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首页> 外文期刊>Journal of computer sciences >Comparison of Exact Algorithms for Rectilinear Distance Single-Source Capacitated Multi-Facility Weber Problems
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Comparison of Exact Algorithms for Rectilinear Distance Single-Source Capacitated Multi-Facility Weber Problems

机译:直线距离单源容量多设施韦伯问题精确算法的比较

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Problem statement: The objective of this study is to develop efficient exact algorithms for a single source capacitated multi-facility location problem with rectilinear distance. This problem is concerned with locating m capacitated facilities in the two dimensional plane to satisfy the demand of n customers with minimum total transportation cost which is proportional to the rectilinear distance between the facilities and their customers. Approach: Two exact algorithms are proposed and compared. The first algorithm, decomposition algorithm, uses explicit branching on the allocation variables and then solve for location variable corresponding to each branch as the original Mixed Integer Programming (MIP) formulation with nonlinear objective function of the problem. For the other algorithm, the new formulation of the problem is first created by making use of a well-known condition for the optimal facility locations. The problem is considered as a p-median problem and the original formulation is transformed to a binary integer programming problem. The classical exact algorithm based on this formulation which is branch-and-bound algorithm (implicit branching) is then used. Results: Computational results show that decomposition algorithm can provide the optimum solution for larger size of the studied problem with much less processing time than the implicit branching on the discrete reformulated problem. Conclusion: The decomposition algorithm has a higher efficiency to deal with the studied NP-hard problems but is required to have efficient MIP software to support.
机译:问题陈述:本研究的目的是针对具有直线距离的单源容量多设施定位问题开发有效的精确算法。这个问题涉及到在二维平面上安置m个设施齐全的设施,以满足n个客户的需求,而总运输成本最小,该成本与设施与其客户之间的直线距离成比例。方法:提出并比较了两种精确算法。第一种算法,分解算法,在分配变量上使用显式分支,然后求解每个分支对应的位置变量,作为具有问题非线性目标函数的原始混合整数规划(MIP)公式。对于其他算法,首先通过使用最佳条件的最佳条件来创建问题的新公式。该问题被视为p中值问题,原始公式被转换为二进制整数编程问题。然后使用基于此公式的经典精确算法,即分支定界算法(隐式分支)。结果:计算结果表明,与离散的重构问题的隐式分支相比,分解算法可以为较大的研究问题提供最佳解决方案,并且处理时间短得多。结论:分解算法在解决NP问题方面具有较高的效率,但需要有高效的MIP软件来支持。

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