Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy al constraints while meeting demand requirement of packed products from various product fam-ilies. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore, we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromo-somes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to de-termine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for com-parison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, al heuristics show the capability to solve large instances within reason-able computational time. In al problem instances, genetic algorithm averagely outperforms ant colony optimiza-tion and Tabu search with slightly longer computational time.