Multi-Objective Combinatorial Optimization Problems and Solution Methods

1st Edition - February 9, 2022
Editors: Mehdi Toloo, Siamak Talatahari, Iman Rahimi
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 7 9 9 - 1
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 8 0 0 - 4

Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considere… Read more

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Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considered metaheuristic, mathematical programming, heuristic, hyper heuristic and hybrid approaches. In other words, the book presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice. Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science, hence many optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic and algebraic techniques.

This book covers this important topic as computational optimization has become increasingly popular as design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice.

Cover Image
Title Page
Copyright
Dedication
Table of Contents
Contributors
Editors Biography
Preface
Acknowledgments
Chapter 1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps
Abstract
1.1 Introduction
1.2 Methodology
1.3 Data and basic statistics
1.4 Results and discussion
1.5 Conclusions and direction for future research
References
Chapter 2 The fundamentals and potential of heuristics and metaheuristics for multiobjective combinatorial optimization problems and solution methods
Abstract
2.1 Introduction
2.2 Multiobjective combinatorial optimization
2.3 Heuristics concepts
2.4 Metaheuristics concepts
2.5 Heuristics and metaheuristics examples
2.6 Evolutionary algorithms (EA)
2.7 Genetic algorithms (GA)
2.8 Simulated annealing
2.9 Particle swarm optimization (PSO)
2.10 Scatter search (SS)
2.11 Greedy randomized adaptive search procedures (GRASP)
2.12 Ant-colony optimization
2.13 Clustering search
2.14 Hybrid metaheuristics
2.15 Differential evolution (DE)
2.16 Teaching learning–based optimization (TLBO)
2.17 Discussion
2.18 Conclusions
2.19 Future trends
References
Chapter 3 A survey on links between multiple objective decision making and data envelopment analysis
Abstract
3.1 Introduction
3.2 Preliminary discussion
3.3 Application of MODM concepts in the DEA methodology
3.4 Classification of usage of DEA in MODM
3.5 Discussion and conclusion
References
Chapter 4 Improved crow search algorithm based on arithmetic crossover—a novel metaheuristic technique for solving engineering optimization problems
Abstract
4.1 Introduction
4.2 Materials and methods
4.3 Results and discussion
4.4 Conclusion
Acknowledgments
References
Chapter 5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm
Abstract
5.1 Introduction
5.2 GROM and MOGROM
5.3 Simulation results, investigation, and analysis
5.4 Conclusion
References
Chapter 6 Multiobjective charged system search for optimum location of bank branch
Abstract
6.1 Introduction
6.2 Multiobjective backgrounds
6.3 Utilized methods
6.4 Analytic Hierarchy Process
6.5 Model formulation
6.6 Implementation and results
6.7 Conclusions
References
Chapter 7 Application of multiobjective Gray Wolf Optimization in gasification-based problems
Abstract
7.1 Introduction
7.2 Systems description
7.3 Modeling
7.4 Multicriteria Gray Wolf Optimization
7.5 Results and discussion
References
Chapter 8 A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning
Abstract
8.1 Introduction
8.2 Problem formulation
8.3 Multiobjective optimization principle
8.4 Nondominated sorting genetic algorithm-II
8.5 Simulation results
8.6 Conclusion
Acknowledgment
References
Chapter 9 A multiobjective Cuckoo Search Algorithm for community detection in social networks
Abstract
9.1 Introduction
9.2 Related works
9.3 Proposed model
9.4 Evaluation and results
9.5 Conclusion and future works
References
Chapter 10 Finding efficient solutions of the multicriteria assignment problem
Abstract
10.1 Introduction
10.2 The basic AP
10.3 Restated MCAP and DEA: models and relationship
10.4 Finding efficient solutions using DEA
10.5 Numerical examples
10.6 Conclusion
Acknowledgments
References
Chapter 11 Application of multiobjective optimization in thermal design and analysis of complex energy systems
Abstract
11.1 Introduction
11.2 Types of optimization problems
11.3 Optimization of energy systems
11.4 Literature survey on the optimization of complex energy systems
11.5 Thermodynamic modeling of energy systems
11.6 Thermoeconomics methodology for optimization of energy systems
11.7 Sensitivity analysis of energy systems
11.8 Example of application (case study)
11.9 Conclusions
References
Chapter 12 A multiobjective nonlinear combinatorial model for improved planning of tour visits using a novel binary gaining-sharing knowledge- based optimization algorithm
Abstract
12.1 Introduction
12.2 Tourism in Egypt: an overview
12.2 Tourism in Cairo
12.3 PTP versus both the TSP and KP
12.4 Mathematical model for planning of tour visits
12.5 A real application case study
12.6 Proposed methodology
12.7 Experimental results
12.8 Conclusions and points for future studies
References
Chapter 13 Variables clustering method to enable planning of large supply chains
Abstract
13.1 Introduction
13.2 SCP at a glance
13.3 SCP instances as MOCO models
13.4 Orders clustering for mix-planning
13.5 Variables clustering for the general SCP paradigm
13.6 Conclusions
References
Index

Mehdi Toloo

Dr. Mehdi Toloo is a Full Professor in the Faculty of Economics, Technical University of Ostrava, and Faculty of Business Administration, University of Economics, Prague, Czech Republic. He received his Masters of Science in Applied Mathematics and his Ph.D. in Operations Research. Dr. Toloo’s areas of interest include Operations Research, Decision Analysis, Performance Evaluation, Multi-Objective Programming, and Mathematical Modelling. He has contributed to numerous international conferences as a chair, keynote speaker, and member of the scientific committee. He is an area editor for the Elsevier journal Computers and Industrial Engineering and an associate editor for RAIRO-Operations Research. His publications include the book Introduction to Scientific Computing: 100 Problems and Solutions in Pascal and papers in top-tier journals such as Applied Mathematics and Computers, Applied Mathematic Modeling, Expert Systems with Applications, and Computers and Mathematics with Applications.

Affiliations and expertise

Department of Business Transformation, Surrey Business School, University of Surrey, Guildford GU2 7XH, United Kingdom. Department of Systems Engineering, Faculty of Economics, Technical University of Ostrava, Ostrava, Czech Republic Department of Operations Management & Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman

Siamak Talatahari

Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author Xin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm; Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures and Infrastructures, all published by as Insights by Elsevier.

Affiliations and expertise

Department of Civil Engineering, University of Tabriz, Tabriz, Iran. School of Civil and Environment Engineering, University of New South Wales, Sydney, Australia.

Iman Rahimi

Iman Rahimi, PhD , earned his BSc (Applied Mathematics) in 2009, MSc (Applied Mathematics – Operations Research) in 2011 and his PhD in the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia, in 2017. He is now working at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, as a research scholar. His research interests include machine learning and multiobjective optimization. He also has edited three books entitled "Evolutionary Computation in Scheduling", "Big Data Analytics in Supply Chain Management: Theory and Applications", and "Multi-Objective Combinatorial Optimization Problems and Solution Methods" with Wiley, CRC Press (Taylor & Francis Group), and Elsevier, respectively. He has served as an editor for the following journals: International Journal of Renewable Energy Technology (IJRET) and International Journal of Advanced Heuristic and Meta-Heuristic Algorithms. Besides, he has acted as a reviewer for "International Journal of Production Research (Q1 JRC ranking)" and "Research in Transportation Business & Management (Q3 JCR ranking)". He also received several awards namely, research grants from "University of Tabriz (Iran)", "Iran National Science Foundation", "International Research Scholarship (Australia)", "Faculty of Engineering and Information Technology Scholarship (Australia)", and "Nicolas Baudin Scholarship" from Embassy of France in Australia.

Affiliations and expertise

University of Technology Sydney, Sydney, Australia.