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Smart Logistics and Sustainability
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Smart Logistics and Sustainability

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

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 26637

Special Issue Editors

Prof. Dr. Guowei Hua

E-Mail Website
Guest Editor
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Interests: logistics management and supply chain; facility location; operation management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. T. C. Edwin Cheng

E-Mail Website
Guest Editor
PolyU Business School, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: e-business and e-commerce; information systems management; innovation and technology management; operations management; quality management; scheduling science; supply chain management
Special Issues, Collections and Topics in MDPI journals
Mr. Liming He

E-Mail Website
Guest Editor
China Federation Logistics & Purchasing
Interests: sustainability, smart logistics, supply chain management
Prof. Dr. Martin Dresner

E-Mail Website
Guest Editor
Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, USA
Interests: air transport management and policy; supply chain management
Prof. Dr. Mingke He

E-Mail Website
Guest Editor
School of Logistics, Beijing Wuzi University, Beijing 101149, China
Interests: logistics and supply chain management; sustainability

Special Issue Information

Dear Colleagues,

With the rapid development of big data, cloud computing, Internet of things, and artificial intelligence, more and more new technologies have been introduced into logistics, such as logistics robots, the Automated Guided Vehicle (AGV), Kiva, the Unmanned Aerial Vehicle (UAV), the unmanned warehouse, and the unmanned distribution centre; accordingly, logistics has become more intelligent and more automated. Additionally, operations and decision-making in logistics needs little or no manpower.

Besides new technologies, smart logistics has new management philosophies, e.g., part to picker, anticipatory logistics or prejudgement delivery, and online learning decision-making; it also has new application scenarios, such as smart warehousing (a preposing warehouse), which produces many new problems and new challenges, such as warehousing optimization; transportation optimization; and distribution optimization, such as the vehicle routing problem of AGV and the optimal deployment of mobile-ranks.

Operations modes in smart logistics change, including packaging, reverse logistics, stocking, order picking, routing, and others. All these are closely related to sustainability. The purpose of this Special Issue (SI) is to publish high-quality analytical papers addressing emerging sustainability issues arising in the smart logistics, producing significant findings that facilitate the decision-making processes of individual consumers and E-business companies. Original and high-quality research fitting the SI’s theme that is neither published nor currently under review by any other journal is welcome. Papers selected for this Special Issue will be subject to a peer-review procedure.

Prof. Dr. Guowei Hua
Prof. Dr. T.C.Edwin Cheng
Mr. Liming He
Prof. Dr. Martin Dresner
Prof. Dr. Mingke He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart logistics
  • sustainability
  • Automated Guided Vehicle (AGV)
  • Unmanned Aerial Vehicle (UAV)
  • anticipatory logistics
  • carbon emission

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Published Papers (6 papers)

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Research

17 pages, 2446 KiB  
Article
Impact of Time Restriction and Logistics Sprawl on Urban Freight and Environment: The Case of Beijing Agricultural Freight
by Boshuai Zhao, Juliang Zhang and Wenchao Wei
Sustainability 2019, 11(13), 3675; https://doi.org/10.3390/su11133675 - 4 Jul 2019
Cited by 9 | Viewed by 3198
Abstract
Time restriction and logistics sprawl (e.g., relocating logistics facilities), as two popular urban policies, usually affect the urban freight and environmental burden, but their combination might lead to unexpected results. This paper analyzes the impact of time restriction and logistics sprawl on urban [...] Read more.
Time restriction and logistics sprawl (e.g., relocating logistics facilities), as two popular urban policies, usually affect the urban freight and environmental burden, but their combination might lead to unexpected results. This paper analyzes the impact of time restriction and logistics sprawl on urban freight and local environments based on a Beijing agricultural freight case through traffic simulation. The data is derived through a freight demand forecasting method. Based on the data, this paper constructs four groups of scenarios to represent different policies (or combined policies) and then conducts macro-simulation to obtain the economic and environmental indicators. Results show that (1) time restriction can increase the freight costs and slightly decrease local emissions, while logistics sprawl can increase both costs and emissions; (2) the joint implementation of the two policies are proved to be positive in economic and environmental aspects because it helps freight carriers adopt a new strategy to improve delivery efficiency; (3) urban freight policies are closely related to the freight carriers because different responses from carriers can lead to different policy effects. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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17 pages, 514 KiB  
Article
Performance Analysis of Three Intelligent Algorithms on Route Selection of Fishbone Layout
by Li Zhou, Zhaochan Li, Ning Shi, Shaohua Liu and Ke Xiong
Sustainability 2019, 11(4), 1148; https://doi.org/10.3390/su11041148 - 21 Feb 2019
Cited by 12 | Viewed by 4293
Abstract
The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for [...] Read more.
The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for logistics benefits is becoming more important. The appropriate use of technologies related to IoT to improve logistics efficiency, such as cloud computing, mobile computing and data mining, has become a topic of considerable research interest. Picking operations are currently an extremely important and cumbersome aspect of logistics center tasks. To shorten the picking distance and improve work efficiency, this paper uses the genetic algorithm, ant colony algorithm and cuckoo algorithm to optimize the picking path in a fishbone-layout warehouse and establishes an optimized model of the warehouse picking path under the fishbone layout. Data-mining technology is used to simulate the model and obtain the simulation data under the condition of multiple orders. The results provide a theoretical basis for the study of the fishbone-layout picking path model and has certain practical significance for the efficient operation of logistics enterprises. Through optimization, it is conducive to the sustainable development of enterprises and to achieving long-term profitability. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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19 pages, 778 KiB  
Article
Crowd Logistics Platform’s Informative Support to Logistics Performance: Scale Development and Empirical Examination
by Mingyu Zhang, Yuhuan Xia, Shuang Li, Wenbing Wu and Shuxiang Wang
Sustainability 2019, 11(2), 451; https://doi.org/10.3390/su11020451 - 16 Jan 2019
Cited by 17 | Viewed by 5133
Abstract
Using the organization information processing theory, we explored the process through which the informative support of crowd logistics platforms affects logistics performance. After collecting data from 321 respondents from two crowd logistics companies in China, we proposed and tested the theoretical framework empirically [...] Read more.
Using the organization information processing theory, we explored the process through which the informative support of crowd logistics platforms affects logistics performance. After collecting data from 321 respondents from two crowd logistics companies in China, we proposed and tested the theoretical framework empirically using SEM. To conduct the empirical study, we developed scales for platform’s informative support and the degree of logistics resources-demand match, respectively. The results indicate that a platform’s informative support improves logistics performance via two mediators, i.e., logistics resources-demand match and logistics agility. Moreover, a platform’s ease of use moderates the indirect process through which its informative support promotes logistics performance via logistics resources-demand match. However, a platform’s ease of use has no significant effect on the indirect process of its informative support affecting logistics performance via logistics agility. This paper extends our understanding on how the informative support of crowd logistics platforms predicts logistics performance. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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16 pages, 1189 KiB  
Article
Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets
by Minfang Huang, Qiong Guo, Jing Liu and Xiaoxu Huang
Sustainability 2018, 10(11), 3931; https://doi.org/10.3390/su10113931 - 29 Oct 2018
Cited by 19 | Viewed by 5011
Abstract
Online retail orders, especially online supermarket orders, have been highlighted to have several distinguished features from traditional online retailers. These include a huge amount of daily orders and orders containing multiple items. Tens of thousands of Stock Keeping Units (SKUs) sold by online [...] Read more.
Online retail orders, especially online supermarket orders, have been highlighted to have several distinguished features from traditional online retailers. These include a huge amount of daily orders and orders containing multiple items. Tens of thousands of Stock Keeping Units (SKUs) sold by online retailers have to be stored at multiple storage zones due to the limit capacity of one area, and ordered items should to be picked with a parallel picking strategy. What is the most efficient and accurate method of picking, sorting and packaging the ordered items from SKUs for online orders? This paper focuses on scheduling the three processes of order picking problems in a warehouse for an online supermarket. Referring to the principle of the mixed-model assembly line, it presents a new optimization method of group order picking. With an objective of minimizing the picking and packaging time, this paper studies order batching and order sequencing. In order batching, considering the workload balance, it builds a mathematical optimization model and applies a bi-objective genetic algorithm to solve it. Then an order batching sequencing model is built, and a solving algorithm based on Pseudo-Boolean Optimization is developed. Case study and sensitivity analyses are conducted to verify the effectiveness of the method. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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15 pages, 1738 KiB  
Article
Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization
by Daqing Wu, Jiazhen Huo, Gefu Zhang and Weihua Zhang
Sustainability 2018, 10(10), 3791; https://doi.org/10.3390/su10103791 - 19 Oct 2018
Cited by 18 | Viewed by 4226
Abstract
This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will [...] Read more.
This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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18 pages, 4942 KiB  
Article
Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives
by Qi Liu, Jiahao Liu and Dunhu Liu
Sustainability 2018, 10(10), 3760; https://doi.org/10.3390/su10103760 - 18 Oct 2018
Cited by 10 | Viewed by 2718
Abstract
This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an [...] Read more.
This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an improved multi-objective particle swarm optimization (MOPSO) process and an entropy weight method-based evaluation process. The MOPSO process is utilized to obtain a set of Pareto optimal solutions, and the entropy weight method-based evaluation process is utilized to select the final solution from Pareto optimal solutions. Numerical experiments are conducted based on large-scale GPS data. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated. Moreover, the comparison of single-objective and multi-objective models validates the efficiency and necessity of the proposed multi-objective model in public charging station location problems. Full article
(This article belongs to the Special Issue Smart Logistics and Sustainability)
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