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Genetic and Hybrid Algorithm Approaches to Flow Shop Scheduling

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Metaheuristics for Production Scheduling

: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods;

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A Book of Open Shop Scheduling

to be executed in any order, contrary to flow and job shops where the order is pre-specified. The author brings the field up to date with;

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Tuning Innovation with Biotechnology

approaches optimal intelligent tuning using a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and illustrates hybrid GA-PSO for;

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Applications of Firefly Algorithm and its Variants

in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use;

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Cellular Manufacturing Systems

such as heuristics, mathematical models, networks models, genetic algorithm approaches, artificial neural networks, knowledge-based algorithms;

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A Genetic Algorithm for Resource-Constrained Project Scheduling

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Evolutionary Computation in Scheduling

role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm;

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Parameters in 2-stage Flow Shop Scheduling Models

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Sequencing & Scheduling with Inaccurate Data

the last decades, several approaches have been developed for sequencing and scheduling with inaccurate data, depending on whether the data;

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Complex Scheduling

and genetic algorithms, and dynamic programming are presented. They are used in exact or heuristic procedures to solve the introduced complex;

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Vehicle Scheduling in Port Automation

, the fastest algorithm for solving the minimum cost flow problem, and develops four new advanced algorithms. In order to verify and validate;

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Optimized Cloud Based Scheduling

assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and;

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Optimized Cloud Based Scheduling

assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and;

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Recent Advances in Swarm Intelligence and Evolutionary Computation

, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as;

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Hybrid Artificial Intelligence and IoT in Healthcare

, and mobility-related problems in healthcare. The book discusses the convergence of AI and the hybrid approaches in healthcare which optimizes;

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Principles of Sequencing and Scheduling

An up-to-date and comprehensive treatment of the fundamentals of scheduling theory, including recent advances and state-of-the-art topics;

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Modern Heuristic Optimization Techniques

systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power;

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Genetic Algorithms

The aim of this book is to present state-of-the-art information regarding the practical applications of genetic algorithms. It discusses;

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Production Scheduling Handbook

topics like rescheduling strategies, policies and procedures for production scheduling; flow shop scheduling; and heuristic and metaheuristic;

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Hybrid Feedback Control

A comprehensive introduction to hybrid control systems and design Hybrid control systems exhibit both discrete changes, or jumps, and;

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Travelling Salesman Problem(tsp)Optimization Through Genetic Algorithm

techniques to solve TSP. One of the best approaches to find an optimal solution to TSP is Genetic Algorithm, which gets the optimal solution from;

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The Disassembly Line

methodologies Exhaustive search Genetic algorithm Ant colony optimization Greedy algorithm Greedy/adjacent element hill climbing hybrid Greedy/2;

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Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling

job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore;

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Advances in Swarm and Computational Intelligence

; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid;

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Advances in Swarm and Computational Intelligence

; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid;

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Advances in Swarm and Computational Intelligence

; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid;

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