Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. (June 2016) - Liu, Chunfeng; Wang, Jufeng; Leung, Joseph Y.-T.
Highlights: Worker assignment and production planning decisions are made simultaneously. Bottleneck workstation may transfer due to learning and forgetting effects. Late delivery and production in advance result in backorder and holding costs. The hybrid bacteria foraging algorithm embeds a heuristic and evolution operators. The superiority of proposed algorithm over other metaheuristics is illustrated. Abstract: We consider a joint decision model of worker assignment and production planning in a dynamic cellular manufacturing system of fiber connector manufacturing industry. On one hand, due to the learning and forgetting effects of workers, the production rate of each workstation will often change. Thus, the bottleneck workstation may transfer to another one in the next period. It is worthwhile to reassign multi-skilled workers such that the production rate of bottleneck workstation may increase. On the other hand, because of the limited production capacity and variety of orders, late delivery or production in advance often occurs at each period. The parts with operational sequence need to be dispatched to the desirable cells for processing. The objective is to minimize backorder cost and holding cost of inventory. To solve this complicated problem, we propose an efficient hybrid bacteria foraging algorithm (HBFA) with elaborately designed solution representation and bacteria evolution operators. A two-phase based heuristic is embedded in the HBFA to generate a high quality initial solution for further search. We tested our algorithm using randomly generated instances by comparing with the original bacteria foraging algorithm (OBFA), discrete bacteria foraging algorithm (DBFA), hybrid genetic algorithm (HGA) and hybrid simulated annealing (HSA). Our results show that the proposed HBFA has better performance than the four compared algorithms with the same running time.