Evolutionary Computation in Scheduling

Amir H. Gandomi, Ali Emrouznejad, Mo M. Jamshidi, Kalyanmoy Deb, Iman Rahimi, Amir H. Gandomi, Ali Emrouznejad, Mo M. Jamshidi, Kalyanmoy Deb, Iman Rahimi

• Publisher: Wiley
• Year: 2020
• Print ISBN: 9781119573845
• eBook ISBN: 9781119573876
• Edition: 1
• Topic: Mathematics
• Subtopic: Operations
• Index: Mathematics

Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems

This 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 Problems

Evolutionary 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.

1
Evolutionary Computation in Scheduling: A Scientometric Analysis

Amir H. Gandomi¹, Ali Emrouznejad², and Iman Rahimi³

¹ Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

² Aston Business School, Aston University, Birmingham, UK

³ Young Researchers and Elite Club, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

1.1 Introduction

Evolutionary computation (EC) is known as a powerful tool for global optimization‐inspired nature. Technically, EC is also known as a family of population‐based algorithms which could be addressed as metaheuristic or stochastic optimization approaches. The term “stochastic” is used because of the nature of these algorithms, such that a primary set of potential solutions (initial population) is produced and updated, iteratively. Another generation is made by eliminating the less desired solutions stochastically. Increasing the fitness function of the algorithm resulted from evolving the population. A metaheuristic term refers to the fact that these algorithms are defined as higher‐level procedures or heuristics considered to discover, produce, or choose a heuristic which is an adequately good solution for an optimization problem [1, 2]. Applications of metaheuristics can be found in the literature, largely [3–9]. Swarm intelligence algorithms are also a family of EC, based on a population of simple agents which are interacting with each other in an environment. The inspiration for these algorithms often comes from nature, while these algorithms behave stochastically and the agents possess a high level of intelligence as a colony. The most common used algorithms reported in literature are: particle swarm optimization, ant colony optimization, the Bees algorithm, and the artificial fish swarm algorithm [10–15].

Scheduling and planning problems are generally complex, large‐scale, challenging issues, and involve several constraints [16–19]. To find a real solution, most real‐world problems must be formulated as discrete or mixed‐variable optimization problems [16, 20]. Moreover, finding efficient and lower‐cost procedures for frequent use of the system is crucially important. Although several solutions are suggested to solve the problems mentioned above, there is still a severe need for more cost‐effective methods. As a result of their complexity, real‐world scheduling problems are challenging to solve using derivative‐based and local optimization algorithms. A possible solution to cope with this limitation is to use global optimization algorithms, such as EC techniques [21]. Lately, EC and its branches have been used to solve large, complex real‐world problems which cannot be solved using classical methods [22–24]. Another critical problem is that several aspects can be considered to optimize systems simultaneously, such as time, cost, quality, risk, and efficiency. Therefore, several objectives should usually be considered for optimizing a real‐world scheduling problem.

This is while there are usually conflicts between the considered objectives, such as cost‐quality, cost‐efficiency, and quality‐cost‐time. In this case, the multi‐objective optimization concept offers key advantages over the traditional mathematical algorithms. In particular, evolutionary multi‐objective computations (EMC) is known as a reliable way to handle these problems in the industrial domain [22,25–27].

With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. The spectrum of real‐w...

Table of contents

• Cover
• Table of Contents
• List of Contributors
• Editors’ Biographies
• Preface
• Acknowledgments
• 1 Evolutionary Computation in Scheduling
• 2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems
• 3 Advanced Ant Colony Optimization in Healthcare Scheduling
• 4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence
• 5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization
• 6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure
• 7 Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic Optimization
• 8 Task Scheduling in Cloud Environments
• 9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms
• 10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies
• Index
• End User License Agreement