Risk-Aware Multi-Objective Optimization for Reciprocal Capacity Sharing under Market Volatility
Keywords:
capacity sharing, multi-objective optimization, risk management, market volatility, reciprocal exchange, fairness, socio-technical systemsAbstract
Reciprocal capacity sharing, wherein independent firms mutually exchange production, storage, or logistics resources, has become a strategic mechanism for improving system-wide utilization and resilience. However, market volatility introduces deep uncertainties that degrade the stability and fairness of such arrangements, necessitating a risk-aware and multi-objective decision framework. This paper develops a conceptual architecture and a system-level analysis of risk-aware multi-objective optimization for reciprocal capacity sharing under volatile market conditions. We formulate the problem as a simultaneous balancing of economic efficiency, risk exposure, service reliability, and inter-participant fairness, while preserving the incentive-compatible reciprocity that sustains long-term cooperation. The study examines the structural trade-offs, governance infrastructures, and policy implications embedded in these systems, drawing on multi-disciplinary insights from operations management, robust optimization, behavioral economics, and digital governance. A thorough discussion of architecture design reveals how trust indices, forward-looking risk measures, and volatility-conditioned scenario generation can be integrated without compromising computational tractability or real-world accountability. The paper further addresses deployment challenges related to data sharing, algorithmic fairness, and regulatory oversight, arguing that the viability of reciprocal capacity ecosystems depends as much on institutional design as on technical optimization. By synthesizing these dimensions, we outline pathways toward sustainable, fair, and volatility-resilient capacity sharing networks and identify critical directions for future empirical and computational research.
References
1. Yu, Y., Benjaafar, S., & Gerchak, Y. (2015). Capacity sharing and cost allocation among independent firms with congestion. Production and Operations Management, 24(8), 1285–1301. https://doi.org/10.1111/poms.12322
2. Fang, Y., & Shou, B. (2015). Managing supply uncertainty under capacity sharing agreements. Naval Research Logistics, 62(2), 89–105. https://doi.org/10.1002/nav.21616
3. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006
4. Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. https://doi.org/10.1109/TEVC.2013.2281535
5. Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21–41. https://doi.org/10.21314/JOR.2000.038
6. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. https://doi.org/10.1287/opre.1030.0065
7. Benth, F. E., & Saltyte Benth, J. (2004). The normal inverse Gaussian distribution and spot price modelling in energy markets. International Journal of Theoretical and Applied Finance, 7(2), 177–192. https://doi.org/10.1142/S0219024904002360
8. Guo, X., & Jiang, B. (2019). Optimal capacity sharing strategies in a supply chain with demand uncertainty. International Journal of Production Research, 57(8), 2457–2473. https://doi.org/10.1080/00207543.2018.1518603
9. Cachon, G. P., & Lariviere, M. A. (1999). Capacity choice and allocation: strategic behavior and supply chain performance. Management Science, 45(8), 1091–1108. https://doi.org/10.1287/mnsc.45.8.1091
10. Bertsimas, D., Farias, V. F., & Trichakis, N. (2011). The price of fairness. Operations Research, 59(1), 17–31. https://doi.org/10.1287/opre.1100.0865
11. Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: an introduction. Journal of Operations Management, 25(6), 1075–1082. https://doi.org/10.1016/j.jom.2007.01.012
12. Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public goods experiments. American Economic Review, 90(4), 980–994. https://doi.org/10.1257/aer.90.4.980
13. Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61(2), 35–51. https://doi.org/10.1177/002224299706100203
14. Özer, Ö., Zheng, Y., & Chen, K. Y. (2011). Trust in forecast information sharing. Management Science, 57(6), 1111–1137. https://doi.org/10.1287/mnsc.1110.1334
15. Hu, X., & Caldentey, R. (2023). Trust and reciprocity in firms’ capacity sharing. Manufacturing & Service Operations Management, 25(4), 1436-1450.
16. Li, J., & Kouvelis, P. (2019). Sharing capacity in supply chains under demand and supply uncertainty. Manufacturing & Service Operations Management, 21(4), 775–794. https://doi.org/10.1287/msom.2018.0727
17. Ghosh, R., Longo, F., Naik, V. K., & Trivedi, K. S. (2013). Modeling and performance analysis of large scale IaaS clouds. Future Generation Computer Systems, 29(5), 1216–1234. https://doi.org/10.1016/j.future.2012.12.018
18. Wang, Z., & Liu, P. (2021). Multi-objective optimization for integrated energy system considering risk. Energy, 226, 120355. https://doi.org/10.1016/j.energy.2021.120355
19. Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press.
20. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
