運用基因演算法求解考慮提早/延遲成本與軟時窗限制之整合生產與配送排程問題

Abstract

Nowadays many companies are adapting the make-to-order (MTO) business model in order to reduce their inventory and increase their net profit. The success of this model is based on finding a good balance between reducing the transportation costs and at the same time increasing the customer satisfaction. This study considers an integrated production and distribution scheduling problem in which jobs, ordered by different customers, are processed by one of the identical parallel machines and then delivered by a third-party logistics service provider with unlimited number of capacitated vehicles within a pre-specified soft time windows to different geographical areas. The objective of this problem is to find a joint production and vehicle routing schedule, so that the total earliness penalty, the total tardiness penalty and the distribution costs can be minimized. This problem has been shown to be NP-hard. This study provided a mathematical formulation to describe the problem and designed a genetic algorithm to find near optimal solutions. This new genetic algorithm with two-layers chromosome was proven to be effective for solving the Integrated Production and Distribution Scheduling with Soft Time Windows. Also some factors, such as number of jobs, population size and number of populations’ generations were shown to have a significant impact on the final solution. This study integrated model was proven to be more effective in finding the best solution rather than the sequential model, but the execution time was longer.

Nowadays many companies are adapting the make-to-order (MTO) business model in order to reduce their inventory and increase their net profit. The success of this model is based on finding a good balance between reducing the transportation costs and at the same time increasing the customer satisfaction. This study considers an integrated production and distribution scheduling problem in which jobs, ordered by different customers, are processed by one of the identical parallel machines and then delivered by a third-party logistics service provider with unlimited number of capacitated vehicles within a pre-specified soft time windows to different geographical areas. The objective of this problem is to find a joint production and vehicle routing schedule, so that the total earliness penalty, the total tardiness penalty and the distribution costs can be minimized. This problem has been shown to be NP-hard. This study provided a mathematical formulation to describe the problem and designed a genetic algorithm to find near optimal solutions. This new genetic algorithm with two-layers chromosome was proven to be effective for solving the Integrated Production and Distribution Scheduling with Soft Time Windows. Also some factors, such as number of jobs, population size and number of populations’ generations were shown to have a significant impact on the final solution. This study integrated model was proven to be more effective in finding the best solution rather than the sequential model, but the execution time was longer.