IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i6d10.1007_s10668-022-02282-x.html
   My bibliography  Save this article

Cooperation and coopetition among retailers-third party logistics providers alliances under different risk behaviors, uncertainty demand and environmental considerations

Author

Listed:
  • Nafiseh Fallahi

    (Islamic Azad University, South Tehran Branch)

  • Ashkan Hafezalkotob

    (Islamic Azad University, South Tehran Branch)

  • Sadigh Raissi

    (Islamic Azad University, South Tehran Branch)

  • Vahidreza Ghezavati

    (Islamic Azad University, South Tehran Branch)

Abstract

Optimal profits for third-party logistics providers (3PLs) can be analyzed using the networks model to determine decision-making processes within transshipment and logistics, distribution networks, etc. Increasing academic attention is currently being focused upon fields examining 3PLs' operations within logistics networks. This paper studies cooperative game theory (CGT) of retailers-3PLs that make an alliance with each other with a specified demand function. The logistics network involves several suppliers and retailers-3PLs alliances, a distribution graph consisting of several nodes and arcs as well as multiple customers. Retailers-3PLs purchase the same goods from suppliers and sell to customers after shipping via the network; they also consider environmental issues to reduce pollutants and emissions fines. The proposed nonlinear programming (NLP) model aims to find the best flow and price of goods under cooperation conditions among retailers-3PLs by analyzing their risk levels. Controlling uncertainty in the models is accomplished by Mulvey's robust approach. In a general coalition, fair profit distribution methods are applied to share the profits among retailers-3PLs under different risk situations. We conduct a numerical analysis to present the application of our proposed model and find whether coalitions and cooperation between retailers-3PLs reduce costs and increase profits. Finally, we report the sensitivity analysis results regarding the penalties imposed for pollutant emissions, along with suggestions for future research. The results reveal that since their profit is greater in the coalition mode, they tend to cooperate with each other, whatever the amount of pollution fines be.

Suggested Citation

  • Nafiseh Fallahi & Ashkan Hafezalkotob & Sadigh Raissi & Vahidreza Ghezavati, 2023. "Cooperation and coopetition among retailers-third party logistics providers alliances under different risk behaviors, uncertainty demand and environmental considerations," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 5597-5633, June.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:6:d:10.1007_s10668-022-02282-x
    DOI: 10.1007/s10668-022-02282-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02282-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02282-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hsiao, H.I. & Kemp, R.G.M. & van der Vorst, J.G.A.J. & (Onno) Omta, S.W.F., 2010. "A classification of logistic outsourcing levels and their impact on service performance: Evidence from the food processing industry," International Journal of Production Economics, Elsevier, vol. 124(1), pages 75-86, March.
    2. Gong, Fengmei & Kung, David S. & Zeng, Tong, 2018. "The impact of different contract structures on IT investment in logistics outsourcing," International Journal of Production Economics, Elsevier, vol. 195(C), pages 158-167.
    3. Zhang, Jian & Nault, Barrie R. & Tu, Yiliu, 2015. "A dynamic pricing strategy for a 3PL provider with heterogeneous customers," International Journal of Production Economics, Elsevier, vol. 169(C), pages 31-43.
    4. M. P. M. Hendriks & D. Armbruster & M. Laumanns & E. Lefeber & J. T. Udding, 2012. "Design Of Robust Distribution Networks Run By Third Party Logistics Service Providers," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1-23.
    5. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    6. Yan, Wei & Xiong, Yu & Xiong, Zhongkai & Guo, Nian, 2015. "Bricks vs. clicks: Which is better for marketing remanufactured products?," European Journal of Operational Research, Elsevier, vol. 242(2), pages 434-444.
    7. Hafezalkotob, Ashkan & Makui, Ahmad, 2015. "Cooperative maximum-flow problem under uncertainty in logistic networks," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 593-604.
    8. Cai, Xiaoqiang & Chen, Jian & Xiao, Yongbo & Xu, Xiaolin & Yu, Gang, 2013. "Fresh-product supply chain management with logistics outsourcing," Omega, Elsevier, vol. 41(4), pages 752-765.
    9. Shuai Huang & Zhi-Ping Fan & Xiaohuan Wang, 2019. "Optimal operational strategies of supply chain under financing service by a 3PL firm," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3405-3420, June.
    10. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    11. Wu, Qing & Mu, Yinping & Feng, Yi, 2015. "Coordinating contracts for fresh product outsourcing logistics channels with power structures," International Journal of Production Economics, Elsevier, vol. 160(C), pages 94-105.
    12. Mohammaditabar, Davood & Ghodsypour, Seyed Hassan & Hafezalkotob, Ashkan, 2016. "A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 87-97.
    13. Huang, Min & Song, Min & Lee, Loo Hay & Ching, Wai Ki, 2013. "Analysis for strategy of closed-loop supply chain with dual recycling channel," International Journal of Production Economics, Elsevier, vol. 144(2), pages 510-520.
    14. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    15. Li, Yina & Xu, Xuejun & Zhao, Xiande & Yeung, Jeff Hoi Yan & Ye, Fei, 2012. "Supply chain coordination with controllable lead time and asymmetric information," European Journal of Operational Research, Elsevier, vol. 217(1), pages 108-119.
    16. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Evangelos Gkanatsas & Harold Krikke, 2020. "Towards a Pro-Silience Framework: A Literature Review on Quantitative Modelling of Resilient 3PL Supply Chain Network Designs," Sustainability, MDPI, vol. 12(10), pages 1-25, May.
    2. Huang, Edward & Goetschalckx, Marc, 2014. "Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 508-518.
    3. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.
    4. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    5. Arkajyoti De & Surya Prakash Singh, 2022. "Analysis of Competitiveness in Agri-Supply Chain Logistics Outsourcing: A B2B Contractual Framework," Sustainability, MDPI, vol. 14(11), pages 1-33, June.
    6. Masoud Hekmatfar & M. R. M. Aliha & Mir Saman Pishvaee & Tomasz Sadowski, 2023. "A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite," Mathematics, MDPI, vol. 11(24), pages 1-41, December.
    7. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.
    8. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    9. Ouhimmou, Mustapha & Nourelfath, Mustapha & Bouchard, Mathieu & Bricha, Naji, 2019. "Design of robust distribution network under demand uncertainty: A case study in the pulp and paper," International Journal of Production Economics, Elsevier, vol. 218(C), pages 96-105.
    10. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    11. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    12. Jihee Han & KwangSup Shin, 2016. "Evaluation mechanism for structural robustness of supply chain considering disruption propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 135-151, January.
    13. Zhinan Li & Qinming Liu & Chunming Ye & Ming Dong & Yihan Zheng, 2022. "Achieving Resilience: Resilient Price and Quality Strategies of Fresh Food Dual-Channel Supply Chain Considering the Disruption," Sustainability, MDPI, vol. 14(11), pages 1-24, May.
    14. Tsai, Jung-Fa, 2007. "An optimization approach for supply chain management models with quantity discount policy," European Journal of Operational Research, Elsevier, vol. 177(2), pages 982-994, March.
    15. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    16. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    17. Xiaoxu Chen & Peng Xu & Thomas Walker & Shengzhong Huang, 2019. "Pricing and Ordering Decisions in a Retailer Dominant Channel Involving a Third-Party Logistics Provider," Complexity, Hindawi, vol. 2019, pages 1-15, July.
    18. Seyed Babak Ebrahimi & Ehsan Bagheri, 2022. "A multi-objective formulation for the closed-loop plastic supply chain under uncertainty," Operational Research, Springer, vol. 22(5), pages 4725-4768, November.
    19. Lai, K.K. & Wang, Ming & Liang, L., 2007. "A stochastic approach to professional services firms' revenue optimization," European Journal of Operational Research, Elsevier, vol. 182(3), pages 971-982, November.
    20. Yaser Taghinezhad, 2019. "Optimisation model for a chain logistics problem involving chilled food under conditions of uncertainty," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(2), pages 103-116.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:25:y:2023:i:6:d:10.1007_s10668-022-02282-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.