A Two-Stage Multi-Objective Genetic Algorithm for a Flexible Job Shop Scheduling Problem with Lot Streaming

The work in this paper is motivated by a recently published article which the authors developed an efficient two-stage genetic algorithm for comprehensive model of flexible job-shop scheduling problem (FJSP). In paper, we extend application to solve lot streaming FJSP while at same time expanding incorporate multiple objectives. objective function terms included our current are minimization (1) makespan, (2) maximum sublot flowtime, (3) total flow time, (4) job (5) (6) finish-time separation, (7) (8) machine load, (9) and (10) load difference. Numerical examples presented illustrate greater need multi-objective optimization larger problems, interaction various terms, their relevance providing better solution quality. ability jointly optimize all also investigated. results show that can generate initial solutions highly improved terms. It outperforms regular convergence speed final quality solving streaming. We demonstrate high-performance parallel computation further improve performance algorithm. Nevertheless, sequential with single CPU uses many CPUs, asserting superiority proposed