Shipping Logistics Network Optimization with Stochastic Demands for Construction Waste Recycling: A Case Study in Shanghai, China

Ping Wu, Yue Song () and Xiangdong Wang
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Ping Wu: China Academy of Transportation Sciences, Beijing 100029, China
Yue Song: College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China
Xiangdong Wang: College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China

Sustainability, 2025, vol. 17, issue 3, 1-18

Abstract: In this paper, we introduce a shipping logistics network optimization method for construction waste recycling. In our case, construction waste is transported by a relay mode integrating land transportation, shipping transportation, and land transportation. Under the influence of urban economic life, the quantity (demand) of construction waste is uncertain and stochastic. Considering the randomness of construction waste generation, a two-stage stochastic integer programming model for the design of a shipping logistics network for construction waste recycling is proposed, and an accurate algorithm based on Benders decomposition is presented. Based on an actual case in Shanghai, numerical experiments are carried out to evaluate the efficacy of the proposed model and algorithm. Based on an actual case study in Shanghai, numerical experiments demonstrate that the proposed model can help to reduce transportation costs of construction waste. Sensitivity analysis highlights that factors like the penalty for untransported waste and capacity constraints play a crucial role in network optimization decisions. The findings provide valuable theoretical support for developing more efficient and sustainable logistics networks for construction waste recycling.

Keywords: construction waste recycling; logistics network optimization; uncertainty; two-stage stochastic integer programming; benders decomposition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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