Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning

This article presents a two-stage approach to solve a multi-dynamic virtual cellular manufacturing system in multi-market allocation and production planning. The first stage identifies the plant locations and grouping parts-machines-workers' assignment to virtual cells, while the second stage determines the production volume for each plant, the allocated amounts to each market and the operations' allocation to the virtual cells. The goal of the proposed mathematical model is to minimize total expected cost consisting of holding, outsourcing, inter-cell material handling, external transportation, fixed cost for producing each part in each plant as well as machine and labour salaries. It is assumed that the parts' processing times on each machine with workers as well as the products' demands are stochastic and described by discrete scenario enrichment using the sample average approximation and Latin hypercube sampling techniques. To solve such a stochastic model, an efficient method based on the genetic algorithm is then utilized. Also, Taguchi method is employed so as to evaluate the effects of different operators and parameters on the performance of genetic algorithm. Finally, computational experiments on randomly generated test problems are presented to demonstrate the performance and power of developed model in handling uncertainty.