A Hybrid Genetic Algorithm for Dual-Resource Constrained Job Shop Scheduling Problem

This paper presents a new scheduling approach based on Genetic Algorithm. This algorithm is developed to address the scheduling problem in manufacturing systems constrained by both machines and heterogeneous workers, which is called as Dual Resource Constrained Job Shop Scheduling Problem. In this algorithm, the evolutionary experience of parent chromosomes is inherited by branch population, which prevents premature and retain excellent gene. Some other optimization mechanisms, such as the elite evolutionary operator, the roulette selection operator with sector partition, the scheduling strategy based on compressed time window, are proposed to improve the algorithm. The performances of proposed approach are verified according to simulation experiments with random benchmark instances while related discussions are represented at last.