International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Optimization Multi-Objective Cost and Delay Rate in Delivering Orders in the Three- Echelon Reverse Supply Chain Based On Cost Management

Document Type : Original Article

Authors
Morteza Ghahremani 1
Reza Ehtesham Rasi 2
Sadegh Abedi 3
1 ph.D.student,Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin.
3 Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
10.30495/ijfma.2023.58255.1564
Abstract
Developing and establishing a logistics network is a strategic decision that its impacts last many years. Because of changes in customer’s demand over time a logistics network should be developed in an efficient manner that can respond to uncertainties. The objective of this research is to define the number of returned products to minimize total cost and delay time of reverse logistics. In this research, a fuzzy bi-objective optimization model was introduced in the reverse logistics system. The aim of this research is to determine the number of returned products that should be delivered to be recovered, processed and re manufactured in different time periods so that the total cost of reverse logistics and delay time to be minimized. To deal with ambiguity in the reverse logistics network, a fuzzy approach has been applied. To solve the problem in large scale, 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 the two algorithms were compared and superiority of each of them was discussed.
Keywords
Reverse Logistics
Optimization
Fuzzy
Metaheuristic Algorithms
Cost managemnt
  • Abidi, Naseem. Bandyopadhayay, Asit. Gupta, Vishal.2017. Sustainable Supply Chain Management:
  • A Three Dimensional Framework and Performance Metric for Indian IT Product Companies. International Journal of Information Systems and Supply Chain Management,10(1),29-52.
  • 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.
  • 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.
  • 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.
  • AMIRI, E. & MAHMOUDI, S. 2016. Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Applied Soft Computing, 41, 15-21.
  • BAGHERI, N. R., BARADARANKAZEMZADE, R. & ASADI, R. 2013. IDENTIFYING AND RANKING OF THE SUCCESS FACTORS IN AUTOMOTIVE REVERSE LOGISTICS THROUGH INTERPRETIVE STRUCTURAL MODELING (ISM).
  • BEHNAMIAN, J. & GHOMI, S. F. 2014. Multi-objective fuzzy multiprocessor flowshop scheduling. Applied soft computing, 21, 139-148.
  • CHOPRA, S. 2003. Designing the distribution network in a supply chain. Transportation Research Part E: Logistics and Transportation Review, 39, 123-140.
  • CHRISTOPHER, M. 2016. Logistics & supply chain management, Pearson UK.
  • Chan, H.K., 2007. A pro-active and collaborative approach to reverse logistics – a case study.
  • Production Planning & Control, London 18 (4), 350–360.
  • CULLINANE, S., BROWNE, M., WHITEING, A. & MCKINNON, P. A. 2010. Green Logistics: improving the environmental sustainability of logistics, Kogan Page.
  • 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.
  • 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.
  • 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.
  • Jingshan, Q. hao, W. Bo, L.(2018).Analysis on the Construction of Enterprise Financial Shared Service Center System . China Business Theory.22: 119-120.
  • JAYARAMAN, V., GUIDE JR, V. & SRIVASTAVA, R. 1999. A closed-loop logistics model for remanufacturing. Journal of the operational research society, 50, 497-508.
  • JAYARAMAN, V., PATTERSON, R. A. & ROLLAND, E. 2003. The design of reverse distribution networks: Models and solution procedures. European journal of operational research, 150, 128-149.
  • JAYARAMAN, V. & PIRKUL, H. 2001. Planning and coordination of production and distribution facilities for multiple commodities. European journal of operational research, 133, 394-408.
  • KESKIN, B. B. & ÜSTER, H. 2007. Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem. European Journal of Operational Research, 182, 663-682.
  • 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.
  • 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.
  • LISTEŞ, O. & DEKKER, R. 2005. A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160, 268-287.
  • LIU, B. & LIU, Y.-K. 2002. Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10, 445-450.
  • 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.
  • 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.
  • Rasi,E.R.2015.Identify and prioritize the factors affecting Quality costs of automotive products in mass production phase (Case study: Iran Khodro and Saipa). Journal of Management Accounting and Auditing Knowledge,4(14),75-86.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • RAJABIOUN, R. 2011. Cuckoo optimization algorithm. Applied soft computing, 11, 5508-5518.
  • 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.
  • Stock, J.R., 2001. The 7 deadly sins of reverse logistics. Material Handling Management, 56 (3), MHS5–MHS11.
  • 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.
  • 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.
  • 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.
  • ZADEH, L. A. 1999. Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 100, 9-34.
  • 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.
  • Zhang,YuHang.Wang,Ying.2019. Supply Chain Decisions Considering Heterogeneous Consumer Greenness Preference and Reservation Utilities. International Journal of Information Systems and Supply Chain Management,12(1),1-21