A Hybrid Heuristic Algorithm for Maximizing the Resilience of Underground Logistics Network Planning

Abstract

In recent times, there has been a sharp increase in the congestion of ground transportation, the scarcity of land resources, and various disasters. Hence, there is an urgent need to find an effective and sustainable approach to transportation. The construction of an underground logistics network, where transportation activities occur beneath the surface of the ground, is anticipated to emerge as a future trend. This study aims to formulate a resilient-maximizing plan for the underground logistics network, ensuring optimal meeting of transportation demands in the aftermath of ground disasters. Accordingly, a two-stage linear programming model is established to determine the layout plan for the most resilient underground logistics network. The first phase of the model is designed to generate viable layouts for the underground logistics network, while the second phase is dedicated to evaluating the resilience of the proposed layout plan. During the evaluation of network resilience, Monte Carlo simulations are used to simulate disaster scenarios. Given the inherent complexity of the model, the traditional solver cannot efficiently solve the problem. Thus, a new hybrid heuristic algorithm is designed to obtain solutions that maximize network resilience. The results show the effectiveness of the designed algorithm and the significant improvement in network resilience achieved by numerical experiments. Moreover, sensitivity analyses are conducted to reveal the relationships between resilience and budget, as well as resilience and the capacity of underground pipelines. It has a significant impact on sustainability when making decisions regarding network planning.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

“…Guo [40] and Fahimnia [41] proposed methods for planning underground logistics and industrial logistics systems, respectively. With technological innovation, more innovative methods and models are applied to urban logistics spatial planning [42], such as the spatial design of a logistics network system and distribution paths based on hybrid heuristic algorithms [43] and genetic algorithms [44], and the planning and control model of intelligent logistics facilities, spaces, and parks based on the logistics 4.0 framework [45].…”