Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming. (August 2017) - Feng, Hanxin; Da, Wen; Xi, Lifeng; Pan, Ershun; Xia, Tangbin
Highlights: Developing a comprehensive model for the integrated cell formation and worker assignment problem. Incorporating many cellular manufacturing systems design attributes. Proposing a hybrid approach combining combinatorial particle swarm optimization and linear programming (GA-LP). Consideration of worker over-assignment reduces worker quantity, improve utilization rate, and generate cost saving. The proposed approach outperforms CPLEX, GA, CPSO and GA-LP. Abstract: Both cell design and human issues are important factors for successful implementation of cellular manufacturing. To better implement cellular manufacturing, we investigate the integrated cell formation and worker assignment problem (ICFWAP). A comprehensive linear model is developed for the ICFWAP to determine the optimal allocation of machines, parts and workers. Specific characteristics of this model include the simultaneous consideration of production planning, coexistence of alternative process routings, lot splitting, workload balancing between cells and worker over-assignment to multiple cells. Motivated by the inefficiency of exact approaches, this paper proposes a hybrid approach combining combinatorial particle swarm optimization and linear programming (CPSO-LP) to efficiently solve real-sized problems. In CPSO-LP, decision variables corresponding to part routing selection and part operation assignment are fixed and other variables are allowed to be changed. CPLEX is then used to solve the reduced LP problem. Numerical experiments validate the proposed model. Results reveal that worker over-assignment can reduce the number of workers hired and improve labor utilization rate. The better efficiency and effectiveness of CPSO-LP are proved by comparisons with CPLEX, a genetic algorithm (GA), CPSO, and a hybrid approach combining GA and LP.