Supply chain coordination based on mean-variance risk optimisation: pricing, warranty, and full-refund decisions

ABSTRACT

This study focuses on optimising sales of different brands of a single-product supply chain model that consists of several manufacturers and a retailer. The price and quality of the products drive competition between manufacturers who sell a single product through a retailer to the customers. This study aims at maximising the profit values of the retailer and manufacturers, simultaneously. Accordingly, four scenarios are defined with respect to the different contracts, including the cost sharing, profit sharing, revenue sharing, and buyback. Mean-variance risk management is applied to the proposed models. A full-refund return policy and warranty are also considered. A novel hybrid metaheuristic that combines the advantages of the group search optimiser (GSO) and human behaviour-based optimisation (HBBO) algorithms, entitled ‘GSO-HBBO’ algorithm is provided to find the high-quality solutions in fewer numbers of the iterations. The performance of the GSO-HBBO algorithm is compared with the GSO and HBBO algorithms based on different measures such as quality of the generated solutions and CPU-Time. The results show that the presented algorithm generates much better solutions than GSO and HBBO algorithms in a reasonable time. The managerial insights confirmed that the profit-sharing and buyback contracts make the most profit for both manufacturer and retailer.

KEYWORDS:

• Revenue sharing contract
• inventory
• buyback contract
• warranty policy
• group search optimiser
• human behaviour-based optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study can be released upon request.

Additional information

Notes on contributors

Ata Allah Taleizadeh

Ata Allah Taleizadeh received the B.Sc. degree in industrial engineering from the Islamic Azad University of Qazvin, and the M.Sc. and PhD degrees from the Iran University of Science and Technology, both in industrial engineering. He is currently an Associate Professor of Industrial Engineering, at University of Tehran. He serves as an Editor/Associated Editor/Editorial Board Member for a number of international journals and has published extensively in reputable and leading journals such as European Journal of Operational Research, OMEGA; International Journal of Management Sciences, IEEE Transactions On Systems, Man, and Cybernetics: Systems, International Journal of Production Economics, Annals of Operation Researches, Service Sciences, Transportation Research: Part E, etc. His research area includes Pricing and Revenue Optimization, Inventory Control, and Supply Chain Management.

Alireza Amjadian

Alireza Amjadian held his M.Sc. in Industrial Engineering from Kharazmi University, Tehran, Iran. His main fields of interests are Inventory and Supply chain modeling, Pricing, Machine learning, Data mining, & Decision making. Optimization as another aspect of his fields of interests represents broad spectrum of Exact, Heuristic, and Meta-heuristic algorithms for solving the MINLP, NLP, MILP, and MIP models of SCs, inventory systems. In addition, optimum Lot-sizing and Replenishment in the integrated inventory systems such as EPQ, EOQ, and EGQ models make up an important part of his research interests.

S. Ehsan Hashemi-Petroodi

S. Ehsan Hashemi-Petroodi is Assistant Professor of production systems and Industry 4.0 in the Department of Operations Management and Information Systems at Kedge Business School, campus in Bordeaux, France. He received a Ph.D. in Industrial Engineering and Operations Research from the IMT Atlantique (Institute Mines Telecom), campus in Nantes, France in 2021. After his Ph.D. he has been a post-doctoral researcher and the leader of process planning optimization task in the European project ASSISTANT – LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments – consisting of 12 industrial and academic partners. He has been also involving in some other national and European projects on reconfigurable manufacturing systems and logistics. His research focuses on combinatorial optimization, robust optimization, assembly line design and balancing, workforce and process planning, and decision aid systems. His main results are based on the exact mathematical programming methods and their intelligent connection with heuristic algorithms. His contributions have been published in leading peer-reviewed scientific journals as International Journal of Production Economics, International Journal of Production Research, Omega-The International Journal of Management Science, etc. and presented at major international conferences.

Ilkyeong Moon

Ilkyeong Moon is a Professor of Industrial Engineering at Seoul National University in Korea. He received his B.S. and M.S. in Industrial Engineering from Seoul National University, and Ph.D. in Operations Research from Columbia University. His research interests include supply chain management, logistics, and inventory management. He published over 160 papers in international journals. He was a former Editor-in-Chief of Journal of the Korean Institute of Industrial Engineers which is a flagship journal of Korean Institute of Industrial Engineers (KIIE). He was a president of KIIE in which he had served from 2019 to 2020. He currently serves as the editor-in-chief of European Journal of IE. He is a fellow of Asia Pacific Industrial Engineering and Management Society and a board member of International Foundation of Production Research and WG 5.7 of IFIP.