Abstract
Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.
Highlights
With the rise of e-commerce and the growth of urbanization in various countries, urban logistics has become critical in ensuring the quality of people’s lives and the sustainability of city development [1]
demand point (DP) 886 has a freight volume of 3088.07 t in the ant colony optimization (ACO) result, and it requires the help of the secondary hubs (SHs) nearby to meet this volume, including SHs 885, 890, and 891, which are dedicated to it. ird, following the logic and search order of the chosen algorithm, some of the hubs were generated through local optimization. ree SHs in the ACO result remained after the points nearby were searched and covered. e superiority of the ACO’s pheromones enables individuals to communicate indirectly through the environment and use the probabilistic search method to effectively avoid local optimal solutions
Given that the primary hubs (PHs) needs to be linked with the logistics parks (LPs) outside of the district, in order to reduce the difficulty of cargo scheduling and improve the clustering effect, the expectant number of clustering centres K was set to 4
Summary
With the rise of e-commerce and the growth of urbanization in various countries, urban logistics has become critical in ensuring the quality of people’s lives and the sustainability of city development [1]. The demand for logistics services is growing rapidly, and the delivery efficiency requirement has become higher [2]. The annual express delivery business growth rate in China exceeded 50% in 2016 [3]. The increasing freight volumes in limited urban areas exacerbate traffic congestion, which has an inevitable impact on energy consumption and environmental pollution. In the Guidelines for National Greenhouse Gas Inventory, it was indicated that petrol consumption in traffic jams is almost twice that during normal driving [4]. As traffic congestion increases, CO2 emissions [5] and PM2.5 [6] increase
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