Metaheuristics for Scheduling in Industrial

This volume presents meta-heuristics approaches for scheduling problems arising in industrial and manufacturing applications. Nowadays, metaheuristics have become a de facto approach to tackle in practice with the complexity of scheduling problems. Early work applied evolutionary computing methods to scheduling problems. The present volume is novel in many respects. First, the proposed approaches comprise a variety of meta-heuristics (Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Scatter Search, Variable Neighborhood Search). Second, in most cases, hybridization is approached as the most effective way to achieve state-of-the art results. Third, and most importantly, the scheduling problems arising in real life applications and real world data instances are solved using these meta-heuristics; these applications comprise reconfigurable manufacturing systems, lot sizing and scheduling in industry, railway scheduling and process, supply chain scheduling and scheduling problem arising in a real-world multi-commodity Oil-derivatives Pipeline.Finally, scheduling problems and meta-heuristics are presented in a comprehensive way making this volume and interesting contribution to the research on scheduling in industrial and manufacturing applications.