...
首页> 外文期刊>Transportation Research >The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks
【24h】

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

机译:自学习粒子群优化方法,用于在多个交叉码头进行物料搬运的情况下,路由多个产品的取货和交付

获取原文
获取原文并翻译 | 示例
           

摘要

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO. (C) 2016 Elsevier Ltd. All rights reserved.
机译:具有对接操作的配送中心中的车辆路径问题(VRP)比传统的更为复杂。本文试图解决具有多个用于处理多种产品的跨码头的配送中心的VRP。在本文中,数学模型旨在最大程度地减少受到一组约束的运营总成本。由于模型的复杂性很高,因此可以使用带有自学习策略的粒子群优化(PSO)变体来解决,即SLPSO。为了验证SLPSO方法的有效性,SLPSO解决了文献中的基准问题和测试问题。 (C)2016 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号