An evolutionary approach to complex job-shop and flexible manufacturing system scheduling

This paper presents a genetic algorithm for generalized job-shop problem solving. The generalization includes feeding times, sequences of set-up dependent operations and jobs with different routings among workcentres including ‘multi-identical’ machines. A formulation as an optimization problem with an original chromosome coding and tailored genetic operators are proposed. The algorithm has been tested with benchmarks given in the literature, and bounds for the minimum completion time are reported in order to evaluate the performance in both generalized job-shop and flexible manufacturing system (FMS) scheduling