Dynamic Integration Of Process Planning And Scheduling With Due Date Assignment

Abstract

As considering among alternative process plans gives a high potential to enhance the production efficiency of manufacturing systems, researchers have investigated the field of integration between decision-making stages including scheduling with due date assignment (SWDDA) and the integrated process planning and scheduling (IPPS). Considering as a potential development from the advantage of SWDDA and IPPS, so far, there are just a few studies working on IPPS with Due Date Assignment (IPPSDDA). In this study, the dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA) is implemented to solve a job shop scheduling problem with random job’s arrival time and fixed period maintenance activity. The objective function is to minimize the raising cost caused by earliness, tardiness, and determine due dates for each job. The metaheuristic algorithm, genetic algorithm (GA), has been developed and proved to be improved by its hybrid with tabu algorithm (TA). Four shop floors different in size have been generated. The performance comparisons of the genetic algorithm (GA) at each shop floor show the efficiency and effectiveness of combining TA into the solving process. In conclusion, computational results show that the proposed combination algorithm (GA/TA) is competitive, give better results than pure metaheuristic (GA), and can effectively generate good solutions for DIPPSDDA problems.