Location and Capacity Optimization of Waste Recycling Centers: Mathematical Models and Numerical Experiments

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development.

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Publication Details

Journal
Applied Sciences
Published
2024-08-11
DOI
https://doi.org/10.3390/app14167039
Citations
2
Primary Topic
Facility Location and Emergency Management
Type
article
Field-Weighted Citation Impact
1.35

Funders

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article

Location and Capacity Optimization of Waste Recycling Centers: Mathematical Models and Numerical Experiments

Wen Yi, Maxwell Fordjour Antwi‐Afari, Huiwen Wang, Shenming Xie et al.
2 citations
Applied Sciences
Facility Location and Emergency Management
1.35
article

Location and Capacity Optimization of Waste Recycling Centers: Mathematical Models and Numerical Experiments

Wen Yi, Maxwell Fordjour Antwi‐Afari, Huiwen Wang, Shenming Xie, Ying Terk Lim
article en
2 citations

Abstract

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development.

Applied SciencesVol. 14(16)
Hong Kong Polytechnic University (HK), Aston University (GB)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 16%
Facility Location and Emergency Management
1.35
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