A Hybrid Genetic Algorithm - based Approach to Solve Parallel Machine Scheduling with Job Delivery Coordination

This paper considers a problem in which orders are processed by either one of two parallel machines and delivered by a single delivery truck to one customer area. Coordination among production stage and distribution stage in the supply chain to achieve ideal overall system performance has become more practical and has received a lot of attention from both industry practitioners and academic researchers. The coordinated scheduling problem of production and distribution operations deals with scheduling orders on the two identical parallel machines and grouping the completed orders into batches for delivery. To solve this complex problem, a regular genetic algorithm (GA) and an efficient approach which is based on a hybrid of GA and a parallel scheduling procedure (PSP) and is called hybrid GA (HGA), are proposed. Experimental results demonstrate that the regular GA and HGA perform very well with respect to the objective function. Besides, the HGA can find even or better solution in a shorter period of time than the regular GA. Thus, the proposed HGA should be the scheduling approach of choice.