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This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

model predictive control --- bulbous bow --- improvement differential evolution algorithm --- evolutionary multi-objective optimization --- location routing problem --- flexible job shop scheduling problem --- basic differential evolution algorithm --- metric measure spaces --- NEAT --- genetic algorithm --- multiobjective optimization --- improved differential evolution algorithm --- performance indicator --- rubber --- averaged Hausdorff distance --- mixture experiments --- U-shaped assembly line balancing --- Genetic Programming --- Local Search --- driving events --- surrogate-based optimization --- single component constraints --- crop planning --- Pareto front --- numerical simulations --- shape morphing --- genetic programming --- economic crops --- local search and jump search --- model order reduction --- optimal solutions --- EvoSpace --- risky driving --- intelligent transportation systems --- optimal control --- IV-optimality criterion --- Bloat --- decision space diversity --- modify differential evolution algorithm --- power means --- driving scoring functions --- open-source framework --- evolutionary computation --- differential evolution algorithm --- vehicle routing problem --- multi-objective optimization

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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Information technology industries --- multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks --- n/a

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Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development.

Technology: general issues --- supply chain optimization --- oil and gas supply chain --- maintenance scheduling --- operation planning --- energy --- order picking --- wave planning --- warehouses --- distribution centers --- mixed integer programming --- non-linear programming --- Hadi-Vencheh model --- multiple criteria ABC inventory classification --- nonlinear weighted product model --- building material distributors --- central composite design (CCD) --- Box-Behnken design (BBD) --- optimal cost --- customer service level --- forecasting --- order planning --- inventory management --- resource-constrained project scheduling problem --- discounted cash flow maximization --- milestones payments --- simulated annealing algorithm --- slotting --- storage strategies --- stackability --- SKU --- product family --- heuristic --- metaheuristics --- scheduling --- injection molding --- hospital catering --- production scheduling --- flexible job shop problem --- mathematical model --- genetic algorithm --- local search method --- iterated local search algorithm --- competitive hub location problem --- network design --- food systems --- rural development --- mathematical programming --- crow search --- process planning --- operation sequencing --- precedence constraints --- manufacturing scheduling --- smart manufacturing --- intelligent manufacturing systems --- scheduling requirements --- cyber-physical production systems --- postman delivery --- vehicle routing problem --- particle swarm optimization algorithm --- differential evolution algorithm --- multi-criteria optimization --- simulation optimization --- production control --- multiple flexible job shop scheduling --- priority rules --- smart health care systems --- planning --- logistic systems --- benchmark --- workload balancing --- identical parallel machines --- normalized sum of square for workload deviations --- maximum completion time --- minimum completion time --- terminal location --- intermodal transportation --- simulated annealing --- mixed integer program --- incomplete networks --- supply chain optimization --- oil and gas supply chain --- maintenance scheduling --- operation planning --- energy --- order picking --- wave planning --- warehouses --- distribution centers --- mixed integer programming --- non-linear programming --- Hadi-Vencheh model --- multiple criteria ABC inventory classification --- nonlinear weighted product model --- building material distributors --- central composite design (CCD) --- Box-Behnken design (BBD) --- optimal cost --- customer service level --- forecasting --- order planning --- inventory management --- resource-constrained project scheduling problem --- discounted cash flow maximization --- milestones payments --- simulated annealing algorithm --- slotting --- storage strategies --- stackability --- SKU --- product family --- heuristic --- metaheuristics --- scheduling --- injection molding --- hospital catering --- production scheduling --- flexible job shop problem --- mathematical model --- genetic algorithm --- local search method --- iterated local search algorithm --- competitive hub location problem --- network design --- food systems --- rural development --- mathematical programming --- crow search --- process planning --- operation sequencing --- precedence constraints --- manufacturing scheduling --- smart manufacturing --- intelligent manufacturing systems --- scheduling requirements --- cyber-physical production systems --- postman delivery --- vehicle routing problem --- particle swarm optimization algorithm --- differential evolution algorithm --- multi-criteria optimization --- simulation optimization --- production control --- multiple flexible job shop scheduling --- priority rules --- smart health care systems --- planning --- logistic systems --- benchmark --- workload balancing --- identical parallel machines --- normalized sum of square for workload deviations --- maximum completion time --- minimum completion time --- terminal location --- intermodal transportation --- simulated annealing --- mixed integer program --- incomplete networks

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Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development.

supply chain optimization --- oil and gas supply chain --- maintenance scheduling --- operation planning --- energy --- order picking --- wave planning --- warehouses --- distribution centers --- mixed integer programming --- non-linear programming --- Hadi-Vencheh model --- multiple criteria ABC inventory classification --- nonlinear weighted product model --- building material distributors --- central composite design (CCD) --- Box–Behnken design (BBD) --- optimal cost --- customer service level --- forecasting --- order planning --- inventory management --- resource-constrained project scheduling problem --- discounted cash flow maximization --- milestones payments --- simulated annealing algorithm --- slotting --- storage strategies --- stackability --- SKU --- product family --- heuristic --- metaheuristics --- scheduling --- injection molding --- hospital catering --- production scheduling --- flexible job shop problem --- mathematical model --- genetic algorithm --- local search method --- iterated local search algorithm --- competitive hub location problem --- network design --- food systems --- rural development --- mathematical programming --- crow search --- process planning --- operation sequencing --- precedence constraints --- manufacturing scheduling --- smart manufacturing --- intelligent manufacturing systems --- scheduling requirements --- cyber-physical production systems --- postman delivery --- vehicle routing problem --- particle swarm optimization algorithm --- differential evolution algorithm --- multi-criteria optimization --- simulation optimization --- production control --- multiple flexible job shop scheduling --- priority rules --- smart health care systems --- planning --- logistic systems --- benchmark --- workload balancing --- identical parallel machines --- normalized sum of square for workload deviations --- maximum completion time --- minimum completion time --- terminal location --- intermodal transportation --- simulated annealing --- mixed integer program --- incomplete networks --- n/a --- Box-Behnken design (BBD)

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

Technology: general issues --- global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption --- global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption