Robustness of Resource Recovery Systems under Feedstock Uncertainty

Wang, Shuming; Ng, Tsan Sheng

doi:10.1111/poms.12944

Cited by 11 publications

(8 citation statements)

References 47 publications

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“…Among the above studies, Xiong et al. (2017) and Wang and Ng (2018) are the most related to our current work. The difference and research gap are explained as follows: (i) Although Xiong et al.…”

Section: Literature Reviewmentioning

confidence: 73%

“…Among the above studies, Xiong et al (2017) and Wang and Ng (2018) are the most related to our current work. The difference and research gap are explained as follows: (i) Although Xiong et al (2017) also studied a resource recovery planning problem in a game-theoretical framework, they focused on the competitive strategies of multiple recovery operators under market competitions.…”

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 93%

“…The study proposed an uncertainty set that combines time series forecasting model and uncertainty budgets for different periods and waste sources and developed an adaptive robust optimization model using the proposed uncertainty set for the problem. More recently, Wang and Ng (2018) investigated a resource recovery technology planning problem that focuses on the impact of both feedstock volume and composition uncertainties on the economic feasibility of the recycling operator. The authors proposed a set of adaptive‐robust‐optimization‐based conditional and composite robustness index models for the recovery performance evaluation and system design optimization under feedstock uncertainty, which are also computationally attractive.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast, our present work investigates a bilevel resource recovery planning with the local authority and private operator under the P3 setting. (ii) Xiong et al (2017) assumed a fixed input of waste condition in their planning model, while Wang and Ng (2018) considered uncertainties of feedstock condition (volume and composition), which are modeled with uncertainty sets without using distributional information. However, in our current work, we model the ambiguity of feedstock condition in the framework of adaptive distributionally robust optimization approach (Bertsimas et al 2018), which allows the (true) distribution to vary in an ambiguity set that is constructed with limited distributional characteristics.…”

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 99%

“…The difference and research gap are explained as follows: (i) Although Xiong et al (2017) also studied a resource recovery planning problem in a game-theoretical framework, they focused on the competitive strategies of multiple recovery operators under market competitions. On the other hand, Wang and Ng (2018) also modeled the resource recovery planning problem from the perspective of the operator. In contrast, our present work investigates a bilevel resource recovery planning with the local authority and private operator under the P3 setting.…”

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 99%

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Robust Bilevel Resource Recovery Planning

Xiong

Wang²,

2021

Production and Operations Management

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I n this study, we consider a resource recovery planning problem under a public-private partnership. The local authority's problem is to determine the waste sorting scheme, and also a cost-sharing budget, to minimize her expected payout, subject to a requirement of economic feasibility of the private operator. Given the budget and sorting scheme, the private operator's problem is to site and operate resource recovery facilities, and his goal is to maximize his expected total profits. A salient feature of the problem is that the feedstock condition (composition and volume) is uncertain, but can be influenced by the sorting-at-source scheme implemented. This feature drives the interactions of the public and private partners. We develop a bilevel resource recovery planning model in the framework of distributionally robust optimization, where a decision-dependent ambiguity set is defined to model the influence of sorting schemes on the feedstock uncertainty. We perform the model analysis on the cost-share ratio, which suggests the importance of sorting investment in achieving the sustainable resource recovery. Computationally, we show that the solutions of the bilevel problem can be obtained by solving one instance of a mixed-integer linear program that does not induce any additional integer variable. Our proposed optimization framework provides a useful decision-analysis tool for effectively utilizing and balancing the instruments of sorting and facility installation investments for resource recovery planning under feedstock ambiguity. For instance, the computational experiments with partial real data in the Singapore context justify several important insights: (i) the suitable sorting scheme implementation serves as a cost-effective instrument that could significantly enhance the utility of the cost sharing; (ii) the sorting dependency of feedstock condition is critical in affecting the private operator's recovery performance; and (iii) the robust planning is effective in hedging against ambiguously poor feedstock condition. These findings should deserve attentions of both public and private sectors in current waste management practice.

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Section: Literature Reviewmentioning

confidence: 73%

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 99%

Section: Resource Recovery Planning Under Uncertaintymentioning

confidence: 99%

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Robust Bilevel Resource Recovery Planning

Xiong

Wang²,

2021

Production and Operations Management

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Towards a circular economy with waste‐to‐resource system optimization

Ng,

Mah,

Zhao

2023

Naval Research Logistics

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Motivated by mounting pressures to achieve environmental sustainability, and the emergence of online waste exchange platforms, in this work we propose an optimization‐based framework for studying waste‐to‐resource formation in the industry. We first develop a linear programming market clearing model comprising self‐interested agents participating in waste‐to‐resource trading. We then embed this in a bi‐level capacity optimization problem under uncertainties in the agents' reserve prices, the goal of which is to maximize the economic savings achieved by waste‐to‐resource. We show that the resulting robust optimization models admit linear mixed integer programming formulations that can be readily computed using available solvers. Furthermore, we provide the formulas for applying an equitable levy and incentive scheme, to enable capacity cost‐sharing among the agents. Numerical studies based on a case of organic waste streams demonstrate the usefulness of the proposed models in generating valuable insights for decision support in the design of waste‐to‐resource markets.

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Bilevel Recycling Facility Location

Saldanha-da-Gama,

Wang

2024

International Series in Operations Research &Amp; Management Science

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Robustness of Resource Recovery Systems under Feedstock Uncertainty

Cited by 11 publications

References 47 publications

Robust Bilevel Resource Recovery Planning

Robust Bilevel Resource Recovery Planning

Towards a circular economy with waste‐to‐resource system optimization

Bilevel Recycling Facility Location

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