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Sustainable Operations and Green Supply Chain
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Sustainable Operations and Green Supply Chain

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 904

Special Issue Editor

Prof. Dr. Fouad Jawab

E-Mail Website
Guest Editor
High School of Technology, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco
Interests: supply chain; industrial engineering; healthcare and hospital logistics; urban freight transport; modelling; transport and mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The general orientation of this Special Issue is to explore in depth the different facets of the transition towards environmentally friendly, socially just and equitable, and economically viable value chains.

In terms of scope, this Special Issue seeks to address the challenges of sustainability in operations, covering a broad spectrum of sectors (manufacturing, agri-food, logistics, etc.) at all levels from supply to reverse logistics, including production and distribution. It aims at exploring the multiple dimensions of sustainability at different scales, local, regional and global.

This Special Issue also aims at focusing on the assessment of environmental and social impacts, by analyzing the methodologies, indicators and tools used. It also aims at examining the levers of action to improve the environmental and social performance of companies, such as certifications (ISO 14001, Ecolabel, etc.), collaboration between chain actors, and the influence of public policies (regulation, taxation, standards, etc.).

Its objective is in line with the cutting-edge work published in the journal Sustainability.

On the theoretical level, this Special Issue aims at taking stock of the most recent research and proposing new conceptual frameworks as well as innovative methodological approaches to analyze and evaluate the multiple facets of sustainability within the supply chain.

On the practical level, this Special Issue seeks to highlight the best practices of economic, political, and social actors in sustainable operations, by presenting concrete tools and case studies from companies in different sectors. It also aims at exploring possible synergies between the different actors in the chain to succeed in this transformation and to present the latest trends and future challenges of the transition to sustainable supply chains.

Prof. Dr. Fouad Jawab
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

  • sustainability
  • supply chain
  • operations
  • performance
  • collaboration
  • environmental impact
  • social impact
  • theory
  • best practices

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

41 pages, 883 KiB  
Article
Dependent-Chance Goal Programming for Sustainable Supply Chain Design: A Reinforcement Learning-Enhanced Salp Swarm Approach
by Yassine Boutmir, Rachid Bannari, Achraf Touil, Mouhsene Fri and Othmane Benmoussa
Sustainability 2025, 17(13), 6079; https://doi.org/10.3390/su17136079 - 2 Jul 2025
Viewed by 224
Abstract
The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration [...] Read more.
The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration and resource allocation that allows trade-off among economic, environmental, social, and resilience objectives. This paper addresses the SSCNDP under hybrid uncertainty, which combines objective randomness got from historical data, and subjective beliefs induced by expert judgment. Building on chance theory, we formulate a dependent-chance goal programming model that specifies target probability levels for achieving sustainability objectives and minimizes deviations from these targets using a lexicographic approach. To solve this complex optimization problem, we develop a hybrid intelligent algorithm that combines uncertain random simulation with Reinforcement Learning-enhanced Salp Swarm Optimization (RL-SSO). The proposed RL-SSO algorithm is benchmarked against standard metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and standard SSO, across diverse problem instances. Results show that our method consistently outperforms these techniques in both solution quality and computational efficiency. The paper concludes with managerial insights and discusses limitations and future research directions. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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34 pages, 1710 KiB  
Article
Logistics Sprawl and Urban Congestion Dynamics Toward Sustainability: A Logistic Regression and Random-Forest-Based Model
by Manal El Yadari, Fouad Jawab, Imane Moufad and Jabir Arif
Sustainability 2025, 17(13), 5929; https://doi.org/10.3390/su17135929 - 27 Jun 2025
Viewed by 381
Abstract
Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several [...] Read more.
Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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