IJAT Vol.14 No.5 pp. 723-733
doi: 10.20965/ijat.2020.p0723


Collaboration Strategy for a Decentralized Supply Chain Using Linear Physical Programming

Tomoaki Yatsuka*1, Aya Ishigaki*1,†, Surendra M. Gupta*2, Yuki Kinoshita*3, Tetsuo Yamada*3, and Masato Inoue*4

*1Tokyo University of Science
2641 Yamazaki, Noda, Chiba 278-8510, Japan

Corresponding author

*2Northeastern University, Boston, USA

*3The University of Electro-Communications, Tokyo, Japan

*4Meiji University, Kawasaki, Japan

February 5, 2020
June 23, 2020
September 5, 2020
single-vendor and multi-buyer, common replenishment epoch, negotiation, Stackelberg game

In recent years, the environment surrounding companies has become more challenging. It has become more difficult for many companies in the manufacturing industry to possess all the skills they need, such as production, warehousing, and retailing, so they need to outsource certain skills. In supply chains with several companies, each has an optimal strategy. Specifically, supply chains where the solution is decided through negotiations with their partners are defined as “decentralized supply chains.” In such situations, collaborative relationships are important. One possible approach is replenishment contracts between vendors and buyers under the condition that demand for each buyer is constant. In a buyer-dominated supply chain, because the vendor cannot choose solutions that lower the satisfaction of buyers, it is difficult to change the replenishment intervals. The common replenishment epochs (CRE) strategy is one of the methods used to address this issue. The vendor integrates the buyers’ replenishment timings using CRE and provides a price discount on the products to compensate for the increase in the cost to the buyers. The price discount rate is calculated based on the worst reduction rate in the costs incurred by the buyers based on the economic order quantity (EOQ) model. The optimal CRE and discount rate are decided such that the cost incurred by vendor is minimized. The increased emphasis on the worst reduction rates can potentially lead to biases in buyer satisfaction, and the price discount rate is overestimated. Then, the cost of the vendor increases. Hence, through the negotiations with less satisfied buyers, the vendor changes the CRE so that their satisfaction is improved and the price discount is lower. As a result, the vendor can reduce its cost. This study develops a model to find an improved solution after the negotiations. If satisfaction of multiple players is regarded as multi-objective, a solution of multi-player decision-making is obtained using multi-objective optimization. Linear physical programming (LPP) has been applied as a form of multi-objective optimization, and it is possible to determine the weight coefficients using the preference ranges of the objective functions. In addition, by considering the buyers’ preference levels, the constraints of the discount rates are relaxed and the vendor’s cost can be reduced. Therefore, this study develops a model based on the CRE strategy using LPP.

Cite this article as:
T. Yatsuka, A. Ishigaki, S. Gupta, Y. Kinoshita, T. Yamada, and M. Inoue, “Collaboration Strategy for a Decentralized Supply Chain Using Linear Physical Programming,” Int. J. Automation Technol., Vol.14 No.5, pp. 723-733, 2020.
Data files:
  1. [1] K. Ma, R. Pal, and E. Gustafsson, “What modelling research on supply chain collaboration informs us? Identifying key themes and future directions through a literature review,” Int. J. of Production Research, Vol.57, No.7, pp. 2203-2225, 2019.
  2. [2] N. Lehoux, S. D’Amours, and A. Langevin, “Inter-firm collaborations and supply chain coordination: review of key elements and case study,” Production Planning & Control, Vol.25, No.10, pp. 858-872, 2014.
  3. [3] T. Vuletic, R. I. Whitfield, W. Wang, A. Duffy, S. Gatchell, H. Prins, and M. Leer-Andersen, “Improving the creation and management of collaborative networks within the European maritime sector,” J. of Industrial Information Integration, Vol.8, pp. 22-37, 2017.
  4. [4] Z. Yu, H. Yan, and T. C. E. Cheng, “Modelling the benefits of information sharing-based partnerships in a two-level supply chain,” J. of the Operational Research Society, Vol.53, pp. 436-446, 2002.
  5. [5] I. Giannoccaro, “Centralized vs. decentralized supply chains: The importance of decision maker’s cognitive ability and resistance to change,” Industrial Marketing Management, Vol.73, pp. 59-69, 2018.
  6. [6] L. Chen, S. Chien, and C. Wei, “Research on Decentralized Supply Chain with Channel-Wide Profit Maximization,” Science J. of Business and Management, Vol.5, No.4, pp. 132-135, 2017.
  7. [7] N. K. Verma, A. Chakraborty, and A. K. Chatterjee, “Joint replenishment of multi retailer with variable replenishment cycle under VMI,” European J. of Operational Research, Vol.233, pp. 787-789, 2014.
  8. [8] N. K. Verma and A. K. Chatterjee, “A multiple-retailer replenishment model under VMI: Accounting for the retailer heterogeneity,” Computers & Industrial Engineering, Vol.104, pp. 175-187, 2017.
  9. [9] M. Moghaddam and S. Y. Nof, “Collaborative service-component integration in cloud manufacturing,” Int. J. of Production Research, Vol.56, Nos.1-2, pp. 677-691, 2018.
  10. [10] M. Kirci, I. Bicer, and R. W. Seifert, “Optimal replenishment cycle for perishable items facing demand uncertainty in a two-echelon inventory system,” Int. J. of Production Research, Vol.57, No.4, pp. 1250-1264, 2019.
  11. [11] J. Geunes, H. E. Romeijn, and W. van den Heuvel, “Improving the efficiency of decentralized supply chains with fixed ordering costs,” European J. of Operation Research, Vol.252, No.3, pp. 815-828, 2016.
  12. [12] Y. Tanimizu, C. Ozawa, Y. Shimizu, B. Orita, K. Iwamura, and N. Sugimura, “Flexible multi-layered dynamic supply chain models with cooperative negotiation,” Int. J. Automation Technol., Vol.7, No.1, pp. 128-135, 2013.
  13. [13] S. Viswanathan and R. Piplani, “Coordinating Supply Chain Inventories Through Common Replenishment Epochs,” European J. of Operational Research, Vol.129, No.2, pp. 277-286, 2001.
  14. [14] N. Jiang, L. L. Zhang, and Y. Yu, “Optimizing cooperative advertizing, profit sharing, and inventory policies in a VMI supply chain: a Nash bargaining model and hybrid algorithm,” IEEE Trans. on Engineering Management, Vol.62, No.4, pp. 449-461, 2015.
  15. [15] M. Haque, S. K. Paul, R. Sarker, and D. Essam, “Managing decentralized supply chain using bilevel with Nash game approach,” J. of Cleaner Production, Vol.266, No.1, pp. 1-20, 2020.
  16. [16] A. Charnes and W. W. Cooper, “Goal programming and multiple objective optimizations: Part 1,” European J. of Operational Research, Vol.1, No.1, pp. 39-54, 1977.
  17. [17] A. Messac, S. M. Gupta, and B. Akbulut, “Linear physical programming: a new approach to multiple objective optimization,” Trans. on Operational Research, Vol.8, No.2, pp. 39-59, 1996.
  18. [18] O. Ondemir and S. M. Gupta, “A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system,” European J. of Operational Research, Vol.233, No.2, pp. 408-419, 2014.
  19. [19] T. Yatsuka, A. Ishigaki, H. Ijuin, Y. Kinoshita, T. Yamada, and M. Inoue, “Mathematical modeling of multi-player multi-objective decision making by linear physical programming,” Proc. of 7th Int. Congress on Advanced Applied Informatics (IIAI-AAI), pp. 706-713, 2018.
  20. [20] T. Yatsuka, A. Ishigaki, Y. Kinoshita, T. Yamada, and M. Inoue, “Control method of effect of robust optimization in multi-player multi-objective decision-making,” American J. of Operations Research, Vol.9, No.4, pp. 175-191, 2019.

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Last updated on Apr. 22, 2024