Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

Abstract

This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different production rates and the required number of workers are available. We propose a three-objective mixed-integer linear programming mathematical model that aims to maximize the manufacturer's total benefit, parts' safety stock (SS) index, and the workforce's physical distance over a finite horizon (one year) by determining the optimal scheduling of the parts on the machines. Since a large production scheduling problem belongs to the Np-Hard category of problems, a non-dominated sorting genetic algorithm, and a non-dominated ranked GA algorithm are developed to solve the presented model in two stages using the empirical data from a Canadian plastic injection mold company. In the first stage, the LP-metrics approach is utilized for validating the meta-heuristics on a reduced-size problem. In the second stage, the validated meta-heuristics are utilized to optimize the company's yearly production schedule. The results indicate both metaheuristics are performing well in determining the optimal solution. Moreover, implementing physical distancing in the company reduces the company's monthly net benefit by around 9.56% compared to the normal operational conditions (without considering physical distancing).