Distributed Flow Shop Scheduling with Dynamic Job Arrival via Enhanced Genetic Algorithm

Distributed flow shop scheduling problems considering dynamic job arrival are important issues in manufacturing systems. To address the problem, a mixed integer programming model is formulated to minimize the maximum completion time. Then, an enhanced genetic algorithm is proposed to solve the model. Genetic operators are performed to quickly explore the global solution space. Meanwhile, a local search method is applied to refine the best solution in population. Finally, the developed method is compared against two well-known meta-heuristics on a set of test instances. Experimental results demonstrate that the proposed method exhibits superior performance in addressing distributed flow shop scheduling problems with dynamic job arrival.