Modelling and a hybrid genetic algorithm for the equity-oriented worker assignment problem in <i>seru</i> production systems

Worker assignment is critical for developing a flexible and human-centered seru production system (SPS). Most studies in the literature focus on worker with objective of maximizing efficiency, which causes an inescapable consequence that workers high proficiency levels experience overwork compared lower levels. From perspective workload equity, we investigate problem SPS, where heterogeneity diverse skill sets taken into account. A mixed integer nonlinear programming model named equity-oriented proposed this to mitigate inequity among by minimizing maximum workers. The linearized solved CPLEX solver small-scale instances. For efficiently solving large-scale instances, design hybrid genetic algorithm combined local search. Computational experiments demonstrate performance algorithm, both terms computing time solution quality. We find using can significantly balance at cost minimal increases total labor hours.