A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic

Ehtesham Rasi, Reza; Mahmoodi, Jamal; Irajpoor, Alireza

doi:10.30495/jsm.2023.1985225.1822

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	A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic
Journal of System Management
دوره 9، شماره 4 - شماره پیاپی 36، دی 2023، صفحه 255-271 اصل مقاله (588.08 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.30495/jsm.2023.1985225.1822
نویسندگان
Reza Ehtesham Rasi^* ¹؛ Jamal Mahmoodi²؛ Alireza Irajpoor³
¹Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran. P. O. Box: 34185-1416 Tel: 0098 (28) 33665275 Fax: 0098 (28) 33665277 ehteshamrasi@qiau.ac.ir
²Ph.D. Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
³Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
چکیده
Purpose – The purpose of this paper is to optimize Cost & time in the three echelon supply chain (SC) network. This paper developed a linear programing (LP) model to consider economic data. Design/methodology/approach – The overall objective of the present study is to use high-quality raw materials, at the same time in post COVID 19 pandemic and the highest profitability is achieved. The model in the problem is solved using two metaheuristic algorithms, namely, Cuckoo and Genetic. Optimization of supply chain performance indicators in minimization of cost and time and maximization of sustainability indexes of the system. Findings – The differences found between the genetic algorithms (GAs) and the LP approaches can be explained by handling the constraints and their various logics. To deal with ambiguity in the reverse logistics network, a fuzzy approach has been applied. To solve the problem in large dimensions, meta-heuristic algorithms of Cuckoo and Genetic were employed by applying MATLAB software. In order to compare two optimization algorithms, a series of sample problems have been generated then the results of two algorithms were compared and superiority of each of them was discussed.
کلیدواژه‌ها
Reverse Logistics؛ Optimization؛ Fuzzy؛ Metaheuristic Algorithms

مراجع
Ahmadpour,M.,Kanaani;Y,G.,Movahhedi,M.(2023). The Structural Model for Evaluating the Performance of the Sustainable Supply Chain of the Service Sector (Case Example: Social Security Organization). Journal of System Management (JSM).9(2).169-181. 301. Doi: 10.30495/JSM.2023.1973196.1716 AGRAWAL, S., SINGH, R. K. & MURTAZA, Q. (2016). Outsourcing decisions in reverse logistics: sustainable balanced scorecard and graph theoretic approach. Resources, Conservation and Recycling, 108, 41-53. https://doi.org/10.1016/j.resconrec.2016.01.004. AKBARI, M. & RASHIDI, H. (2016). A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems. Expert Systems with Applications, 60, 234-248. https://doi.org/10.1016/j.eswa.2016.05.014. ALTIPARMAK, F., GEN, M., LIN, L. & PAKSOY, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & industrial engineering, 51, 196-215. https://doi.org/10.1016/j.cie.2006.07.011. AMIRI, E. & MAHMOUDI, S. (2016). Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Applied Soft Computing, 41, 15-21. https://doi.org/10.1016/j.asoc.2015.12.008. BAGHERI, N. R., BARADARANKAZEMZADE, R. & ASADI, R. (2013). IDENTIFYING AND RANKING OF THE SUCCESS FACTORS IN AUTOMOTIVE REVERSE LOGISTICS THROUGH INTERPRETIVE STRUCTURAL MODELING (ISM).Managment research in Iran.17(1),21-40. Doi:20.1001.1.2322200.1392.17.1.2.6. BEHNAMIAN, J. & GHOMI, S. F. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied soft computing, 21, 139-148. https://doi.org/10.1016/j.asoc.2014.03.031CHRISTOPHER, M. (2016). Logistics & supply chain management, Pearson UK. DEMIREL, E., DEMIREL, N. & GÖKÇEN, H. (2016). A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. Journal of Cleaner Production, 112,2101-2113. https://doi.org/10.1016/j.jclepro.2014.10.079. DU, F. & EVANS, G. W. (2008).A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35, 2617-2634. https://doi.org/10.1016/j.cor.2006.12.020 FARAHANI, R. Z. & ELAHIPANAH, M. (2008). A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, 111, 229-243. https://doi.org/10.1016/j.ijpe.2006.11.028 GAUR, J., AMINI, M. & RAO, A.( 2017)(. Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model. Omega, 66, 212-223. https://doi.org/10.1016/j.omega.2015.11.008 Gurbuz I.B., Ozkan G. (2020).Transform or perish: Preparing the business for a post-pandemic future. IEEE Engineering Management Review. vol. ahead-of-p, no. ahead-of-print:1–6. doi: 10.1109/EMR.2020.3014693 KILIC, H. S., CEBECI, U. & AYHAN, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resources, Conservation and Recycling, 95, 120-132. https://doi.org/10.1016/j.resconrec.2014.12.010 Koonin L.M.(2020). Novel coronavirus disease (COVID-19) outbreak: Now is the time to refresh pandemic plans. Journal of Business Continuity & Emergency Planning. 13(4):1–15. DOI: 10.12691/ijcd-8-2-8. LEE, D.-H. & DONG, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45, 61-71. https://doi.org/10.1016/j.tre.2008.08.002. LI, X. & LIU, B. (2006). A sufficient and necessary condition for credibility measures. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14, 527-535. https://doi.org/10.1142/S0218488506004175 LIU, B. & LIU, Y.-K.(2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10, 445-450. DOI: 10.1109/TFUZZ.2002.800692 LU, H., DU, P., CHEN, Y. & HE, L. (2016). A credibility-based chance-constrained optimization model for integrated agricultural and water resources management: A case study in South Central China. Journal of hydrology, 537, 408-418. https://doi.org/10.1016/j.jhydrol.2016.03.056 MEHLAWAT, M. K. & GUPTA, P.(2014). Credibility-based fuzzy mathematical programming model for portfolio selection under uncertainty. International Journal of Information Technology & Decision Making, 13, 101-135. DOI:10.1016/j.jhydrol.2016.03.056 MELACHRINOUDIS, E., MESSAC, A. & MIN, H. (2005).Consolidating a warehouse network:: A physical programming approach. International Journal of Production Economics, 97, 1-17. https://doi.org/10.1016/j.ijpe.2004.04.009 MENA, C., HUMPHRIES, A. & CHOI, T. Y. (2013). Toward a theory of multi‐tier supply chain management. Journal of Supply Chain Management, 49, 58-77. https://doi.org/10.1108/SCM-02-2014-0070 MIN, H., KO, H. & PARK, B. (2005). A Lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, closed-loop supply chain network design problem. International Journal of Logistics Systems and Management, 1, 382-404. DOI:10.1504/IJLSM.2005.006292. Marzban,S., Shafiee,M., Mozaffari,M.R.,(2022). Four-Stage Supply Chain Design for Perishable Products and Evaluate it by Considering the Triple Dimensions of Sustainability. Journal of System Management (JSM).8(4).109-132. Doi: 10.30495/JSM.2022.1966734.1684 Mohmmadi,H.R.,Rasi, E,R.2022. Multi‐Objective Mathematical Model for Locating Flow Optimization Facilities in Supply Chain of Deteriorating Products. Journal of System Management (JSM),8(1),51-71. Doi: 10.30495/JSM.2022.1911221.1468 PAYNE, R. B. & SORENSEN, M. D.(2005). The cuckoos, Oxford University Press. PEDRAM, A., YUSOFF, N. B., UDONCY, O. E., MAHAT, A. B., PEDRAM, P. & BABALOLA, A. (2017).Integrated forward and reverse supply chain: A tire case study. Waste Management, 60, 460-470. https://doi.org/10.1016/j.wasman.2016.06.029 PERCIVAL, R. V., SCHROEDER, C. H., MILLER, A. S. & LEAPE, J. P. (2017). Environmental regulation: Law, science, and policy, Wolters Kluwer Law & Business. PISHVAEE, M. S., FARAHANI, R. Z. & DULLAERT, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & operations research, 37, 1100-1112. https://doi.org/10.1016/j.cor.2009.09.018. PISHVAEE, M. S. & TORABI, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161, 2668-2683. https://doi.org/10.1016/j.fss.2010.04.010. PISHVAEE, M. S., TORABI, S. A. & RAZMI, J. (2012).Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62, 624-632. https://doi.org/10.1016/j.cie.2011.11.028 Paul,S.K., Chowdhury,Priyabrata.,Md. Abdul Moktadir., Kwok Hung Lau.(2021). Supply chain recovery challenges in the wake of COVID-19 pandemic. Journal of Business Research.136,316-329. Ozceylan.E, T. Paksoy, T. Bektas.(2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation Research Part E: Logistics and Transportation Review, 61,3, 142–164. https://doi.org/10.1016/j.tre.2013.11.001 Queiroz M.M., Ivanov D., Dolgui A., Wamba S.F.(2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. p. ahead-of-print. https://doi.org/10.1007/s10479-020-03685-7. RAJABIOUN, R. (2011). Cuckoo optimization algorithm. Applied soft computing, 11, 5508-5518. https://doi.org/10.1016/j.asoc.2011.05.008. Rasi,Ehtesham.R.(2018). A Cuckoo Search Algorithm Approach for Multi- Objective Optimization in Reverse Logistics Network under Uncertainty Condition. International Journal of Supply and Operations Management, 5, 1, 66-80.Doi: 10.22034/2018.1.5 Razkov,M.,Ivanov,D.,Blackjurst,J.,Nair,Annad. (2022). Adapting supply chain operations in anticipation of and during the COVID-19 pandemic SHAKOURLOO, A., KAZEMI, A. & JAVAD, M. O. M. (2016). A new model for more effective supplier selection and remanufacturing process in a closed-loop supply chain. Applied Mathematical Modelling, 40, 9914-9931. https://doi.org/10.1016/j.apm.2016.06.039 SOWINSKI, R. & HAPKE, M. (2000). Scheduling under fuzziness, Physica-Verlag. STADTLER, H., KILGER, C. & MEYR, H. (2015). Supply chain management and advanced planning. Springer Berlin Heidelberg. ÜLKÜ, M. A. & BOOKBINDER, J. H. (2012). Optimal quoting of delivery time by a third party logistics provider: The impact of shipment consolidation and temporal pricing schemes. European Journal of Operational Research, 221, 110-117. https://doi.org/10.1016/j.ejor.2012.03.021 WOOD, D. A. (2016). Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic, chaos-enhanced, fat-tailed distribution sampling and metaheuristic profiling. Journal of Natural Gas Science and Engineering, 34, 236-252. https://doi.org/10.1016/j.jngse.2016.06.060 ZADEH, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 100, 9-34. https://doi.org/10.1016/S0165-0114(99)80004-9 ZHANG, Y., HUANG, G., LU, H. & HE, L. (2015). Planning of water resources management and pollution control for Heshui River watershed, China: a full credibility-constrained programming approach. Science of The Total Environment, 524, 280-289.
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اخبار و اعلانات

آمار

A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic

Ahmadpour,M.,Kanaani;Y,G.,Movahhedi,M.(2023). The Structural Model for Evaluating the Performance of the Sustainable Supply Chain of the Service Sector (Case Example: Social Security Organization). Journal of System Management (JSM).9(2).169-181. 301. Doi: 10.30495/JSM.2023.1973196.1716

AGRAWAL, S., SINGH, R. K. & MURTAZA, Q. (2016). Outsourcing decisions in reverse logistics: sustainable balanced scorecard and graph theoretic approach. Resources, Conservation and Recycling, 108, 41-53.

https://doi.org/10.1016/j.resconrec.2016.01.004.

AKBARI, M. & RASHIDI, H. (2016). A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems. Expert Systems with Applications, 60, 234-248.

https://doi.org/10.1016/j.eswa.2016.05.014.

ALTIPARMAK, F., GEN, M., LIN, L. & PAKSOY, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & industrial engineering, 51, 196-215. https://doi.org/10.1016/j.cie.2006.07.011.

AMIRI, E. & MAHMOUDI, S. (2016). Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Applied Soft Computing, 41, 15-21.

https://doi.org/10.1016/j.asoc.2015.12.008.

BAGHERI, N. R., BARADARANKAZEMZADE, R. & ASADI, R. (2013). IDENTIFYING AND RANKING OF THE SUCCESS FACTORS IN AUTOMOTIVE REVERSE LOGISTICS THROUGH INTERPRETIVE STRUCTURAL MODELING (ISM).Managment research in Iran.17(1),21-40. Doi:20.1001.1.2322200.1392.17.1.2.6.

BEHNAMIAN, J. & GHOMI, S. F. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied soft computing, 21, 139-148. https://doi.org/10.1016/j.asoc.2014.03.031CHRISTOPHER, M. (2016). Logistics & supply chain management, Pearson UK.