具有时间窗的最优有能力车辆路径问题邓安孟古纳坎蚁群优化

I. A. Soenandi, J. Joice, Budi Marpaung
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引用次数: 4

摘要

近年来,物流和运输成本的最小化已经成为制造公司增加利润的必要条件。一件事是通过优化从每个供应商处获取或运输组件的路线来降低物流和运输成本。以最小化总运输成本为目标的路线优化是车辆路径问题(Vehicle Routing Problems, VRP)中经常遇到的问题。带时间窗的问题有能力车辆路由(CVRPTW)是VRP的一种变体,它考虑了车辆容量和每辆车的服务周期。CVRPTW是一个非多项式困难(NP-Hard)问题,需要一个高效的算法来解决该汽车公司出现的问题。本研究采用蚁群优化(Ant Colony Optimization, ACO)算法,通过多个参数的测试来解决CVRPTW问题。使用蚁群算法的测试结果与该公司采用的方法相比,获得了更快的路径。可在:http://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/1469
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimasi Capacitated Vehicle Routing Problem with Time Windows dengan Menggunakan Ant Colony Optimization
In recent years, minimization of logistics and transportation costs has become essential for manufacturing companies to increase profits. One thing is done to reduce logistics and transportation costs by optimizing the route of taking or transporting components from each supplier. Route optimization to minimize total transportation costs is a problem that often finds in Vehicle Routing Problems (VRP). Problem Capacitated Vehicle Routing with Time Windows (CVRPTW) is one variant of VRP that considers the vehicle capacity and the service period of each vehicle. CVRPTW is a Non-Polynomial Hard (NP-Hard) problem that requires an efficient and effective algorithm in solving problems that occur in this automotive company. This study uses the Ant Colony Optimization (ACO) algorithm by testing using several parameters to solve the CVRPTW problem. The test results using the ACO algorithm obtained a faster route compared to the method applied by the company. Available at: http://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/1469
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