عنوان مقاله [English]
One of the most important decisions that must be made in production systems is determining the product mix. That means which and how much of the product should be made from it in order to increase the final output of the system. Often, in previously existing algorithms, all problem parameters are assumed to be certain and decision-making used to be carried out. The situation studied in this paper is that all the production parameters are in the form of triangular fuzzy numbers. Those production parameters include weekly demand, selling price, cost of raw materials, the processing time of products and the available capacity of resources. In the proposed algorithm, considering the multi-bottleneck, with the help of fuzzy Vikor, the prioritization of production is calculated. Finally, in order to explain the aforementioned method, a numerical example has been discussed.
1- Aryanezhad, M. B., Badri, S. A. and RashidiKomijan, A. (2010). Threshold-based method for elevating the system’s constraint under theory of constraints.International Journal of Production Research, 48(17), 5075–5087.
2- Plenert, G. (1993). Optimized theory of constraints when multiple constrained resources exist.European Journal of Operational Research, 70(1), 126–133.
3- Luebbe, R. and Finch, B. (1992). Theory of constraints and linear programming: a comparison. International Journal of Production Research, 30(6), 1471–1478.
4- Lee, T. N. and Plenert, G. (1993). Optimizing theory of constraints when new product alternatives exist.Production and Inventory Management Journal, 35(6), 51–57.
5- Fredendall, L. D. and Lea, B. R. (1997). Improving the product mix heuristic in the theory of constraints.International Journal of Production Research, 35(6), 1535–1544.
6- Onwbolu, G. C. and Mutingi, M. (2001). A genetic algorithm approach to the theory of constraints product mix problems. Production Planning & Control, 12(1), 21–27.
7- Onwbolu, G. C. and Mutingi, M. (2001). Optimizing the multiple constrained resources product mix problem using genetic algorithms.International Journal of Production Research, 39(9), 1897–1910.
8- Onwbolu, G. C. (2001). Tabu search-based algorithm for the TOC product mix decision. International Journal of Production Research, 39(10), 2065–2076.
9- Aryanezhad, M. B. and Komijan, A. R. (2004). An improved algorithm for optimizing product mix under the theory of constraints.International Journal of Production Research, 42(20), 4221–4233.
10- Mishra, N., Prakash, Tiwari, M. K., Shankar, R. and Chan, T. S. (2005). Hybrid tabu-simulated annealing based approach to solve multi-constraint product mix decision problem. Expert Systems with Applications, 29(2), 446–454.
11- RashidiKomijan, A. and Sadjadi, S. J. (2005). Optimizing Product Mix in a Multi-bottleneck Environment Using Group Decision-Making Approach.Computational Science and Its Applications, 3483, 388–396.
12- Bhattacharya, A. and Vasant, P. (2007). Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP, European Journal of Operational Research, 177(1), 55–70.
13- Tsai, W. H., Lai, C. W. and Chang, J. C. (2007). An algorithm for optimizing joint products decision based on the Theory of Constraints. International Journal of Production Research, 45(15), 3421–3437.
14- Rezaie, K., Nazari-Shirkouhi, S. and Manouchehrabadi, B. (2009). Particle swarm optimization algorithm based approach to solve Theory of constraint product mix problem. Second International Conference on Developments in eSystemsEngineering.
15- Wang, J. Q., Sun, S. D., Si, S. B. and Yang, H. A. (2009). Theory of Constraints product mix optimization based on immune algorithm. International Journal of Production Research, 47(16), 4521–4543.
16- Opricovic, S. and Tzeng, G. H. (2002). Multicriteria planning of postearthquake sustainable reconstruction.Computer-Aided Civil and Infrastructure Engineering, 17(3), 211–220.
17- Opricovic, S., and Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.
18- Zhang, N., and Wei, G. (2013). Extention of Vikor method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37, 4938–4947.
19 - Opricovic, S., and Tzeng, G.-H. (2007). Extended VIKOR method in comparison with outranking methods.European Journal of Operational Research, 178(2), 514–529.
20- Zadeh, L.A., (1965). Fuzzy sets. Information and Control, 338–35.
21- Kaya, T., & Kahraman, C. (2011). Fuzzy multiple criteria forestry decision making based on an integrated Vikor and approach. Expert Systems with Applications, 38, 7326–7333.