逆向物流系统库存控制模型的仿真优化

IF 1.4 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Hanane Rachih, F. Mhada, R. Chiheb
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引用次数: 8

摘要

如今,公司正在认识到他们在保护环境和节约自然资源方面的首要角色和责任。他们专注于一些当代活动,如逆向物流,这在经济上和环境上都是可行的。然而,这样一个倡议的整合需要流程重组和供应链管理,以增加可持续性和最大化利润。在此背景下,本文研究了针对不同销售价格的新产品和再制造产品两种不同需求的逆向物流系统的库存控制模型。该模型由生产和再制造操作之间的单个共享机器组成,而该机器受制于随机故障和维修。分别考察了退货、新产品和再制造产品的三个库存点。同时,本文利用Arena®对该问题进行了离散事件仿真建模。以寻找总成本最小的近最优库存控制策略为目的,建立了基于禁忌搜索和遗传算法的优化模型。通过算例和灵敏度分析,比较各算法的鲁棒性和结果。然后将两种方法的结果与OptQuest®优化工具的结果进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation optimization of an inventory control model for a reverse logistics system
Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.
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来源期刊
Decision Science Letters
Decision Science Letters Decision Sciences-Decision Sciences (all)
CiteScore
3.40
自引率
5.30%
发文量
49
审稿时长
20 weeks
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