×

A preemptive goal programming for multi-objective cooperative games: an application to multi-objective linear production. (English) Zbl 07816769

Summary: The aim of this study is to investigate the fair allocation of multi-objective cooperative games (MOCGs) using a preemptive goal programming (PGP). First, this research establishes the PGP model, where the priority factors of PGP describe the importance degrees of different objectives in MOCGs, the positive and negative deviations of PGP present the relation between the overall payoff and the worth of a coalition, and the weighting factors of negative deviations of PGP express the coalition weights of MOCGs. Second, we investigate the relationships between the solutions of the PGP model and the cores of MOCGs, including the preference core, preference \(p\)-core, and dominance core. Our analysis shows that the optimal solution of PGP belongs to the preference core, and the satisfactory solution of PGP satisfying certain conditions belongs to the dominance core or preference \(p\)-core. Finally, we demonstrate the practical and managerial relevance of our results by applying our findings to a multi-objective linear production to produce certain kinds of goods using different resources for multi-firms with varying objectives and illustrate them numerically.
© 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies.

MSC:

90-XX Operations research, mathematical programming
Full Text: DOI

References:

[1] Alice, H.A., Lienert, J., Helversen, B.V., 2022. Gamified environmental multi‐criteria decision analysis: information on objectives and range insensitivity bias. International Transactions in Operational Research30, 6, 3738-3770. · Zbl 07745344
[2] Bergstresser, K., Yu, P.L., 1977. Domination structures and multicriteria problems in n‐person games. Theory and Decision8, 5-48. · Zbl 0401.90117
[3] Bjorndal, E., Jornsten, K., 2009. Lower and upper bounds for linear production games. European Journal of Operational Research Production, Manufacturing and Logistics196, 2, 476-486. · Zbl 1163.90450
[4] Borrero, D.V., Hinojosa, M.A., Mármol, A.M., 2016. Stable solutions for multiple scenario cost allocation games with partial information. Annals of Operations Research245, 209-226. · Zbl 1406.91214
[5] Chang, C.T., 2011. Multi‐choice goal programming with utility functions. European Journal of Operational Research215, 2, 439-445. · Zbl 1237.90218
[6] Fernández, F.R., Monroy, L., Puerto, J., 1998. Multicriteria goal games. Journal of Optimization Theory and Applications99, 195-208. · Zbl 0915.90269
[7] Fernández, F.R., Hinojosa, M.A., Puerto, J., 2002. Core solutions in vector‐valued games. Journal of Optimization Theory and Applications112, 2, 331-360. · Zbl 1005.91016
[8] Fernández, F.R., Hinojosa, M.A., Puerto, J., 2004. Set‐valued TU‐games. European Journal of Operational Research159, 1, 181-195. · Zbl 1067.90068
[9] González‐Fernández, A.I., Rubio‐MisasM., Ruiz, F., 2020. Goal programming to evaluate the profile of the most profitable insurers: an application to the Spanish insurance industry. International Transactions in Operational Research27, 6, 2976-3006. · Zbl 07767662
[10] Jornsten, K., Lind, K., Tind, J., 1997. Stable payment schemes of TU‐games with multiple criteria, Optimization40, 57-78. · Zbl 0872.90122
[11] Hinojosa, M.A., Mármol, A.M., Monroy, L., Fernández, F.R., 2013. A multi‐objective approach to fuzzy linear production games. International Journal of Information Technology & Decision Making12, 5, 927-943.
[12] Hinojosa, M.A., Mármol, A.M., Thomas, L.C., 2005. Core, least core and nucleolus for multiple scenario cooperative games. European Journal of Operational Research164, 1, 225-238. · Zbl 1132.91332
[13] Komsiyah, S., Meiliana Centika, H.E., 2018. A fuzzy goal programming model for production planning in furniture company. Procedia Computer Science135, 544-552.
[14] LeP.H., NguyenT.D., BektaT., 2020. Efficient computation of the Shapley value for large‐scale linear production games. Annals of Operations Research287, 761-781. · Zbl 1437.91035
[15] LehrerE., TeperR., 2020. Allocation in multi‐agenda disputes: a set‐valued games approach. Games and Economic Behavior122, 440-452. · Zbl 1450.91009
[16] Lozano, S., 2013. DEA production games. European Journal of Operational Research231, 2, 405-413. · Zbl 1317.90106
[17] Lozano, S., Hinojosa, M.A., Mármol, A.M., 2015. Set‐valued DEA production games. Omega52, 92-100.
[18] Mirzaee, H., Naderi, B., Pasandideh, S.H.R., 2018. A preemptive fuzzy goal programming model for generalized supplier selection and order allocation with incremental discount. Computers and Industrial Engineering122, 292-302.
[19] Mohebbi, S., Barnett, K., Aslani, B., 2021. Decentralized resource allocation for interdependent infrastructures resilience: a cooperative game approach. International Transactions in Operational Research28, 6, 3394-3415. · Zbl 07769650
[20] Nishizaki, I., Hayashida, T., Shintomi, Y., 2016. A core‐allocation for a network restricted linear production game. Annals of Operations Research238, 2, 389-410. · Zbl 1347.91075
[21] Nishizaki, I., Sakawa, M., 2001a. Fuzzy and Multi‐objective Games for Conflict Resolution. Physica,Verlag HD, Berlin, Germany. · Zbl 0973.91001
[22] Nishizaki, I., Sakawa, M., 2001b. On computational methods for solutions of multi‐objective linear production programming games. European Journal of Operational Research129, 2, 386-413. · Zbl 0980.90081
[23] Nouweland, A., Vanden Aarts, H., Borm, P., 1989. Multicommodity games. Methods of Operations Research63, 329-338. · Zbl 0726.90102
[24] Osicka, O., Guajardo, M., Oost, T.V., 2019. Cooperative game‐theoretic features of cost sharing in location‐routing. International Transactions in Operational Research27, 4, 2157-2183. · Zbl 07767566
[25] Owen, G., 1975. On the core of linear production games. Mathematical Programming9, 1, 358-370. · Zbl 0318.90060
[26] Puerto, J., Fernández, F.R., Hinojosa, Y., 2008. Partially ordered cooperative games: extended core and Shapley value. Annals of Operations Research158, 1, 143-159. · Zbl 1138.91329
[27] Qin, Z.F., 2018. Uncertain random goal programming. Fuzzy Optimization and Decision Making17, 375-386. · Zbl 1427.90256
[28] Ruiz, L.M., Valenciano, F., Zarzuelo, J.M., 1998. The family of least square values for transferable utility games. Games and Eonomic Behavior, 24, 109-130. · Zbl 0910.90276
[29] Sakawa, M., Nishizaki, I., 1997. The nucleolus in multi‐objective n‐person cooperative games. In Fandel, G. (ed.), Gal, T. (ed.) (eds) Multiple Criteria Decision Making, Springer, Berlin, Germany, pp. 64-73. · Zbl 0898.90141
[30] Tanino, T., 2012. Vector Optimization and Cooperative Games//Recent Developments in Vector Optimization. Springer, Berlin Heidelberg.
[31] Zhu, X.Y., Zhang, T., Cao, Y.Z., 2024. Managing production and inventory in a remanufacturing supply chain with two classes of cores under consignment stock agreement. International Transactions in Operational Research31, 2, 1232-1269. · Zbl 07797311
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.