Mathematical Problems in Engineering

Machine Learning and Computational Intelligence in Supply Chains


Publishing date
01 Jan 2023
Status
Published
Submission deadline
26 Aug 2022

Lead Editor

1University of Defence in Belgrade, Belgrade, Serbia

2Afyon Kocatepe University, Afyonkarahisar, Turkey

3University of Molise, Campobasso, Italy


Machine Learning and Computational Intelligence in Supply Chains

Description

The current world’s context has challenged supply chains, especially with regard to resilience and sustainability. The COVID-19 pandemic brought unprecedented issues to supply chains in terms of maintaining their continuity in delivering products and services. Concurrently, the world is facing many never seen climate issues, which have fostered discussions on how supply chains can produce and deliver products and services in a more sustainable way. At the same time that pandemic and climate issues have arisen in the world, the fourth industrial revolution (Industry 4.0) brings many opportunities for supply chains by the adoption of disruptive technologies. This includes data, information and knowledge technologies, which are integrated with physical technologies allowing for the generation of more efficient, integrated, transparent and smarter supply chain processes.

The advent of wearable devices, Internet of Things, and Internet of Vehicles have stimulated deep transformations in supply chains, not only at the technological level but also at the societal and economic level. Data is generated at a rate of petabytes per day, and given this amount of data, intelligent processing is needed. In addition, because of the advances in high-performance computing, large data sets can now be used for training machine learning algorithms. Specifically, deep learning paradigms enable the sophisticated transformation of data into usable, operational knowledge. Moreover, discussions on how supply chains can act for more sustainable and smart societies (Society 5.0) are also ongoing. There is a demand to further explore the abundant applications of soft computing methods, including deep learning, fuzzy logic, evolutionary methods, and various data mining techniques.

Therefore, this Special Issue aims to answer the key question of how the application of machine learning and computational intelligence can contribute to more sustainable and resilient supply chains. We welcome both original research and review articles with a focus on the application of machine learning and computational intelligence in supply chains.

Potential topics include but are not limited to the following:

  • Big data analytics in supply chains
  • Internet of Things in supply chains
  • Artificial intelligence and machine learning in supply chains
  • Blockchain technology in supply chains
  • Cloud technologies in supply chains
  • Digital twins in supply chains
  • Robotics and autonomous vehicles in supply chains
  • Cobots and multi-agent systems in supply chains
  • Additive manufacturing in supply chains
  • Augmented and virtual reality in supply chains
  • Interoperability of technologies in supply chains
  • AI-based and green-based supply chains
  • Data-driven innovations for planning and management in the supply chains
  • Soft computing methods for supply chains
  • Meta-heuristic algorithms in supply chain
  • Computational intelligence for sustainable supply chains
  • Novel or improved nature-inspired optimization algorithms in supply chains
  • Generative adversarial learning in supply chains
  • Intelligent transportation systems
  • Advanced machine learning and deep networks for supply chains
  • Trend analysis with big data and artificial intelligence for supply chains

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 9631579
  • - Research Article

Cubic Intuitionistic Fuzzy Topology with Application to Uncertain Supply Chain Management

Muhammad Riaz | Khadija Akmal | ... | Daud Ahmad
  • Special Issue
  • - Volume 2022
  • - Article ID 9657703
  • - Research Article

Spherical Fuzzy Information Aggregation Based on Aczel–Alsina Operations and Data Analysis for Supply Chain

Muhammad Riaz | Hafiz Muhammad Athar Farid | ... | Shaista Tanveer
  • Special Issue
  • - Volume 2022
  • - Article ID 3605641
  • - Research Article

Application of Hamacher Aggregation Operators in the Selection of the Cite for Pilot Health Project based on Complex T-spherical Fuzzy Information

Kifayat Ullah | Zareena Kousar | ... | Zeeshan Ali
  • Special Issue
  • - Volume 2022
  • - Article ID 1419804
  • - Research Article

Identification of Encrypted Traffic Using Advanced Mathematical Modeling and Computational Intelligence

Xinlei Liu
  • Special Issue
  • - Volume 2022
  • - Article ID 2316474
  • - Research Article

Short-Term Electrical Load Demand Forecasting Based on LSTM and RNN Deep Neural Networks

Badar ul Islam | Shams Forruque Ahmed
  • Special Issue
  • - Volume 2022
  • - Article ID 6518976
  • - Research Article

Extended Transportation Models Based on Picture Fuzzy Sets

Muhammad Athar Mehmood | Shahida Bashir
  • Special Issue
  • - Volume 2022
  • - Article ID 4182740
  • - Research Article

Innovative Bipolar Fuzzy Sine Trigonometric Aggregation Operators and SIR Method for Medical Tourism Supply Chain

Muhammad Riaz | Dragan Pamucar | ... | Nimra Jamil
  • Special Issue
  • - Volume 2022
  • - Article ID 1839028
  • - Research Article

Fully Bipolar Single-Valued Neutrosophic Transportation Problems

Jamil Ahmed | Shahida Bashir
Mathematical Problems in Engineering
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Acceptance rate12%
Submission to final decision157 days
Acceptance to publication34 days
CiteScore2.600
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