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Supply chain with fuzzy analytic hierarchy process (AHP): A case study | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 1699-1717 اصل مقاله (974.99 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5310 | ||
| نویسندگان | ||
| Mohamed Saad Ibrahim1؛ Barraq Subhi Kaml* 2 | ||
| 1Ministry of Higher Education and Scientific Research, Iraq | ||
| 2Al-Nahrain University, College of Business Economics, Iraq | ||
| تاریخ دریافت: 02 اردیبهشت 1400، تاریخ بازنگری: 04 تیر 1400، تاریخ پذیرش: 10 تیر 1400 | ||
| چکیده | ||
| To achieve real competition in the global market requires the manufacturers to have the ability to meet the needs and demands of their customers, which comes from the optimal planning of the supply chain. In this paper, consideration is given to the supply chain with multi-providers of raw materials, multi-manufacturing locations, multi- centres of selling products to customers in multiple, with instability (fuzzy) of customer demands, holding costs, costs of appointment, retire and training of workforce After building a mathematical model for the supply chain that aims to maximize the net profit and reduce all costs that include production costs, labour, raw materials, storage, transportation, and the cost shortage, the model was improved through a proposal that the decision-maker has a desire to prefer one manufacturing location over another, as the proposal relied on developing a pairwise comparison in the Analytic Hierarchy Process (AHP) when the degree of comparison between factory locations is a fuzzy nature. The results of the proposed model were applied to actual data taken from an industrial organization. | ||
| کلیدواژه‌ها | ||
| Fuzzy supply chain؛ Aggregate production planning؛ graded mean integration method؛ pairwise comparison | ||
| مراجع | ||
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[1] Z. Dai and X. Zheng, Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model, Comput. Indust. Engin. 88 (2015) 444–457. [2] B. S. Kaml, The Methods and Algorithms of Fuzzy Linear Programming Problems with Limited Resources, Doctorate Dissertation, National University of Kyiv, 2015.[3] D. Kumar, Z. Rahman and F.T.S. Chan, A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study, Int. J. Comput. Integ. Manufact. 30(6) (2017) 192–202. [4] C. Lu and X.W. Li, Supply chain modelling using fuzzy sets and possibility theory in an uncertain environment, 6th World Congress on Intelligent Control and Automation, Dalian, (2006), 3608–3612. [5] S.M.J. Mirzapour Al-hashem, H.Maleky and M.B.Aryanezhad, A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty, Int. J. Produc. Econ. 134 (2011) 28-–42. [6] T. Nishia, O. Yoshida, Optimization of multi-period bilevel supply chains, 48th CIRP Conf. MANUFACTURING SYSTEMS - CIRP CMS, Procedia CIRP 41 (2016) 508–513. [7] D. Petrovic, R. Roy and R. Petrovic, Supply chain modelling using fuzzy sets, Int. J. Product. Econ. 59 (1999) 443–453. [8] J.R.N. Quevedo, Fuzzy sets: A way to represent ambiguity and subjectivity, Boletn Mat. 24(1) (2017) 57–88. [9] T.L. Saaty and L. G. Vargas, Decision Making with the Analytic Network Process, Springer New York Heidelberg Dordrecht London, 2013. [10] T.L. Saaty, Decision making with the analytic hierarchy process, Int. J. Serv. Sci. 1(1) (2008) 284–296. [11] L. Schrage, LINGO the Modeling language and optimizer, LINDO Systems Inc. Chicago, Illinois, 2018. | ||
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