Submission Deadline: 28 February 2026 View: 1620 Submit to Special Issue
Prof. Dr. Frank Werner, Otto-Von-Guericke-University, Germany
Dr. Mohammad Shokouhifar, Shahid Beheshti University, Iran
Discrete optimization and scheduling algorithms are essential for addressing a wide range of complex and resource-intensive engineering problems. These algorithms excel in solving issues where decisions must be made discretely, such as scheduling tasks, allocating resources, or selecting a subset of projects. In recent years, numerous discrete optimization methods including integer programming, mixed-integer programming, graph theory, combinatorial optimization, heuristics, metaheuristics, and hyper-heuristics, have been employed to model and solve engineering problems in areas like resource allocation, design, manufacturing, logistics, and operations research. These techniques enable the efficient organization of resources, tasks, and processes, leading to optimal or near-optimal solutions. The use of discrete optimization and scheduling algorithms significantly improves decision-making and operational efficiency across various engineering domains. By leveraging these techniques, engineers can design systems that are more efficient, cost-effective, and robust, ultimately leading to better operational performance in modern engineering practices.
This Special Issue invites experts from either academia or industry to showcase the latest achievements in the applications of discrete optimization and scheduling algorithms for solving real-world problems across various engineering domains. We invite high-quality research papers and review articles on topics including, but not limited to:
· Integer and mixed-integer programming
· Combinatorial optimization problems
· Graph theory and network flows
· Exact search techniques
· Heuristic, metaheuristic, and hyper-heuristic algorithms
· Job-shop and flow-shop scheduling problems
· Project scheduling and management
· Timetabling problems
· Resource allocation problems
· Vehicle routing problems
· Just-in-time optimization for dynamic scheduling
· Manufacturing and production planning
· Healthcare and medical scheduling
· Smart cities and urban planning
· Internet-of-Things (IoT) applications
· Smart manufacturing and supply chain logistics
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