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Solving a continuous location-routing problem by use of a self-organizing map

机译:通过使用自组织映射来解决连续的位置路由问题

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摘要

Purpose - A variant of Kohonen's algorithm for the self-organizing map (SOM) is used to solve a continuous location-routing problem that can be applied to identify potential sites for subsequent selection by a discrete finite set model. The paper aims to show how the algorithm may be customized to fit the problem structure in a way that allows aspects of location and routing to be integrated into the solution procedure. Design/methodology/approach - A set of test instances is used to compare the solutions of the neural network to those obtained by sequential approaches based on a savings procedure. Findings - Compared to the results of the sequential approaches, the neural network yields good results. Research limitations/implications - Future work may cover the expansion of the neural approach to multi-depot and multi-stage problems. Additionally, application of procedures other than the savings procedure should be evaluated with respect to their potential for further enhancing the solution quality of the sequential approaches. Practical implications - This paper shows that strategic location decisions in practical applications with long-term customer relationships can be taken using simultaneously generated routing information on an operational level. Originality/value - The paper provides a new variety of applications for SOM as well as high quality results for the specific type of problem considered.
机译:目的-Kohonen自组织地图(SOM)算法的一种变体用于解决连续的位置路由问题,该问题可用于识别潜在的地点,以通过离散有限集模型进行后续选择。本文旨在展示如何定制算法以适合问题结构,从而将位置和路由方面集成到解决过程中。设计/方法/方法-一组测试实例用于将神经网络的解决方案与通过基于节省程序的顺序方法获得的解决方案进行比较。发现-与顺序方法的结果相比,神经网络产生了良好的结果。研究局限性/意义-未来的工作可能涉及将神经方法扩展到多仓库和多阶段问题。此外,应就节省程序以外的其他程序的应用潜力进行评估,以进一步提高顺序方法的解决方案质量。实际意义-本文表明,在具有长期客户关系的实际应用中,可以使用操作级别上同时生成的路由信息​​来进行战略性位置决策。原创性/价值-本文为所考虑的特定类型的问题提供了SOM的各种新应用以及高质量的结果。

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