Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

A bilevel optimization model is developed to synthesize an optimal waste oil recycling supply chain design in economic and environmental aspects while considering its overall resiliency in addressing major disruptions. In the upper level, optimal re-refineries selection is determined based on the cost and emissions, while the “n-1” contingency is incorporated in the subsequent level to identify the production capacity required for each re-refinery to ensure the functionality of supply chains under disruption. Two regional case studies, comprising the East Malaysia and West Malaysia, are presented to evaluate the effectiveness of the “n-1” contingency strategy. The economic viability of applying “n-1” is different for both cases. Considering a one-month disruption across the asset lifespan, the model anticipates a positive economic gain of 15.79% (in East Malaysia) and 85.71% (in West Malaysia). Nevertheless, applying “n-1” contingency demonstrates better environmental performances in both cases as compared to cases without “n-1”.

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Publication Details

Journal
Industrial & Engineering Chemistry Research
Published
2024-09-30
DOI
https://doi.org/10.1021/acs.iecr.4c02539
Primary Topic
Sustainable Supply Chain Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

Viknesh Andiappan, Sin Yong Teng, Lip Siang Yeo, Bing Shen How et al.
Industrial & Engineering Chemistry Research
Sustainable Supply Chain Management
article

Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

Viknesh Andiappan, Sin Yong Teng, Lip Siang Yeo, Bing Shen How, Sue Lin Ngan, Selena Kian Yii Song
article en

Abstract

A bilevel optimization model is developed to synthesize an optimal waste oil recycling supply chain design in economic and environmental aspects while considering its overall resiliency in addressing major disruptions. In the upper level, optimal re-refineries selection is determined based on the cost and emissions, while the “n-1” contingency is incorporated in the subsequent level to identify the production capacity required for each re-refinery to ensure the functionality of supply chains under disruption. Two regional case studies, comprising the East Malaysia and West Malaysia, are presented to evaluate the effectiveness of the “n-1” contingency strategy. The economic viability of applying “n-1” is different for both cases. Considering a one-month disruption across the asset lifespan, the model anticipates a positive economic gain of 15.79% (in East Malaysia) and 85.71% (in West Malaysia). Nevertheless, applying “n-1” contingency demonstrates better environmental performances in both cases as compared to cases without “n-1”.

Industrial & Engineering Chemistry ResearchVol. 63(40)
Radboud University Nijmegen (NL), National University of Singapore (SG), Radboud University Medical Center (NL), Maastricht University (NL), Radboud Institute for Molecular Life Sciences (NL), National University of Malaysia (MY), Swinburne University of Technology Sarawak Campus (MY)
Universiti Kebangsaan Malaysia, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 71%
Sustainable Supply Chain Management
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Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain — Viknesh Andiappan, Sin Yong Teng, et al. · Industrial & Engineering Chemistry Research (2024) | TGRS Research Map | TGRS