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• 1. Multi-objective combinatorial optimization problems: Social, Keywords, and Journal maps Mehdi Toloo, Siamak Talatahari, Iman Rahimi and Amir H. Gandomi
• 2. The Fundamentals and Potential of Heuristics and Metaheuristics for Multi-Objective Combinatorial Optimization Problems and Solution Methods Ana Carolina Borges Monteiro, Reinaldo Padilha Franca, Rangel Arthur, Yuzo Iano and Reinaldo Padilha Franca
• 3. A survey on links between multiple objective decision making and data envelopment analysis Amineh Ghazi and Farhad Hosseinzadeh Lotfi II. New methods for combinatorial optimization problems
• 4. Improved Crow Search Algorithm Based on Arithmetic Cross Over- A Novel Metaheuristic Technique for Solving Engineering Optimization Problems S N Kumar, A Lenin Fred, R. Jonisha Miriam, Padmanabhan Parasuraman, Balazs Gulyas, Ajay Kumar Haridhas and Nisha Dayana
• 5. MOGROM: Multi-objective Golden Ratio Optimization Algorithm Behrooz Vahidi, Amin Foroughi and Abolfazl Rahiminejad III. Application of random-based methods for combinatorial optimization problems
• 6. Multi-Objective Charged System Search for Optimum Location of Bank Branch Siamak Talatahari
• 7. Application of Multi-objective Grey Wolf Optimization in Gasification-based Problems Siamak Talatahari
• 8. A VDS-NSGA-II Algorithm for Multi-Year Multi-Objective Dynamic Generation and Transmission Expansion Planning Ali Esmaeel Nezhad
• 9. A Multi-Objective Cuckoo Search Algorithm for Community Detection in Social Networks Farhad Soleimanian Gharehchopogh and Shafih Ghafori IV. Application of other methods for combinatorial optimization problems
• 10. Finding efficient solutions of the multi-criteria assignment problem Emmanuel Kwasi Mensah, Esmaeil Keshavarz and Mehdi Toloo
• 11. Application of Multi-objective Optimization in Thermal Design and Analysis of Complex Energy Systems Ali Baghernejad and Elnaz Aslanzadeh
• 12. A Multi-Objective Nonlinear Combinatorial Model for Improved Planning of Tour Visits Using a Novel Binary Gaining-Sharing knowledge-based Optimization Algorithm Ali Wagdy wagdy, Said Hassan, Prachi Agrawal and Talari Ganesh
• 13. Variables Clustering Method to Enable Planning of Large Supply Chains Emilio Bertolotti Sr.
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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.
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• A role of minimum spanning tree.- Discrete Newton method.- An overview of submodular optimization: single- and multi-objectives.- Discrete convex optimization and applications in supply chain management.- Thresholding methods for streaming submodular maximization with a cardinality constraint and its variants.- Nonsubmodular optimization.- On block-structured integer programming and its applications.- Online combinatorial optimization problems with nonlinear objectives.- Solving combinatorial problems with machine learning methods.- Modeling malware propagation dynamics and developing prevention method in wireless sensor networks.- Composed influence in social networks.- Friending.- Optimization on content spread in social network studies.- Interation-aware influence maximization in social networks.- Multi-document extractive summarization as a nonlinear combinatorial optimization- Viral marketing for complementary products.
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Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
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• Decomposition for Multi-Period Network Design
• Solving Network Design Problems via Aggregation
• Approximate Second-Order Cone Robust Optimization.

Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time. Contents Decomposition for Multi-Period Network Design Solving Network Design Problems via Aggregation Approximate Second-Order Cone Robust Optimization Target Groups Researchers, teachers and students in mathematical optimization and operations research Network planners in the field of logistics and beyond < About the Author Dr. Andreas Bärmann is currently working as a postdoctoral researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) at the chair of Economics, Discrete Optimization and Mathematics. His research is focussed on mathematical optimization, especially the optimization of logistic processes.

• Preface - Data Correcting Algorithms in Combinatorial Optimization - The Steiner Ratio of Banach-Minkowski Space - Probabilistic Verification and Non-Approximability - Steiner Trees in Industry - Network-Based Model and Algorithms in Data Mining and Knowledge Discovery - The Generalized Assignment Problem and Extensions - Optimal Rectangular Partitions - Connected Dominating Sets in Sensor Networks and MANETs - Author Index - Subject Index.
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This is a supplementary volume to the major three-volume Handbook of Combinatorial Optimization set. It can also be regarded as a stand-alone volume presenting chapters dealing with various aspects of the subject in a self-contained way.
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• Motivation for Differential Evolution for Permutative-Based Combinatorial Problems.- Differential Evolution for Permutation-Based Combinatorial Problems.- Forward Backward Transformation.- Relative Position Indexing Approach.- Smallest Position Value Approach.- Discrete/Binary Approach.- Discrete Set Handling.
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What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both ?nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem's feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de?ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with ?oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi?ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem's goal is to pack objects of differing weight and value so that the knapsack's total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies ?oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.
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• Preface.- Introduction.- Graphs.- Linear Programming.- Linear Programming Algorithms.- Integer Programming.- Spanning Trees and Arborescences.- Shortest Paths.- Network Flows.- Minimum Cost Flows.- Maximum Matchings.- Weighted Matching.- b-Matchings and T-Joins.- Matroids.- Generalizations of Matroids.- NP-Completeness.- Approximation Algorithms.- The Knapsack Problem.- Bin Packing.- Multicommodity Flows and Edge-Disjoint Paths.- Network Design Problems.- The Traveling Salesman Problem.- Facility Location.- Notation Index.- Author Index.- Subject Index.
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This comprehensive textbook on combinatorial optimization puts special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It has arisen as the basis of several courses on combinatorial optimization and more special topics at graduate level. Since the complete book contains enough material for at least four semesters (4 hours a week), one usually selects material in a suitable way. The book contains complete but concise proofs, also for many deep results, some of which did not appear in a book before. Many very recent topics are covered as well, and many references are provided. Thus this book represents the state of the art of combinatorial optimization. This third edition contains a new chapter on facility location problems, an area which has been extremely active in the past few years. Furthermore there are several new sections and further material on various topics. New exercises and updates in the bibliography were added.From the reviews of the 2nd edition:"This book on combinatorial optimization is a beautiful example of the ideal textbook."- "Operations Resarch Letters 33" (2005), p.2 16-217. "The second edition (with corrections and many updates) of this very recommendable book documents the relevant knowledge on combinatorial optimization and records those problems and algorithms that define this discipline today. To read this is very stimulating for all the researchers, practitioners, and students interested in combinatorial optimization." - "OR News 19" (2003), p.42.
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• A Critical Element-Guided Perturbation Strategy for Iterated Local Search.- A Genetic Algorithm for Net Present Value Maximization for Resource Constrained Projects.- A Hybrid Algorithm for Computing Tours in a Spare Parts Warehouse.- A New Binary Description of the Blocks Relocation Problem and Benefits in a Look Ahead Heuristic.- A Plasmid Based Transgenetic Algorithm for the Biobjective Minimum Spanning Tree Problem.- A Tabu Search Algorithm with Direct Representation for Strip Packing.- An ACO Approach to Planning.- An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem.- Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows.- Binary Exponential Back Off for Tabu Tenure in Hyperheuristics.- Diversity Control and Multi-Parent Recombination for Evolutionary Graph Coloring Algorithms.- Divide-And-Evolve Facing State-of-the-Art Temporal Planners during the 6 th International Planning Competition.- Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm.- Finding Balanced Incomplete Block Designs with Metaheuristics.- Guided Ejection Search for the Job Shop Scheduling Problem.- Improving Performance in Combinatorial Optimisation Using Averaging and Clustering.- Iterated Local Search for Minimum Power Symmetric Connectivity in Wireless Networks.- Metropolis and Symmetric Functions: A Swan Song.- Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks.- Staff Scheduling with Particle Swarm Optimisation and Evolution Strategies.- University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study.
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Metaheuristics have been shown to be e?ective for di?cult combinatorial op- mization problems appearing in a wide variety of industrial, economic, and sci- ti?c domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization, and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satis?ability, and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the ?rst event speci?cally dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current - velopments and applications. Following the general trend of hybrid metaheur- tics and diminishing boundaries between the di?erent classes of metaheuristics, EvoCOP has broadened its scope in recent years and invited submissions on any kind of metaheuristic for combinatorial optimization.
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• 1 Introduction.- 2 Heuristic Methods.- 3 Meta-Heuristics.- 4 Branch-and-Bound.- 5 Branch-and-Cut.- 6 The Linear Ordering Polytope.- 7 Further Aspects.- References.- Index.
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In the last decades, algorithmic advances as well as hardware and software improvements have provided an excellent environment to create and develop solving methods to hard optimization problems. Modern exact and heuristic techniques are dramatically enhancing our ability to solve significant practical problems. This monograph sets out state-of-the-art methodologies for solving combinatorial optimization problems, illustrating them with two well-known problems. This second edition of the book extends the first one by adding to the 'linear ordering problem' (LOP), included in the first edition, the 'maximum diversity problem' (MDP). In this way, we provide the reader with the background, elements and strategies to tackle a wide range of different combinatorial optimization problems. The exact and heuristic techniques outlined in these pages can be put to use in any number of combinatorial optimization problems. While the authors employ the LOP and the MDP to illustrate cutting-edge optimization technologies, the book is also a tutorial on how to design effective and successful implementations of exact and heuristic procedures alike. This monograph provides the basic principles and fundamental ideas that will enable students and practitioners to create valuable applications based on both exact and heuristic technologies. Specifically, it is aimed at engineers, scientists, operations researchers, and other applications specialists who are looking for the most appropriate and recent optimization tools to solve particular problems. The book provides a broad spectrum of advances in search strategies with a focus on its algorithmic and computational aspects.
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• Introduction.- Circuits Without Repetitions. Main Tools.- Combinatorial Optimization Problems. Conventional Circuits.- Multi-Stage and Bi-Criteria Optimization of Matchings in Trees.- Combinatorial Optimization Problems. Syntactical Circuits.- Index.
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This book introduces a fairly universal approach to the design and analysis of exact optimization algorithms for multi-objective combinatorial optimization problems. It proposes the circuits without repetitions representing the sets of feasible solutions along with the increasing and strictly increasing cost functions as a model for such problems. The book designs the algorithms for multi-stage and bi-criteria optimization and for counting the solutions in the framework of this model. As applications, this book studies eleven known combinatorial optimization problems: matrix chain multiplication, global sequence alignment, optimal paths in directed graphs, binary search trees, convex polygon triangulation, line breaking (text justification), one-dimensional clustering, optimal bitonic tour, segmented least squares, optimization of matchings in trees, and 0/1 knapsack problem. The results presented are useful for researchers in combinatorial optimization. This book is also useful as the basis for graduate courses.
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• Combinatorial optimization space.- Solving COPs.- From CS to DCS.- DCS and the studied COPs.- Cuckoo search Random key encoding.
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This book provides a literature review of techniques used to pass from continuous to combinatorial space, before discussing a detailed example with individual steps of how cuckoo search (CS) can be adapted to solve combinatorial optimization problems. It demonstrates the application of CS to three different problems and describes their source code. The content is divided into five chapters, the first of which provides a technical description, together with examples of combinatorial search spaces. The second chapter summarizes a diverse range of methods used to solve combinatorial optimization problems. In turn, the third chapter presents a description of CS, its formulation and characteristics. In the fourth chapter, the application of discrete cuckoo search (DCS) to solve three POCs (the traveling salesman problem, quadratic assignment problem and job shop scheduling problem) is explained, focusing mainly on a reinterpretation of the terminology used in CS and its source of inspiration. In closing, the fifth chapter discusses random-key cuckoo search (RKCS) using random keys to represent positions found by cuckoo search in the TSP and QAP solution space.
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• Preface.
• 1 Introduction. 1.1 Two examples of combinatorial optimization. 1.2 Why study combinatorial optimization using statistical physics? 1.3 Textbooks. Bibliography.
• 2 Algorithms. 2.1 Pidgin Algol. 2.2 Iteration and recursion. 2.3 Divide and conquer. 2.4 Dynamic programming. 2.5 Backtracking. Bibliography.
• 3 Introduction to graphs. 3.1 Basic concepts and graph problems. 3.2 Basic graph algorithms. 3.3 Random graphs. Bibliography.
• 4 Introduction to complexity theory. 4.1 Turing machines. 4.2 Church's thesis. 4.3 Languages. 4.4 The halting problem. 4.5 Class P. 4.6 Class NP. 4.7 Definition of NP completeness. 4.8 NP complete problems. 4.9 Worst case vs. typical case complexity. Bibliography.
• 5 Statistical mechanics of the Ising model. 5.1 Phase transitions. 5.2 Some general notes on statistical mechanics. 5.3 The Curie Weiss model of a ferromagnet. 5.4 The Ising model on a random graph. Bibliography.
• 6 Algorithms and numerical results for vertex covers. 6.1 Definitions. 6.2 Heuristic algorithms. 6.3 Branch and bound algorithm. 6.4 Results: Covering random graphs. 6.5 The leaf removal algorithm. 6.6 Monte Carlo simulations. 6.7 Backbone. 6.8 Clustering of minimum vertex covers. Bibliography.
• 7 Statistical mechanics of vertex covers on a random graph. 7.1 Introduction. 7.2 The first moment bound. 7.3 The hard core lattice gas. 7.4 Replica approach. Bibliography.
• 8 The dynamics of vertex cover algorithms. 8.1 The typical case solution time of a complete algorithm. 8.2 The dynamics of generalized leaf removal algorithms. 8.3 Random restart algorithms. Bibliography.
• 9 Towards new, statistical mechanics motivated algorithms. 9.1 The cavity graph. 9.2 Warning propagation. 9.3 Belief propagation. 9.4 Survey propagation. 9.5 Numerical experiments on random graphs. Bibliography.
• 10 The satisfiability problem. 10.1 SAT algorithms. 10.2 Phase transitions in random K SAT. 10.3 Typical case dynamics of RandomWalkSAT. 10.4 Message passing algorithms for SAT. Bibliography.
• 11 Optimization problems in physics. 11.1 Monte Carlo optimization. 11.2 Hysteric optimization. 11.3 Genetic algorithms. 11.4 Shortest paths and polymers in random media. 11.5 Maximum flows and random field systems. 11.6 Submodular functions and free energy of Potts model. 11.7 Matchings and spin glasses. Bibliography. Index.
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This book offers a concise, comprehensive introduction to the topic of statistical physics of combinatorial optimization, bringing together theoretical concepts and algorithms from computer science with analytical methods from physics. The result bridges the gap between statistical physics and combinatorial optimization, investigating problems taken from theoretical computing, such as the vertex cover problem, with the concepts and methods of theoretical physics. The authors cover rapid developments and analytical methods that are both extremely complex and spread by word of mouth, providing all the necessary basics in required detail. Throughout, the algorithms are shown with examples and calculations, while the proofs are given in a way suitable for graduate students, post docs, and researchers. This book is ideal for newcomers to this young, multidisciplinary field.
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• Multi-Objective Combinatorial Optimization: Problematic and Context.- Approximating Pareto-Optimal Sets Using Diversity Strategies in EvolutionaryMulti-Objective Optimization.- On the Velocity Update in Multi-objective Particle Swarm Optimizers.- Approaching Dynamic Multi-objective Optimization Problems by Using Parallel Evolutionary Algorithms.- ParadisEO-MOEO: A Software Framework for Evolutionary Multi-objective Optimization.- The Multiobjective Traveling Salesman Problem: A Survey and a New Approach.- On the Performance of Local Search for the Biobjective Traveling Salesman Problem.- A Bi-objectiveMetaheuristic for Disaster Relief Operation Planning.
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The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research, engineering, and computer science, to do research in this area. As such, this book is expected to become a valuable reference for those wishing to do research on the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.
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• Global Optimization in Supply Chain Operations.- Solving Real-World Vehicle Routing Problems with Evolutionary Algorithms.- A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems.- An Estimation of Distribution Algorithm for Flowshop Scheduling with Limited Buffers.- Solving Hierarchically Decomposable Problems with the Evolutionary Transition Algorithm.- Electrical Load Forecasting using a Neural-Fuzzy Approach.- Quantised Problem Spaces and the Particle Swarm Algorithm.- A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks.- Ant Colony Optimization and Its Application to the Vehicle Routing Problem with Pickups and Deliveries.- Evolutionary and Ant Colony Optimization Based Approaches for Two-dimensional Strip Packing Problem.- Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization.- A Hybrid Intelligent System for Distributed Dynamic Scheduling.
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Scheduling, planning and packing are ubiquitous problems that can be found in a wide range of real-world settings. These problems transpire in a large variety of forms, and have enormous socio-economic impact. For many years, significant work has been devoted to automating the processes of scheduling, planning and packing using different kinds of methods. However, poor scaling and the lack of flexibility of many of the conventional methods coupled with the fact that most of the real-world problems across the application areas of scheduling, planning and packing nowadays tend to be of large scale, dynamic and full of complex dependencies have made it necessary to tackle them in unconventional ways. This volume, "Natural Intelligence for Scheduling, Planning and Packing Problems", is a collection of numerous natural intelligence based approaches for solving various kinds of scheduling, planning and packing problems. It comprises 12 chapters which present many methods that draw inspiration from nature, such as evolutionary algorithms, neural-fuzzy system, particle swarm algorithms, ant colony optimisation, extremal optimisation, raindrop optimisation, and so on. Problems addressed by these chapters include freight transportation, job shop scheduling, flowshop scheduling, electrical load forecasting, vehicle routing, two-dimensional strip packing, network configuration and forest planning, among others. Along with solving these problems, the contributing authors present a lively discussion of the various aspects of the nature-inspired algorithms utilised, providing very useful and important new insights into the research areas

• Background and State-of-the-Art.- Statement of the Tuning Problem.- F-Race for Tuning Metaheuristics.- Experiments and Applications.- Some Considerations on the Experimental Methodology.- Conclusions.
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Metaheuristics are a relatively new but already established approachto c- binatorial optimization. A metaheuristic is a generic algorithmic template that can be used for ?nding high quality solutions of hard combinatorial - timization problems. To arrive at a functioning algorithm, a metaheuristic needs to be con?gured: typically some modules need to be instantiated and someparametersneedto betuned.Icallthese twoproblems"structural"and "parametric" tuning, respectively. More generally, I refer to the combination of the two problems as "tuning". Tuning is crucial to metaheuristic optimization both in academic research andforpracticalapplications.Nevertheless, relativelylittle researchhasbeen devoted to the issue. This book shows that the problem of tuning a me- heuristic can be described and solved as a machine learning problem. Using the machine learning perspective, it is possible to give a formal de?nitionofthetuningproblemandtodevelopagenericalgorithmfortunin g metaheuristics.Moreover, fromthemachinelearningperspectiveitispossible tohighlightsome?awsinthecurrentresearchmethodologyandtostatesome guidelines for future empirical analysis in metaheuristics research. This book is based on my doctoral dissertation and contains results I have obtained starting from 2001 while working within the Metaheuristics Net- 1 work. During these years I have been a?liated with two research groups: INTELLEKTIK, Technische Universitat Darmstadt, Darmstadt, Germany and IRIDIA, Universite Libre de Bruxelles, Brussels, Belgium. I am the- fore grateful to the research directors of these two groups: Prof. Wolfgang Bibel, Dr. Thomas Stutzle, Prof. Philippe Smets, Prof. Hugues Bersini, and Prof. Marco Dorigo.
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• Part I Reviews of the Field.- Hyperheuristics: Recent Developments.- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation.- Part II New Techniques and Applications.- An Efficient Hyperheuristic for Strip-Packing Problems.- Probability-driven simulated annealing for optimizing digital FIR filters.- RASH: A Self-adaptive Random Search Method.- Market Based Allocation of Transportation Orders to Vehicles in Adaptive Multi-Objective Vehicle Routing.- A Simple Evolutionary Algorithm with Self-Adaptation for Multi-Objective Nurse Scheduling.- Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design.- Adaptive Estimation of Distribution Algorithms.- Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm.- Evolution of Descent Directions.- "Multiple Neighbourhood" Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods.- Automated Parameterisation of a Metaheuristic for the Orienteering Problem.
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One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself - endowing it with smart mechanisms to self-adapt to the problem - has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
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• Algorithms for Network Design.- Polynomial Approximation Schemes for the Max-Min Allocation Problem under a Grade of Service Provision.- A Linear Time Algorithm for Computing the Most Reliable Source on a Tree with Faulty Vertices.- A 5/3-Approximation Algorithm for Joint Replenishment with Deadlines.- A PTAS for Node-Weighted Steiner Tree in Unit Disk Graphs.- Bioinformatics.- DNA Library Screening, Pooling Design and Unitary Spaces.- Improved Algorithms for the Gene Team Problem.- Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting.- Combinatorics and Its Applications.- Generalized Russian Cards Problem.- Computing the Transitive Closure of a Union of Affine Integer Tuple Relations.- Matching Techniques Ride to Rescue OLED Displays.- Computational Geometry.- On Open Rectangle-of-Influence Drawings of Planar Graphs.- An Effective Hybrid Algorithm for the Circles and Spheres Packing Problems.- Variable-Size Rectangle Covering.- On-Line Multiple-Strip Packing.- Game Theory.- A Cost-Sharing Method for the Soft-Capacitated Economic Lot-Sizing Game.- Improved Bounds for Facility Location Games with Fair Cost Allocation.- Graph Algorithms.- Two-Level Heaps: A New Priority Queue Structure with Applications to the Single Source Shortest Path Problem.- On Construction of Almost-Ramanujan Graphs.- A 2log2(n)-Approximation Algorithm for Directed Tour Cover.- Approximation Algorithms for Max 3-Section Using Complex Semidefinite Programming Relaxation.- Graph Theory.- Hamiltonian Decomposition of Some Interconnection Networks.- Infinite Family from Each Vertex k-Critical Graph without Any Critical Edge.- A Note on Edge Choosability and Degeneracy of Planar Graphs.- A Sufficient and Necessary Condition for the Forcing Number of a Bipartite Graph Being Equal to the Minimum Number of Trailing Vertices.- On Integrity of Harary Graphs.- A Note on n-Critical Bipartite Graphs and Its Application.- Network Models and Problems.- Real-Time Algorithm Scheme for n-Vehicle Exploration Problem.- Deterministically Estimating Data Stream Frequencies.- Positive Influence Dominating Set in Online Social Networks.- On-line Algorithms.- Optimal Algorithms for the Online Time Series Search Problem.- A Risk-Reward Competitive Analysis for the Newsboy Problem with Range Information.- Optimal Semi-online Algorithm for Scheduling on a Batch Processing Machine.- A Note on Online Scheduling for Jobs with Arbitrary Release Times.- Size-Problems.- Size-Constrained Tree Partitioning: A Story on Approximation Algorithm Design for the Multicast k-Tree Routing Problem.- On Disjoint Shortest Paths Routing on the Hypercube.- A New Approach for Rearrangeable Multicast Switching Networks.- Scheduling.- Bicriteria Scheduling on Single-Machine with Inventory Operations.- Approximation Algorithm for Minimizing the Weighted Number of Tardy Jobs on a Batch Machine.- Scheduling with Rejection to Minimize the Makespan.- Scheduling Problems in Cross Docking.- Makespan Minimization with Machine Availability Constraints.- A Mathematical Programming Approach for Online Hierarchical Scheduling.- Recoverable Robust Timetables on Trees.- Roulette Wheel Graph Colouring for Solving Examination Timetabling Problems.- Integrated Production and Delivery Scheduling with Disjoint Windows.- Wireless and Optical Networks.- Fault-Tolerant Routing: k-Inconnected Many-to-One Routing in Wireless Networks.- A Branch-and-Cut Algorithm for the Minimum Energy Symmetric Connectivity Problem in Wireless Networks.- Minimum Energy Broadcast Routing in Ad Hoc and Sensor Networks with Directional Antennas.- Approximating the Multicast Traffic Grooming Problem in Unidirectional SONET/WDM Rings.- An Algorithm with Better Approximation Ratio for Multicast Traffic in Unidirectional SONET/WDM Rings.
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TheThirdAnnualInternationalConferenceonCombinatorialOptimizati onand Applications, COCOA2009, tookplace in Huangshan, China, June 10-12,2009. Past COCOA conferences were held in Xi'an, China (2007) and Newfoundland, Canada (2008). COCOA2009providedaforumforresearchersworkingintheareasofcom- natorial optimization and its applications. In addition to theoretical results, the conference is particularly focused on recent works on experimental and applied research of general algorithmic interest. The Program Committee received 103 submissions from 17 countries and regions: Brazil, Canada, China, Denmark, France, Germany, Hong Kong, India, Italy, Japan, Korea, Malaysia, Poland, Switzerland, Taiwan, UK, and USA. Among the 103 submissions, 50 papers were selected for presentation at the conference and are included in this volume. Some of these will be selected for publicationinaspecialissueofJournalof Combinatorial Optimization, aspecial issue of Theoretical Computer Science, and a special issue of Discrete Mat- matics, Algorithms and Applications under the standard refereeing procedure. In addition to the selected papers, the conference also included one invited p- sentation by Panos M. Pardalos (University of Florida, USA). We thank the authors for submitting their papers to the conference. We are grateful to the members of the Program Committee and the external referees for their work within demanding time constraints. We thank the Organizing Committee for their contribution to make the conference a success. We also thank Donghyun Kim for helping us create and update the conference website and maintain the Springer Online Conference Service system.
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• One: Techniques.- 0 Introduction to the Techniques of Combinatorial Optimization.- 0
• .1. The General Problem.- 0
• .2. Important Combinatorial Optimization Problems.- 0
• .3. The Fundamental Algorithm, Efficiency and the Digital Computer.- 1 Linear Programming and Extensions.- 1
• .1. An Introduction to Linear Programming.- 1
• .2. The Transportation Problem.- 1
• .3. The Assignment Problem.- 2 Solution Techniques.- 2
• .1. Integer Programming.- 2
• .2. Dynamic Programming.- 2
• .3. Complexity.- 2
• .4. Heuristic Problem Solving.- 3 Optimization on Graphs and Networks.- 3
• .1. Minimal Spanning Trees.- 3
• .2. Shortest Paths.- 3
• .3. The Maximum-Flow Problem.- 3
• .4. The Minimum-Cost-Flow Problem.- 3
• .5. Activity Networks.- Two: Applications.- 4 Some Applications of Combinatorial Optimization Techniques.- 4
• .1. Facilities Layout.- 4
• .2. The Traveling Salesman Problem.- 4
• .3. The Vehicle Scheduling Problem.- 4
• .4. Car Pooling.- 4
• .5. Evolutionary Tree Construction.- 5 Appendix.- 5
• .1. Linear Algebra.- 5
• .2. Graph Theory.- Further Reading.
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The major purpose of this book is to introduce the main concepts of discrete optimization problems which have a finite number of feasible solutions. Following common practice, we term this topic combinatorial optimization. There are now a number of excellent graduate-level textbooks on combina- torial optimization. However, there does not seem to exist an undergraduate text in this area. This book is designed to fill this need. The book is intended for undergraduates in mathematics, engineering, business, or the physical or social sciences. It may also be useful as a reference text for practising engineers and scientists. The writing of this book was inspired through the experience of the author in teaching the material to undergraduate students in operations research, engineering, business, and mathematics at the University of Canterbury, New Zealand. This experience has confirmed the suspicion that it is often wise to adopt the following approach when teaching material of the nature contained in this book. When introducing a new topic, begin with a numerical problem which the students can readily understand; develop a solution technique by using it on this problem; then go on to general problems. This philosophy has been adopted throughout the book. The emphasis is on plausibility and clarity rather than rigor, although rigorous arguments have been used when they contribute to the understanding of the mechanics of an algorithm.
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• Preface. Preface to the Second Editor. Part I.
• Chapter I. Introduction.
• Chapter II. Submodular Systems and Base Polyhedra.
• Chapter III. Neoflows.
• Chapter IV. Submodular Analysis.
• Chapter V. Nonlinear Optimizaation with Submodular Constraints. Part II.
• Chapter VI. Submodular Function Minimization.
• Chapter VII. Discrete Convex Analysis. References. Index.
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It has widely been recognized that submodular functions play essential roles in efficiently solvable combinatorial optimization problems. Since the publication of the 1st edition of this book fifteen years ago, submodular functions have been showing further increasing importance in optimization, combinatorics, discrete mathematics, algorithmic computer science, and algorithmic economics, and there have been made remarkable developments of theory and algorithms in submodular functions. The 2nd edition of the book supplements the 1st edition with a lot of remarks and with new two chapters: "Submodular Function Minimization" and "Discrete Convex Analysis." The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete algorithms in the fields of mathematics, operations research, computer science, and economics. Key features: - Self-contained exposition of the theory of submodular functions. - Selected up-to-date materials substantial to future developments. - Polyhedral description of Discrete Convex Analysis. - Full description of submodular function minimization algorithms. - Effective insertion of figures. - Useful in applied mathematics, operations research, computer science, and economics.
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• W. Hochstattler and J. Wiehe, The Chromatic Polynomial of a Digraph.- J. Diaz et al., On List k-Coloring Convex Bipartite Graphs.- E. Kubicka et al., Total chromatic sum for trees.- S. Ghosal and S. C. Ghosh, An incremental search heuristic for coloring vertices of a graph.- S. Bandopadhyay et al., Improved Bounds on the Span of L(1,2)-edge Labeling of Some Infinite Regular Grids.- E. Althaus and S. Ziegler, Optimal Tree Decompositions Revisited: A Simpler Linear-Time FPT Algorithm.- H. Kerivin and A. Wagler, On superperfection of edge intersection graphs of paths.- L. Liberti et al., A cycle-based formulation for the Distance Geometry Problem.- P. Samer and D. Haugland, The unsuitable neighbourhood inequalities for the fixed cardinality stable set polytope.- Lucas L. S. Portugal et al., Relating hypergraph parameters of generalized power graphs.- A. Nixon, Assur decompositions of direction-length frameworks.- M. Hiller et al., On the Burning Number of p-Caterpillars.- J. Boeckmann and C. Thielen, An Approximation Algorithm for Network Flow Interdiction with Unit Costs and Two Capacities.- T. Bacci and S. Nicoloso, On the benchmark instances for the Bin Packing Problem with Conflicts.- Barbara M. Anthony and Alison M. Marr, Directed Zagreb Indices.- F. Couto et al., Edge Tree Spanners.- S. Khalife, Sequence graphs: characterization and counting of admissible elements.- L. Burahem Martins et al., On solving the time window assignment vehicle routing problem via iterated local search.- M. Barbato et al., Synchronized Pickup and Delivery Problems with Connecting FIFO Stack.- A. Teymourifar et al., A Comparison Between Simultaneous and Hierarchical Approaches to Solve a Multi-Objective Location-Routing Problem.- M. Bodirsky et al., Piecewise Linear Valued Constraint Satisfaction Problems with Fixed Number of Variables.- M. Cacciola et al., A Lagrangian approach to Chance Constrained Routing with Local Broadcast.- P. Detti et al., A metaheuristic approach for biological sample transportation in healthcare.- Diego M. Pinto and G. Stecca, Optimal Planning of Waste Sorting Operations through Mixed Integer Linear Programming.- G. Micheli et al., Selecting and Initializing Representative Days for Generation and Transmission Expansion Planning with High Shares of Renewables.- T. Bacci et al., Start-up/Shut-down MINLP formulations for the Unit Commitment with Ramp Constraints.- J. Lee et al., Gaining or Losing Perspective for Piecewise-Linear Under-Estimators of Convex Univariate Functions.- M. Aprile et al., Recognizing Cartesian products of matrices and polytopes.- A. Frank, Special subclass of Generalized Semi-Markov Decision Processes with discrete time.- R. Seccia et al., Coupling Machine Learning and Integer Programming for Optimal TV Promo Scheduling.- F. Mendoza-Granada and M. Villagra, A Distributed Algorithm for Spectral Sparsification of Graphs with Applications to Data Clustering.
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This book highlights new and original contributions on Graph Theory and Combinatorial Optimization both from the theoretical point of view and from applications in all fields. The book chapters describe models and methods based on graphs, structural properties, discrete optimization, network optimization, mixed-integer programming, heuristics, meta-heuristics, math-heuristics, and exact methods as well as applications. The book collects selected contributions from the CTW2020 international conference (18th Cologne-Twente Workshop on Graphs and Combinatorial Optimization), held online on September 14-16, 2020. The conference was organized by IASI-CNR with the contribution of University of Roma Tre, University Roma Tor Vergata, and CNRS-LIX and with the support of AIRO. It is addressed to researchers, PhD students, and practitioners in the fields of Graph Theory, Discrete Mathematics, Combinatorial Optimization, and Operations Research.
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