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Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply...
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Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3571

Special Issue Editors

Dr. Xue-Ming Yuan

E-Mail Website
Guest Editor
NUS Business School, National University of Singapore, Singapore 119245, Singapore
Interests: data science; predictive and prescriptive analytics; supply chain analytics; logistics and supply chain management; stochastic models; algorithms and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Yu

E-Mail Website
Guest Editor
Department of Industrial Engineering, UiT—The Arctic University of Norway, 8514 Narvik, Norway
Interests: reverse logistics; sustainable logistics; optimization; simulation; smart logistics in Industry 4.0/5.0; digital logistics twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's dynamic and ever-evolving business landscape, logistics and supply chain management have become increasingly intricate, necessitating efficient decision-making processes to ensure seamless operations for goods, information, and resources. Leveraging the transformative potential of Industry 4.0 technologies, an unprecedented abundance of data becomes available, presenting unique opportunities to derive valuable insights and intelligent decision-making in logistics and supply chain management. Mastering the art of harnessing the available data to comprehend past events, monitor current happenings, predict future trends, and execute optimal decisions has emerged as a key competitive advantage for companies across industries. Data analytics, particularly predictive analytics and prescriptive analytics, play pivotal roles in addressing the dynamic and uncertain business environments prevalent in logistics and supply chain management. By integrating cutting-edge technologies and data-driven methodologies, this field now faces unprecedented opportunities and challenges.

The primary goal of this Special Issue is to curate groundbreaking research on data-driven algorithms and their practical applications in enabling efficient decision-making within logistics and supply chain management. We invite original contributions that delve into the theoretical, methodological, and practical aspects of data-driven algorithms, with a special focus on their profound influence on optimal decision-making processes across various domains within logistics and supply chain management. This Special Issue welcomes exemplary contributions from both academia and industry, centered on data-driven algorithms for optimal decision-making in logistics and supply chain management. We are encouraging the submission of original papers encompassing a wide array of topics, including, but not limited to:

  • Autonomous warehouse systems: Exploring advanced automation techniques to optimize warehouse operations and enhance overall efficiency.
  • Big data analytics: Unveiling the hidden treasures in vast data sets, empowering businesses to make data-backed optimal decisions.
  • Consumer behavior modeling and prediction: Utilizing data-driven approaches to better understand and anticipate consumer behavior patterns.
  • Data-driven sales & operations planning (S&OP): Enhancing the planning process through data-backed insights to optimize sales and operations coordination.
  • Digital supply chain management: Examining the pivotal role of digital technologies in revolutionizing supply chain operations.
  • End-to-end supply chain integration and optimization: Addressing challenges and opportunities in achieving comprehensive supply chain optimization.
  • Intelligent demand forecasting: Implementing AI-driven forecasting techniques for more accurate demand predictions.
  • Intelligent manufacturing systems: Integrating data-driven intelligence into manufacturing processes for enhanced efficiency and adaptability.
  • Intelligent transportation systems: Leveraging data analytics to optimize transportation operations and logistics.
  • Inventory track and trace: Implementing real-time tracking solutions to optimize inventory management.
  • Learning algorithms: Incorporating learning algorithms to develop intelligent systems capable of adaptability and continuous improvement.
  • Mathematical models: Utilizing mathematical models to provide a rigorous foundation for decision-making processes.
  • Predictive maintenance systems: Utilizing predictive analytics to optimize maintenance operations and minimize downtime.
  • Predictive inventory management and optimization: Optimizing inventory levels based on predictive analytics to reduce costs and improve responsiveness.
  • Route optimization in transportation and distribution: Applying data-driven algorithms to optimize route planning and delivery logistics.
  • Supplier selection intelligence: Employing data-driven methods for optimal supplier selection and management.
  • Sustainable supply chain management: Investigating how data-driven optimization can support environmentally responsible supply chain practices.

Dr. Xue-Ming Yuan
Dr. Hao Yu
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. Mathematics 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 2600 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

  • big data analytics
  • data-driven algorithms
  • optimal decision making
  • intelligent systems
  • learning algorithms
  • mathematical models

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Published Papers (4 papers)

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Research

23 pages, 5296 KiB  
Article
Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach
by Selin Çabuk and Rızvan Erol
Mathematics 2024, 12(13), 2138; https://doi.org/10.3390/math12132138 - 7 Jul 2024
Viewed by 566
Abstract
In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and [...] Read more.
In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders. Full article
(This article belongs to the Special Issue Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management)
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26 pages, 4659 KiB  
Article
Robust Truck Transit Time Prediction through GPS Data and Regression Algorithms in Mixed Traffic Scenarios
by Adel Ghazikhani, Samaneh Davoodipoor, Amir M. Fathollahi-Fard, Mohammad Gheibi and Reza Moezzi
Mathematics 2024, 12(13), 2004; https://doi.org/10.3390/math12132004 - 28 Jun 2024
Viewed by 464
Abstract
To enhance safety and efficiency in mixed traffic scenarios, it is crucial to predict freight truck traffic flow accurately. Issues arise due to the interactions between freight trucks and passenger vehicles, leading to problems like traffic congestion and accidents. Utilizing data from the [...] Read more.
To enhance safety and efficiency in mixed traffic scenarios, it is crucial to predict freight truck traffic flow accurately. Issues arise due to the interactions between freight trucks and passenger vehicles, leading to problems like traffic congestion and accidents. Utilizing data from the Global Positioning System (GPS) is a practical method to enhance comprehension and forecast the movement of truck traffic. This study primarily focuses on predicting truck transit time, which involves accurately estimating the duration it will take for a truck to travel between two locations. Precise forecasting has significant implications for truck scheduling and urban planning, particularly in the context of cross-docking terminals. Regression algorithms are beneficial in this scenario due to the empirical evidence confirming their efficacy. This study aims to achieve accurate travel time predictions for trucks by utilizing GPS data and regression algorithms. This research utilizes a variety of algorithms, including AdaBoost, GradientBoost, XGBoost, ElasticNet, Lasso, KNeighbors, Linear, LinearSVR, and RandomForest. The research provides a comprehensive assessment and discussion of important performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2). Based on our research findings, combining empirical methods, algorithmic knowledge, and performance evaluation helps to enhance truck travel time prediction. This has significant implications for logistical efficiency and transportation dynamics. Full article
(This article belongs to the Special Issue Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management)
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10 pages, 386 KiB  
Article
DE-MKD: Decoupled Multi-Teacher Knowledge Distillation Based on Entropy
by Xin Cheng, Zhiqiang Zhang, Wei Weng, Wenxin Yu and Jinjia Zhou
Mathematics 2024, 12(11), 1672; https://doi.org/10.3390/math12111672 - 27 May 2024
Viewed by 480
Abstract
The complexity of deep neural network models (DNNs) severely limits their application on devices with limited computing and storage resources. Knowledge distillation (KD) is an attractive model compression technology that can effectively alleviate this problem. Multi-teacher knowledge distillation (MKD) aims to leverage the [...] Read more.
The complexity of deep neural network models (DNNs) severely limits their application on devices with limited computing and storage resources. Knowledge distillation (KD) is an attractive model compression technology that can effectively alleviate this problem. Multi-teacher knowledge distillation (MKD) aims to leverage the valuable and diverse knowledge distilled by multiple teacher networks to improve the performance of the student network. Existing approaches typically rely on simple methods such as averaging the prediction logits or using sub-optimal weighting strategies to fuse distilled knowledge from multiple teachers. However, employing these techniques cannot fully reflect the importance of teachers and may even mislead student’s learning. To address this issue, we propose a novel Decoupled Multi-Teacher Knowledge Distillation based on Entropy (DE-MKD). DE-MKD decouples the vanilla knowledge distillation loss and assigns adaptive weights to each teacher to reflect its importance based on the entropy of their predictions. Furthermore, we extend the proposed approach to distill the intermediate features from multiple powerful but cumbersome teachers to improve the performance of the lightweight student network. Extensive experiments on the publicly available CIFAR-100 image classification benchmark dataset with various teacher-student network pairs demonstrated the effectiveness and flexibility of our approach. For instance, the VGG8|ShuffleNetV2 model trained by DE-MKD reached 75.25%|78.86% top-one accuracy when choosing VGG13|WRN40-2 as the teacher, setting new performance records. In addition, surprisingly, the distilled student model outperformed the teacher in both teacher-student network pairs. Full article
(This article belongs to the Special Issue Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management)
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19 pages, 583 KiB  
Article
An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling
by Ke Yang and Dazhi Pan
Mathematics 2023, 11(20), 4384; https://doi.org/10.3390/math11204384 - 21 Oct 2023
Viewed by 1325
Abstract
The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated [...] Read more.
The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS. Full article
(This article belongs to the Special Issue Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management)
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