基于导向局部搜索的多目标灌顶现场劳动力调度算法

Abdullah Alsheddy, E. Tsang
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引用次数: 3

摘要

基于赋权的劳动力调度是一种让员工参与决策的新方法。它使员工能够在时间表中提出自己的偏好。在这种方法中,员工的参与是通过在雇主的目标上增加一个额外的目标来模拟的,这个目标代表了员工的总体满意度。因此,调度问题成为一个双目标优化问题,其任务是最大化组织目标和员工满意度。本文采用一种基于Pareto的局部搜索元启发式算法——导引Pareto局部搜索(Guided Pareto local search, GPLS)来解决这一问题,导引局部搜索是导引局部搜索的扩展,可以包含多目标场景。计算实验表明,与标准的帕累托局部搜索和单目标优化器相比,GPLS是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A guided local search based algorithm for the multiobjective empowerment-based field workforce scheduling
Empowerment-based workforce scheduling is a new approach that involves employees in the decision making. It enables employees to suggest their own preferences in the schedule. Employee involvement in this approach is modelled by adding to the employer's objective an additional objective that represents the overall employees' satisfaction rate. Thus, the scheduling problem becomes a biobjective optimization problem, where the task is to maximize both organizational objective(s) and employees' satisfaction level. In this paper, this problem is approached by a Pareto based local search metaheuristic, Guided Pareto Local Search (GPLS) which is an extension to the guided local search to contain multiobjective scenarios. Computational experiments show the effectiveness of GPLS, compared to a standard Pareto local search and a single-objective optimizer.
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