A multi-objective location and channel model for ULS network

Liang, Hejun; Yuan, Guanghui; Han, Jingti; Sun, Lily

doi:10.1007/s00521-018-3636-5

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In this section, we compare the method introduced in this paper with the multi-objective location and channel model for the ULS network proposed by Hejun et al [ 46 ]. Both approaches plan logistics networks based on actual data such as geographical location, population density, and freight traffic conditions, and both achieved desirable outcomes in alleviating traffic congestion and reducing logistics costs during a logistics network simulation in Nanjing’s Xianlin district.…”

Section: Example and Results Analysismentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

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The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

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Section: Example and Results Analysismentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

Self Cite

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“…Currently, research on the planning of underground logistics networks is focused on layout and path planning, aiming at the minimum total cost. For example, a hybrid planning model was proposed to distribute freight between an underground network and maritime highways to obtain a layout plan for an underground logistics network with the goal of cost-minimizing [10,17,18]. In addition, some researchers have developed a weighted set coverage model for node positioning in underground logistics networks aiming at the lowest total cost [19], while others have developed a planning method based on the subway system [11].…”

Section: Underground Logistics Networkmentioning

confidence: 99%

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

Xue,

Fang,

Peng

et al. 2023

Applied Sciences

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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.

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“…e optimal ULS nodes, tunnel layout, and transport route network flows were formulated via a biobjective mixedinteger linear programming model considering minimal costs and maximal system utilization [9]. Liang et al [10] established a multiobject ULS network planning model, including hub location and tunnel linking, and used agglomerative hierarchical clustering to determine the location of the first-level hubs and a greedy algorithm (GA) to determine the locations of the second-level hubs covered by each first-level hub. Ren et al [11] constructed a set-covering problem (SCP) model to determine the locations of the firstlevel hubs, and their results were optimized according to freight volume and the cargo handling capacity of the hubs.…”

Section: Logistics Location For Uls Networkmentioning

confidence: 99%

“…Moreover, the location of the hubs affects the number of underground nodes and the total length of the tunnels, and underground engineering is extremely expensive. Determining the logistics location for a ULS is a complicated and systematic task for which optimization algorithms have been designed considering various parameters, such as costs, distances, regional congestion, and freight volumes [9][10][11][12]. However, the current location approaches assume that important parameters, such as regional congestion and freight volumes, are known parameters.…”

Section: Introductionmentioning

confidence: 99%

Design of a Support System for Complicated Logistics Location Integrating Big Data

Cheng

Pan

2021

Advances in Civil Engineering

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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.

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A multi-objective location and channel model for ULS network

Cited by 8 publications

References 6 publications

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

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

Design of a Support System for Complicated Logistics Location Integrating Big Data

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