Abstract
Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.
Highlights
Traffic congestion has become a critical social issue in several metropolis and cities worldwide
Models. e regional logistics network design (RLND) model constructed in this study aims to minimize the design and operation costs of the logistics network to satisfy the regional logistics demand
(1) e computational result displayed in Table 2 is analyzed by encoding the heuristic algorithm based on Lagrangian relaxation (HALR) algorithm and applying a Genetic Algorithm (GA) toolbox with MATLAB R2010(a) on a personal computer launched Intel Core (TM) i7, 1.80 GHz, and 4 GB RAM. e results show the approximate optimal solution and the difference in value between its upper and lower bounds
Summary
Traffic congestion has become a critical social issue in several metropolis and cities worldwide. Some of the major cities in China have achieved success regarding traffic congestion reduction. Compared with 2016, the maximum traffic-congested index during peak hours reduced by 4.8% [1]. Such progress may be attributed to the specific demand management mechanism and control policy, public transport system optimization, and emerging individual travel modes, such as ride-sharing demand-responsive transport and shared bikes [2]. Researchers seek to discover new ways of achieving traffic congestion relief in the roadway freight transport system [4]
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