Robustness of Resource Recovery Systems under Feedstock Uncertainty

Shuming Wang and Tsan Sheng Ng

Production and Operations Management, 2019, vol. 28, issue 3, 628-649

Abstract: Recovery of resources from waste streams present important opportunities in mitigating environmental and energy challenges in many countries. In this paper, we develop an optimization‐based approach to study the robustness of resource recovery systems in achieving economic feasibility under feedstock uncertainty. Two key characteristics of the feedstock that influence recovery performance are its volume and composition. We propose models of feedstock robustness functions to perform robustness analysis with respect to these two characteristics, and also propose a composite robustness index as the optimization criterion for the problem of technology and capacity planning of recovery systems. We show that the proposed models have computationally attractive reformulations for the robustness analysis and optimization problems. In particular, the robustness analysis model solves either a small number of linear programs in several special cases, or a small number of linear mixed integer programs in general. Correspondingly, the design optimization problem can be solved either via the solution of a small number of linear mixed integer programs, or via a cutting plane approach. Finally, we demonstrate, through some numerical studies, the insights and values of using the proposed models in evaluation and optimization of organic waste‐to‐energy recovery systems.

Date: 2019
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