Taylor & Francis Group

Deep Learning Applications in Operations Research, Advances in Computational Collective Intelligence

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Explore deep learning models in operations research AI with real-world case studies in collective intelligence and smart decision systems.

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Additional information

Additional information

Authors

Aryan & Mallik, Biswadip Basu & Mukherjee, Chaudhary, Gunjan & Kar, Rahul

Publisher

Taylor & Francis Group

Published On

2025-11-15

Language

English

Format

pdf

Size (MB)

15.17 MB

Rating

⭐️⭐️⭐️⭐️⭐️ 4.33

Description

Deep Learning Applications in Operations Research

 

Deep Learning Operations Research explores how modern artificial intelligence and deep neural networks are transforming operations research, decision-making, and optimization across industries. This comprehensive course bridges theoretical models with practical deep learning techniques used to solve real-world business and scientific challenges.

Course Description

In today’s data-driven world, operational efficiency and intelligent automation are vital for success. This course, Deep Learning Applications in Operations Research, Advances in Computational Collective Intelligence, integrates the principles of machine learning, optimization algorithms, and computational collective intelligence to enhance decision-making systems. You will gain the ability to model complex problems, automate decision processes, and apply neural network-based optimization in logistics, supply chain, scheduling, and predictive analytics.

Through hands-on examples and interactive case studies, learners will discover how to combine reinforcement learning, evolutionary computation, and optimization algorithms to create smarter operational strategies. Additionally, the course highlights how collective intelligence frameworks, including swarm intelligence and multi-agent systems, can be combined with deep learning for innovative problem-solving.

What You’ll Learn

  • Fundamentals of deep learning and its applications in operations research.
  • Optimization techniques using neural networks and reinforcement learning.
  • Integration of computational collective intelligence in decision models.
  • Case studies on logistics, manufacturing, and data-driven planning.
  • Building AI-powered systems for real-world operational problems.

Requirements

  • Basic knowledge of Python programming.
  • Understanding of linear algebra and statistics.
  • Familiarity with machine learning concepts is recommended.

About the Publication

This course is developed by industry experts and academic professionals specializing in artificial intelligence and operations research. The publication combines insights from recent studies in computational intelligence and applied AI to help learners stay ahead in technological innovation.

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Additional information

Authors

Aryan & Mallik, Biswadip Basu & Mukherjee, Chaudhary, Gunjan & Kar, Rahul

Publisher

Taylor & Francis Group

Published On

2025-11-15

Language

English

Format

pdf

Size (MB)

15.17 MB

Rating

⭐️⭐️⭐️⭐️⭐️ 4.33

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