| 废旧汽车再制造逆向物流网络模型优化 |
| 投稿时间：2017-03-28 修订日期：2017-11-07 点此下载全文 |
| 引用本文：董贵颖,胡坚堃,黄有方.废旧汽车再制造逆向物流网络模型优化[J].上海海事大学学报,2018,39(1):60-66. |
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| 基金项目:国家自然科学基金（41505001）；上海市科学技术委员会科研计划（14DZ2280200） |
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| 中文摘要:针对市场对汽车再制造产品的需求不确定和再制造设施投入成本高的问题，引入多等级设施、市场需求率和回收率，通过对废旧汽车再制造逆向物流网络涉及到的各项成本和收入进行权衡，建立一个以收益最大为目标的混合整数规划模型，并用离散粒子群优化算法对模型进行求解，确定再制造物流网络中各设施的数量、位置和等级，以及各设施间的物流量分配。对市场需求率、回收率、设施能力等参数进行灵敏度分析,研究各参数对网络模型的影响。通过仿真实例验证模型和算法的有效性。 |
| 中文关键词:再制造物流 逆向物流 网络设计 设施能力 市场需求 离散粒子群优化（DPSO）算法 |
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| Model optimization of end-of-life automobile remanufacturing reverse logistics network |
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| Abstract:In view of the uncertain market demand of automobile remanufactured products and the high cost of remanufacturing facilities, introducing the multi grade facilities, the market demand rate and the recovery rate, considering each cost and revenue in the reverse logistics network of end of life automobile remanufacturing, a mixed integer programming model with the objective of maximizing profit is constructed. The discrete particle swarm optimization (DPSO) algorithm is used to solve the model. The number, location and grade of the facilities in the remanufacturing reverse logistics network and the flow distribution among the facilities are determined. The sensitivity analysis on the market demand rate, the recovery rate and the facility capacity is given, and their influence on the network model is studied. Through simulating an instance, the validity of the model and the algorithm is verified. |
| keywords:remanufacturing logistics reverse logistics network design facility capacity market demand discrete particle swarm optimization (DPSO) algorithm |
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