Miguel Mujica Mota – författare

The two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3–5, 2023.

The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: ​environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.

The two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3–5, 2023.

The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: ​environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.

Presenting techniques, case-studies and methodologies that combine the use of simulation approaches with optimization techniques for facing problems in manufacturing, logistics, or aeronautical problems, this book provides solutions to common industrial problems in several fields, which range from manufacturing to aviation problems, where the common denominator is the combination of simulation’s flexibility with optimization techniques’ robustness.

Providing readers with a comprehensive guide to tackle similar issues in industrial environments, this text explores novel ways to face industrial problems through hybrid approaches (simulation-optimization) that benefit from the advantages of both paradigms, in order to give solutions to important problems in service industry, production processes, or supply chains, such as scheduling, routing problems and resource allocations, among others.

This book presents for the first time a methodology that combines the power of a modelling formalism such as colored petri nets with the flexibility of a discrete event program such as SIMIO. Industrial practitioners have seen the growth of simulation as a methodology for tacking problems in which variability is the common denominator. Practically all industrial systems, from manufacturing to aviation are considered stochastic systems. Different modelling techniques have been developed as well as mathematical techniques for formalizing the cause-effect relationships in industrial and complex systems. The methodology in this book illustrates how complexity in modelling can be tackled by the use of coloured petri nets, while at the same time the variability present in systems is integrated in a robust fashion. The book can be used as a concise guide for developing robust models, which are able to efficiently simulate the cause-effect relationships present in complex industrial systemswithout losing the simulation power of discrete-event simulation. In addition SIMIO’s capabilities allows integration of features that are becoming more and more important for the success of projects such as animation, virtual reality, and geographical information systems (GIS).

Building on the author’s earlier Applied Simulation and Optimization, this book presents novel methods for solving problems in industry, based on hybrid simulation-optimization approaches that combine the advantages of both paradigms. The book serves as a comprehensive guide to tackling scheduling, routing problems, resource allocations and other issues in industrial environments, the service industry, production processes, or supply chains and aviation. Logistics, manufacturing and operational problems can either be modelled using optimization techniques or approaches based on simulation methodologies. Optimization techniques have the advantage of performing efficiently when the problems are properly defined, but they are often developed through rigid representations that do not include or accurately represent the stochasticity inherent in real systems. Furthermore, important information is lost during the abstraction process to fit each problem into theoptimization technique. On the other hand, simulation approaches possess high description levels, but the optimization is generally performed through sampling of all the possible configurations of the system. The methods explored in this book are of use to researchers and practising engineers in fields ranging from supply chains to the aviation industry.