Designing a fuzzy multi-objective mathematical model of four-stage perishable supply chain using an operational, financial and marketing approach under Benders’ uncertainty state and decomposition analysis

Fallah, Fatemeh; Azimi, Parham; Sharifi, Mani

doi:10.22075/ijnaa.2023.31485.4642

Designing a fuzzy multi-objective mathematical model of four-stage perishable supply chain using an operational, financial and marketing approach under Benders’ uncertainty state and decomposition analysis

Document Type : Research Paper

Authors

Fatemeh Fallah ¹

Parham Azimi ¹

Mani Sharifi ²

¹ Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

² Department of Information Systems and Analytic, Farmer School of Business, Miami University, Oxford, Ohio, USA

10.22075/ijnaa.2023.31485.4642

Abstract

This article solves the mathematical model of supply, planning, storage, and distribution in a supply chain using an agile supply chain. In modern supply chains, addressing financial risks in purchases and distribution, transportation for supply and distribution of products and lost policies or the storage of products in perishable products supply chains is critical. This article investigates a direct supply chain model for the executive policies of manufacturing companies and a four-stage supply chain problem using the operational financial and marketing approach under an uncertainty state, Lagrangian relaxation and Benders’ decomposition evaluation. This study introduced and provided the concepts of supply chains in perishable products. Considering supply chain issues and uncertainty in this environment, a fuzzy mathematical model was provided. In the end, a literature review has shown that most research has used heuristic and meta-heuristic algorithms due to supply chain models being NP-HARD., which are inefficient due to the approximation of optimal solutions in this domain. Meanwhile, the study used a Benders decomposition and Lagrangian relaxation algorithm for mathematical solutions, which would reduce the model’s solving time and provide accurate answers.

Keywords

Lagrangian relaxation

Benders relaxation

perishable supply chain

multi-objective optimization

[1] H. Arani and S.A. Torabi, Integrated material-financial supply chain master planning under mixed uncertainty, Inf. Sci. 423 (2017), 96–114.

[2] J.F. Benders, Partitioning procedures for solving mixed-variables programming problems, Comput. Manag. Sci. 2 (2005), no. 1, 3–19.

[3] B. Dehning, V.J. Richardson, and R.W. Zmud, The financial performance effects of IT-based supply chain management systems in manufacturing firms, J. Oper. Manag. 25 (2006), no. 4, 806–824.

[4] A. Dolgui, M.K. Tiwari, Y. Sinjana, S.K. Kumar, and Y.-J. Son, Optimising integrated inventory policy for perishable items in a multi-stage supply chain, Int. J. Prod. Res. 56 (2017), no. 1–2, 902–925.

[5] B. Elahi and M. Franchetti, A multi-objective proposed mathematical model for a health care supply chain with perishable product, IIE Ann. Conf. Proc., Institute of Industrial and Systems Engineers (IISE), 2015, pp. 1514.

[6] P. Golinska-Dawson, Logistics Operations and Management for Recycling and Reuse, Springer Berlin Heidelberg, 2020.

[7] M. Imran, M.S. Habib, A. Hussain, N. Ahmed, and A.M. Al-Ahmari, Inventory routing problem in supply chain of perishable products under cost uncertainty, Mathematics 8 (2020), no. 3, 382.

[8] R. Ismailpour, A. Azar, and M. Shah Mohammadi, Presenting an interpretative structural model from among supplier selection indicators using corporate social responsibility, Sci.-Res. Quart. Ind. Manag. Stud. 47 (2017), 45–70.

[9] K. Kianfar, Maximizing profit in a supply chain by considering advertising and price elasticity of demand, Comput. Ind. Eng. 135 (2019), 265–274.

[10] M. Liu, X. Liu, F. Chu, F. Zheng, and C. Chu, Distributionally robust inventory routing problem to maximize the service level under limited budget, Transport. Res. Part E: Logistics Transport. Rev. 126 (2019), 190–211.

[11] E. Malinowski, M.H. Karwan, J.M. Pinto, and L. Sun, A mixed-integer programming strategy for liquid helium global supply chain planning, Transport. Res. Part E: Logistics Transport. Rev. 110 (2018), 168–188.

[12] M.M. Parast and M. Shekarian, The impact of supply chain disruptions on organizational performance: A literature review, Revisit. Supply Chain Risk 7 (2018), 367–389.

[13] R. Patidar and S. Agrawal, A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: a case study problem, Benchmark.: Int. J. 27 (2020), no. 8, 2341–2363.

[14] R. Pellegrino, N. Costantino and D. Tauro, Supply chain finance: a supply chain-oriented perspective to mitigate commodity risk and pricing volatility, J. Purch. Supply Manag. 25 (2019), no. 2, 118–133.

[15] S. Tavakkoli Moghaddam, M. Javadi and S.M. Hadji Molana, A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization, J. Ind. Eng. Int. 15 (2018), no. 4, 709–721.