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A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

机译:同时拾取和交付的车辆路径问题修改的荟萃启发式方法

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The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection? Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.
机译:这项工作的目的是基于增强的VNS元启发式制定智能优化软件,以通过同时取电和交付(VRPSPD)来解决车辆路由问题。基于增强的可变邻域搜索开发的优化系统,采用扰动机制和自适应选择机制,作为本工作中呈现的简单但有效的优化方法。通过将基于扰动的变量邻域搜索(PVNS)与自适应选择组合来组成的解决方案方法?控制扰动方案的机制(ASM)。代替随机方法,在算法中选择的扰动方案选择基于搜索的每个扰动方案的经验选择。 ASM帮助算法在搜索过程中使用大多数成功的扰动方案来获得更多多样化程度和从局部最佳条件跳跃。提出了一种具有众所周知的精确方法的比较分析,以在有限的计算时间中测试生成的VRPSPD基准实例中的解决方法。然后进行液化石油气配送公司提供的VRPSPD情景的测试。测试结果证实,解决方案方法对具有更大尺寸实例提供最佳解决方案的精确方法解决方案的优异性能,并且与经销商公司使用的基本VNS和原始路线规划技术相比,成功获得了大量改进。

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