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Gresilient supplier assessment and order allocation planning

Author

Listed:
  • Ahmed Mohammed

    (Muscat University)

  • Irina Harris

    (Cardiff University)

  • Anthony Soroka

    (Cardiff University)

  • Mohamed Naim

    (Cardiff University)

  • Tim Ramjaun

    (Cardiff University)

  • Morteza Yazdani

    (Universidad Loyola Andalucia)

Abstract

Companies are under pressure to re-engineer their supply chains to ‘go green’ while simultaneously improving their resilience to cope with unexpected disruptions where the supplier selection decision plays a strategic role. We present a new approach to supplier evaluation and allocating the optimal order quantity from each supplier with respect to green and resilience (gresilience) characteristics. An integrated framework that considers traditional business, green and resilience criteria and sub-criteria was developed, followed by a calculation of importance weight of criteria and sub-criteria using analytical hierarchy process (AHP). We evaluate suppliers using the technique for order of preference by similarity to ideal solution (TOPSIS). The obtained weights from AHP and TOPSIS were integrated into a developed multi-objective programming model used as an order allocation planner and the ε-constraint method was used to solve the multi-objective optimization problem. TOPSIS was applied to select the final Pareto solution based on its closeness from the ideal solution. The applicability and effectiveness of the proposed approach was illustrated using a real case study through a comparatively meaningful ranking of suppliers. The study provides a helpful aid for managers seeking to improve their supply chain resilience along with ‘go green’ responsibilities.

Suggested Citation

  • Ahmed Mohammed & Irina Harris & Anthony Soroka & Mohamed Naim & Tim Ramjaun & Morteza Yazdani, 2021. "Gresilient supplier assessment and order allocation planning," Annals of Operations Research, Springer, vol. 296(1), pages 335-362, January.
  • Handle: RePEc:spr:annopr:v:296:y:2021:i:1:d:10.1007_s10479-020-03611-x
    DOI: 10.1007/s10479-020-03611-x
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    References listed on IDEAS

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    Cited by:

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    2. Rudiah Md Hanafiah & Nur Hazwani Karim & Noorul Shaiful Fitri Abdul Rahman & Saharuddin Abdul Hamid & Ahmed Maher Mohammed, 2022. "An Innovative Risk Matrix Model for Warehousing Productivity Performance," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    3. George Mutugu Mwangi & Stella Despoudi & Oscar Rodriguez Espindola & Konstantina Spanaki & Thanos Papadopoulos, 2022. "A planetary boundaries perspective on the sustainability: resilience relationship in the Kenyan tea supply chain," Annals of Operations Research, Springer, vol. 319(1), pages 661-695, December.
    4. Mehdi Keshavarz-Ghorabaee, 2023. "Sustainable Supplier Selection and Order Allocation Using an Integrated ROG-Based Type-2 Fuzzy Decision-Making Approach," Mathematics, MDPI, vol. 11(9), pages 1-33, April.
    5. João Pires Ribeiro & Ana Paula F. D. Barbosa-Póvoa, 2023. "A responsiveness metric for the design and planning of resilient supply chains," Annals of Operations Research, Springer, vol. 324(1), pages 1129-1181, May.
    6. Ahmed Mohammed & Morteza Yazdani & Amar Oukil & Ernesto D. R. Santibanez Gonzalez, 2021. "A Hybrid MCDM Approach towards Resilient Sourcing," Sustainability, MDPI, vol. 13(5), pages 1-30, March.
    7. Aida Rezaei & Amir Aghsami & Masoud Rabbani, 2021. "Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1036-1072, December.
    8. Mohammed, Ahmed & Lopes de Sousa Jabbour, Ana Beatriz & Koh, Lenny & Hubbard, Nicolas & Chiappetta Jabbour, Charbel Jose & Al Ahmed, Teejan, 2022. "The sourcing decision-making process in the era of digitalization: A new quantitative methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    9. Rukiye Kaya & Said Salhi & Virginia Spiegler, 2023. "A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge," Annals of Operations Research, Springer, vol. 320(1), pages 205-234, January.

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