Intelligent Systems and Dynamic Scheduling: Optimization and Management
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 140
Special Issue Editors
Interests: reliability optimization; intelligent fault diagnosis and prediction; intelligent maintenance decision; intelligent dynamic scheduling optimization
Special Issue Information
Dear Colleagues,
This Special Issue, "Intelligent Systems and Dynamic Scheduling: Optimization and Management", delves deeply into the amalgamation of mathematical theories and practical methodologies to drive advancements in manufacturing, logistics, and transportation systems. It aims to provide further insights into the mathematical aspects covered in the collection, including specific mathematical models and algorithms.
This Special Issue explores novel mathematical approaches that can significantly enhance the efficiency of dynamic scheduling and intelligent manufacturing/logistics processes. Various optimization algorithms are investigated, ranging from linear programming, which uses mathematical models to optimize resource allocation, to evolutionary computation, which employs iterative processes inspired by biological evolution to solve complex optimization problems.
The intricate interplay between predictive analytics, which uses statistical models and forecasting techniques to analyze current and historical facts, and real-time decision making is a recurring theme. This integration is crucial for dynamic scheduling, where decisions must be made rapidly and accurately based on current system conditions.
The importance of stochastic models is emphasized throughout the volume. These models, which incorporate randomness and probabilistic distributions, are essential for capturing the uncertainties inherent in production systems and supply chains. By modeling these uncertainties, decision makers can better anticipate and mitigate risks.
Collaborative discussions also highlight the role of discrete-event simulations, which use mathematical models to simulate the operation of complex systems. These simulations are crucial for understanding the nuances of scheduling policies and for testing the effectiveness of different scheduling strategies before implementation.
In summary, this Special Issue showcases pioneering research that bridges the gap between theoretical mathematics and practical applications in the field of intelligent systems and dynamic scheduling. The aim is not only to highlight current innovations but also to inspire future research that continues to push the boundaries of what is achievable within this interdisciplinary domain.
Dr. Yaqiong Lv
Dr. Shuzhu Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- mathematical modeling
- intelligent systems
- dynamic scheduling
- evolutionary computation
- intelligent fault diagnosis
- combinatorial optimization
- operations research
- intelligent computing
- real-time decision making
- reinforcement learning
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