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Promotion and Optimization toward Sustainable Urban Logistics Systems

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 9325

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Special Issue Editors

Prof. Dr. Yong Wang

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Guest Editor

School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Interests: logistics transportation; logistics delivery; supply chain management

Prof. Dr. Muqing Du

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Guest Editor

College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: optimization model and algorithm of traffic network; traffic network carrying capacity; traffic planning and management

Prof. Dr. Hui Zhang

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Guest Editor

School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
Interests: big data analysis of transportation

Special Issue Information

Dear Colleagues,

The logistics resource configuration provides a better understanding of intelligent logistics, multimodal transport, resource-sharing modes, coordinated configuration of logistics resources, etc. This interest is supported by recent new advances of emerging information technologies, such as the Internet of Things, big data, cloud computing, and blockchain, which bring opportunities for logistics enterprises to overcome obstacles to collaboration. In addition, collaboration among logistics enterprises is a promising approach to increase enterprise operational efficiency by sharing the limited logistics resources under different situations and promotes the sustainable development of urban logistics systems in the long term. Several issues have been raised on the use of traditional models and algorithms, such as the new model establishment and solution algorithm issues of the logistics transport system, warehousing, and the validation of collaborative schemes.

Potential topics include but are not limited to the following:

Logistics schemes and performance evaluation;
Resource-sharing modes and strategies;
Collaborative mechanisms (i.e., how to facilitate collaboration among transport entities) in logistics network operations;
Collaborative logistics system ;
Sustainable logistics network modeling;
Application of intelligent algorithms for solving logistics network modeling;
Multiperiod resource configuration in the time–space logistics network;
Vehicle routing and scheduling with dynamic customer demands;
Application of emerging technologies in logistics network.

Prof. Dr. Yong Wang
Prof. Dr. Muqing Du
Prof. Dr. Hui Zhang
Guest Editors

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Keywords

collaborative mechanisms
sustainable logistics
resource sharing
vehicle routing
solution algorithms

Published Papers (4 papers)

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Research

19 pages, 8913 KiB

Open AccessArticle

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

by Rong Cao, Xuehui Chen, Jianmin Jia and Hui Zhang

Sustainability 2023, 15(11), 8688; https://0-doi-org.brum.beds.ac.uk/10.3390/su15118688 - 27 May 2023

Cited by 1 | Viewed by 1053

Abstract

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are. Full article

(This article belongs to the Special Issue Promotion and Optimization toward Sustainable Urban Logistics Systems)

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25 pages, 6109 KiB

Open AccessArticle

Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint

by Yong Liu, Zhicheng Yue, Yong Wang and Haizhong Wang

Sustainability 2023, 15(4), 3594; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043594 - 15 Feb 2023

Cited by 4 | Viewed by 2092

Abstract

As an important support of the e-commerce industry, the express delivery industry is particularly important in national development. Low loading rates caused by numerous types of containers and cost increases caused by low loading and unloading efficiency are still remaining issues in the process of goods delivery and packing. This study introduced the pallet with telescopic support height as the middle to address these issues and proposed a distribution scheme based on the constraints of three-dimensional pallet loading with a time window. First, combining the path optimization of the time window and cargo loading, a solution model was established to solve the existing express delivery problem with the lowest total delivery cost and the highest average vehicle loading rate. In addition, the multi-objective problem was transformed through the multi-objective linear weighting method. Second, we cluster the customer nodes. In order to solve the large number of gaps generated by the hierarchy theory, we adopt the descending order of cargo volume as the initial sequence and design the coding and decoding for path optimization and pallet loading, solving the problem through the simulated anneal-genetic algorithm. Finally, the effectiveness of the algorithm is obtained through the comparison with other algorithms and the simple three-dimensional loading and distribution scheme by using examples. It is proved that the optimization of three-dimensional packing for express delivery using pallets as carriers can not only meet the high loading rate but also improve the loading and unloading speed, reduce the time penalty cost, and improve the operability of loading. This paper provides decision reference and method support for path optimization under three-dimensional loading constraints. Full article

(This article belongs to the Special Issue Promotion and Optimization toward Sustainable Urban Logistics Systems)

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31 pages, 4733 KiB

Open AccessArticle

Electric Vehicle Charging Station Location-Routing Problem with Time Windows and Resource Sharing

by Yong Wang, Jingxin Zhou, Yaoyao Sun, Xiuwen Wang, Jiayi Zhe and Haizhong Wang

Sustainability 2022, 14(18), 11681; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811681 - 17 Sep 2022

Cited by 3 | Viewed by 2261

Abstract

Electric vehicles (EVs) are widely applied in logistics companies’ urban logistics distribution, as fuel prices increase and environmental awareness grows. This study introduces an EV charging station (CS) location-routing problem with time windows and resource sharing (EVCS-LRPTWRS). Resource sharing, among multiple depots within multiple service periods is proposed to adjust the transportation resource configuration for a sustainable logistics development. Solving the EVCS-LRPTWRS involves a periodic CS location selection and a multi-depot multi-period EV routing optimization. A bi-objective nonlinear programming model is proposed to formulate the EVCS-LRPTWRS with a minimum total operating cost and number of EVs. A hybrid algorithm combining the Gaussian mixture clustering algorithm (GMCA) with the improved nondominated sorting genetic algorithm-II (INSGA-II) is designed to address the EVCS-LRPTWRS. The GMCA is employed to assign customers to appropriate depots in various service periods in order to reduce the computational complexity. The INSGA-II is adopted to obtain the Pareto optimal solutions by using the CS insertion operation to select CS locations and integrating the elite retention mechanism to ensure a stable and excellent performance. The superiority of the hybrid algorithm is proven by comparison with the other three algorithms (i.e., multi-objective genetic algorithm, multi-objective particle swarm optimization, and multi-objective ant colony optimization). An empirical study of the EVCS-LRPTWRS in Chongqing City, China is conducted. Then, four types of service period divisions and three scenarios of resource sharing modes are further analyzed and discussed. The empirical results demonstrate the validity and practicability of the proposed solution method in realizing a sustainable operation in EV distribution networks. Full article

(This article belongs to the Special Issue Promotion and Optimization toward Sustainable Urban Logistics Systems)

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37 pages, 11724 KiB

Open AccessArticle

Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows

by Yong Wang, Jiayi Zhe, Xiuwen Wang, Yaoyao Sun and Haizhong Wang

Sustainability 2022, 14(11), 6709; https://0-doi-org.brum.beds.ac.uk/10.3390/su14116709 - 31 May 2022

Cited by 8 | Viewed by 3148

Abstract

Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development. Full article

(This article belongs to the Special Issue Promotion and Optimization toward Sustainable Urban Logistics Systems)

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