Minimizing the number of late jobs for the permutation flowshop problem with secondary resources

This paper considers a permutation flowshop problem with secondary resources with the objective of minimizing the number of tardy jobs. The number of secondary resources assigned to the machines (workcenters), as well as the allocation of resources among the various machines, will play a significant role in the time required to process each job by its specified due date. This problem finds application in a large number of environments including manufacturing, maintenance, warehousing operations, as well as in healthcare. The research presents a lower bound for the permutation flowshop problem and evaluates its performance against the optimal solution for small, medium, and large instances. Several heuristics, including neighborhood search and simulated annealing, are presented to generate the secondary resource assignment and the allocation of jobs to the schedule. The computational complexity of the lower bound and computational examples for the heuristics are discussed.