Coronavirus Optimization Algorithms for Minimizing Earliness, Tardiness, and Anticipation of Due Dates in Permutation Flow Shop Scheduling

This study addresses a permutation flow shop scheduling problem to minimize a linear combination of earliness, tardiness, and assigned due dates. The production environment under study has several real-world applications, such as manufacturing, industrial maintenance, integrated production–distribution systems, potential disruptive environments, and other scenarios in which the due date is defined during sales negotiations with the customer. Four mixed-integer linear programming formulations were proposed and computationally evaluated, and their results were employed to measure the solution quality of the metaheuristics. Due to the complexity of the tackled problem, four coronavirus optimization algorithms based on recent optimization literature were implemented to solve it. A statistical analysis based on ANOVA (analysis of variance) and Tukey tests was performed to evaluate if the difference between the metaheuristics is statistically significant. The computational results demonstrated the superiority of the proposed metaheuristic Covid, especially in large-sized instances, outperforming an algorithm based on an iterated greedy algorithm (IGA) that we implemented for this problem.