This paper introduces a new generic problem to the literature of Workforce Scheduling and Routing Problem. In this problem, multiple workers are assigned to a shared vehicle based on their qualifications and customer demands, and then the route is formed so that a traveler may be dropped off and picked up later to minimize total flow time. We introduced a mixed-integer linear programming model for the problem. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) algorithm was developed with problem-specific heuristics and a decomposition-based constructive upper bound algorithm (UBA). To analyze the impact of newly introduced policies, service area, difficulty of service, distribution of care, and number of demand nodes type instance characteristics are considered. The empirical analyses showed that the ALNS algorithm presents solutions with up to 35% less total flow time than the UBA. The implementation of the proposed drop-off and pick-up (DP) and vehicle sharing policies present up to 24% decrease in total flow time or provide savings on the total cost of service especially when the demand nodes are located in small areas like in urban areas.
翻译:本文为劳动力排期和出勤问题文献提出了一个新的通用问题。在此问题上,多工人被分配到一个基于其资格和客户要求的共用车辆上,然后形成路线,以便将旅行者丢弃,然后取走,以尽量减少总流量时间。我们为该问题采用了混合整数线性编程模式。为解决这一问题,开发了一个适应性大邻里搜索算法,配有特定问题的超速和基于分解的建设性高约束算法(UBA)。为了分析新推出的政策、服务领域、服务困难、护理分配和需求节点类型特点的影响,考虑了经验分析结果分析表明,ALNS算法提出的解决办法比UBA总流时少35%。拟议的辍学和集款(DP)和车辆共享政策的实施在总流量时间上减少了24%,或者在服务总成本方面节省了费用,特别是在需求节点位于像城市地区这样的小地区的情况下。</s>