An Integrated Decision Making Model for Manufacturing Cell Formation and Supplier Selection

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

2 Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

3 Department of Mechanical and Industrial Engineering, Khayyam University, Mashhad, Iran

Abstract

Optimization of the complete manufacturing and supply process has become a critical ingredient for gaining a competitive advantage. This article provides a unified mathematical framework for modeling manufacturing cell configuration and raw material supplier selection in a two-level supply chain network. The commonly used manufacturing design parameters along with supplier selection and a subcontracting approach are incorporated into our mathematical model. To the authors’ knowledge, there is no single model which integrates all of these attributes simultaneously. A sensitivity analysis is also performed to study the effects of this integration. An efficient meta-heuristic based on Genetic Algorithm (GA) search procedure is employed to effectively solve the model in medium and large scales. We improve the GA search mechanism by proper combination of linear programming optimization technique and GA in a cooperative framework. Computational results show that our hybrid solution technique can find satisfactory solutions in a timely manner.

Keywords

Main Subjects


Aalaei, A., & Davoudpour, H. (2016). Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: A case study. Engineering Applications of Artificial Intelligence, 47, 3-15.
Aalaei, A., & Davoudpour, H. (2016). Two bounds for integrating the virtual dynamic cellular manufacturing problem into supply chain management. Journal of Industrial and Management Optimization, 12(3), 907-930.
Aalaei, A., & Davoudpour, H. (2017). A Robust Optimization Model for Cellular Manufacturing System into Supply Chain Management. International Journal of Production Economics, 183(C), 667-679.
Akinc, U. (1993). Selecting a set of vendors in a manufacturing environment. Journal of Operations Management, 11(2), 107-122.
Benhalla, S., Gharabi, A., & Olivier, C. (2011). Multi-plant cellular manufacturing design within a supply chain. Journal of Operations and Logistics, 4(1), II.1-II.17.
Defersha, F. M., & Chen, M. (2008). A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. European Journal of Operational Research, 187(1), 46-69.
Heydari, H., Paydar, M. M., & Mahdavi, I. (2017). An integrated model of cellular manufacturing and supplier selection considering product quality. Journal of Optimization in Industrial Engineering, 22, 39-48.
Jayaswal, S., & Adil, G. K. (2004). Efficient algorithm for cell formation with sequence data, machine replications and alternative process routings. International Journal of Production Research, 42(12), 2419-2433.
Mahdavi, I., Paydar, M. M., Solimanpur, M., & Heidarzade, A. (2009). Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Systems with Applications, 36(3), 6598-6604.
Noktehdan, A., Seyedhosseini, S., & Saidi-Mehrabad, M. (2016). A meta-heuristic algorithm for the manufacturing cell formation problem based on grouping efficacy. International Journal of Advanced Manufacturing Technology, 82(1), 25-37.
Papaioannou, G., & Wilson, J. M. (2010). The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research. European Journal of Operational Research, 206(3), 509-521.
Paydar, M. M., & Saidi-Mehrabad, M. (2013). A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy. Computers & Operations Research, 40(4), 980-990.
Paydar, M. M., & Saidi-Mehrabad, M. (2015). Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. International Journal of Computer Integrated Manufacturing, 28(3), 251-265.
Paydar, M. M., Saidi-Mehrabad, M., & Teimoury, E. (2014). A robust optimization model for generalized cell formation problem considering machine layout and supplier selection. International Journal of Computer Integrated Manufacturing, 27(8), 772-786.
Qi, X. (2007). Order splitting with multiple capacitated suppliers. European Journal of Operational Research, 178(2), 421-432.
Rabbani, M., Bavil-Oliaei, M. T., Farrokhi-Asl, H., & Mobini, M. (2017). A new mathematical model in cell formation problem with consideration of inventory and backorder: Genetic and particle swarm optimization algorithms. Iranian journal of management studies, 10(4), 819-852.
Rao, P. P., & Mohanty, R. P. (2003). Impact of cellular manufacturing on supply chain management: Exploration of interrelationships between design issues. International Journal of Manufacturing Technology Management, 5(5/6), 507-520.
Saxena, L. K., & Jain, P. K. (2011). An integrated model of dynamic cellular manufacturing and supply chain system design. International Journal of Advanced Manufacturing Technology, 62(1-4), 385-404.
Schaller, J. (2008). Incorporating cellular manufacturing into supply chain design. International Journal of Production Research, 46(17), 4925-4945.
Treleven, M., & Schweikhart, S. B. (1988). A risk/benefit analysis of sourcing strategies: Single vs. multiple sourcing. Journal of operations Management, 7(3-4), 93-114.
Wemmerlov, U., & Johnson, D. J. (1997). Cellular manufacturing at 46 user plants: Implementation experiences and performance improvements. International Journal of Production Research, 35(1), 29-49.
Wu, T. H., Chung, S. H., & Chang, C. C. (2009). Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings. Expert Systems with Applications, 36(2), 3652-3661.
Wu, X., Chu, C. H., Wang, Y., & Yue, D. (2007). Genetic algorithms for integrating cell formation with machine layout and scheduling. Computers & Industrial Engineering, 53(2), 277-289.