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A resource allocation model to choose the best portfolio of economic resilience plans: a possibilistic stochastic programming model

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Listed:
  • Shima Pashapour
  • Ali Azadeh
  • Ali Bozorgi-Amiri
  • Abbas Keramati
  • Seyed Farid Ghaderi

Abstract

Economic resilience is defined as a tool capable of reducing the losses caused by disasters. It can be defined in two major concepts. Static economic resilience is the effective allocation of available resources and dynamic economic resilience refers to accelerating the recovery process through the repair and rebuilding of the capital stock. In this research, the performance of a petrochemical plant in the face of crisis is investigated. For this, a bi-objective mathematical model that considers cost and resilience capability as objective functions is developed to choose the best portfolio of static and dynamic plans. To solve the mathematical model, a weighted augmented ε-constraint method and a multi-stage possibilistic stochastic programming (MSPSP) approach are employed. The numerical results showed that the proposed approach is effective in optimising the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans. [Received: 11 January 2019; Revised: 9 July 2019; Revised: 13 August 2019; Accepted: 13 August 2019]

Suggested Citation

  • Shima Pashapour & Ali Azadeh & Ali Bozorgi-Amiri & Abbas Keramati & Seyed Farid Ghaderi, 2020. "A resource allocation model to choose the best portfolio of economic resilience plans: a possibilistic stochastic programming model," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 14(3), pages 301-334.
    • Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:301-334
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    Cited by:

    1. Ahmad Jafari Ghezelhesar & Ali Bozorgi-Amiri, 2022. "A novel approach to selection of resilient measures portfolio under disruption and uncertainty: a case study of e-payment service providers," Operational Research, Springer, vol. 22(5), pages 5477-5527, November.

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