Book Title: Evolutionary Computation in Scheduling
- Amir H. Gandomi, Assistant Professor of Analytics & Information Systems, Stevens Institute of Technology, NJ, USA, email: email@example.com
- Ali Emrouznejad, Professor and Chair of Business Analytics, Aston University, UK, email: firstname.lastname@example.org
- Mo M Jamshidi, Lutcher Brown Endowed Chair and Professor of Electrical and Computer Engineering, University of Texas at San Antonio, USA, email: email@example.com
- Kalyanmoy Deb, Koenig Endowed Chair and Professor of Electrical and Computer Engineering, Michigan State University, MI, USA, email: firstname.lastname@example.org
- Iman Rahimi, Young Researchers and Elite Club, Iran, email: email@example.com
Scheduling and planning problems are generally complex, large-scale, challenging issues, and involve several constraints. To find a practical solution for these problems, most real-world problems must be formulated as discrete or mixed variable optimization problems. Moreover, finding efficient and lower cost procedures for frequent use of the system is crucially important. Although several solutions are proposed to solve the issues mentioned above, there is still a serious need for more cost-effective approaches. By cause of their complexity, the real-world scheduling problems are difficult to solve using derivative-based and local optimization algorithm. A viable solution to cope with this limitation is to employ global optimization algorithms, such as the EC techniques. Lately, EC and its branches have been used to solve large, complex real-world problems which cannot be solved using classical methods. Another critical problem is that several aspects can be considered to optimize systems simultaneously such as time, cost, quality, risk, efficiency, etc. 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 is known as a reliable way to handle these problems in the industrial domain. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques.
Aim of the Book:
This book intends to show a diversity of single, multi, and many-objective scheduling problems that have been solved using evolutionary computations including evolutionary algorithms and swarm intelligence in the following topics, but are not limited to:
- Evolutionary approaches to Big data scheduling problems
- Scheduling of healthcare organizations
- Scheduling and timetable aircraft industry
- School timetabling problem
- Scheduling in manufacturing and industry systems
- Scheduling and logistics system
We highly encourage submission of reviews and survey chapters as the book is intended to be a good reference on its topic for students and scholars as follows:
- Introduction and computational issues to Scheduling problems
- Evolutionary Computation techniques include single, multi, and many objectives (state of the art).
Your proposal should include the following:
Chapter proposal: 200-500 words.
List of contributors with affiliations.
Your estimate of the total number of printed pages per chapter.
Your estimate of the total number of figures and tables.
Selection process and timeline
Since timeliness is crucial to the success of this editorial project, we would assume the following schedule:
- Chapter proposals Nov 30, 2018 (Extended)
- Decisions from editors Dec 15, 2018
- Full submission of chapters Feb 15, 2019
- Feedback of reviews Apr 15, 2019
- Revised chapter submission May 15, 2019
- Final decision notifications Jun 15, 2019
To submit your proposal, please click HERE