Designing a Green-Resilient Supply Chain Network for Perishable Products Considering a Pricing Reduction Strategy to Manage Optimal Inventory: A Column Generation-based Approach

Document Type : Research Paper

Authors

1 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

2 Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

10.22070/jqepo.2023.17599.1256

Abstract

This paper aims to design an integrated green supply chain by considering several drivers of resiliency for perishable products. Supply chain resiliency seeks to prevent the chain from moving toward unfavorable conditions and to manage and mitigate disruptions. To minimize the effects of disruption, new strategies are investigated, including horizontal cooperation between distribution centers, emergency inventory reserves in production and distribution centers, and contracts with backup suppliers. Additionally, to avoid resource wastage, the strategy of reducing the selling price of products nearing their expiration date is considered, allowing the model to control the optimal amount of production and inventory. In addition to resiliency, all supply chain members must adopt a green supply chain approach to conduct production and distribution activities in a manner that reduces environmental damage. Therefore, optimal routing is implemented to deliver products at the minimum possible cost and with the least pollution. A column generation decomposition algorithm is developed to improve solution time as the model becomes complex and unsolvable or takes a long time to solve with standard mixed-integer programming solvers. After designing an integrated supply chain for perishable products, we initially solved several test cases to determine the applicability of the column generation method to these issues. Subsequently, we employed the proposed solution method for a specific case study on the PEGAH dairy industry. A sensitivity analysis was conducted on the product's shelf life to examine variations in optimal production and inventory levels. The results indicated that as the product's lifetime decreases, a lower quantity of the product is produced and stored.

Keywords

References

Adenso-Díaz, B., Lozano, S., & Palacio, A. (2017). Effects of dynamic pricing of perishable products on revenue and waste. Applied Mathematical Modelling, 45, 148-164.
Aloui, A., Hamani, N., Derrouiche, R., & Delahoche, L. (2022). Assessing the benefits of horizontal collaboration using an integrated planning model for two-echelon energy efficiency-oriented logistics networks design. International journal of systems science: operations & logistics, 9(3), 302-323.
Arabsheybani, A., & Khasmeh, A. A. (2023). Robust and resilient supply chain network design considering risks in food industry: Flavour industry in Iran. In Sustainable Logistics Systems Using AI-based Meta-Heuristics Approaches (pp. 89-100). Routledge.
Babaeinesami, A., Ghasemi, P., Chobar, A. P., Sasouli, M. R., & Lajevardi, M. (2022). A New wooden supply chain model for inventory management considering environmental pollution: A genetic algorithm. Foundations of Computing and Decision Sciences, 47(4), 383-408.
Bahrampour, P., Najafi, S. E., Hosseinzadeh Lotfi, F., & Edalatpanah, A. (2022). Development of scenario-based mathematical model for sustainable closed loop supply chain considering reliability of direct logistics elements. Journal of Quality Engineering and Production Optimization, 7(2), 232-266.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260, 120842.
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
Christopher, M., & Lee, H. (2004). Mitigating supply chain risk through improved confidence. International journal of physical distribution & logistics management, 34(5), 388-396.
Daneshvar, A., Radfar, R., Ghasemi, P., Bayanati, M., & Pourghader Chobar, A. (2023). Design of an optimal robust possibilistic model in the distribution chain network of agricultural products with high perishability under uncertainty. Sustainability, 15(15), 11669.
Daroudi, S., Kazemipoor, H., Najafi, E., & Fallah, M. (2021). The minimum latency in location routing fuzzy inventory problem for perishable multi-product materials. Applied Soft Computing, 110, 107543.
Delgado, M., Verdegay, J. L., & Vila, M. A. (1989). A general model for fuzzy linear programming. Fuzzy Sets and systems, 29(1), 21-29.
Deng, X., Yang, X., Zhang, Y., Li, Y., & Lu, Z. (2019). Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain. Computers & Industrial Engineering, 135, 1175-1187.
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
Foroozesh, N., Karimi, B., Mousavi, S. & Mojtahedi, M. (2023). A hybrid decision-making method using robust programming and interval-valued fuzzy sets for sustainable-resilient supply chain network design considering circular economy and technology levels. Journal of Industrial Information Integration, 33, 100440.
Francis, V. (2008). Supply chain visibility: lost in translation?. Supply chain management: An international journal, 13(3), 180-184.
Garcia-Herreros, P., Wassick, J. M., & Grossmann, I. E. (2014). Design of resilient supply chains with risk of facility disruptions. Industrial & Engineering Chemistry Research, 53(44), 17240-17251.
Goli, A., Tirkolaee, E. B., & Weber, G. W. (2020). A perishable product sustainable supply chain network design problem with lead time and customer satisfaction using a hybrid whale-genetic algorithm. Logistics operations and management for recycling and reuse, 99-124.
Goodarzian, F., Shishebori, D., Bahrami, F., Abraham, A., & Appolloni, A. (2023). Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2 emissions and water consumption. International Journal of Systems Science: Operations & Logistics, 10(1), 2009932.
Hamidieh, A., Arshadi Khamseh, A., & Naderi, B. (2018). A resilient supply chain network design model with a novel fuzzy programming method under uncertainty and disruptions: a real industrial approach. Journal of Quality Engineering and Production Optimization, 3(2), 27-50.
Hasan-Zadeh, A. (2023). Developing a new formulation and exact solution approach for continuous time optimal control model: application to coordinating a supplier-manufacturer supply chain. Journal of Quality Engineering and Production Optimization.
Hashemi-Amiri, O., Ghorbani, F., & Ji, R. (2023). Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach. Computers & Industrial Engineering, 175, 108845.
Hemmati, H., Baradaran Kazemzadeh, R., Nikbaksh, E., & Nakhai Kamalabadi, I. (2022). Designing an agile supply chain network for perishable products with resilient suppliers. Journal of Industrial and Systems Engineering, 14(3), 121-148.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation research part E: logistics and transportation review, 125, 285-307.
Jabbarzadeh, A., Fahimnia, B., Sheu, J. B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191.
Keshmiry Zadeh, K., Harsej, F., Sadeghpour, M., & Molani Aghdam, M. (2021). A multi-objective multi-echelon closed-loop supply chain with disruption in the centers. Journal of Quality Engineering and Production Optimization, 6(2), 31-58.
Leventhal, B., & Breur, T. (2012). Intelligent markdown pricing. Journal of direct, data and digital marketing practice, 13, 207-220.
 
Mehrbanfar, M., Bozorgi-Amiri, A., & Nasiri, M. M. (2020). A mathematical programming model for sustainable agricultural supply chain network design under uncertainty. Journal of Quality Engineering and Production Optimization, 5(1), 189-220.
Millar, M. (2015). Global supply chain ecosystems: Strategies for competitive advantage in a complex, connected world. Kogan Page Publishers.
Mirmajlesi, S. R., & Shafaei, R. (2016). An integrated approach to solve a robust forward/reverse supply chain for short lifetime products. Computers & Industrial Engineering, 97, 222-239.
Mohammadi Jozani, S., Safaei, F., & Messi Bidgoli, M. (2022). An Integrated Production-Distribution-Routing Problem under an Unforeseen Circumstance within a Competitive Framework. Journal of Quality Engineering and Production Optimization, 7(1), 121-159.
Noya, I., Aldea, X., Gasol, C. M., González-García, S., Amores, M. J., Colón, J., ... & Boschmonart-Rives, J. (2016). Carbon and water footprint of pork supply chain in Catalonia: From feed to final products. Journal of environmental management, 171, 133-143.
Ouhader, H. (2020). Assessing the economic and environmental benefits of horizontal cooperation in delivery: Performance and scenario analysis. Uncertain Supply Chain Management, 8(2), 303-320.
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, 109, 9-22.
Rahimi, M., Baboli, A., & Rekik, Y. (2017). Inventory routing problem for perishable products by considering customer satisfaction and green criteria. In Dynamics in Logistics: Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany (pp. 445-455). Springer International Publishing.
Rajesh, R. (2019). A fuzzy approach to analyzing the level of resilience in manufacturing supply chains. Sustainable Production and Consumption, 18, 224-236.
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & industrial engineering, 126, 657-672.
Sarkis, J., Zhu, Q., & Lai, K. H. (2011). An organizational theoretic review of green supply chain management literature. International journal of production economics, 130(1), 1-15.
Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471-484.
Shi, Y., Zhang, Z., Tiwari, S., & Yang, L. (2023). Pricing and replenishment strategy for a perishable product under various payment schemes and cap-and-trade regulation. Transportation Research Part E: Logistics and Transportation Review, 174, 103129.
Shokouhifar, M., & Goli, A. (2023). Designing a resilient–sustainable supply chain network of age-differentiated blood platelets using vertical–horizontal transshipment and grey wolf optimizer. International Journal of Environmental Research and Public Health, 20(5), 4078.
Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation research part e: logistics and transportation review, 79, 22-48.
Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: definition, review and theoretical foundations for further study. International journal of production research, 53(18), 5592-5623.
Wang, J., Liu, S., & Su, J. (2017, March). Identifying vulnerable nodes in supply chain based on the risk transmission model. In 2017 6th International Conference on Industrial Technology and Management (ICITM) (pp. 1-5). IEEE.
Yang, T., & Fan, W. (2016). Information management strategies and supply chain performance under demand disruptions. International Journal of Production Research, 54(1), 8-27.
Yavari, M., Enjavi, H., & Geraeli, M. (2020). Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products. Research in Transportation Business & Management, 37, 100552.
Journal of Quality Engineering and Production Optimization
Volume 8, Issue 1
May 2023
Pages 171-196

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