可变邻域搜索:一个高约束劳动力调度问题的案例研究

Kenneth N. Reid, Jingpeng Li, J. Swan, A. McCormick, G. Owusu
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引用次数: 7

摘要

本文描述了一种结合Metropolis-Hastings接受的可变邻域搜索(VNS)来解决现场服务操作(FSO)公司典型的高度受限的劳动力调度问题。首先设计了一种改进的贪心算法,生成满足所有硬约束和部分软约束的初始解;然后使用VNS交换不太有希望的组合,不断向最优解决方案移动,直到满足整理要求,这要么是一个令人满意的平均适应度集作为参数,要么是一个小时的时间允许。与独立贪婪算法相比,该方法的结果是有希望的,当与预期需求数据和实际员工数据参数化时,该方法的适应度平均提高了10.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variable Neighbourhood Search: A case study for a highly-constrained workforce scheduling problem
This paper describes a Variable Neighbourhood Search (VNS) combined with Metropolis-Hastings acceptance to tackle a highly constrained workforce scheduling problem typical of field service operations (FSO) companies. A refined greedy algorithm is firstly designed to create an initial solution which meets all hard constraints and satisfies some of the soft constraints. The VNS is then used to swap out less promising combinations, continually moving towards a optimal solution until meeting finishing requirements, which are either a satisfactory mean fitness set as a parameter, or a time allowance of one hour. The results of this approach are promising when compared to the stand-alone greedy algorithm, and have showed an average of 10.3% increase in fitness when parameterized with expected demand data and real employee data.
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