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Combined demand and capacity sharing with best matching decisions in enterprise collaboration

Author

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  • Moghaddam, Mohsen
  • Nof, Shimon Y.

Abstract

Demand and capacity sharing (DCS) among entities within a supply network are common practice, and have become attractive strategies for competing and non-competing supply enterprises (SEs). Examples include airlines, test and assembly factories, and outsourced maintenance and logistics providers. The purpose: maximize profit and resource utilization, enable timely delivery to customers in spite of uncertain market demands and unexpected capacity shortages, and maximize the overall stability. DCS protocols are defined for the SEs with capacity shortage, known as demand sharing SEs, to utilize excess capacities of other SEs, known as capacity sharing SEs, thus fulfilling their current customers' demand more effectively, while eliminating excess inventory of capacity sharing SEs. These DCS roles vary over time. High frequency of DCS decisions could impose additional costs to the Collaborative Network of SEs (CNSE) in terms of transactions, negotiations, and lateral transshipment of stocks between SEs. Attention must be paid to the inevitable costs of collaboration for DCS. Best Matching (BM) protocol is proposed to minimize the DCS costs through dynamic matching of SEs and customers with respect to the customers' demand and SEs' available capacity to share. BM protocol is also applied for finding the best matches between DCS proposals during collaboration negotiations among SEs. A novel Mixed-Integer Programming (MIP) formulation is developed for modeling and analyzing the combined DCS–BM decisions. The DCS–BM model is then validated using the Queuing Theory. It is shown mathematically and through numerical experiments that the DCS–BM model: (1) outperforms the previous non-collaborative models in terms of resource utilization and stability, and (2) provides a dominating strategy, compared with both collaborative and non-collaborative models, for optimizing the total CNSE profit and service level.

Suggested Citation

  • Moghaddam, Mohsen & Nof, Shimon Y., 2014. "Combined demand and capacity sharing with best matching decisions in enterprise collaboration," International Journal of Production Economics, Elsevier, vol. 148(C), pages 93-109.
  • Handle: RePEc:eee:proeco:v:148:y:2014:i:c:p:93-109
    DOI: 10.1016/j.ijpe.2013.11.015
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    References listed on IDEAS

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    Cited by:

    1. Yilmaz, Ibrahim & Yoon, Sang Won & Seok, Hyesung, 2017. "A framework and algorithm for fair demand and capacity sharing in collaborative networks," International Journal of Production Economics, Elsevier, vol. 193(C), pages 137-147.
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    3. Mohsen Moghaddam & Shimon Y. Nof & Ehud Menipaz, 2016. "Design and administration of collaborative networked headquarters," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7074-7090, December.
    4. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    5. Li, Wenjie & Asadabadi, Ali & Miller-Hooks, Elise, 2022. "Enhancing resilience through port coalitions in maritime freight networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 1-23.
    6. Ying Cheng & Luning Bi & Fei Tao & Ping Ji, 2020. "Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1707-1720, October.
    7. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    8. Chen, Xu & Peng, Ying & Wang, Xiaojun & Wang, Pengfei, 2024. "Capacity sharing between competing manufacturers: A collective good or a detrimental effect?," International Journal of Production Economics, Elsevier, vol. 268(C).

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