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Two-stage mean-risk stochastic optimization model for port cold storage capacity under pelagic fishery yield uncertainty

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  • Liu, Zhimin
  • Qu, Shaojian
  • Goh, Mark
  • Wu, Zhong
  • Huang, Ripeng
  • Ma, Gang

Abstract

The problem of the optimal capacity of cold storage for pelagic fisheries under uncertain harvesting/production is studied. We establish a two-stage mean-risk stochastic optimization model, by considering the uncertainty of pelagic fishery yield and the risk measure of the cold storage cost loss. Applying a Benders-type scenario decomposition method, a modified cutting decomposition algorithm is proposed to solve the two-stage mean-risk stochastic optimization model, yielding the optimal capacity and maximal expected return of cold storage simultaneously. Further, the effects of the refrigeration cost, storage fee, weight of the conditional value-at-risk, and the confidence level on the expected profit are analyzed. We compare the modified cutting decomposition algorithm with a multi-cutting decomposition algorithm, to validate the proposed algorithm based on the computational time and the number of iterations.

Suggested Citation

  • Liu, Zhimin & Qu, Shaojian & Goh, Mark & Wu, Zhong & Huang, Ripeng & Ma, Gang, 2020. "Two-stage mean-risk stochastic optimization model for port cold storage capacity under pelagic fishery yield uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318680
    DOI: 10.1016/j.physa.2019.123338
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    Cited by:

    1. Liu, Zhimin & Wu, Zhong & Ji, Ying & Qu, Shaojian & Raza, Hassan, 2021. "Two-stage distributionally robust mixed-integer optimization model for three-level location–allocation problems under uncertain environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).

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