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Evolutionary Computation in Scheduling

 E-Book
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ISBN-13:
9781119573869
Veröffentl:
2020
Einband:
E-Book
Seiten:
368
Autor:
Amir H. Gandomi
eBook Typ:
PDF
eBook Format:
Reflowable
Kopierschutz:
2 - DRM Adobe
Sprache:
Englisch
Beschreibung:

Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problemsThis book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches.Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book:* Provides a representative sampling of real-world problems currently being tackled by practitioners* Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence* Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling ProblemsEvolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
List of Contributors viiEditors' Biographies xiPreface xvAcknowledgments xvii1 Evolutionary Computation in Scheduling: A Scientometric Analysis 1Amir H. Gandomi, Ali Emrouznejad, and Iman Rahimi2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems: A Detailed Analysis 11P. Deepalakshmi and K. Shankar3 Advanced Ant Colony Optimization in Healthcare Scheduling 37Reza Behmanesh, Iman Rahimi, Mostafa Zandieh, and Amir H. Gandomi4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence 73S. Sarathambekai and K. Umamaheswari5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization 105Prabina Pattanayak and Preetam Kumar6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure 137Amir Mohammad Fathollahi-Fard, Abbas Ahmadi, and Mohsen S. Sajadieh7 Application of Sub-Population Scheduling Algorithm in Multi-Population Evolutionary Dynamic Optimization 169Javidan Kazemi Kordestani and Mohammad Reza Meybodi8 Task Scheduling in Cloud Environments: A Survey of Population-Based Evolutionary Algorithms 213Fahimeh Ramezani, Mohsen Naderpour, Javid Taheri, Jack Romanous, and Albert Y. Zomaya9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms 257Jiayi Liu, Wenjun Xu, Zude Zhou, and Duc Truong Pham10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies 299Mohammad Sadegh Javadi, Ali Esmaeel Nezhad, Seyed-Ehsan Razavi, Abdollah Ahmadi, and João P.S. CatalãoIndex 327

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