Improved Bacterial Foraging Algorithm for Cell Formation and Product Scheduling Considering Learning and Forgetting Factors in Cellular Manufacturing Systems

Abstract

This article designs a joint decision model to solve cell formation and product scheduling problems together in cellular manufacturing systems. Multifunctional machines and multiskilled workers need to be grouped and assigned to work cells. Because of the latter's learning and forgetting effects, the production rate of each operation that requires the same machine function and is handled by the same pair of a machine and worker is different. Each product with an operation sequence is allowed to move from one machine to another for processing its subsequent operation, which may reduce product processing time at the expense of additional product movement time. In order to solve this intertwined optimization problem, an improved bacterial foraging algorithm (IBFA) is proposed to minimize makespan. In IBFA, a bacterium returns to the best position, once a worse tumble or swimming result occurs. Each bacterium can, thus, always go ahead toward favorable positions in the chemotactic procedure. In IBFA's reproduction strategy, half of the best bacteria are retained; while new bacteria are produced via crossover operations, and selectively retained. Through this method, high-quality and diversified population is produced. Moreover, in the reproduction and elimination-dispersal strategies of IBFA, the best bacterium can be retained to the subsequent generation through a population sorting method. Computational experiments and t-test are conducted to show that the proposed algorithm has the better performance than the classical one, genetic algorithm and two hybridized bacterial foraging algorithms given the same computational budget.