2021/11/21 by Jangwon Park, Park, Jangwon, Evangelos Vrettos +1
Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Scheduling and Optimization Algorithms #Scheduling and Timetabling Solutions #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2111.10845
openalex publication_date 2021/11/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Employee rostering is a process of assigning available employees to open\nshifts. Automating it has ubiquitous practical benefits for nearly all\nindustries, such as reducing manual workload and producing flexible,\nhigh-quality schedules. In this work, we develop a hybrid methodology which\ncombines Mixed-Integer Linear Programming (MILP) with scatter search, an\nevolutionary algorithm, having as use case the optimization of employee\nrostering for Swissgrid, where it is currently a largely manual process. The\nhybrid methodology guarantees compliance with labor laws, maximizes employees'\npreference satisfaction, and distributes workload as uniformly as possible\namong them. Above all, it is shown to be a robust and efficient algorithm,\nconsistently solving realistic problems of varying complexity to\nnear-optimality an order of magnitude faster than an MILP-alone approach using\na state-of-the-art commercial solver. Several practical extensions and use\ncases are presented, which are incorporated into a software tool currently\nbeing in pilot use at Swissgrid.\n