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Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future...
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Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 281

Special Issue Editor

Dr. Muhammad Noman Shafique

E-Mail Website
Guest Editor
Department of Environment and Planning, University of Aveiro, Aveiro, Portugal
Interests: supply chain management; logistics; digital business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that this Special Issue of the MDPI journal Information is currently accepting articles on “Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions”.

Reducing carbon footprints, managing resource scarcity, ensuring sustainable procurement, and minimizing waste are just a few of the major concerns regarding sustainable supply chains. With the global economy becoming more integrated and dynamic, artificial intelligence (AI) is becoming crucial in tackling these issues. AI-driven predictive analytics, real-time decision-making, and enhanced transparency are revolutionizing supply chain planning and management.

Even though supply chain management is progressively relying on AI, several important issues and unfulfilled research gaps remain to be considered. These include addressing ethical aspects, AI-driven decision-making, integrating AI into circular and multi-tiered supply chains, and ensuring accountability and transparency in AI applications. Additionally, the integration of AI with sustainability creates unique challenges, such as combining social, environmental, and economic objectives while supporting operational resilience and efficiency.

This Special Issue invites the submission of original research that can advance our understanding of AI’s contribution to sustainable supply chains. As existing research often overlooks this relationship, authors are highly encouraged to align their studies with the United Nation’s Sustainable Development Goals (SDGs).

Topics of interest include but are not limited to the following:

  • AI and circular economy in supply chain management.
  • AI-enabled transparency and traceability in global supply chains.
  • Ethical considerations in AI applications for sustainability.
  • The role of AI in circular supply chains and reverse logistics.
  • AI-powered tools for monitoring and reducing carbon footprints.
  • Using AI to achieve economic, environmental, and social goals in supply chains.
  • Applying AI in industries such as manufacturing, logistics, retail, and agriculture.
  • The use of AI in risk management for sustainable supply chains.
  • Case studies on AI’s impact on supply chain resilience and sustainability.
  • Challenges and future directions for AI-driven sustainable supply chains.

We welcome the submission of theoretical, methodological, and applied research from academia, industry, and policymakers that focus on the topics mentioned above. Authors should demonstrate how their research contributes to and promotes the SDGs while providing practical implications from their findings. We especially encourage the inclusion of innovative frameworks or strategies that can leverage AI in the context of a circular economy and sustainable supply chain management.

Dr. Muhammad Noman Shafique
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. Information is an international peer-reviewed open access monthly 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 1800 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

  • artificial intelligence (AI) in supply chains
  • sustainable supply chain management practices
  • predictive analytics for supply chain optimization
  • supply chain transparency and traceability
  • supply chain resilience

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

Published Papers (1 paper)

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Research

26 pages, 1886 KiB  
Article
Path Planning with Adaptive Autonomy Based on an Improved A∗ Algorithm and Dynamic Programming for Mobile Robots
by Muhammad Aatif, Muhammad Zeeshan Baig, Umar Adeel and Ammar Rashid
Information 2025, 16(8), 700; https://doi.org/10.3390/info16080700 (registering DOI) - 17 Aug 2025
Abstract
Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of [...] Read more.
Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved A∗ (I-A∗) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Supply Chains: Innovations, Applications, and Future Directions)
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