Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rahmad Syah, M. Elveny, Enni Soerjati, J. W. Guerrero, Rawya Read Jowad, Wanich Suksatan, S. Aravindhan, O. Voronkova, D. Mavaluru
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引用次数: 1

Abstract

Abstract Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.
用多目标优化方法优化队列网络中多级位置分配问题
摘要在配电网中使用集线器是一种有效的方法。本文在排队网络的框架内设计了一个位置分配问题的模型,在排队网络中,服务有几个级别,客户必须经过这些级别才能完成服务。该模型的目的是在潜在地点中定位适当数量的设施,并分配客户。该模型是一个多目标非线性混合整数规划模型。目标函数包括客户和系统中的等待时间以及系统中的等候时间的总和,并将设施中失业的最大可能性降至最低。为了求解该模型,将ε约束方法的精确解技术用于多目标优化,并计算问题的Pareto解。此外,对该问题进行了敏感性分析,结果表明该问题对客户需求率具有敏感性。基于所获得的结果,可以得出结论,所提出的模型能够极大地求和系统中的客户和等待时间,并降低所有设施在几个级别上的最大失业概率。还可以通过为每个客户选择车辆来进一步开发该车型。
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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