Multi-Objective Combinatorial Optimization Problems and Solution Methods

Front cover
Half title
Title
Copyright
Dedication
Contents
Contributors
Editors Biography
Preface
Acknowledgments
Chapter
1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps
	1.1 Introduction
	1.2 Methodology
	1.3 Data and basic statistics
	1.4 Results and discussion
		1.4.1 Mapping the cognitive space
		1.4.2 Mapping the social space
	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
	2.1 Introduction
	2.2 Multiobjective combinatorial optimization
	2.3 Heuristics concepts
	2.4 Metaheuristics concepts
	2.5 Heuristics and metaheuristics examples
		2.5.1 Tabu search
	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
	3.1 Introduction
	3.2 Preliminary discussion
		3.2.1 Multiple objective decision making
		3.2.2 Data envelopment analysis
	3.3 Application of MODM concepts in the DEA methodology
		3.3.1 Classical DEA models
		3.3.2 Target setting
		3.3.3 Value efficiency
		3.3.4 Secondary goal models
		3.3.5 Common set of weights
		3.3.6 DEA-discriminant analysis
		3.3.7 Efficient units and efficient hyperplanes
	3.4 Classification of usage of DEA in MODM
		3.4.1 Efficient points
	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
	4.1 Introduction
	4.2 Materials and methods
		4.2.1 Crow search optimization
		4.2.2 Arithmetic crossover based on genetic algorithm
		4.2.3 Hybrid CO algorithm
	4.3 Results and discussion
	4.4 Conclusion
	Acknowledgments
	References
Chapter
5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm
	5.1 Introduction
		5.1.1 Definition of multiobjective problems
(MOPs)
		5.1.2 Literature review
		5.1.3 Background and related work
	5.2 GROM and MOGROM
		5.2.1 MOGROM
	5.3 Simulation results, investigation, and analysis
		5.3.1 First class
		5.3.2 Second class
		5.3.3 Third class
		5.3.4 Fourth class
		5.3.5 Fifth class
	5.4 Conclusion
	References
Chapter
6 Multiobjective charged system search for optimum location of bank branch
	6.1 Introduction
	6.2 Multiobjective backgrounds
		6.2.1 Dominance and Pareto Front
		6.2.2 Performance metrics
		6.2.2.2 Coverage of Two Sets
(CS)
	6.3 Utilized methods
		6.3.1 NSGA-II algorithm
		6.3.2 MOPSO algorithm
		6.3.3 MOCSS algorithm
	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
	7.1 Introduction
	7.2 Systems description
		7.2.1 Downdraft gasifier
		7.2.2 Waste-to-energy plant
	7.3 Modeling
	7.4 Multicriteria Gray Wolf Optimization
	7.5 Results and discussion
		7.5.1 Optimization at the gasifier level
		7.5.2 Optimization at the WtEP Level
	References
Chapter
8 A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning
	8.1 Introduction
	8.2 Problem formulation
		8.2.1 Master problem
		8.2.2 Slave problem
		8.2.3 TC assessment objective of the MMDGTEP problem
		8.2.4 EENSHL-II evaluation procedure of the MMDGTEP problem
	8.3 Multiobjective optimization principle
	8.4 Nondominated sorting genetic algorithm-II
		8.4.1 Computational flow of NSGA-II
		8.4.2 VDS-NSGA-II
		8.4.3 Methodology
		8.4.4 VIKOR decision making
	8.5 Simulation results
	8.6 Conclusion
	Acknowledgment
	References
Chapter
9 A multiobjective Cuckoo Search Algorithm for community detection in social networks
	9.1 Introduction
	9.2 Related works
	9.3 Proposed model
		9.3.1 Community diagnosis
		9.3.2 Multiobjective optimization
		9.3.3 CD based on MOCSA
		9.3.4 Fitness function
	9.4 Evaluation and results
	9.5 Conclusion and future works
	References
Chapter
10 Finding efficient solutions of the multicriteria assignment problem
	10.1 Introduction
	10.2 The basic AP
	10.3 Restated MCAP and DEA: models and relationship
		10.3.1 The multicriteria assignment problem
(MCAP)
		10.3.2 Data envelopment analysis
		10.3.3 An integrated DEA and MCAP
	10.4 Finding efficient solutions using DEA
		10.4.1 The two-phase algorithm
		10.4.2 The proposed algorithm
	10.5 Numerical examples
	10.6 Conclusion
	Acknowledgments
	References
Chapter
11 Application of multiobjective optimization in thermal design and analysis of complex energy systems
	11.1 Introduction
		11.1.1 System boundaries
		11.1.2 Optimization criteria
		11.1.3 Variables
		11.1.4 The mathematical model
		11.1.5 Suboptimization
	11.2 Types of optimization problems
		11.2.1 Single-objective optimization
		11.2.2 Multiobjective optimization
	11.3 Optimization of energy systems
		11.3.1 Thermodynamic optimization and economic optimization
		11.3.2 Thermoeconomic optimization
	11.4 Literature survey on the optimization of complex energy systems
	11.5 Thermodynamic modeling of energy systems
		11.5.1 Mass balance
		11.5.2 Energy balance
		11.5.3 Entropy balance
		11.5.4 Exergy balance
		11.5.5 Energy efficiency
		11.5.6 Exergy efficiency
	11.6 Thermoeconomics methodology for optimization of energy systems
		11.6.1 The SPECO method
		11.6.2 The F (fuel)
and P (product) rules
	11.7 Sensitivity analysis of energy systems
	11.8 Example of application
(case study)
		11.8.1 Integrated biomass trigeneration system
		11.8.2 Results and discussion
		11.8.3 Sensitivity analysis
	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
	12.1 Introduction
	12.2 Tourism in Egypt: an overview
		12.2.1 Tourism in Egypt
		12.2.2 Tourism in Cairo
		12.2.3 Planning of tour visits
	12.3 PTP versus both the TSP and KP
		12.3.1 The Traveling Salesman Problem and its variations
		12.3.2 Multiobjective 0–1 KP
		12.3.3 Basic differences between PTP and both the TSP and KP
	12.4 Mathematical model for planning of tour visits
	12.5 A real application case study
		12.5.1 Ramses Hilton Hotel
	12.6 Proposed methodology
		12.6.1 Gaining Sharing Knowledge-based optimization algorithm 
(GSK)
		12.6.2 Binary Gaining Sharing Knowledge-based optimization algorithm
(BGSK)
	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
	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
Back cover
Blank Page
Blank Page
Blank Page
Blank Page