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Sustainable Logistics Operations and Management
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Sustainable Logistics Operations and Management

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

Deadline for manuscript submissions: 5 June 2025 | Viewed by 7251

Special Issue Editor

Prof. Dr. Yu Tian

E-Mail Website
Guest Editor
School of Business, Sun Yat-sen University, Guangzhou 510275, China
Interests: logistics management; project management; marketing channel management

Special Issue Information

Dear Colleagues,

Logistics activities affect two systems: the economy and the environment. Logistics operations and management are receiving more attention with the process of social reproduction. On the one hand, as economic activity increases, the environmental impact of logistics activities becomes more severe. For example, waste increases air pollution, noise pollution, and resource waste; on the other hand, business and organizational modes are constantly changing in the process of economic development, and traditional logistics operation and management methods are difficult to adapt to this new challenge. Therefore, there is an urgent need to explore solutions that may solve environmental and economic concerns in order to promote the operation and management of logistics for sustainable growth. This Special Issue intends to provide an overview of solutions and recent advances in sustainable logistics operations and management. The purpose of this Special Issue is to provide selected contributions on advances in theory, methodology, and applications of sustainable logistics operations and management in social reproduction processes.

This Special Issue contains, but is not limited to, the following topics: sustainable logistics operation; sustainable logistics management; green logistics; green supply chain management; innovation of logistics organization mode; crowdsourcing logistics and sustainable development of urban logistics; environmental consequences of logistical operations and management; solutions for sustainable logistics operation and management; future prospects of sustainable logistics operation and management.

Prof. Dr. Yu Tian
Guest Editor

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

  • sustainable logistics
  • operations
  • management
  • green logistics
  • mode innovation

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Published Papers (3 papers)

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Research

18 pages, 965 KiB  
Article
Supply Chain Performance with a Downside-Risk-Averse Retailer and Strategic Customers
by Ling Zhao, Anquan Zou, Minghua Xiong and Jun Liu
Sustainability 2023, 15(4), 3646; https://doi.org/10.3390/su15043646 - 16 Feb 2023
Viewed by 1421
Abstract
Predicting future promotion information on markdowns, customers can maximize their utilities by deciding when to buy. With this strategic behavior, this paper investigates a downside-risk-averse retailer’s integrated stock and pricing problem using a single case study method. Analyzing effects of the downside risk [...] Read more.
Predicting future promotion information on markdowns, customers can maximize their utilities by deciding when to buy. With this strategic behavior, this paper investigates a downside-risk-averse retailer’s integrated stock and pricing problem using a single case study method. Analyzing effects of the downside risk aversion and strategic customers is our purpose. By exploring a two-phase newsvendor model with a retailer selling to strategic customers, our work determines the downside-risk-averse retailer’s equilibrium ordering level and selling price. On this basis, effects of the downside risk aversion and the strategic behavior on the retailer’s optimum decisions and profit are analyzed. We find that the reverse effect of the strategic behavior can be mitigated by the retailer’s downside risk constraint. We also extend the model to a decentralized supply chain case. It is found that a low (high) downside risk aversion would mean that the supply chain profit in the decentralized case can (cannot) dominate the centralized under some (any) wholesale price contracts when customers are strategic. In addition, for different risk aversions, we also construct contracts to optimize the supply chain profit. Our results will provide reference evidence of making operational management decisions for the downside-risk-averse retailer in the case of strategic customers. Full article
(This article belongs to the Special Issue Sustainable Logistics Operations and Management)
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26 pages, 290 KiB  
Article
Evaluation of the Effectiveness of and the Extent to Which Large and Medium Logistics Organisations Report on Social Sustainability—The Case of South Africa
by Tumo Paulus Kele and Mokheseng Makhetha
Sustainability 2022, 14(22), 14917; https://doi.org/10.3390/su142214917 - 11 Nov 2022
Viewed by 1992
Abstract
Despite the importance of the logistics sector to the South African economy and the significant negative impact of the sector on South African society, little research has been conducted to determine the extent to which South African logistics companies report their social sustainability [...] Read more.
Despite the importance of the logistics sector to the South African economy and the significant negative impact of the sector on South African society, little research has been conducted to determine the extent to which South African logistics companies report their social sustainability and the effectiveness of their social sustainability reporting. The objectives of this study were to determine the extent to which South African logistics companies report on social sustainability performance and to evaluate the effectiveness of social sustainability reporting practices of South African logistics companies. A documentary analysis of the sustainability information of the logistics companies was conducted using a control list and a judgment scale. A purposive sample of 50 companies was used. The majority of the companies in the sample are private companies that are not listed. Of the sample group, 20% are family-owned, and 16% of the companies are publicly listed in the Johannesburg Stock Exchange (JSE). The results indicate that social sustainability reporting by logistics companies is very low. The range of scores is from a minimum of 0% to a maximum of 57%. Only two companies attained a score above 50%. About 61% of the companies did not disclose any of the stated themes of social sustainability, while 25% of the companies disclosed the themes in narrative form, and 12.1% disclosed them relative to prior periods of disclosure by the companies. Only 1.4% disclosed themes relative to the targets set by the company, and 0.3% of the companies disclosed them relative to industry standards. Regarding the effectiveness of social sustainability reporting, nine companies (18%) had a score of 50% and above. Only 13 companies (26%) have a score of 40% or higher. This is indicative of the fact that, in general, road logistics companies are not effective in their reporting of social sustainability activities. We endeavour for the study to assist South African logistics companies in being aware of elements to consider when reporting on their social sustainability, as well as assist them in improving their reporting. Full article
(This article belongs to the Special Issue Sustainable Logistics Operations and Management)
17 pages, 1845 KiB  
Article
Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method
by Ya Li and Zhanguo Wei
Sustainability 2022, 14(20), 13478; https://doi.org/10.3390/su142013478 - 19 Oct 2022
Cited by 5 | Viewed by 2889
Abstract
With the growth of e-commerce and the recurrence of the novel coronavirus pneumonia outbreak, the global logistics industry has been deeply affected. People are forced to shop online, which leads to a surge in logistics needs. Conversely, the novel coronavirus can also be [...] Read more.
With the growth of e-commerce and the recurrence of the novel coronavirus pneumonia outbreak, the global logistics industry has been deeply affected. People are forced to shop online, which leads to a surge in logistics needs. Conversely, the novel coronavirus can also be transmitted through goods, so there are some security risks. Thus, in the post-epidemic era, the analysis of regional logistics needs can serve as a foundation for logistics planning and policy formation in the region, and it is critical to find a logistics needs forecasting index system and a effective method to effectively exploit the logistics demand information in recent years. In this paper, we use the freight volume to assess the logistics needs, and the Long short-term memory (LSTM) network to predict the regional logistics needs based on time series and impact factors. For the first time, the Changsha logistics needs prediction index system is built in terms of e-commerce and the post-epidemic era and compared with some well-known methods such as Grey Model (1,1), linear regression model, and Back Propagation neural network. The findings show that the LSTM network has the smallest prediction errors, and the logistics needs are not affected by the epidemic. Therefore, the authors suggest that the government and businesses pay more attention to regional logistics needs forecasting, choosing scientific prediction methods. Full article
(This article belongs to the Special Issue Sustainable Logistics Operations and Management)
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