Multi-Objective Mathematical Model for Locating Flow Optimization Facilities in Supply Chain of Deteriorating Products
محورهای موضوعی : Business Strategy
Hamidreza Mohammadi
1
(Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Reza Ehtesham Rasi
2
(Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
کلید واژه: Deteriorating Products, Metaheuristic, Location-routing, Supply Chain,
چکیده مقاله :
Managing supply chain operations in a reliable manner is a significant concern for decision-makers in competitive industries. In recent years consumers and legislation have been pushing companies to design their activities in such a way as to reduce negative environmental impacts more and more. It is therefore important to examine the optimization of total supply chain costs and environmental impacts together. However, because of the recycling of deteriorated products, the environmental impacts of deteriorating items are more significant than those of non-deteriorating ones. The subject of supply chain of deteriorating products, simultaneously considering costs and environment has gained attention in the academia and from the industry. Particularly for deteriorating and seasonal products, such as fresh produce, the issues of timely supply and disposal of the deteriorated products are of high concerns. The objective of this paper is to develop multi objective mathematical model and to propose a new replenishment policy in a centralized supply chain for deteriorating products. In this model, the manufacturer produces a new product and delivers it to a distant market, and then the distributor buys the product and sells it to the end consumers. This study presents a new mathematical model of the location-routing problem (LRP) of facilities in supply chain network (SCN) for deteriorating products through taking environmental considerations, cost, delivery time and customer satisfaction into account across the entire network and customer satisfaction. In order to solve the model, the combination of the two red deer algorithm (RDA) and annealing simulation (AS) was proposed. We then perform the network optimization in SCN and provide some managerial insights. Finally, more promising directions are suggested for future research.
Abdallah, T., Diabat, A., Simchi-Levi, D. (2010). A carbon sensitive supply chain network problem with green procurement. In: 2010 40th International Conference on Computers and Industrial Engineering (CIE), pp. 1–6. DOI: 10.1109/ICCIE.2010.5668278
Azami Adel., Mehrabad,Mohammad. (2021). A production and distribution planning of perishable products with a fixed lifetime under vertical competition in the seller-buyer systems: A real-world application.JournalofManufacturing Systems,Vol.58(PartA),pp.223-247.DOI: 10.1016/j.jmsy.2020.12.001
Babazadeh,R.Ghaderi,H.Pishvaee,S.(2019).A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty. Computers & Chemical Engineering, 124,pp.364-380.Doi: 10.1016/j.jmsy.2020.12.001
Boronoos,Mehdi.,Torabi,SeyedAli.,Mousazadeh,Mohammad.(2019). A Bi-objective Mathematical Model for Closed-loop Supply Chain Network Design Problem. Journal of Quality Engineering and Production Optimization, Vol.4(1),pp.85-98.Doi : 10.22070/JQEPO.2019.3688.1086.
Chica, M. Juan, A. Bayliss, C. Cordon, O.; Kelton, D.(2020). Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation. SORT Stat. Oper. Res. Trans. Vol.44, pp. 1–24.Doi: 10.2436/20.8080.02.104.
Cole, R., & Aitken, J. (2019). The role of intermediaries in establishing a sustainable supply chain. Journal of Purchasing and Supply Management.Vol.26(2),pp.100-123.
DOI:10.1016/J.PURSUP.2019.04.001
Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering.Vol19(3),pp.338-352. Doi: 10.1016/j.cie.2018.04.007
Dey, K., & Saha, S. (2018). Influence of procurement decisions in two-period green supply chain. Journal of cleaner production, Vol.190(6),pp. 388-402.Doi: 10.1016/j.jclepro.2018.04.114
De Keizer, M., Akkerman, R., Grunow, M., Bloemhof, J. M., Haijema, R., & van der Vorst, J. G. (2017). Logistics network design for perishable products with heterogeneous quality decay. European Journal of Operational Research, 262(2), pp.535-549. Doi: 10.1016/j.ejor.2017.03.049
Ebner,J.,Young,P.,Geraghty,J.(2019). Intelligent self-designing production control strategy: Dynamic allocation hybrid pull-type mechanism applicable to closed-loop supply chains. Computers & Industrial Engineering .135, pp. 1127-1144. Doi: 10.1016/j.cie.2019.04.005
Gitinavard,H.Shirazi,M,A.Ghodsypour,S.H.(2019). A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty. Scientia iranica,Vol. 26(5),pp.2952-2970.Doi: 10.24200/SCI.2018.5047.1060
Katsaliaki K, Mustafee N, Kumar S. (2014) A game-based approach towards facilitating decision making for perishable products: an example of blood supply chain. Expert Syst Appl,Vol. 41(9),pp.4043–4059. Doi: 10.1016/j.eswa.2013.12.038
Keshteli,M,H., Fard,F.(2018). Sustainable closed-loop supply chain network design with discount supposition. Neural Computing and Applications.31,pp.5343-5377.Doi: 10.1007/s00521-018-3369-5
Khezerlou,H,S.,Vahdan,B.,Yazdani,M.(2020). A Bi-Objective Optimization Model to Design a Reliable Biomass Supply Chain Network under Uncertainty and Congestion Effect. Journal of Quality Engineering and Production Optimization,Vol.5(1),pp.19-32.Doi : 10.22070/JQEPO.2020.5149.1130
Kovaˇci´c D, Bogataj M (2013). Reverse logistics facility location using cyclical model of extended MRP theory. Cent Eur J Oper Res.Vol. 21(1),pp.41–57. Doi: 10.1007/s10100-012-0251-x
Kaur, H., & Singh, S. P. (2018). Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Computers & Operations Research.Vol.98, pp.301-321.Doi: 10.1016/j.cor.2017.05.008
Li, L., Dababneh, F., & Zhao, J. (2018). Cost-effective supply chain for electric vehicle battery remanufacturing. Applied energy, Vol.226, pp.277-286. Doi: 10.1016/j.apenergy.2018.05.115
Mogale, D. G., Kumar, M., Kumar, S. K., & Tiwari, M. K. (2018). Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transportation Research Part E: Logistics and Transportation Review,Vol. 111, pp.40-69.Doi: 10.1016/j.tre.2018.01.004
Mohammadi,H.R.Ehtesham,rasi,R.Mohtashami,Ali.(2020).A Hybrid Mehta-Heuristic algorithm for optimization location-routing problem of facilities in four echelon supply chain. Journal of Industrial Engineering International,16(4),pp.14-31.Doi: 10.30495/JIEI.2020.678787
Nurjann,K,P.Carvalho,S,M.Costa,L.(2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. Int. J. Production Economics,183(3),pp.421-432.Doi: 10.1016/j.ijpe.2016.08.028
Pettersson, A. I., & Segerstedt, A. (2013). Measuring supply chain cost. International Journal of Production Economics.Vol.143(2), pp.357-363. Doi: 10.1016/j.ijpe.2012.03.012
Pellegrino, R., Costantino, N., & Tauro, D. (2019). Supply Chain Finance: A supply chain-oriented perspective to mitigate commodity risk and pricing volatility. Journal of Purchasing and Supply Management.Vol. 25(2), pp.118-133.Doi : 10.1016/j.pursup.2018.03.004
Oh, J., & Jeong, B. (2019). Tactical supply planning in smart manufacturing supply chain. Robotics and Computer-Integrated Manufacturing, Vol.55, pp.217-233.Doi : 10.1016/j.rcim.2018.04.003
Moradi, M.,Salavati,A.,Shafie,R.(2021). Determining and Modeling the Factors Affecting the Promotion of Customer Satisfaction of Electricity Distribution Companies.Jouranl of system management. Vol.7(3),pp.163-183.Doi: 10.30495/JSM.2021.1942263.1533
Rafie-Majd, Z., Pasandideh, S. H. R., & Naderi, B. (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & Chemical Engineering.Vol.109, pp.9-22.Doi: 10.1016/j.rcim.2018.04.003
Rasi,Ehtesha,Reza.(2018). A Cuckoo Search Algorithm Approach for Multi Objective Optimization in Reverse Logistics Network under Uncertainty Condition. International Journal of Supply and Operations Management (IJSOM).Vol.5(1),pp.66-80.Doi: 10.22034/2018.1.5
Reza,Ehtesham,Rasi, Sohanian,Mehdi.(2020). A multi-objective optimization model for sustainable supply chain network with using genetic algorithm. Journal of Modelling in Management,Vol.15(1),pp.1-14.Doi: 10.1108/JM2-06-2020-0150
Sun, S., & Wang, X. (2019). Promoting traceability for food supply chain with certification. Journal of Cleaner Production.Vol. 217, pp.658-665.Doi: 10.1016/j.jclepro.2019.01.296
Saif-Eddine, A. S., El-Beheiry, M. M., & El-Kharbotly, A. K. (2019). An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal.Vol.10(1), pp.63-76.Doi: 10.1016/j.asej.2018.09.002
Shirazi,A.,Chaghoushi,A.Mahdiraji,A.Safari,B.(2020). Coordinating the Two-Echelon Supply Chain of Perishable Products with Uncertain Demand: A Game-Theoretic Approach. . Jouranl of system management. Vol.6(4),pp.103-138.Doi: 10.30495/JSM.2021.1910315.1368
Tavakkoli Moghaddam, S., Javadi, M. & Hadji Molana, S.M.(2019). A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization. J Ind Eng Int, Vol. 15, pp.709–721. Doi:10.1007/s40092-018-0287-1.
Tarvirdizadeh,D.,Mirzaeidaryani,S,.AmirKhiz,A.Pasbani,A.Azimi,H.(2021). A Hybrid Approach in Designing and Validating a Green Quality Management Model in the Food Industry. Jouranl of system management. Vol.7(1),pp.191-232.Doi: 10.30495/JSM.2021.1926288.1456
Vakili,R.Akbarpour,Mohsen.S,.Gitinavard,H.(2020).Multi-echelon green open-location-routing problem: A robust-based stochastic optimization approach. Scientia Iranica, Article in press.Doi: 10.24200/SCI.2020.52149.2564
Wu, T., Zhang, L. G., & Ge, T. (2019). Managing financing risk in capacity investment under green supply chain competition. Technological Forecasting and Social Change.Vol. 143, pp.37-44. Doi: 10.1016/j.techfore.2019.03.005
Wang, X., Guo, H., Yan, R., & Wang, X. (2018). Achieving optimal performance of supply chain under cost information asymmetry. Applied Mathematical Modelling.Vol. 53, pp.523-539.Doi: 10.1016/j.apm.2017.09.002
Wang,R.Lu,Shileil.Feng,Wei.(2020). A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost.Energy.197(1),pp.116-162.Doi: 10.1016/j.energy.2019.116723
XIAO, Y. B., Jian, C. H. E. N., & XU, X. L. (2008). Fresh product supply chain coordination under CIF business model with long distance transportation. Systems Engineering-Theory & Practice, Vol.28(2),pp.19-34.Doi: 10.1016/S1874-8651(09)60009-0
Zhang, C.-T., Liu, L.-P.(2013). Research on coordination mechanism in three-level green supply chain under non-cooperative game. Appl. Math. Model. 37 (5), pp.3369–3379.Doi: 10.1016/j.apm.2012.08.006
Zhang, S., Lee, C. K. M., Wu, K., & Choy, K. L. (2016). Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels. Expert Systems with Applications.Vol. 65, pp.87-99.Doi: 10.1016/j.eswa.2016.08.037
Zhang, L., Guan, L., Kuo, Y. H., & Shen, H. (2019). Push or Pull? Perishable Products with Freshness-Keeping Effort. Asia-Pacific Journal of Operational Research, Vol.36(01),pp.44-63.Doi: 10.1142/S0217595919500088
