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Coordinating biomass supply chains for remote communities: a comparative analysis of non-cooperative and cooperative scenarios

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  • Fereshteh Mafakheri
  • Dotun Adebanjo
  • Audley Genus

Abstract

The absence of economies of scale is a major barrier in use of renewable energy sources in small and dispersed off-grid remote communities. For example, in northern Canada, diesel is currently the main source of electricity and heat generation. Coordination of biomass supply chains could play a key role in improving the cost efficiency and reliability of bioenergy generation through bundled ordering and creation of storage hubs. In this study, a supply chain management model with multiple suppliers and multiple end-user communities is formulated. The proposed model enables us to analyse and compare the outcomes of adopting a cooperative coordination strategy (with a joint pay-off for communities) versus a non-cooperative coordination strategy (with individual payoffs for communities). Other peculiar attributes of the proposed model rest in the addressing of restricted ordering schedules and quantities (due to unavailability of pathways) by advocating nonlinear ordering and distribution costs (to incorporate quantity discounts) achieved through coordinated and/or collective inventories. A real biomass supply chain case study of three northernmost Nunavik communities in Quebec is considered to show the applicability of the model and provide insights for uptake of bioenergy sources in remote off-grid communities.

Suggested Citation

  • Fereshteh Mafakheri & Dotun Adebanjo & Audley Genus, 2021. "Coordinating biomass supply chains for remote communities: a comparative analysis of non-cooperative and cooperative scenarios," International Journal of Production Research, Taylor & Francis Journals, vol. 59(15), pages 4615-4632, August.
  • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:15:p:4615-4632
    DOI: 10.1080/00207543.2020.1767312
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    Cited by:

    1. Jiang, Zhong-Zhong & He, Na & Huang, Song, 2021. "Government penalty provision and contracting with asymmetric quality information in a bioenergy supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    2. Yunusoglu, Pinar & Ozsoydan, Fehmi Burcin & Bilgen, Bilge, 2024. "A machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain," Applied Energy, Elsevier, vol. 362(C).

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