A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic
Subject Areas : Optimization
Reza Ehtesham Rasi
1
(Department 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)
Jamal Mahmoodi
2
(Ph.D. Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
Alireza Irajpoor
3
(Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
Keywords:
Abstract :
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.
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.
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.
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.
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.
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.
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.
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.
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.