Operations Research and Intelligent Computing for System Optimization

Dear Colleagues,

The fields of Operations Research (OR) and Computer Science have long been at the forefront of optimizing complex systems across various domains. From transportation, logistics, and supply chain management to scheduling, finance, and resource allocation, OR techniques have played a crucial role in improving efficiency, decision making, and overall system performance. On the other hand, Computer Science has contributed advancements in algorithmic design, computational intelligence, and data analytics, revolutionizing how we approach complex problems.

In recent years, the integration of Intelligent Computing techniques, such as Artificial Intelligence (AI), Machine Learning (ML), and Metaheuristics, has opened new possibilities for tackling challenging optimization problems. AI and ML algorithms enable systems to learn from data, adapt to changing conditions, and make intelligent decisions. Metaheuristics, inspired by natural processes or intelligent search procedures, offer powerful optimization algorithms capable of navigating large search spaces and finding high-quality solutions.

This Special Issue aims to explore the synergies between Operations Research and Intelligent Computing in the context of Systems Optimization, with a focus on applications in different sectors. We seek original research articles, reviews, and case studies that demonstrate the integration and utilization of OR techniques and Intelligent Computing methodologies for optimizing complex systems. Potential topics of interest include but are not limited to:

• Intelligent optimization algorithms that combine OR techniques and AI/ML methods for system optimization.
• Applications of AI and ML techniques in solving optimization problems in domains such as transportation, healthcare, finance, energy, manufacturing, and telecommunications.
• Metaheuristic approaches integrated with OR models for system optimization, including biased‒randomized algorithms, GRASP, iterated local search, evolutionary algorithms, swarm intelligence, simulated annealing, ant colony optimization, etc.
• Simheuristic approaches combining metaheuristics with simulation to deal with systems under uncertainty.
• Learnheuristic approaches combining metaheuristics with ML to deal with dynamic systems.
• Decision support systems that leverage the integration of OR and Intelligent Computing techniques to enhance decision-making processes.
• Hybrid approaches that combine OR models with intelligent algorithms to solve complex optimization problems.
• Real-world case studies demonstrating the benefits and effectiveness of the integration between Operations Research and Intelligent Computing in various sectors.
• Performance evaluation and benchmarking of optimization algorithms, considering both traditional OR methods and Intelligent Computing techniques, in the context of system optimization.

We look forward to receiving your contributions and making this Special Issue an exceptional platform for advancing the field of Operations Research and Intelligent Computing in the context of Systems Optimization, opening new frontiers of knowledge and fostering practical applications across diverse sectors.

Dr. Javier Panadero
Prof. Dr. Angel A. Juan
Guest Editors

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• operations research
• intelligent computing
• systems optimization
• artificial intelligence
• machine learning
• metaheuristics
• simulation
• hybrid intelligent algorithms

Title: Two synched capacitated-VRP problem with time windows
Authors: Esther Fernández-Bravo (UPM) · Hugo Larzabal (baobab soluciones) · Álvaro García-Sánchez (UPM) · Carlos A. Méndez (UNL) · Miguel Ortega-Mier (UPM)
Affiliation: UPM
Abstract: Several companies have to deal with decisions in which two different kind of resources (for example, trucks and working teams of people) have to be coordinated. Each resource has its own routes (with its own constraints like time windows) but both of them have to join in the client site to perform an activity to the client. In this paper, we define the two synched capacitated-VRPs with time windows problem (2S-CVRPTW). To solve the problem, we first suggest a MILP mathematical formulation with a full-space approach as the solution strategy. Later on, we propose three decomposed formulations arising from the original MILP problem as to study their ability to quickly find feasible solutions. Finally, we consider two local search algorithms based on heuristic techniques for improving the initial solutions.