> Treating On-Demand Bonds as Cash-In-Hand: Analyzing the Use of “Unconscionability” as a Ground for Challenging Claims for Payment under On-Demand Bonds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asanga%20Gunawansa">Asanga Gunawansa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shenella%20Fonseka"> Shenella Fonseka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On-demand bonds, also known as unconditional bonds, are commonplace in the construction industry as a means of safeguarding the employer from any potential non-performance by a contractor. On-demand bonds may be obtained from commercial banks, and they serve as an undertaking by the issuing bank to honour payment on demand without questioning and/or considering any dispute between the employer and the contractor in relation to the underlying contract. Thus, whether or not a breach had occurred under the underlying contract, which triggers the demand for encashment by the employer, is not a question the bank needs to be concerned with. As a result, an unconditional bond allows the beneficiary to claim the money almost without any condition. Thus, an unconditional bond is as good as cash-in-hand. In the past, establishing fraud on the part of the employer, of which the bank had knowledge, was the only ground on which a bank could dishonour a claim made under an on-demand bond. However, recent jurisprudence in common law countries shows that courts are beginning to consider unconscionable conduct on the part of the employer in claiming under an on-demand bond as a ground that contractors could rely on the prevent the banks from honouring such claims. This has created uncertainty in connection with on-demand bonds and their liquidity. This paper analyzes recent judicial decisions in four common law jurisdictions, namely, England, Singapore, Hong Kong, and Sri Lanka, to identify the scope of using the concept of “unconscionability” as a ground for preventing unreasonable claims for encashment of on-demand bonds. The objective of this paper is to argue that on-demand bonds have lost their effectiveness as “cash-in-hand” and that this is, in fact, an advantage and not an impediment to international commerce, as the purpose of such bonds should not be to provide for illegal and unconscionable conduct by the beneficiaries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fraud" title="fraud">fraud</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20guarantees" title=" performance guarantees"> performance guarantees</a>, <a href="https://publications.waset.org/abstracts/search?q=on-demand%20bonds" title=" on-demand bonds"> on-demand bonds</a>, <a href="https://publications.waset.org/abstracts/search?q=unconscionability" title=" unconscionability"> unconscionability</a> </p> <a href="https://publications.waset.org/abstracts/158271/treating-on-demand-bonds-as-cash-in-hand-analyzing-the-use-of-unconscionability-as-a-ground-for-challenging-claims-for-payment-under-on-demand-bonds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158271.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">105</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">4134</span> Statistical Classification, Downscaling and Uncertainty Assessment for Global Climate Model Outputs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Queen%20Suraajini%20Rajendran">Queen Suraajini Rajendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Sai%20Hung%20Cheung"> Sai Hung Cheung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20downscaling" title="statistical downscaling">statistical downscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20climate%20model" title=" global climate model"> global climate model</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/18056/statistical-classification-downscaling-and-uncertainty-assessment-for-global-climate-model-outputs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18056.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">368</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">4133</span> Epistemic Uncertainty Analysis of Queue with Vacations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baya%20Takhedmit">Baya Takhedmit</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Abbas"> Karim Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofiane%20Ouazine"> Sofiane Ouazine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The vacations queues are often employed to model many real situations such as computer systems, communication networks, manufacturing and production systems, transportation systems and so forth. These queueing models are solved at fixed parameters values. However, the parameter values themselves are determined from a finite number of observations and hence have uncertainty associated with them (epistemic uncertainty). In this paper, we consider the M/G/1/N queue with server vacation and exhaustive discipline where we assume that the vacation parameter values have uncertainty. We use the Taylor series expansions approach to estimate the expectation and variance of model output, due to epistemic uncertainties in the model input parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epistemic%20uncertainty" title="epistemic uncertainty">epistemic uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=M%2FG%2F1%2FN%20queue%20with%20vacations" title=" M/G/1/N queue with vacations"> M/G/1/N queue with vacations</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric%20sensitivity%20analysis" title=" non-parametric sensitivity analysis"> non-parametric sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series%20expansion" title=" Taylor series expansion"> Taylor series expansion</a> </p> <a href="https://publications.waset.org/abstracts/63375/epistemic-uncertainty-analysis-of-queue-with-vacations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63375.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">433</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">4132</span> Uncertainty in Risk Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mueller%20Jann">Mueller Jann</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoffmann%20Christian%20Hugo"> Hoffmann Christian Hugo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventional quantitative risk management in banking is a risk factor of its own, because it rests on assumptions such as independence and availability of data which do not hold when rare events of extreme consequences are involved. There is a growing recognition of the need for alternative risk measures that do not make these assumptions. We propose a novel method for modeling the risk associated with investment products, in particular derivatives, by using a formal language for specifying financial contracts. Expressions in this language are interpreted in the category of values annotated with (a formal representation of) uncertainty. The choice of uncertainty formalism thus becomes a parameter of the model, so it can be adapted to the particular application and it is not constrained to classical probabilities. We demonstrate our approach using a simple logic-based uncertainty model and a case study in which we assess the risk of counter party default in a portfolio of collateralized loans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=risk%20model" title="risk model">risk model</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20monad" title=" uncertainty monad"> uncertainty monad</a>, <a href="https://publications.waset.org/abstracts/search?q=derivatives" title=" derivatives"> derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=contract%20algebra" title=" contract algebra"> contract algebra</a> </p> <a href="https://publications.waset.org/abstracts/28143/uncertainty-in-risk-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28143.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">4131</span> Estimation of Uncertainty of Thermal Conductivity Measurement with Single Laboratory Validation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowaluck%20Ukrisdawithid">Saowaluck Ukrisdawithid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The thermal conductivity of thermal insulation materials are measured by Heat Flow Meter (HFM) apparatus. The components of uncertainty are complex and difficult on routine measurement by modelling approach. In this study, uncertainty of thermal conductivity measurement was estimated by single laboratory validation approach. The within-laboratory reproducibility was 1.1%. The standard uncertainty of method and laboratory bias by using SRM1453 expanded polystyrene board was dominant at 1.4%. However, it was assessed that there was no significant bias. For sample measurement, the sources of uncertainty were repeatability, density of sample and thermal conductivity resolution of HFM. From this approach to sample measurements, the combined uncertainty was calculated. In summary, the thermal conductivity of sample, polystyrene foam, was reported as 0.03367 W/m·K ± 3.5% (k = 2) at mean temperature 23.5 °C. The single laboratory validation approach is simple key of routine testing laboratory for estimation uncertainty of thermal conductivity measurement by using HFM, according to ISO/IEC 17025-2017 requirements. These are meaningful for laboratory competent improvement, quality control on products, and conformity assessment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20laboratory%20validation%20approach" title="single laboratory validation approach">single laboratory validation approach</a>, <a href="https://publications.waset.org/abstracts/search?q=within-laboratory%20reproducibility" title=" within-laboratory reproducibility"> within-laboratory reproducibility</a>, <a href="https://publications.waset.org/abstracts/search?q=method%20and%20laboratory%20bias" title=" method and laboratory bias"> method and laboratory bias</a>, <a href="https://publications.waset.org/abstracts/search?q=certified%20reference%20material" title=" certified reference material"> certified reference material</a> </p> <a href="https://publications.waset.org/abstracts/115436/estimation-of-uncertainty-of-thermal-conductivity-measurement-with-single-laboratory-validation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115436.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">153</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">4130</span> Agile Supply Chains and Its Dependency on Air Transport Mode: A Case Study in Amazon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabiana%20Lucena%20Oliveira">Fabiana Lucena Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Aristides%20da%20Rocha%20Oliveira%20Junior"> Aristides da Rocha Oliveira Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article discusses the dependence on air transport mode of agile supply chains. The agile supply chains are the result of the analysis of the uncertainty supply chain model, which ranks the supply chain, according to the respective product. Thus, understanding the Uncertainty Model and life cycle of products considered standard and innovative is critical to understanding these. The innovative character in the intersection of supply chains arising from the uncertainty model with its most appropriate transport mode. Consider here the variables availability, security and freight as determinants for choosing these modes. Therefore, the research problem is: How agile supply chains maintains logistics competitiveness, as these are dependent on air transport mode? A case study in Manaus Industrial Pole (MIP), an agglomeration model that includes six hundred industries from different backgrounds and billings, located in the Brazilian Amazon. The sample of companies surveyed include those companies whose products are classified in agile supply chains , as innovative and therefore live with the variable uncertainty in the demand for inputs or the supply of finished products. The results confirm the hypothesis that the dependency level of air transport mode is greater than fifty percent. It follows then, that maintain agile supply chain away from suppliers base is expensive (1) , and continuity analysis needs to be remade on each twenty four months (2) , consider that additional freight, handling and storage as members of the logistics costs (3) , and the comparison with the upcoming agile supply chains the world need to consider the location effect (4). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20model" title="uncertainty model">uncertainty model</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20transport%20mode" title=" air transport mode"> air transport mode</a>, <a href="https://publications.waset.org/abstracts/search?q=competitiveness" title=" competitiveness"> competitiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics" title=" logistics "> logistics </a> </p> <a href="https://publications.waset.org/abstracts/26173/agile-supply-chains-and-its-dependency-on-air-transport-mode-a-case-study-in-amazon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26173.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">511</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">4129</span> A Robust Optimization Model for the Single-Depot Capacitated Location-Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdolsalam%20Ghaderi">Abdolsalam Ghaderi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location-routing%20problem" title="location-routing problem">location-routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title=" robust optimization"> robust optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a> </p> <a href="https://publications.waset.org/abstracts/83797/a-robust-optimization-model-for-the-single-depot-capacitated-location-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83797.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">269</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">4128</span> Airport Investment Risk Assessment under Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elena%20M.%20Capitanul">Elena M. Capitanul</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20Nunes%20Cosenza"> Carlos A. Nunes Cosenza</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20El%20Moudani"> Walid El Moudani</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20Mora%20Camino"> Felix Mora Camino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The construction of a new airport or the extension of an existing one requires massive investments and many times public private partnerships were considered in order to make feasible such projects. One characteristic of these projects is uncertainty with respect to financial and environmental impacts on the medium to long term. Another one is the multistage nature of these types of projects. While many airport development projects have been a success, some others have turned into a nightmare for their promoters. This communication puts forward a new approach for airport investment risk assessment. The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk. Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming. An illustration of the proposed approach is provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=airports" title="airports">airports</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=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/14093/airport-investment-risk-assessment-under-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14093.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">413</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">4127</span> Decision Making Approach through Generalized Fuzzy Entropy Measure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20D.%20Arora">H. D. Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjali%20Dhiman"> Anjali Dhiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertainty is found everywhere and its understanding is central to decision making. Uncertainty emerges as one has less information than the total information required describing a system and its environment. Uncertainty and information are so closely associated that the information provided by an experiment for example, is equal to the amount of uncertainty removed. It may be pertinent to point out that uncertainty manifests itself in several forms and various kinds of uncertainties may arise from random fluctuations, incomplete information, imprecise perception, vagueness etc. For instance, one encounters uncertainty due to vagueness in communication through natural language. Uncertainty in this sense is represented by fuzziness resulting from imprecision of meaning of a concept expressed by linguistic terms. Fuzzy set concept provides an appropriate mathematical framework for dealing with the vagueness. Both information theory, proposed by Shannon (1948) and fuzzy set theory given by Zadeh (1965) plays an important role in human intelligence and various practical problems such as image segmentation, medical diagnosis etc. Numerous approaches and theories dealing with inaccuracy and uncertainty have been proposed by different researcher. In the present communication, we generalize fuzzy entropy proposed by De Luca and Termini (1972) corresponding to Shannon entropy(1948). Further, some of the basic properties of the proposed measure were examined. We also applied the proposed measure to the real life decision making problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=entropy" title="entropy">entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20entropy" title=" fuzzy entropy"> fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20fuzzy%20entropy" title=" generalized fuzzy entropy"> generalized fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a> </p> <a href="https://publications.waset.org/abstracts/26513/decision-making-approach-through-generalized-fuzzy-entropy-measure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26513.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">448</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">4126</span> Uncertainty Estimation in Neural Networks through Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20James">Ashish James</a>, <a href="https://publications.waset.org/abstracts/search?q=Anusha%20James"> Anusha James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20estimation" title="uncertainty estimation">uncertainty estimation</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=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/153501/uncertainty-estimation-in-neural-networks-through-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153501.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">135</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">4125</span> Establishment of the Regression Uncertainty of the Critical Heat Flux Power Correlation for an Advanced Fuel Bundle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Q.%20Yuan">L. Q. Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Yang"> J. Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siddiqui"> A. Siddiqui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CHF%20experiment" title="CHF experiment">CHF experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=CHF%20correlation" title=" CHF correlation"> CHF correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20uncertainty" title=" regression uncertainty"> regression uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Method" title=" Monte Carlo Method"> Monte Carlo Method</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20Series%20Method" title=" Taylor Series Method"> Taylor Series Method</a> </p> <a href="https://publications.waset.org/abstracts/77556/establishment-of-the-regression-uncertainty-of-the-critical-heat-flux-power-correlation-for-an-advanced-fuel-bundle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77556.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">416</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">4124</span> Soft Computing Employment to Optimize Safety Stock Levels in Supply Chain Dairy Product under Supply and Demand Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riyadh%20Jamegh">Riyadh Jamegh</a>, <a href="https://publications.waset.org/abstracts/search?q=Alla%20Eldin%20Kassam"> Alla Eldin Kassam</a>, <a href="https://publications.waset.org/abstracts/search?q=Sawsan%20Sabih"> Sawsan Sabih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to overcome uncertainty conditions and inability to meet customers' requests due to these conditions, organizations tend to reserve a certain safety stock level (SSL). This level must be chosen carefully in order to avoid the increase in holding cost due to excess in SSL or shortage cost due to too low SSL. This paper used soft computing fuzzy logic to identify optimal SSL; this fuzzy model uses the dynamic concept to cope with high complexity environment status. The proposed model can deal with three input variables, i.e., demand stability level, raw material availability level, and on hand inventory level by using dynamic fuzzy logic to obtain the best SSL as an output. In this model, demand stability, raw material, and on hand inventory levels are described linguistically and then treated by inference rules of the fuzzy model to extract the best level of safety stock. The aim of this research is to provide dynamic approach which is used to identify safety stock level, and it can be implanted in different industries. Numerical case study in the dairy industry with Yogurt 200 gm cup product is explained to approve the validity of the proposed model. The obtained results are compared with the current level of safety stock which is calculated by using the traditional approach. The importance of the proposed model has been demonstrated by the significant reduction in safety stock level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inventory%20optimization" title="inventory optimization">inventory optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20computing" title=" soft computing"> soft computing</a>, <a href="https://publications.waset.org/abstracts/search?q=safety%20stock%20optimization" title=" safety stock optimization"> safety stock optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=dairy%20industries%20inventory%20optimization" title=" dairy industries inventory optimization"> dairy industries inventory optimization</a> </p> <a href="https://publications.waset.org/abstracts/102674/soft-computing-employment-to-optimize-safety-stock-levels-in-supply-chain-dairy-product-under-supply-and-demand-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102674.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">125</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">4123</span> Decision Making under Strict Uncertainty: Case Study in Sewer Network Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhen%20Wu">Zhen Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Lupien%20St-Pierre"> David Lupien St-Pierre</a>, <a href="https://publications.waset.org/abstracts/search?q=Georges%20Abdul-Nour"> Georges Abdul-Nour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20criteria" title="decision criteria">decision criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=sewer%20network%20planning" title=" sewer network planning"> sewer network planning</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=strict%20uncertainty" title=" strict uncertainty"> strict uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/64139/decision-making-under-strict-uncertainty-case-study-in-sewer-network-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64139.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">559</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">4122</span> Competition and Cooperation of Prosumers in Cournot Games with Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong-Heng%20Shi">Yong-Heng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Hao"> Peng Hao</a>, <a href="https://publications.waset.org/abstracts/search?q=Bai-Chen%20Xie"> Bai-Chen Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solar prosumers are playing increasingly prominent roles in the power system. However, its uncertainty affects the outcomes and functions of the power market, especially in the asymmetric information environment. Therefore, an important issue is how to take effective measures to reduce the impact of uncertainty on market equilibrium. We propose a two-level stochastic differential game model to explore the Cournot decision problem of prosumers. In particular, we study the impact of punishment and cooperation mechanisms on the efficiency of the Cournot game in which prosumers face uncertainty. The results show that under the penalty mechanism of fixed and variable rates, producers and consumers tend to take conservative actions to hedge risks, and the variable rates mechanism is more reasonable. Compared with non-cooperative situations, prosumers can improve the efficiency of the game through cooperation, which we attribute to the superposition of market power and uncertainty reduction. In addition, the market environment of asymmetric information intensifies the role of uncertainty. It reduces social welfare but increases the income of prosumers. For regulators, promoting alliances is an effective measure to realize the integration, optimization, and stable grid connection of producers and consumers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cournot%20games" title="Cournot games">Cournot games</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20market" title=" power market"> power market</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=prosumer%20cooperation" title=" prosumer cooperation"> prosumer cooperation</a> </p> <a href="https://publications.waset.org/abstracts/163232/competition-and-cooperation-of-prosumers-in-cournot-games-with-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163232.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span 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