Skip to main content

Advertisement

Log in

A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The coordination of the planning operations across the manufacturing supply chains (MSC) is considered as a major component of supply chain management. As centralized coordination requires relevant information sharing, alternative approaches are needed to synchronize production plans between partners of MSC characterized by decentralized decision making systems with limited information sharing. In this paper, a bi-level fuzzy-based negotiation approach is proposed in order to model collaborative planning between MSC partners. During negotiation, each manufacturer is optimizing a bi-objective planning model. In order to generate optimal production plans, a genetic algorithm is used. To evaluate the exchanged proposals and the satisfaction degree of each partner, the fuzzy logic approach is adopted in the both negotiation levels. The main result of the developed approach consists in a collaborative decision making mechanism allowing the MSC partners to define their optimal production plans while considering the whole negotiating process with the pre-negotiation and post-negotiation stages. Computational tests done for different MSC structures show the effectiveness of the proposed mechanism, which ensures the satisfaction of the manufacturers and the optimality of the final solution. By comparing the results with the ones obtained considering a centralized planning approach, it is shown that the developed negotiation mechanism yields to near optimal solutions with insignificant gaps from the global optimal solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Agrawal, N., Rangaiah, G., Ray, a, & Gupta, S. (2007). Design stage optimization of an industrial low-density polyethylene tubular reactor for multiple objectives using NSGA-II and its jumping gene adaptations. Chemical Engineering Science, 62(9), 2346–2365. doi:10.1016/j.ces.2007.01.030.

    Article  Google Scholar 

  • Arshinder, Kanda, A., & Deshmukh, S. G. (2007). Coordination in supply chains: An evaluation using fuzzy logic. Production Planning & Control, 18(5), 420–435. doi:10.1080/09537280701430994.

    Article  Google Scholar 

  • Atkin, T. S., & Rinehart, L. M. (2006). The effect of negotiation practices on the relationship between suppliers and customers. Negotiation Journal, 22(January), 47–65. doi:10.1111/j.1571-9979.2006.00085.x.

    Article  Google Scholar 

  • Bandyopadhyay, S., & Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24(4), 707–716. doi:10.1007/s10845-011-0617-2.

    Article  Google Scholar 

  • Barnes, J., & Liao, Y. (2012). The effect of individual, network, and collaborative competencies on the supply chain management system. International Journal of Production Economics, 140(2), 888–899. doi:10.1016/j.ijpe.2012.07.010.

    Article  Google Scholar 

  • Bensmaine, A., Dahane, M., & Benyoucef, L. (2011). Simulation-based NSGA-II approach for multi-unit process plans generation in reconfigurable manufacturing system. In IEEE 16th conference on emerging technologies and factory automation (pp. 1–8). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6059047

  • Bichler, M., Kersten, G., & Strecker, S. (2003). Towards a structured design of electronic negotiations. Group Decision and Negotiation, 12(4), 311–335. Retrieved from http://www.springerlink.com/index/VW76372275563832.pdf

  • Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180. doi:10.1016/j.jom.2010.12.008.

    Article  Google Scholar 

  • Cárdenas-Barrón, L. E., Taleizadeh, A. A., & Treviño-Garza, G. (2012). An improved solution to replenishment lot size problem with discontinuous issuing policy and rework, and the multi-delivery policy into economic production lot size problem with partial rework. Expert Systems with Applications, 39(18), 13540–13546. doi:10.1016/j.eswa.2012.07.012.

    Article  Google Scholar 

  • Cárdenas-Barrón, L. E., Teng, J. T., Treviño-Garza, G., Wee, H. M., & Lou, K. R. (2012). An improved algorithm and solution on an integrated production-inventory model in a three-layer supply chain. International Journal of Production Economics, 136(0925), 384–388. doi:10.1016/j.ijpe.2011.12.013.

    Article  Google Scholar 

  • Cárdenas-Barrón, L. E., & Treviño-Garza, G. (2014). An optimal solution to a three echelon supply chain network with multi-product and multi-period. Applied Mathematical Modelling, 38, 1911–1918. doi:10.1016/j.apm.2013.09.010.

    Article  Google Scholar 

  • Cárdenas-Barrón, L. E., Treviño-Garza, G., & Wee, H. M. (2012). A simple and better algorithm to solve the vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Systems with Applications, 39, 3888–3895. doi:10.1016/j.eswa.2011.09.057.

    Article  Google Scholar 

  • Cárdenas-Barrón, L. E., Treviño-Garza, G., Widyadana, G. A., & Wee, H.-M. (2014). A constrained multi-products EPQ inventory model with discrete delivery order and lot size. Applied Mathematics and Computation, 230, 359–370. doi:10.1016/j.amc.2013.12.077.

    Article  Google Scholar 

  • Cheng, C., Chan, C., & Lin, K. (2006). Intelligent agents for e-marketplace: Negotiation with issue trade-offs by fuzzy inference systems. Decision Support Systems, 42(2), 626–638. doi:10.1016/j.dss.2005.02.009.

    Article  Google Scholar 

  • Coutinho, C., Cretan, A., da Silva, C. F., Ghodous, P., & Jardim-Goncalves, R. (2014). Service-based negotiation for advanced collaboration in enterprise networks. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0857-4

  • Deb, K., & Agrawal, S. (2002). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Evolutionary Computation, IEEE Transactions, 6(2), 182–197. Retrieved from http://www.springerlink.com/index/181751v0v1125574.pdf

  • Díaz-Madroñero, M., & Peidro, D. (2011). A fuzzy goal programming approach for collaborative supply chain master planning. In D. Onkal (Ed.), Supply chain management—Pathways for research and practice. doi:10.5772/20642.

  • Dudek, G., & Stadtler, H. (2005). Negotiation-based collaborative planning between supply chains partners. European Journal of Operational Research, 163(3), 668–687. doi:10.1016/j.ejor.2004.01.014.

    Article  Google Scholar 

  • Dudek, G., & Stadtler, H. (2007). Negotiation-based collaborative planning in divergent two-tier supply chains. International Journal of Production Research, 45(2), 465–484. doi:10.1080/00207540600584821.

    Article  Google Scholar 

  • Ertogral, K., & Wu, S. D. (2000). Auction-theoretic coordination of production planning in the supply chain. IIE Transactions, 32(10), 931–940. doi:10.1080/07408170008967451.

    Google Scholar 

  • Fang, F., & Wong, T. N. (2010). Applying hybrid case-based reasoning in agent-based negotiations for supply chain management. Expert Systems with Applications, 37(12), 8322–8332. doi:10.1016/j.eswa.2010.05.052.

    Article  Google Scholar 

  • Fathi, A., & Mozaffari, A. (2014). Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map. Journal of Intelligent Manufacturing, 25, 775–795. doi:10.1007/s10845-012-0718-6.

    Article  Google Scholar 

  • Fink, a. (2004). Supply chain coordination by means of automated negotiations. Proceedings of the 37th annual Hawaii international conference on system sciences, 2004, 10 pp. doi:10.1109/HICSS.2004.1265206

  • Guner Goren, H., Tunali, S., & Jans, R. (2010). A review of applications of genetic algorithms in lot sizing. Journal of Intelligent Manufacturing, 21(4), 575–590. doi:10.1007/s10845-008-0205-2.

    Article  Google Scholar 

  • Hadaya, P., & Cassivi, L. (2012). Joint collaborative planning as a governance mechanism to strengthen the chain of IT value co-creation. The Journal of Strategic Information Systems, 21(3), 182–200. doi:10.1016/j.jsis.2012.03.001.

    Article  Google Scholar 

  • Hernandez, J. E., Mula, J., Poler, R., & Pavon, J. (2011). A multiagent negotiation based model to support the collaborative supply chain planning process. Studies in Informatics and Control, 20(1), 43–54. Retrieved from \(<\text{ Go } \text{ to } \text{ ISI }>\)://WOS:000288526500005.

  • İnkaya, T., & Akansel, M. (2015). Coordinated scheduling of the transfer lots in an assembly-type supply chain: A genetic algorithm approach. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1041-9

  • Jap, S. (1999). Pie-expansion efforts: Collaboration processes in buyer–supplier relationships. Journal of Marketing Research, 36(4), 461. doi:10.2307/3152000.

    Article  Google Scholar 

  • Jolai, F., Razmi, J., & Rostami, N. K. M. (2011). A fuzzy goal programming and meta heuristic algorithms for solving integrated production: Distribution planning problem. Central European Journal of Operations Research, 19(4), 547–569. doi:10.1007/s10100-010-0144-9.

    Article  Google Scholar 

  • Jung, H., Jeong, B., & Lee, C.-G. (2008). An order quantity negotiation model for distributor-driven supply chains. International Journal of Production Economics, 111(1), 147–158. doi:10.1016/j.ijpe.2006.12.054.

    Article  Google Scholar 

  • Kumar, R. S., Tiwari, M. K., & Goswami, a. (2014). Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production-inventory system. Journal of Intelligent Manufacturing, doi:10.1007/s10845-014-0921-8

  • Li, X., & Wang, Q. (2007). Coordination mechanisms of supply chain systems. European Journal of Operational Research, 179(1), 1–16. doi:10.1016/j.ejor.2006.06.023.

    Article  Google Scholar 

  • Lin, Y.-I., Chou, Y.-W., Shiau, J.-Y., & Chu, C.-H. (2011). Multi-agent negotiation based on price schedules algorithm for distributed collaborative design. Journal of Intelligent Manufacturing, 24(3), 545–557. doi:10.1007/s10845-011-0609-2.

    Article  Google Scholar 

  • Lingxiao, Y., & Liangyou, S. (2013). Multi-objective manufacturer order fulfillment based on NSGA-II algorithm. International Journal of Digital Content Technology and Its Applications, 7(6), 1267–1275. doi:10.4156/jdcta.vol7.issue6.145.

    Article  Google Scholar 

  • Mamdani, E. H., & Assilian, S. (1975). Experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.

    Article  Google Scholar 

  • Margaliot, M. (2007). Mathematical modeling of natural phenomena: A fuzzy logic approach. In Fuzzy Logic (Vol. 215, pp. 113–134). Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-71258-9_7

  • Martínez-de-Albéniz, V., & Simchi-Levi, D. (2013). Supplier–buyer negotiation games: Equilibrium conditions and supply chain efficiency. Production and Operations Management, 22(2), 397–409. doi:10.1111/j.1937-5956.2012.01374.x.

    Article  Google Scholar 

  • Mintu-Wimsatt, A., & Graham, J. L. (2004). Testing a negotiation model on Canadian anglophone and Mexican exporters. Journal of the Academy of Marketing Science, 32(3), 345–356. doi:10.1177/0092070304266123.

    Article  Google Scholar 

  • Mohebbi, S., & Shafaei, R. (2012). e-Supply network coordination: The design of intelligent agents for buyer–supplier dynamic negotiations. Journal of Intelligent Manufacturing, 23(3), 375–391. doi:10.1007/s10845-009-0377-4.

    Article  Google Scholar 

  • Murugan, P., Kannan, S., & Baskar, S. (2009). NSGA-II algorithm for multi-objective generation expansion planning problem. Electric Power Systems Research, 79(4), 622–628. doi:10.1016/j.epsr.2008.09.011.

    Article  Google Scholar 

  • Nyaga, G. N., Whipple, J. M., & Lynch, D. F. (2010). Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? Journal of Operations Management, 28(2), 101–114. doi:10.1016/j.jom.2009.07.005.

    Article  Google Scholar 

  • Paulraj, A., Lado, Aa, & Chen, I. J. (2008). Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships. Journal of Operations Management, 26(1), 45–64. doi:10.1016/j.jom.2007.04.001.

    Article  Google Scholar 

  • Ramsay, J. (2004). Serendipity and the realpolitik of negotiations in supply chains. Supply Chain Management: An International Journal, 9(3), 219–229.

    Article  Google Scholar 

  • Rodriguez-Rodriguez, R., Gonzalez, P. P., & Leisten, R. (2011). From competitive to collaborative supply networks: A simulation study. Applied Mathematical Modelling, 35(3), 1054–1064. doi:10.1016/j.apm.2010.07.050.

    Article  Google Scholar 

  • Scarbrough, H. (2000). The HR implications of supply chain relationships. Human Resource Management Journal, 10(1), 5–17. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1748-8583.2000.tb00010.x/full

  • Scavarda, M., Reyes Levalle, R., Lee, S., & Nof, S. Y. (2015). Collaborative e-work parallelism in supply decisions networks: The chemical dimension. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-015-1054-4

  • Seifert, D. (2003). Collaborative planning, forecasting, and replenishment: How to create a supply chain advantage (1st ed.). New York: AMACOM.

    Google Scholar 

  • Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396–419. doi:10.1016/j.tre.2006.11.001.

    Article  Google Scholar 

  • Shukla, R., Garg, D., & Agarwal, A. (2014). An integrated approach of fuzzy AHP and fuzzy TOPSIS in modeling supply chain coordination. Production & Manufacturing Research, 2(1), 415–437. doi:10.1080/21693277.2014.919886.

    Google Scholar 

  • Stadtler, H. (2009). A framework for collaborative planning and state-of-the-art. OR Spectrum, 31(1), 5–30. doi:10.1007/s00291-007-0104-5.

    Article  Google Scholar 

  • Stank, T. (2001). Supply chain collaboration and logistical service performance. Journal of Business Logistics, 22(1), 29–48. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/j.2158-1592.2001.tb00158.x/full

  • Sugeno, M., & Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1(1), 7. doi:10.1109/TFUZZ.1993.390281.

    Article  Google Scholar 

  • Terpend, R., Tyler, B. B., Krause, D. R., & Handfield, R. B. (2008). Buyer-supplier relationships: Derived value over two decades. Journal of Supply Chain Management, 44(2), 28–55. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1745-493X.2008.00053.x/full

  • Türkşen, I., & Zarandi, M. (1999). Production planning and scheduling: Fuzzy and crisp approaches. In H.-J. Zimmermann (Ed.), Practical applications of fuzzy technologies (pp. 479–529). Springer, New York. doi:10.1007/978-1-4615-4601-6_15.

  • Wang, C., & Dargahi, F. (2013). Service customization under capacity constraints: An auction-based model. Journal of Intelligent Manufacturing, 24(5), 1033–1045. doi:10.1007/s10845-012-0689-7.

    Article  Google Scholar 

  • Wang, G., Wong, T. N., & Yu, C. (2013). A computational model for multi-agent E-commerce negotiations with adaptive negotiation behaviors. Journal of Computational Science, 4(3), 135–143. doi:10.1016/j.jocs.2011.10.003.

    Article  Google Scholar 

  • Xu, X., & Meng, Z. (2014). Coordination between a supplier and a retailer in terms of profit concession for a two-stage supply chain. International Journal of Production Research, 52(7), 2122–2133. doi:10.1080/00207543.2013.854940.

    Article  Google Scholar 

  • Zachariassen, F. (2008). Negotiation strategies in supply chain management. International Journal of Physical Distribution & Logistics Management, 38(10), 764–781. doi:10.1108/09600030810926484.

    Article  Google Scholar 

  • Zadeh, L. (1965). Fuzzy sets. Information and Control, 353, 338–353. Retrieved from http://www.sciencedirect.com/science/article/pii/S001999586590241X

  • Zhang, Y., Wang, L., & Gao, J. (2015). Supplier collaboration and speed-to-market of new products: The mediating and moderating effects. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-1021-5.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wafa Ben Yahia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ben Yahia, W., Ayadi, O. & Masmoudi, F. A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains. J Intell Manuf 28, 1987–2006 (2017). https://doi.org/10.1007/s10845-015-1085-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1085-x

Keywords

Navigation