A ROBUST CREDIBILITY-BASED FUZZY PROGRAMMING FOR PRODUCTION-ROUTING PROBLEMS WITH CELL FORMATION

Production-routing problem (PRP) integrates different decisions along the supply chain simultaneously. Studies on PRP only determine the amount and time of production in a linear and continuous production system. While for the supply chain with scattered and different customers, it is necessary to have a flexible production system such as a cellular manufacturing system (CMS). The proposed model incorporates the cell formation problem (CFP) into a PRP. This designing problem in CMS determines the allocation of machines and products to cells and reduces the cost of intercellular movements. We apply a Robust credibility-based fuzzy programming (RCFP) approach versus the possibilistic chance-constrained programming (PCCP) for demand uncertainty. We optimize the model by Teaching-Learning-based Optimization (TLBO) and simulated annealing (SA) algorithms. The convergence in results shows the effectiveness of both algorithms, especially the TLBO algorithm. Furthermore, RCFP leads to a more robust solution with the intelligent choice of the confidence level for satisfying chance constraints and less sensitivity to the changes in non-deterministic parameters