Cyclic Production Scheduling with Remanufacturing

Abstract

The cyclic production scheduling problem is concerned with the development of a least-cost cyclic production schedule for a set of tasks required to produce a set of products over a given production cycle and according to a given delivery schedule. Recently, remanufacturing of used products has become an important area of research due to its environmental and economic benefits. Consequently, many organizations integrated manufacturing and remanufacturing operations to optimize their forward and reverse supply chain processes. There are some studies that have been conducted on cyclic production scheduling, remanufacturing, as well as cyclic production scheduling with remanufacturing systems, which have led to the development of different optimization approaches. But to the best of our knowledge, none of them considered cyclic deliveries in order to build cyclic production schedules integrating both manufacturing and remanufacturing. Therefore, the focus of this research is to develop a mixed integer linear programing model for an integrated cyclic production scheduling with remanufacturing system to satisfy a given cyclic delivery schedule while minimizing the total holding and setup costs under the assumption of a delivery cycle of one week discretized into smaller periods. A sensitivity analysis is conducted by varying the model parameters to study the limits of the developed model and illustrate the effect of these variations on the total holding and setup costs as well as the computational time. The results of the sensitivity analysis indicate that thirty minutes would be the best period length for the developed model. These results also validate the capability of the developed model in solving the problem at hand and finding the optimal, near optimal, and good feasible solutions for a period length equal to thirty minutes. Further, increasing the number of decision variables or increasing the amount of scheduled deliveries and returns will result in the computational time to increase exponentially. Also, varying the holding cost or increasing the return rate will result in an increase in the computational time. Finally, increasing the holding cost or increasing the return rate will result in an increase in the total holding and setup costs.