A ZERO AND ONE MIXED MATHEMATICAL MODEL FOR DETERMINE THE OPTIMAL LAYOUT IN ADVANCED MOLD ASSEMBLY UNIT IN THE IRAN KHODRO FACTORY
DOI:
https://doi.org/10.58885/ijmh.v04i1.09.rnKeywords:
Facilities, Facilities Layout, Zero and one modelling.Abstract
The equipment layout is one of the most important issues in the manufacturing company, because with the proper alignment equipment can reduce material transportation costs significantly. This article presents a mathematical model to determine optimal facilities layout in advanced mold Iran Khodro factory's assembly unit. The model mentioned model is kind of zero and one mixed that is derived from Tompkins. The objective function of model is to minimize transportation costs. The model is solved by Lingo software and its output determines the best location of machines.
References
N. A. Z. Abidin and B. Ingirige. Identification of the “Pathogenic” Effects of Disruptions to Supply Chain Resilience in Construction, Procedia engineering, 212 (2018). 467-474.
C. Bode and S. M. Wagner. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions, Journal of Operations Management, 36 (2015). 215-228.
C. Cao, C. Li, Q. Yang, Y. Liu and T. Qu. A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters, Journal of Cleaner Production, 174 (2018). 1422-1435.
E. Fernandez, V. Bogado, E. Salomone and O. Chiotti. Framework for modelling and simulating the supply process monitoring to detect and predict disruptive events, Computers in Industry, 80 (2016). 30-42.
J. He, F. Alavifard, D. Ivanov and H. Jahani. A real-option approach to mitigate disruption risk in the supply chain, (2018).
Y. Higuchi, T. Inui, T. Hosoi, I. Takabe and A. Kawakami. The impact of the Great East Japan Earthquake on the labor market—need to resolve the employment mismatch in the disaster-stricken areas, Japan Labor Review,9(4) (2012) 4-21.
H. G. Huntington. Measuring oil supply disruptions: A historical perspective, Energy Policy, 115 (2018). 426-433.
M. Kamalahmadi and M. M. Parast. An assessment of supply chain disruption mitigation strategies, International Journal of Production Economics, 184 (2017). 210-230.
M. Kumar, P. Basu and B. Avittathur. Pricing and sourcing strategies for competing retailers in supply chains under disruption risk, European Journal of Operational Research, 265(2) (2018). 533-543.
C. Li, X. Qi and D. Song. Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events, Transportation Research Part B: Methodological, 93 (2016). 762-788.
H. S. Loh and V. Van THAI. Cost consequences of a port-related supply chain disruption, The Asian Journal of Shipping and Logistics, 31(3) (2015). 319-340.
H. S. Loh, Q. Zhou, V.V. Thai, Y. D. Wong and K. F. Yuen. Fuzzy comprehensive evaluation of port-centric supply chain disruption threats, Ocean & Coastal Management, 148 (2017). 53-62.
K. Park, H. Min and S. Min, Inter-relationship among risk taking propensity, supply chain security practices, and supply chain disruption occurrence, Journal of Purchasing and Supply Management, 22(2) (2016). 120-130.
R. Ravindran, R. Ufuk Bilsel, V. Wadhwa and T. Yang. Risk adjusted multicriteria supplier selection models with applications, International journal of production research, 48(2) (2010). 405-424.
T. Sawik. Disruption Mitigation and Recovery in Supply Chains using Portfolio Approach, (2018).
T. Schoenherr, V. Rao Tummala and T. P. Harrison. Assessing supply chain risks with the analytic hierarchy process: providing decision support for the offshoring decision 9,9. (2008).
J. Tokui, K. Kawasaki and T. Miyagawa. The economic impact of supply chain disruptions from the Great East Japan earthquake, Japan and the World Economy, 41 (2017). 59-70.
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