In this paper, a decision-support is developed for a strategic problem of identifying target prices for the single buyer to negotiate with multiple suppliers to achieve common goal of maintaining sustained business environment. For this purpose, oligopolistic-competitive equilibrium prices of suppliers are suggested to be considered as target prices. The problem of identifying these prices is modeled as a multi-leader-single-follower bilevel programming problem involving linear constraints and bilinear objective functions. Herein, the multiple suppliers are considered leaders competing in a Nash game to maximize individual profits, and the buyer is a follower responding with demand-order allocations to minimize the total procurement-cost. Profit of each supplier is formulated on assessing respective operational cost to fulfill demand-orders by integrating aggregate-production-distribution-planning mechanism into the problem. A genetic-algorithm-based technique is designed in general for solving large-scale instances of the variant of bilevel programming problems with multiple leaders and single follower, and the same is applied to solve the modeled problem. The developed decision support is appropriately demonstrated on the data of a leading FMCG manufacturing firm, which manufactures goods through multiple sourcing.
Keywords: Competitive target prices, Price negotiations, bilevel programming, multi-leader-single-follower bilevel game, aggregate-production-distribution-planning, genetic algorithm
@article{RO_2022__56_1_293_0,
author = {Kumar, Akhilesh and Gupta, Anjana and Mehra, Aparna},
title = {A bilevel game model for ascertaining competitive target prices for a buyer in negotiation with multiple suppliers},
journal = {RAIRO. Operations Research},
pages = {293--330},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {1},
doi = {10.1051/ro/2021185},
mrnumber = {4376283},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021185/}
}
TY - JOUR AU - Kumar, Akhilesh AU - Gupta, Anjana AU - Mehra, Aparna TI - A bilevel game model for ascertaining competitive target prices for a buyer in negotiation with multiple suppliers JO - RAIRO. Operations Research PY - 2022 SP - 293 EP - 330 VL - 56 IS - 1 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021185/ DO - 10.1051/ro/2021185 LA - en ID - RO_2022__56_1_293_0 ER -
%0 Journal Article %A Kumar, Akhilesh %A Gupta, Anjana %A Mehra, Aparna %T A bilevel game model for ascertaining competitive target prices for a buyer in negotiation with multiple suppliers %J RAIRO. Operations Research %D 2022 %P 293-330 %V 56 %N 1 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021185/ %R 10.1051/ro/2021185 %G en %F RO_2022__56_1_293_0
Kumar, Akhilesh; Gupta, Anjana; Mehra, Aparna. A bilevel game model for ascertaining competitive target prices for a buyer in negotiation with multiple suppliers. RAIRO. Operations Research, Tome 56 (2022) no. 1, pp. 293-330. doi: 10.1051/ro/2021185
[1] and , Unit commitment using a new integer coded genetic algorithm. Eur. Trans. Electr. Power 19 (2009) 1161–1176. | DOI
[2] and , A study on the use of heuristics to solve a bilevel programming problem. Int. Trans. Oper. Res. 22 (2015) 861–882. | MR | DOI
[3] , and , A systematic review on supplier selection and order allocation problems. J. Ind. Eng. Int. 15 (2019) 267–289. | DOI
[4] , and , Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. Int. J. Prod. Res. 53 (2015) 383–408. | DOI
[5] and , Hierarchical design of an integrated production and 2-echelon distribution system. Eur. J. Oper. Res. 118 (1999) 464–484. | Zbl | DOI
[6] , Practical Bilevel Optimization: Algorithms and Applications Nonconvex Optimization and its Applications. Springer, USA (1998). | MR | Zbl | DOI
[7] , and , Hybrid real coded genetic algorithm solution to economic dispatch problem. Comput. Electr. Eng. 29 (2003) 407–419. | Zbl | DOI
[8] , and , A real-coded genetic algorithm for training recurrent neural networks. neural networks 14 (2001) 93–105. | DOI
[9] , , and , A bilevel model for toll optimization on a multicommodity transportation network. Transp. Sci. 35 (2001) 345–358. | Zbl | DOI
[10] and , A goal programming model for purchase planning. J. Purchasing Mater. Manage. 19 (1983) 27–34.
[11] , and , A new approach for solving linear bilevel problems using genetic algorithms. Eur. J. Oper. Res. 188 (2008) 14–28. | MR | Zbl | DOI
[12] , and , Bilevel model for production-distribution planning solved by using ant colony optimization. Comput. Oper. Res. 38 (2011) 320–327. | Zbl | DOI
[13] , and , A heuristic algorithm for a supply chain’s production-distribution planning. Comput. Oper. Res. 61 (2015) 110–121. | MR | DOI
[14] and , Integrated inventory models considering the two-level trade credit policy and a price-negotiation scheme. Eur. J. Oper. Res. 205 (2010) 47–58. | Zbl | DOI
[15] , and , A trust-region method for nonlinear bilevel programming: algorithm and computational experience. Comput. Optim. App. 30 (2005) 211–227. | MR | Zbl | DOI
[16] , and , An overview of bilevel optimization. Ann. Oper. Res. 153 (2007) 235–256. | MR | Zbl | DOI
[17] Competition Commission of India, Provisions relating to cartels, Competition Act, 2002. Competition Commission of India (2002).
[18] , and , Integrated operations planning and revenue management for rail freight transportation. Transp. Res. Part B Methodol. 46 (2012) 100–119. | DOI
[19] , and , Network-constrained economic dispatch using real-coded genetic algorithm. IEEE Trans. Power Syst. 18 (2003) 198–205. | DOI
[20] , Adapting operator probabilities in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1989) 61–69.
[21] , Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York (1991).
[22] and , A new crossover operator for real coded genetic algorithms. Appl. Math. Comput. 188 (2007) 895–912. | MR | Zbl | DOI
[23] and , A new mutation operator for real coded genetic algorithms. Appl. Math. Comput. 193 (2007) 211–230. | MR | Zbl | DOI
[24] , , and , A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl. Math. Comput. 212 (2009) 505–518. | MR | Zbl | DOI
[25] and , An integrated multiobjective decision making process for supplier selection and order allocation. Omega 36 (2008) 76–90. | DOI
[26] , Foundations of Bilevel Programming, 1st edition. Springer, US (2002). | MR | Zbl
[27] and , Bilevel road pricing: theoretical analysis and optimality conditions. Ann. Oper. Res. 196 (2012) 223–240. | MR | Zbl | DOI
[28] , , and , Integrated product line design and supplier selection: a multi-objective optimization paradigm. Comput. Ind. Eng. 70 (2014) 150–158. | DOI
[29] , , and , New formulations and valid inequalities for a bilevel pricing problem. Oper. Res. Lett. 36 (2008) 141–149. | MR | Zbl | DOI
[30] , An analysis of vendor selection systems and decisions. J. Purchasing 2 (1966) 5–17. | DOI
[31] , and , A simulation-optimization approach using genetic search for supplier selection. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat No03EX693) (2003) 1260–1267.
[32] and , Pricing strategies and models. Ann. Rev. Control 34 (2010) 101–110. | DOI
[33] , , and , A smoothing heuristic for a bilevel pricing problem. Eur. J. Oper. Res. 174 (2006) 1396–1413. | MR | Zbl | DOI
[34] and , Real-coded genetic algorithms and interval-schemata. Found. Genet. Algorithms 2 (1993) 187–202.
[35] , and , Genetic algorithm optimisation of an integrated aggregate production-distribution plan in supply chains. Int. J. Prod. Res. 50 (2012) 81–96. | DOI
[36] , , and , A review and critique on integrated production-distribution planning models and techniques. J. Manuf. Syst. 32 (2013) 1–19. | DOI
[37] , , , , and , Designing an intelligent decision support system for effective negotiation pricing: a systematic and learning approach. Decis. Support Syst. 96 (2017) 49–66. | DOI
[38] , Minimum cost allocation of tenders. J. Oper. Res. Soc. 25 (1974) 389–398. | Zbl | DOI
[39] , , and , Particle swarm optimization for bi-level pricing problems in supply chains. J. Global Optim. 51 (2011) 245–254. | MR | Zbl | DOI
[40] , and , Buyer opportunism in strategic supplier relationships: triggers, manifestations and consequences. J. Purchasing Supply Manage. 26 (2020) 100581. | DOI
[41] and , Hybrid genetic algorithm for multi-time period production/distribution planning. Comput. Ind. Eng. 48 (2005) 799–809. | DOI
[42] and , Supply chain model with price- and trade credit-sensitive demand under two-level permissible delay in payments. Int. J. Syst. Sci. 44 (2013) 937–948. | MR | Zbl | DOI
[43] , Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition. Addison-Wesley Longman Publishing Co., Inc., USA (1989). | Zbl
[44] , Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Syst. 5 (1991) 139–167. | MR | Zbl
[45] and , A bilevel approach to operational decision making of a distribution company in competitive environments. IEEE Trans. Power Syst. 27 (2012) 1797–1807. | DOI
[46] , and , Pay-as-bid versus marginal pricing: the role of suppliers strategic behavior. Int. J. Electr. Power Energy Syst. 42 (2012) 350–358. | DOI
[47] , , and , Linear bilevel programming solution by genetic algorithm. Comput. Oper. Res. 29 (2002) 1913–1925. | MR | Zbl | DOI
[48] , , and , Fuzzy tools to improve genetic algorithms. In: Proc of the Second European Congress on Intelligent Techniques and Soft Computing (1994) 1532–1539.
[49] , and , Tuning fuzzy logic controllers by genetic algorithms. Int. J. Approximate Reasoning 12 (1995) 299–315. | MR | Zbl | DOI
[50] , Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA (1975). | MR | Zbl
[51] and , Gauss-Seidel method for multi-leader-follower games. J. Optim. Theory App. 180 (2019) 651–670. | MR | DOI
[52] , and , Supplier selection and order quantity allocation: a comprehensive model. J. Supply Chain Manage. 35 (1999) 50–58. | DOI
[53] , and , Nearness and bound relationships between an integer-programming problem and its relaxed linear-programming problem. J. Optim. Theory App. 98 (1998) 55–63. | MR | Zbl | DOI
[54] , and , A bilevel programming model for operative decisions on special trains: an Indian Railways perspective. J. Rail Transp. Planning Manage. 8 (2018) 184–206. | DOI
[55] , and , A bilevel programming model for a cohesive decision-making on strategic pricing and production distribution planning for a small-scale supplier. Int. Game Theory Rev. 22 (2020) 2040009. | MR | DOI
[56] and , Bilevel programming and price setting problems. Ann. Oper. Res. 240 (2016) 141–169. | MR | DOI
[57] , and , A bilevel model of taxation and its application to optimal highway pricing. Manage. Sci. 44 (1998) 1608–1622. | Zbl | DOI
[58] , and , Social costs of setting high aspirations in competitive negotiation. Negotiation Conflict Manage. Res. 6 (2013) 1–12. | DOI
[59] , and , Quantitative vendor evaluation. J. Purchasing Mater. Manage. 12 (1976) 19–28.
[60] and , Solving multi-leader-common-follower games. Optim. Methods Softw. 25 (2010) 601–623. | MR | Zbl | DOI
[61] and , An evolutionary algorithm for solving bilevel programming problems using duality conditions. Math. Prob. Eng. 2012 (2012) 1–14. | MR | Zbl
[62] and , A hybrid genetic algorithm for solving nonlinear bilevel programming problems based on the simplex method. In: Proceedings – Third International Conference on Natural Computation, ICNC 2007. Vol. 4 (2007) 91–95. | DOI
[63] , and , Solution for integer linear bilevel programming problems using orthogonal genetic algorithm. J. Syst. Eng. Electron. 25 (2014) 443–451. | DOI
[64] , Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Comput. Ind. Eng. 55 (2008) 676–694. | DOI
[65] and , Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Syst. App. 36 (2009) 3367–3377. | DOI
[66] and , A multi-objective supplier selection model under stochastic demand conditions. Int. J. Prod. Econ. 105 (2007) 150–159. | DOI
[67] and , A logistics production-distribution scheme based on intelligent computing. Comput. Modell. New Technol. 18 (2014) 1332–1336.
[68] , , and , Managing opportunism in a developing interfirm relationship: the interrelationship of calculative and loyalty commitment. Ind. Marketing Manage. 39 (2010) 844–852. | DOI
[69] , , and , Multilevel decision-making: a survey. Inf. Sci. 346, 347 (2016) 463–487. | MR | DOI
[70] and . Application of genetic algorithms in chemometrics. In: Proceedings of the Third International Conference on Genetic Algorithms, edited by . Morgan Kaufmann Publishers, San Mateo (1989) 170–176.
[71] , and , An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy. Appl. Math. Comput. 183 (2006) 903–915. | MR | Zbl | DOI
[72] , and , Applying mixed integer programming to the design of a distribution logistic network.Int. J. Ind. Eng. Theory App. Pract. 13 (2006) 207–218.
[73] , and , Genetic algorithm based approach to bi-level linear programming. RAIRO-Oper. Res. 28 (1994) 1–21. | MR | Zbl | Numdam | DOI
[74] and , A modeling framework and local search solution methodology for a production-distribution problem with supplier selection and time-aggregated quantity discounts. Appl. Math. Modell. 68 (2019) 198–218. | MR | DOI
[75] , , and , An optimal differential pricing in smart grid based on customer segmentation. In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 – Proceedings. 2018–January (2017) 1–6. DOI: . | DOI
[76] , Genetic algorithm. Stud. Comput. Intell. 780 (2019) 43–55.
[77] and , Predictive models for the breeder genetic algorithm I. Continuous parameter optimization. Evol. Comput. 1 (1993) 25–49. | DOI
[78] , and , The key role of opportunism in business relationships. Marketing Intell. Planning 29 (2011) 436–449. | DOI
[79] and , The Strategy and Tactics of Pricing: A Guide to Growing More Profitably, 6th edition. Taylor & Francis, Routledge (2018).
[80] , and , Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. J. Supply Chain Manage. 37 (2001) 28–37. | DOI
[81] and , A fuzzy bi-objective MILP approach to integrate sales, production, distribution and procurement planning in a FMCG supply chain. Soft Comput. 23 (2019) 4871–4890. | DOI
[82] , , and , An enhanced integer coded genetic algorithm to optimize PWRs. Prog. Nucl. Energy 53 (2011) 449–456. | DOI
[83] and , Detection of collusion in government procurement auctions. J. Purchasing Supply Manage. 17 (2011) 207–221. | DOI
[84] , and , Opportunism, governance structure and relational norms: an interactive perspective. J. Bus. Res. 77 (2017) 131–139. | DOI
[85] , and , A matheuristic for aggregate production-distribution planning with mould sharing. Int. J. Prod. Econ. 145 (2013) 29–37. | DOI
[86] , Equivalence class analysis of genetic algorithms. Complex Syst. 5 (1991) 183–205. | MR | Zbl
[87] , and , Agent-based cloud service negotiation architecture using similarity grouping approach. Int. J. Wavelets, Multiresolution Inf. Process. 18 (2020) 1941015. | MR | DOI
[88] , and , A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Appl. Math. Comput. 188 (2007) 786–800. | MR | Zbl | DOI
[89] , and , Equilibria in an oligopolistic electricity pool with stepwise offer curves. IEEE Trans. Power Syst. 27 (2012) 752–761. | DOI
[90] , , and , A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. Int. J. Prod. Econ. 166 (2015) 226–237. | DOI
[91] , and , A review on bilevel optimization: from classical to evolutionary approaches and applications. IEEE Trans. Evol. Comput. 22 (2018) 276–295. | DOI
[92] , Strategic supplier selection: understanding long-term buyer relationships. Bus. Horizons 31 (1988) 75–81. | DOI
[93] , and , Does supplier opportunism lead to buyer opportunism? A social capital perspective. J. Bus. Ind. Marketing 35 (2019) 362–384. | DOI
[94] and , Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Power Syst. Res. 79 (2009) 374–381. | DOI
[95] , and , A matrix real-coded genetic algorithm to the unit commitment problem. Electr. Power Syst. Res. 76 (2006) 716–728. | DOI
[96] and , A methodology for strategic sourcing. Eur. J. Oper. Res. 154 (2004) 236–250. | Zbl | DOI
[97] , , , , and , Multi-agent decision support tool to enable interoperability among heterogeneous energy systems. Appl. Sci. (Switzerland) 8 (2018) 328.
[98] , Optimal price discounting and lot-sizing policies for perishable items in a supply chain under advance payment scheme and two-echelon trade credits. Int. J. Prod. Econ. 139 (2012) 459–472. | DOI
[99] and , Coordinated supply chain management. Eur. J. Oper. Res. 94 (1996) 1–15. | Zbl | DOI
[100] and , A genetic algorithm for the linear transportation problem. IEEE Trans. Syst. Man Cybern. 21 (1991) 445–452. | MR | Zbl | DOI
[101] , and , An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handeling scheme. IEEE Trans. Syst. Man Cybern. Part C: App. Rev. 35 (2005) 221–232. | DOI
[102] , and , Vendor selection criteria and methods. Eur. J. Oper. Res. 50 (1991) 2–18. | Zbl | DOI
[103] , , and , Bi-level vendor–buyer strategies for a time-varying product price. Appl. Math. Comput. 219 (2013) 9670–9680. | MR | Zbl | DOI
[104] , Genetic algorithms for real parameter optimization. In: Foundations of Genetic Algorithms (1991) 205–218. | MR
[105] , and , Modeling private highways in networks with entry-exit based toll charges. Transp. Res. Part B Methodol. 38 (2004) 191–213. | DOI
[106] , , and , Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using genetic algorithm. Expert Syst. App. 38 (2011) 14773–14777. | DOI
[107] , Genetic-algorithms-based approach for bilevel programming models. J. Transp. Eng. 126 (2000) 115–119. | DOI
[108] , and , A hybrid differential evolution algorithm for solving nonlinear bilevel programming with linear constraints. In: Proceedings of the 5th IEEE International Conference on Cognitive Informatics, ICCI 2006. Vol. 1 (2006) 126–131.
Cité par Sources :





