A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty

Qiaofeng Li (), Halit Üster () and Zhi-Hai Zhang ()
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Qiaofeng Li: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China; Smart Supply Chain Y, JD.com, Beijing 100101, China
Halit Üster: Department of Operations Research and Engineering Management, Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275
Zhi-Hai Zhang: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China

Transportation Science, 2023, vol. 57, issue 5, 1296-1320

Abstract: This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored.

Keywords: biomass logistics network; risk-averse farmer; robust optimization; Benders decomposition (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:57:y:2023:i:5:p:1296-1320

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