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AI for Sustainable and Resilient Operations Management
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AI for Sustainable and Resilient Operations Management

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

Deadline for manuscript submissions: 25 June 2026 | Viewed by 3688

Special Issue Editors

Dr. S. Mahdi Homayouni

E-Mail Website
Guest Editor
School of Business, Social and Decision Sciences, Constructor University, Campus Ring 1, 28759 Bremen, Germany
Interests: operations management; supply chain analytics; operational sustainability; energy efficiency; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Reinaldo Gomes

E-Mail Website
Guest Editor
INESC TEC—Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: operations management; supply chain resilience; risk management; decision support systems

Special Issue Information

Dear Colleagues,

Achieving sustainable and resilient operations in a rapidly changing world is a pressing challenge that calls for innovative strategies and technologies. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges, offering unprecedented opportunities for improving resource efficiency, reducing environmental impacts, and enhancing the adaptability of systems to climate and social changes. This Special Issue focuses on the integration of AI in operations management to support sustainability and resilience, emphasizing the development of smart systems and data-driven approaches that align with global climate and sustainability goals.

The scope of this Special Issue spans theoretical advancements, methodological innovations, and empirical studies that explore how AI can transform operations management for sustainability. It invites contributions that delve into AI applications in sustainable production, green supply chains, intelligent digital twins, predictive operations models, and circular economy supply chains. Additionally, this Special Issue encourages research that investigates organizational, societal, and behavioural aspects of AI adoption for sustainable practices.

Research areas may include, but are not limited to, the following:

  1. AI-driven optimization for sustainable supply chains and logistics.
  2. Predictive analytics for operations management.
  3. The role of AI in fostering circular economy and closed-loop systems.
  4. AI applications in renewable energy systems and grid management.
  5. Smart decision-making systems for resilient and sustainable supply chains.
  6. Integration of AI with IoT and digital twins for sustainability.
  7. Behavioral and organizational impacts of AI on promoting sustainable practices.
  8. AI-enabled frameworks for assessing and mitigating risks in operations.
  9. Ethical and governance considerations of using AI in sustainable systems.
  10. The role of AI in policy-making and planning for resilient cities and communities.

This Special Issue seeks to complement existing literature by focusing on the practical and theoretical integration of AI in sustainability-related challenges, providing actionable insights, innovative methodologies, and a roadmap for future research in smart and resilient operations management.

Dr. S. Mahdi Homayouni
Dr. Reinaldo Gomes
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 250 words) can be sent to the Editorial Office for assessment.

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

  • AI in operations management
  • resilient supply chains
  • intelligent digital twins
  • smart decision support systems
  • AI-driven optimization
  • predictive analytics
  • green logistics

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

Published Papers (4 papers)

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Research

49 pages, 2496 KB  
Article
Adaptive Lead-Time Prediction for Resilient and Sustainable Supply Chains
by Ibrahim Mutambik
Sustainability 2026, 18(10), 4748; https://doi.org/10.3390/su18104748 - 10 May 2026
Viewed by 776
Abstract
Reliable prediction of supplier lead times is important for understanding resilience in complex adaptive supply chains, which function as socio-technical systems characterized by high variability, dynamic interactions, and operational unpredictability. This study proposes a simulation-based adaptive lead-time prediction framework that unifies uncertainty-aware statistical [...] Read more.
Reliable prediction of supplier lead times is important for understanding resilience in complex adaptive supply chains, which function as socio-technical systems characterized by high variability, dynamic interactions, and operational unpredictability. This study proposes a simulation-based adaptive lead-time prediction framework that unifies uncertainty-aware statistical modeling, digital twin-enabled simulation, IoT-linked operational adjustment, and AI-driven temporal learning within a single system-oriented architecture. Semi-synthetic datasets are used to emulate lead-time variability and disruption patterns across multiple operating scenarios under intermediate and elevated levels of uncertainty. The novelty of the study lies not in the use of individual techniques in isolation, but in their integration within a closed-loop predictive framework that links probabilistic modeling, adaptive correction, and digital twin-based system updating. The results indicate that the baseline statistical model performs satisfactorily under stable conditions; however, its performance declines significantly when exposed to parameter variations and extreme disruptions. Under high-variability conditions, for example, RMSE at μ = 3.0 and σ = 1.2 decreases from 65.00 weeks in the baseline model to 13.45 weeks in the IoT-adaptive model and to 3.00 weeks in the AI-enhanced model. These findings show that the proposed framework improves predictive accuracy, robustness, and adaptability relative to both the baseline statistical and IoT-adaptive alternatives. Overall, the proposed framework contributes to supply chain analytics by providing an integrated and simulation-based proof-of-concept for resilient lead-time prediction in complex supply environments. Its sustainability relevance should be understood as prospective: although the study does not directly measure emissions, energy use, or waste reduction, improved predictive stability and adaptive decision support may inform future sustainability-oriented planning and empirical evaluation. Full article
(This article belongs to the Special Issue AI for Sustainable and Resilient Operations Management)
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22 pages, 6594 KB  
Article
A Hybrid Physics-Based and AI-Enabled Framework for Mine Road Infrastructure Maintenance Using Inertial Sensors
by Wioletta Koperska, Paweł Stefaniak, Artur Skoczylas, Maria Stachowiak and Dariusz Janik
Sustainability 2026, 18(9), 4402; https://doi.org/10.3390/su18094402 - 30 Apr 2026
Viewed by 407
Abstract
Maintaining road infrastructure in underground mines is critical for ensuring efficient transportation, reducing fuel consumption, extending the lifespan of machines, and providing operator safety and comfort. At the same time, the operation of heavy machinery on uneven roads, and the presence of loose [...] Read more.
Maintaining road infrastructure in underground mines is critical for ensuring efficient transportation, reducing fuel consumption, extending the lifespan of machines, and providing operator safety and comfort. At the same time, the operation of heavy machinery on uneven roads, and the presence of loose rock fragments make it impossible to keep roads in consistently good condition, necessitating continuous condition monitoring and appropriate maintenance planning. This paper proposes a framework based on a single inertial sensor mounted on a mining vehicle for road quality assessment and vehicle speed estimation. The developed methods have a hybrid character, combining the physical interpretability of inertial data with unsupervised AI-based techniques. The integrated analytical system, combining road surface quality assessment with vehicle speed analysis, serves as a decision-supporting tool for pinpointing road segments that are critical for maintenance, safety, transport efficiency, and machine wear. The proposed approach was validated using data collected from haul trucks operating under real-world conditions. The system has the potential to support more efficient and sustainable management of mine road maintenance by reducing unnecessary interventions, resource consumption, and the negative environmental and safety impacts associated with haulage operations. Full article
(This article belongs to the Special Issue AI for Sustainable and Resilient Operations Management)
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32 pages, 1249 KB  
Article
AI-Enabled Flexible Design of Resilient Forest-to-Bioenergy Supply Chains Under Wildfire Disruption Risk
by Reinaldo Gomes, João Pires Ribeiro, Ruxanda Godina Silva and Ricardo Soares
Sustainability 2026, 18(4), 2086; https://doi.org/10.3390/su18042086 - 19 Feb 2026
Viewed by 418
Abstract
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to [...] Read more.
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed. Full article
(This article belongs to the Special Issue AI for Sustainable and Resilient Operations Management)
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27 pages, 394 KB  
Article
Does AI Application Enhance Corporate ESG Performance? The Role of Human Capital Structure
by Yingying Qi and Guohua Yu
Sustainability 2025, 17(24), 11100; https://doi.org/10.3390/su172411100 - 11 Dec 2025
Viewed by 1162
Abstract
Existing research has focused chiefly on the impact of artificial intelligence (AI) on economic growth. This study developed an AI dictionary using machine learning methods. Based on data from 3646 Shanghai- and Shenzhen-listed A-share companies from 2011 to 2022 and a panel mediation [...] Read more.
Existing research has focused chiefly on the impact of artificial intelligence (AI) on economic growth. This study developed an AI dictionary using machine learning methods. Based on data from 3646 Shanghai- and Shenzhen-listed A-share companies from 2011 to 2022 and a panel mediation effect model, the relationships between AI application, human capital structure adjustment, and corporate ESG performance were examined. Theoretical research suggests that when corporates adopt AI, demand for high-skilled labor will increase while some low-skilled positions will be replaced. This leads to optimization of the human capital structure, which in turn improves corporate ESG performance. The results of the mechanism examination show that enhancing corporate ESG performance through AI use is achieved by modifying the human capital structure. Analysis of heterogeneity finds that for non-state-owned, large-sized, and non-technology-intensive corporates, the impact of AI applications on corporate ESG performance is more pronounced. This research further deepens the understanding of AI’s role in the corporate governance process at the micro-corporate level and offers suggestions to promote the development of AI technology. Full article
(This article belongs to the Special Issue AI for Sustainable and Resilient Operations Management)
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