Special Issue "Applications of AI and Machine Learning Models for Logistics and Supply Chain Management"

A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 5091

Special Issue Editors

Poole College of Management, North Carolina State University, Campus Box 7229, Raleigh, NC 27695, USA
Interests: strategic sourcing; supplier development; bioproducts; supply market intelligence; supply chain re-design; logistics
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: logistics systems; supply chain management; applications of artificial intelligence (machine learning, deep learning, natural language processing, etc.); decision-support systems; integration of production and supply chain; internet-of-things; Industry 4.0
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Interests: intelligent logistics; transportation and logistics system design; logistics technology; logistics platforms; vehicle routing problem; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

The goal of this special issue is to accelerate the adoption of machine learning and related AI tools and technologies in supply chain management and logistics. The exponentially increasing volume of data that is being generated by IoT-enabled sensors on products, equipment, and transport vehicles is enabling a next generation of smart supply chains and logistics networks. New tools of artificial intelligence—from data analytics and machine learning to natural language processing—are well suited to the task of turning this data into actionable information that enables faster and more responsive supply chains. For example, machine learning makes it possible to discover fine-grained and complex patterns in data that can improve decision-making for functions ranging from warehouse and inventory management to transportation planning. Pattern recognition can also help to both predict and recover from disruptive events along the supply chain. And AI-enabled vehicle routing can increase fleet efficiencies and reduce costs. Machine learning combined with computer vision power robots and drones that are increasingly used to monitor and manage warehouses and distribution centers. Going forward, cognitive supply chains that can reason and make decisions based on what has been learned, will further drive competitive differentiation. Many open questions and challenges remain about the ability of machine learning and other digital technologies to drive transformational change in supply chain management and logistics. This special issue will address the need for changes that utilize data to drive improvements in operations performance.

Papers are welcome that provide high-quality research about theoretical and empirical studies of machine learning applied to all aspects of supply chains and logistical systems. Authors are cordially invited to submit high-quality, original research papers, review articles, empirical studies and case studies (for teaching and training). Manuscripts may be submitted until August 31, 2021, on topics including, but not limited to, application of data analytics, machine learning and other AI tools to:

  • Supply chain planning and optimization
  • Predictive and prescriptive supply chain analytics
  • Demand planning and inventory management
  • Data-driven revenue management and pricing
  • Automation of supply chain operations
  • Supply chain disruption management
  • Intelligent logistics networks and services
  • Warehouse automation
  • Real-time fleet monitoring and management
  • Routing and delivery optimization
  • Digital platforms for procurement and supply chain operations
  • Supplier selection and performance tracking
  • Robotic process automation
  • Autonomous delivery vehicles
  • Blockchain for supply chain transparency and security
  • Real-time product tracking and tracing
  • Autonomous mobile robotics
  • AI-powered visual quality inspection systems
  • Virtual assistants for procurement and supply chain security
  • Anomaly detection and fraud management

Prof. Dr. Robert Handfield
Prof. Dr. Noel Greis
Dr. Linning Cai
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. Logistics is an international peer-reviewed open access quarterly 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 1200 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.


  • machine learning
  • artificial intelligence
  • intelligent logistics
  • Supply Chain 4.0
  • robotic process
  • automation

Published Papers (1 paper)

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18 pages, 832 KiB  
Rolling Cargo Management Using a Deep Reinforcement Learning Approach
Logistics 2021, 5(1), 10; https://doi.org/10.3390/logistics5010010 - 08 Feb 2021
Cited by 3 | Viewed by 3391
Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an autonomous [...] Read more.
Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an autonomous tug master to manage rolling cargo and perform loading and unloading operations while avoiding collisions with static and dynamic obstacles along the way. The artificial intelligence agent, representing the tug master, is trained and evaluated in a challenging environment based on the Unity3D learning framework, called the ML-Agents, and using proximal policy optimization. The agent is equipped with sensors for obstacle detection and is provided with real-time feedback from the environment thanks to its own reward function, allowing it to dynamically adapt its policies and navigation strategy. The performance evaluation shows that by choosing appropriate hyperparameters, the agents can successfully learn all required operations including lane-following, obstacle avoidance, and rolling cargo placement. This study also demonstrates the potential of intelligent autonomous systems to improve the performance and service quality of maritime transport. Full article
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