Intelligent Optimization Approach to Cell Formation and Product Scheduling for Multifactory Cellular Manufacturing Systems Considering Supply Chain and Operational Error
Chunfeng Liu, Jufeng Wang, MengChu Zhou, Tao Zhou
Abstract
This study designs a joint decision model to solve cell formation and product scheduling problems together in multifactory cellular manufacturing systems. If a product is processed in a factory with a small logistics cost of its raw material, the logistics cost of the product delivered to the distributor may be large. If managers prefer the pair of equipment unit and worker with a high production rate, operational error rate may be large. Meanwhile, it influences the labor cost, raw material cost, and completion time of each product. Managers can assign the products of a type to several cells for processing. If a cell receives fewer products of a type, it can complete their processing and becomes available to process the products of other types. However, some cells receiving more products of this type may be delayed to handle the products of other types. If a type of product in a cell has a priority to be processed, its backorder cost may be none or reduced, but this schedule may delay other product types with lower priority in the cell, and give rise to their backorder cost. For solving the intertwined optimization problem, an improved bacterial foraging algorithm with a priority rule-based heuristic (IBFAP) is developed to maximize profit. Experiments are conducted to show that IBFAP outperforms the improved bacterial foraging algorithm with no heuristic, the classical bacterial foraging algorithm, genetic algorithm, and simulated annealing.