Parallel-machine scheduling with load-balancing and sequence-dependent setups

Abstract

In many practical manufacturing environments, setups consume a significant amount of industrial resources. Therefore, reducing setups in a non-identical parallel machine environment will significantly enhance a company's performance level. In this thesis, the problem of minimizing total completion time with load balancing and sequence-dependent setups in a non-identical parallel machine environment was studied. A mathematical model for minimizing total completion time with a workload-balancing constraint is presented. Since this problem is an NP-hard problem, some simple heuristics and a genetic algorithm were developed for efficient scheduling of resources. Both were tested on random data.