An Efficient Elite-Based Simulation-Optimization Approach for Stochastic Resource Allocation Problems in Manufacturing and Service Systems

Chun-Chih Chiu, James T. Lin
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引用次数: 2

Abstract

Stochastic resource allocation problems (SRAPs) involve determining the optimal configuration of a limited resource to achieve an objective function under given constraints and random effects in manufacturing systems (MSs) and service systems (SSs). The problems are traditionally solved by determining the optimal solution. It is generally preferable to determine as many global optima as possible, or at least a small set of diverse but good candidates, to help the decision-maker rapidly adopt alternative solutions from the set if one solution is unsuitable. However, many local or global optima occur in SRAPs in MSs and SSs due to the interaction between random system factors, such as processing time uncertainty and machine failure rates. Thus, enhancing the searching efficiency of algorithms for SRAPs is a challenge. This study proposes an efficient simulation–optimization approach, called elite-based particle swarm optimization (EPSO), using an optimal replication allocation strategy (ORAS) (i.e., EPSO[Formula: see text], to address three types of SRAPs from the literature. Three simulation models were constructed to evaluate the system performance under random factors. We developed a novel EPSO to explore and exploit the solution space. We created an elite group (EG) that includes multiple solutions, and each solution of the EG has a statistically nonsignificant difference from the current optimal solution. The new feature of EPSO updates the velocity and position of the particles in the design space based on multiple global optima from the EG to enhance diversity and prevent premature convergence. We propose an ORAS to allocate a limited number of replications to each solution. Three numerical experiments were performed to verify the effectiveness and efficiency of EPSO[Formula: see text] compared with other simulation–optimization approaches, namely particle swarm optimization (PSO) and the genetic algorithm (GA) with both optimal computing budget allocation (OCBA) and the ORAS. The experimental results reveal that the solution quality of EPSO improved compared with that of PSO and GA, and the ORAS provides a more efficient allocation of the number of replications compared with the OCBA in the three experiments. Finally, the proposed approach also provides an elite set at the end of the algorithm, instead of a single optimal solution, to support decision-making.
制造与服务系统随机资源分配问题的高效精英仿真优化方法
在制造系统和服务系统中,随机资源分配问题涉及在给定约束和随机效应下确定有限资源的最佳配置以实现目标函数的问题。传统上通过确定最优解来解决这些问题。通常最好确定尽可能多的全局最优,或者至少确定一组多样但良好的候选,以便在一个解决方案不合适的情况下帮助决策者迅速从一组解决方案中采用替代解决方案。然而,由于随机系统因素(如加工时间不确定性和机器故障率)之间的相互作用,在MSs和SSs中的srap中会出现许多局部或全局最优。因此,提高srap算法的搜索效率是一个挑战。本研究提出了一种高效的模拟优化方法,称为基于精英的粒子群优化(EPSO),该方法使用最优复制分配策略(ORAS)(即EPSO[公式:见文本])来解决文献中的三种类型的srap。建立了三个仿真模型来评估系统在随机因素下的性能。我们开发了一种新颖的EPSO来探索和利用解决方案空间。我们创建了一个包含多个解的精英组(EG), EG的每个解与当前最优解的差异在统计上不显著。EPSO的新特性是基于EG的多个全局最优来更新粒子在设计空间中的速度和位置,以增强多样性和防止过早收敛。我们建议使用ORAS为每个解决方案分配有限数量的副本。通过三个数值实验验证了EPSO[公式:见文本]与其他模拟优化方法(即粒子群优化(PSO)和遗传算法(GA)同时具有最优计算预算分配(OCBA)和ORAS)的有效性和效率。实验结果表明,与PSO和GA相比,EPSO的溶液质量有所提高,ORAS在三个实验中比OCBA更有效地分配了重复数。最后,该方法还在算法的最后提供了一个精英集,而不是单一的最优解,以支持决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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