SCHEDULING OF PARALLEL MACHINES WITH JOB DELIVERY COORDINATION

This paper examines an integrated scheduling model for production and distribution operations. In this model, a set of jobs involving different amounts of storage space in delivery trucks was processed by either one of two parallel machines and delivered by a single truck to one customer area. The objective was to minimize the time required for all jobs to be completed and delivered to the customer area, and the truck returned to the factory; this variable was denoted as Cm. This problem was shown to be NP-hard in the strong sense. Previous related studies have focused on developing optimization-based solutions; however, such optimization-based approaches require a significant amount of computing time to produce an optimal solution. The time required to compute a production schedule is very important in business practice, and thus, we need to develop an effective heuristic to solve these problems. Two heuristics (H1 and H3) and a GA-based algorithm were developed by this study, and simulation experiments were conducted to evaluate the performance of the proposed approaches. The experimental results showed that if transportation time is short or truck capacity is large, then the proposed heuristic H3 should be the scheduling approach of choice; otherwise, the GA-based algorithm is the optimal scheduling approach.