PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A coalition formation framework for horizontal supply chain collaboration

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: Horizontal logistics collaboration offers a great opportunity for companies to reduce their distribution costs and therefore improve the efficiency of the supply chain. By forming a coalition, companies have the potential to become more profitable. However, the selection of a coalition structure is a difficult task for decision makers. The decision maker needs to identify and choose the best possible partner(s) in order to carry out a joint plan with respect to many criteria. The purpose of this paper is to propose a coalition formation framework for horizontal supply chain collaboration. Methods: To identify and choose the best possible partner(s) in order to carry out a joint plan with respect to many criteria, the methodology of decision-making in a multi-criteria group was used; it was implemented by combining the analytical hierarchy process, the Choquet integral and the Shapley value. In order to demonstrate the validity of the proposed method, numerical examples related to different coalition structures are presented. They demonstrate both the advantages and the feasibility of the proposed framework. Results: The simulation study generated several insights that may help companies to make better partner choices during the design phase of the coalition. The paper solves a coalition-formation problem for cooperative replenishment with multiple firms. Given the significant savings that may be realized thanks to horizontal collaboration, the main interest of the potential collaborating firms is to figure out how the collaborating group should be formed. In order to reach a cooperative decision that fulfills the requirements of individual firms, a multiple criteria hesitant fuzzy decision-making method that uses Shapley value-based E-VIKOR and TOPSIS methods was developed. The proposed procedures not only perfect the existing methods of MCDM with hesitant fuzzy information, but also develop a new direction of hesitant fuzzy theory in the practical decision-making problem of coalition formation in horizontal cooperation. Conclusions: Horizontal cooperation may create synergy effects for cooperating entrepreneurs. Logistics is a good area to analyze the possibility of horizontal cooperation because of the fairly clear set of variables that describe it. The selection of appropriate coalition partners is a very difficult task because it is necessary to comply with a multi-partner collaborative environment. There are many parameters that have a significant role, so the decision-maker has to take them all into consideration. MCDM methods can simplify the decision-making process for coalition formation, but there are also certain other considerations that should be taken into account.
Czasopismo
Rocznik
Strony
357--372
Opis fizyczny
Bibliogr. 51 poz., rys., tab.
Twórcy
  • Faculty of Economics, University of Maria Curie-Skłodowska, Lublin, Poland
  • Faculty of Economics, University of Maria Curie-Skłodowska, Lublin, Poland
Bibliografia
  • 1. Abideen, A. Z., Sorooshian, S., Sundram, V. P. K., & Mohammed, A. (2023). Collaborative insights on horizontal logistics to integrate supply chain planning and transportation logistics planning–A systematic review and thematic mapping. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100066.
  • 2. Audy, J. F., D’Amours, S., & Rönnqvist, M. (2012). An empirical study on coalition formation and cost/savings allocation. International Journal of Production Economics, 136(1), 13-27, https://www.doi.org/10.1016/j.ijpe.2011.08.027
  • 3. Audy, J. F., Lehoux, N., D'Amours, S., & Rönnqvist, M. (2012). A framework for an efficient implementation of logistics collaborations. International transactions in operational research, 19(5), 633-657, https://www.doi.org/10.1111/j.1475-3995.2010.00799.x
  • 4. Badraoui, I., van der Lans, I. A., Boulaksil, Y., & van der Vorst, J. G. (2024). Horizontal logistics collaboration success factors: expectations versus reality. Benchmarking: An International Journal, 31(1), 29-52.
  • 5. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with applications, 39(17), 13051-13069, https://www.doi.org/10.1016/j.eswa.2012.05.056
  • 6. Bleeke, J., & Ernst, D. (2002). Is your strategic alliance really a sale?. Strategy: critical perspectives on business and management, 4(4). Available on the Internet: https://hbr.org/1995/01/is-your-strategic-alliance-really-a-sale (06/09/2021)
  • 7. Bozorg-Haddad, O., Zolghadr-Asli, B., & Loáiciga, H. A. (2021). A handbook on multi-attribute decision-making methods. John Wiley & Sons.
  • 8. Chang, Y. H., Yeh, C. H., & Chang, Y. W. (2013). A new method selection approach for fuzzy group multicriteria decision making. Applied Soft Computing, 13(4), 2179-2187, https://www.doi.org/10.1016/j.asoc.2012.12.009
  • 9. Chen, T. H., & Chen, J. M. (2005). Optimizing supply chain collaboration based on joint replenishment and channel coordination. Transportation Research Part E: Logistics and Transportation Review, 41(4), 261-285, https://www.doi.org/10.1016/j.tre.2004.06.003
  • 10. Cruijssen, F., Bräysy, O., Dullaert, W., Fleuren, H., & Salomon, M. (2007). Joint route planning under varying market conditions. International Journal of Physical Distribution & Logistics Management, https://www.doi.org/10.1108/09600030710752514
  • 11. Cruijssen, F., Cools, M., & Dullaert, W. (2007). Horizontal cooperation in logistics: opportunities and impediments. Transportation Research Part E: Logistics and Transportation Review, 43(2), 129-142, https://www.doi.org/10.1016/j.tre.2005.09.007
  • 12. Cuervo, D. P., Vanovermeire, C., & Sörensen, K. (2016). Determining collaborative profits in coalitions formed by two partners with varying characteristics. Transportation Research Part C: Emerging Technologies, 70, 171-184, https://www.doi.org/10.1016/j.trc.2015.12.011
  • 13. D’Amours, S., & Rönnqvist, M. (2010). Issues in collaborative logistics. In Energy, natural resources and environmental economics (pp. 395-409). Springer, Berlin, Heidelberg, https://www.doi.org/10.1007/978-3-642-12067-1_22
  • 14. Defryn, C., & Sörensen, K. (2018). Multi-objective optimisation models for the travelling salesman problem with horizontal cooperation. European Journal of Operational Research, 267(3), 891-903, https://www.doi.org/10.1016/j.ejor.2017.12.028
  • 15. Defryn, C., Sörensen, K., & Dullaert, W. (2019). Integrating partner objectives in horizontal logistics optimisation models. Omega, 82, 1-12, https://www.doi.org/10.1016/j.omega.2017.11.008
  • 16. Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of management review, 23(4), 660-679, https://www.doi.org/10.5465/amr.1998.1255632
  • 17. Eren Akyol, D., & Sarısakal, O. (2023). Horizontal cooperation in subcontracting: A case study in home textile industry. Mathematical Problems in Engineering, 2023(1), 8256116.
  • 18. Ghaderi, H., Dullaert, W., & Amstel, W. P. V. (2016). Reducing lead-times and lead-time variance in cooperative distribution networks. International Journal of Shipping and Transport Logistics, 8(1), 51-65, https://www.doi.org/10.1504/IJSTL.2016.073316
  • 19. Gou, Q., Zhang, J., Liang, L., Huang, Z., & Ashley, A. (2014). Horizontal cooperative programmes and cooperative advertising. International Journal of Production Research, 52(3), 691-712, https://www.doi.org/10.1080/00207543.2013.827809
  • 20. Govindan, K., Kadziński, M., Ehling, R., & Miebs, G. (2019). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega, 85, 1-15, https://www.doi.org/10.1016/j.omega.2018.05.007
  • 21. Guajardo, M., Jörnsten, K., & Rönnqvist, M. (2016). Constructive and blocking power in collaborative transportation. OR spectrum, 38(1), 25-50, https://www.doi.org/10.1007/s00291-015-0413-z
  • 22. Guajardo, M., Rönnqvist, M., Flisberg, P., & Frisk, M. (2018). Collaborative transportation with overlapping coalitions. European Journal of Operational Research, 271(1), 238-249, https://www.doi.org/10.1016/j.ejor.2018.05.001
  • 23. Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
  • 24. Ji, H., Yang, S., Jia, B., Zhang, M., & Su, B. (2024). Study on Location Selection of Urban Two-Level Joint Express Delivery Stations Considering Fair Cost Allocation among Enterprises. Transportation Research Record, 03611981241239651.
  • 25. Juan, A. A., Faulin, J., Pérez-Bernabeu, E., & Jozefowiez, N. (2014). Horizontal cooperation in vehicle routing problems with backhauling and environmental criteria. Procedia-Social and Behavioral Sciences, 111, 1133-1141, https://www.doi.org/10.1016/j.sbspro.2014.01.148
  • 26. Kaya, T., & Kahraman, C. (2011). An integrated fuzzy AHP–ELECTRE methodology for environmental impact assessment. Expert Systems with Applications, 38(7), 8553-8562, https://www.doi.org/10.1016/j.eswa.2011.01.057
  • 27. c, S., Laabidi, A., & Abdelaziz, F. B. (2011). Single supplier multiple cooperative retailers inventory model with quantity discount and permissible delay in payments. Computers & Industrial Engineering, 60(1), 164-172, https://www.doi.org/10.1016/j.cie.2010.10.014
  • 28. Leitner, R., Meizer, F., Prochazka, M., & Sihn, W. (2011). Structural concepts for horizontal cooperation to increase efficiency in logistics. CIRP Journal of Manufacturing Science and Technology, 4(3), 332-337, https://www.doi.org/10.1016/j.cirpj.2011.01.009
  • 29. Mrabti, N., Hamani, N., & Delahoche, L. (2023). A new metric for gain sharing assessment in collaborative distribution: the sustainability and flexibility rate. International Journal of Systems Science: Operations & Logistics, 10(1), 2038714.
  • 30. Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
  • 31. Prakash, C., & Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66-78, https://www.doi.org/10.1016/j.spc.2016.04.001
  • 32. Raue, J. S., & Wieland, A. (2015). The interplay of different types of governance in horizontal cooperations. The International Journal of Logistics Management, https://www.doi.org/10.1108/IJLM-08-2012-0083
  • 33. Raut, R., Kharat, M., Kamble, S., & Kumar, C. S. (2018). Sustainable evaluation and selection of potential third-party logistics (3PL) providers. Benchmarking: An International Journal, https://www.doi.org/10.1108/BIJ-05-2016-0065
  • 34. Saenz, M. J., Ubaghs, E., Cuevas, A. I., Saenz, M. J., Ubaghs, E., & Cuevas, A. I. (2015). Vertical collaboration and horizontal collaboration in supply chain. Enabling horizontal collaboration through continuous relational learning, 7-10.
  • 35. S. B. Jouida, S. Krichen, and W. Klibi. Coalition-formation problem for sourcing contract design in supply networks. European Journal of Operational Research, 257(2):539–558, 2017, https://www.doi.org/10.1016/j.ejor.2016.07.040
  • 36. Serrano-Hernandez, A., Faulin, J., Hirsch, P., & Fikar, C. (2018). Agent-based simulation for horizontal cooperation in logistics and transportation: From the individual to the grand coalition. Simulation Modelling Practice and Theory, 85, 47-59, https://www.doi.org/10.1016/j.simpat.2018.04.002
  • 37. Shapley, L. S., & Shubik, M. (1953). Solutions of n-person games with ordinal utilities. Econometrica, 21(2), 348-349.
  • 38. Simatupang, T. M., & Sridharan, R. (2002). The collaborative supply chain. The international journal of logistics management, 13(1), 15-30, https://www.doi.org/10.1108/09574090210806333
  • 39. Sombattheera, C., & Ghose, A. (2006, June). A pruning-based algorithm for computing optimal coalition structures in linear production domains. In Conference of the Canadian Society for Computational Studies of Intelligence (pp. 13-24). Springer, Berlin, Heidelberg, https://www.doi.org/10.1007/11766247_2
  • 40. Soysal, M., Bloemhof-Ruwaard, J. M., Haijema, R., & van der Vorst, J. G. (2018). Modeling a green inventory routing problem for perishable products with horizontal collaboration. Computers & Operations Research, 89, 168-182, https://www.doi.org/10.1016/j.cor.2016.02.003
  • 41. Tavana, M., Mavi, R. K., Santos-Arteaga, F. J., & Doust, E. R. (2016). An extended VIKOR method using stochastic data and subjective judgments. Computers & Industrial Engineering, 97, 240-247, https://www.doi.org/10.1016/j.cie.2016.05.013
  • 42. Tadić, S., Krstić, M., & Kovač, M. (2023). Assessment of city logistics initiative categories sustainability: case of Belgrade. Environment, Development and Sustainability, 25(2), 1383-1419.
  • 43. Uzun, B., Taiwo, M., Syidanova, A., & Uzun Ozsahin, D. (2021). The technique for order of preference by similarity to ideal solution (TOPSIS). Application of multi-criteria decision analysis in environmental and civil engineering, 25-30.
  • 44. Vaidogas, E. R., & Sakenaite, J. (2011). Multi-attribute decision-making in economics of fire protection. Engineering Economics, 22(3), https://www.doi.org/10.5755/j01.ee.22.3.516
  • 45. Wan, S. P., Wang, Q. Y., & Dong, J. Y. (2013). The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers. Knowledge-Based Systems, 52, 65-77, https://www.doi.org/10.1016/j.knosys.2013.06.019
  • 46. Wang, X., Kopfer, H., & Gendreau, M. (2014). Operational transportation planning of freight forwarding companies in horizontal coalitions. European Journal of Operational Research, 237(3), 1133-1141, https://www.doi.org/10.1016/j.ejor.2014.02.056
  • 47. Wei, G. (2012). Hesitant fuzzy prioritized operators and their application to multiple attribute decision making. Knowledge-Based Systems, 31, 176-182, https://www.doi.org/10.1016/j.knosys.2012.03.011
  • 48. Wei, G., & Zhang, N. (2014). A multiple criteria hesitant fuzzy decision making with Shapley value-based VIKOR method. Journal of Intelligent & Fuzzy Systems, 26(2), 1065-1075, https://www.doi.org/10.3233/IFS-130798
  • 49. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.
  • 50. Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37(7), 4938-4947, https://www.doi.org/10.1016/j.apm.2012.10.002
  • 51. Zhao, N., Xu, Z., & Liu, F. (2015). Uncertainty measures for hesitant fuzzy information. International Journal of Intelligent Systems, 30(7), 818-836, https://www.doi.org/10.1002/int.21714
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e9173fc6-8e1e-4125-8aea-c05f21ced845
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.