Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern

Abstract

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network design problem within a simplified boundary, whereas several critical system features (e.g., stochastic freight demand and co-modality) were omitted. To bridge the knowledge gaps, this paper proposes a reliable modeling approach to solve an urban surface-underground integrated logistics network (USUIL) design problem with a holistic consideration of cost-benefit objectives, road-ULS modal split, and demand uncertainties. To begin with, we introduce a city-wide hub-and-spoke USUIL network structure with a two-tier parallel distribution process. Five major external benefits of underground goods movement are formulated using real-world data. Next, a deterministic model is developed to optimize the network decisions including hub-satellite location-allocation, deployment of tunnels and pipelines, logistics service assignment, and flow routing. Then, to deal with the influences of case-wise demand uncertainties, the model is reformulated into a reliable one by incorporating the ideas of solution robustness and stochastic equivalents. A two-stage heuristic-based optimization tool that hybridizes the adaptive immune genetic algorithm, plant growth simulation algorithm, and Clarke-Wright saving algorithm was proposed to solve the problem. Finally, numerical experiments are conducted on the Beijing city case to validate the proposed method, and the best network schemes obtained from different models and scenarios are compared. Results show that a greater robust control factor in our model helps the network to lower the integrated freight transport cost while improving the social-environmental benefit at the expense of deploying more facilities. The reliable model performs well in balancing the conservativeness (i.e., less capacity insufficiency risk) and optimality (i.e., less cost due to redundant facilities) of the USUIL network design when faced with diverse demand cases.