{"product_id":"evolutionary-computation-in-scheduling-isbn-9781119573845","title":"Evolutionary Computation in Scheduling","description":"\u003cp\u003e\u003cb\u003ePresents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEvolutionary Computation in Scheduling\u003c\/i\u003e 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a representative sampling of real-world problems currently being tackled by practitioners\u003c\/li\u003e \u003cli\u003eExamines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence\u003c\/li\u003e \u003cli\u003eConsists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eEvolutionary Computation in Scheduling \u003c\/i\u003eis ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.\u003c\/p\u003e \u003cp\u003eList of Contributors vii\u003c\/p\u003e \u003cp\u003eEditors’ Biographies xi\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003e1 Evolutionary Computation in Scheduling: A Scientometric Analysis 1\u003cbr\u003e\u003ci\u003eAmir H. Gandomi, Ali Emrouznejad, and Iman Rahimi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems: A Detailed Analysis 11\u003cbr\u003e\u003ci\u003eP. Deepalakshmi and K. Shankar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Advanced Ant Colony Optimization in Healthcare Scheduling 37\u003cbr\u003e\u003ci\u003eReza Behmanesh, Iman Rahimi, Mostafa Zandieh, and Amir H. Gandomi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence 73\u003cbr\u003e\u003ci\u003eS. Sarathambekai and K. Umamaheswari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization 105\u003cbr\u003e\u003ci\u003ePrabina Pattanayak and Preetam Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks’ Departure 137\u003cbr\u003e\u003ci\u003eAmir Mohammad Fathollahi-Fard, Abbas Ahmadi, and Mohsen S. Sajadieh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Application of Sub-Population Scheduling Algorithm in Multi-Population Evolutionary Dynamic Optimization 169\u003cbr\u003e\u003ci\u003eJavidan Kazemi Kordestani and Mohammad Reza Meybodi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Task Scheduling in Cloud Environments: A Survey of Population-Based Evolutionary Algorithms 213\u003cbr\u003e\u003ci\u003eFahimeh Ramezani, Mohsen Naderpour, Javid Taheri, Jack Romanous, and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms 257\u003cbr\u003e\u003ci\u003eJiayi Liu, Wenjun Xu, Zude Zhou, and Duc Truong Pham\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies 299\u003cbr\u003e\u003ci\u003eMohammad Sadegh Javadi, Ali Esmaeel Nezhad, Seyed‐Ehsan Razavi, Abdollah Ahmadi, and João P.S. Catalão\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAMIR H. GANDOMI, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Professor of Data Science at University of Technology Sydney, Australia. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eALI EMROUZNEJAD, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Professor and Chair of Business Analytics at Aston University, UK. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMO M. JAMSHIDI, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Lutcher Brown Endowed Chair and Professor of Electrical and Computer Engineering at the University of Texas at San Antonio, USA. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKALYANMOY DEB, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Koenig Endowed Chair and Professor of Electrical and Computer Engineering at Michigan State University, USA. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eIMAN RAHIMI, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a member of the Young Researchers and Elite Club, Isfahan (Khorasgan) Branch at Islamic Azad University, Iran.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePresents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEvolutionary Computation in Scheduling\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides a representative sampling of real-world problems currently being tackled by practitioners\u003c\/li\u003e \u003cli\u003eExamines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence\u003c\/li\u003e \u003cli\u003eConsists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eEvolutionary Computation in Scheduling\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989179220197,"sku":"NP9781119573845","price":134.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119573845.jpg?v=1761783108","url":"https:\/\/k12savings.com\/es\/products\/evolutionary-computation-in-scheduling-isbn-9781119573845","provider":"K12savings","version":"1.0","type":"link"}