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• Preface.
• 1. Introduction. 1.1 Combinatorial Optimization. 1.2 Optimization Methods. 1.3 States, Moves, and Optimality. 1.4 Local Search. 1.5 Optimal versus Final Solution. 1.6 Single versus Multicriteria Constrained Optimization. 1.7 Convergence Analysis of Iterative Algorithms. 1.8 Markov Chains. 1.9 Parallel Processing. 1.10 Summary and Organization of the Book. References. Exercises.
• 2. Simulated Annealing (SA). 2.1 Introduction. 2.2 Simulated Annealing Algorithm. 2.3 SA Convergence Aspects. 2.4 Parameters of the SA Algorithm. 2.5 SA Requirements. 2.6 SA Applications. 2.7 Parallelization of SA. 2.8 Conclusions and Recent Work. References. Exercises.
• 3. Genetic Algorithms (GAs). 3.1 Introduction. 3.2 Genetic Algorithm. 3.3 Schema Theorem and Implicit Parallelism. 3.4 GA Convergence Aspects. 3.5 GA in Practice. 3.6 Parameters of GAs. 3.7 Applications of GAs. 3.8 Parallelization of GA. 3.9 Other Issues and Recent Work. 3.10 Conclusions. References. Exercises.
• 4. Tabu Search (TS). 4.1 Introduction. 4.2 Tabu Search Algorithm. 4.3 Implementation--Related Issues. 4.4 Limitations of Short--Term Memory. 4.5 Examples of Diversifying Search. 4.6 TS Convergence Aspects. 4.7 TS Applications. 4.8 Parallelization of TS. 4.9 Other Issues and Related Work. 4.10 Conclusions. References. Exercises.
• 5. Simulated Evolution (SimE). 5.1 Introduction. 5.2 Historical Background. 5.3 Simulated Evolution Algorithm. 5.4 SimE Operators and Parameters. 5.5 Comparison of SimE, SA, and GA. 5.6 SimE Convergence Aspects. 5.7 SimE Applications. 5.8 Parallelization of SimE. 5.9 Conclusions and Recent Work. References. Exercises.
• 6. Stochastic Evolution (StocE). 6.1 Introduction. 6.2 Historical Background. 6.3 Stochastic Evolution Algorithm. 6.4 Stochastic Evolution Convergence Aspects. 6.5 Stochastic Evolution Applications. 6.6 Parallelization of Stochastic Evolution. 6.7 Conclusions and Recent Work. References. Exercises.
• 7. Hybrids and Other Issues. 7.1 Introduction. 7.2 Overview of Algorithms. 7.3 Hybridization. 7.4 GA and Multiobjective Optimization. 7.5 Fuzzy Logic for Multiobjective Optimization. 7.6 Artificial Neural Networks. 7.7 Quality of the Solution. 7.8 Conclusions. References. Exercises. About the Authors. Index.
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Iterative Computer Algorithms with Applications in Engineering describes in-depth the five main iterative algorithms for solving hard combinatorial optimization problems: Simulated Annealing, Genetic Algorithms, Tabu Search, Simulated Evolution, and Stochastic Evolution. The authors present various iterative techniques and illustrate how they can be applied to solve several NP-hard problems. For each algorithm, the authors present the procedures of the algorithm, parameter selection criteria, convergence property analysis, and parallelization. There are also several real-world examples that illustrate various aspects of the algorithms. The book includes an introduction to fuzzy logic and its application in the formulation of multi-objective optimization problems, a discussion on hybrid techniques that combine features of heuristics, a survey of recent research work, and examples that illustrate required mathematical concepts. The unique features of this book are: An integrated and up-to-date description of iterative non-deterministic algorithms; Detailed descriptions of Simulated Evolution and Stochastic Evolution; A level of treatment suitable for first year graduate student and practicing engineers; Parallelization aspects and particular parallel implementations; A brief survey of recent research work; Graded exercises and an annotated bibliography in each chapter.
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• Discrete Problems from Applications.- Basics, Notation and Data Structures.- Minimum Spanning Trees.- Linear Programming Duality.- Shortest Paths.- Maximal Flows.- Minimum-Cost Flows.- Matching.- Weighted Matching.
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Graph algorithms are easy to visualize and indeed there already exists a variety of packages to animate the dynamics when solving problems from graph theory. Still it can be difficult to understand the ideas behind the algorithm from the dynamic display alone. CATBox consists of a software system for animating graph algorithms and a course book which we developed simultaneously. The software system presents both the algorithm and the graph and puts the user always in control of the actual code that is executed. In the course book, intended for readers at advanced undergraduate or graduate level, computer exercises and examples replace the usual static pictures of algorithm dynamics. For this volume we have chosen solely algorithms for classical problems from combinatorial optimization, such as minimum spanning trees, shortest paths, maximum flows, minimum cost flows, weighted and unweighted matchings both for bipartite and non-bipartite graphs. Find more information at http://schliep.org/CATBox/.
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• A metaheuristic approach for solving Monitor Placement Problem
• A VNS-based heuristic for the minimum number of resources under a perfect schedule
• BVNS for Overlapping Community Detection
• A Simulation-Based Variable Neighborhood Search Approach for Optimizing Cross-Training Policies
• Multi-Objective Variable Neighborhood Search for improving software modularity
• An Effective VNS for Delivery Districting
• BVNS for the Minimum Sitting Arrangement problem in a cycle
• Assigning Multi-Skill Confgurations to Multiple Servers with a Reduced VNS
• Multi-Round Infuence Maximization: A Variable Neighborhood Search Approach
• A VNS based heuristic for a 2D Open Dimension Problem
• BVNS for the bi-objective multi row equal facility layout problem.

This volume constitutes the proceedings of the 9th International Conference on Variable Neighborhood Search, ICVNS 2023, held in Abu Dhabi, United Arab Emirates, in October 2022. The 11 full papers presented in this volume were carefully reviewed and selected from 29 submissions. The papers describe recent advances in methods and applications of variable neighborhood search.

Solving combinatorial optimization problems can often lead to runtime growing exponentially as a function of the input size. But important real-world problems, industrial applications, and academic research challenges, may demand exact optimal solutions. In such situations, parallel processing can reduce the runtime from days or months, typical when one workstation is used, to a few minutes or even seconds. Partners of the CEC-sponsored SCOOP Project (Solving Combinatorial Optimization Problems in Parallel) contributed, on invitation, to this book; much attention was paid to competent coverage of the topic and the style of writing. Readers will include students, scientists, engineers, and professionals interested in the design and implementation of parallel algorithms for solving combinatorial optimization problems.
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• Which are the most critical nodes in a network? A Basic Variable Neighborhood Search approach.- Variable Neighborhood Descent Branching applied to the Green Electric Vehicle Routing Problem with Time Window and Mixed Fleet.- A Variable Neighborhood Heuristic for Facility Locations in Fog Computing.- A GRASP/VND Heuristic for the Generalized Steiner Problem with Node-Connectivity Constraints and Hostile Reliability.- Max-Diversity Orthogonal Regrouping of MBA Students using a GRASP/VND Heuristic.- Scheduling in parallel machines with two servers: the restrictive case.- Reduced Variable Neighbourhood Search for the generation of controlled circular data.- Sequential and Parallel Scattered Variable Neighborhood Search for Solving Nurikabe.- A Hybrid VNS for the Multi-Product Maritime Inventory Routing Problem.- Simplicial vertex heuristic in solving the Railway arrival and departure paths assignment problem.- Scheduling of Patients in Emergency Departments with a Variable Neighborhood Search.- A GRASP/VND Heuristic for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows.- Using K-means and Variable Neighborhood Search for Automatic Summarization of Scientific Articles.- BVNS approach for the Order Processing in Parallel Picking Workstations .
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This volume constitutes the proceedings of the 8th International Conference on Variable Neighborhood Search, ICVNS 2021, held in Abu Dhabi, United Arab Emirates, in March 2021.The 14 full papers presented in this volume were carefully reviewed and selected from 27 submissions. The papers describe recent advances in methods and applications of variable neighborhood search. Chapter "Reduced Variable Neighbourhood Search for the Generation of Controlled Circular Data" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
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• A Reduced Variable Neighborhood Search Approach for Feature Selection in Cancer Classification.- Basic VNS for a Variant of the Online Order Batching Problem.- A VNS-Based Algorithm for the Mammography Unit Location Problem.- A Hybrid Heuristic Algorithm for the Dial-a-Ride Problem.- Multi-Objective Basic Variable Neighborhood Search for Portfolio Selection.- Local Search Approach for the (r
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This volume constitutes the post- conference proceedings of the 7th International Conference on Variable Neighborhood Search, ICVNS 2019, held in Rabat, Morocco, in October 2019. The 13 full papers presented in this volume were carefully reviewed and selected from 46 submissions. The papers describe recent advances in methods and applications of variable neighborhood search. .
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• Intro
• Preface
• Organization
• Contents
• Efficiently Approximating High-Dimensional Pareto Frontiers for Tree-Structured Networks Using Expansion and Compression
• 1 Introduction
• 2 Problem Formulation
• 3 The Expansion Method
• 4 The Compression Method
• 5 Experiments
• 5.1 Experimental Setup
• 5.2 Evaluation Method
• 5.3 Experimental Results
• 5.4 Ablation Study
• 6 Conclusion
• References
• Objective-Based Counterfactual Explanations for Linear Discrete Optimization
• 1 Introduction
• 2 Background
• 2.1 Counterfactual Explanations

• 2.2 Nearest Counterfactual Explanations
• 2.3 Inverse Combinatorial Optimization
• 3 Problem Definition
• 3.1 Existence of an Explanation
• 4 The NCXplain Algorithm
• 5 Experimental Method
• 5.1 Forward Problems
• 5.2 NCEMILP Instances
• 5.3 Computational Details
• 6 Experimental Results
• 7 Limitations and Future Work
• 8 Related Work
• 9 Conclusion
• References
• Column Elimination for Capacitated Vehicle Routing Problems
• 1 Introduction
• 2 Column Formulation for CVRP
• 3 Decision Diagram Formulation for CVRP
• 3.1 From Dynamic Programming to Decision Diagrams

• 3.2 Dynamic Programming for Route Relaxations
• 3.3 Exact and Relaxed Decision Diagrams
• 3.4 Constrained Network Flow Formulation
• 4 Column Elimination Procedure
• 5 Lagrangian Relaxation
• 6 Cutting Planes
• 7 Reduced Cost-Based Arc Fixing
• 8 Experimental Results
• 9 Conclusion
• References
• Cutting Plane Selection with Analytic Centers and Multiregression
• 1 Introduction
• 2 Related Work
• 3 Contributions and Methodology
• 3.1 Analytic Center-Based Methods
• 3.2 Multiple LP Solutions
• 3.3 Properties and Limitations of the Distance Measures
• 3.4 Multi-output Regression

• 4 Experiments
• 4.1 Root Node Results
• 4.2 Branch and Bound Generalisation
• 4.3 Regression Model Results
• 5 Conclusion
• References
• Handling Symmetries in Mixed-Integer Semidefinite Programs
• 1 Introduction
• 2 Computing Symmetries
• 3 Symmetry Detection
• 4 Computational Results
• References
• A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination
• 1 Introduction
• 2 Reducing a DBLP to a MILP: A Worked Example
• 3 Symbolic Calculus with Case Representation
• 3.1 Case Representation
• 3.2 Basic Case Operators

• 4 Symbolic Reduction of a DBLP to a MILP
• 4.1 Symbolic Minimization of Linear Piecewise Linear Functions
• 4.2 Symbolic Minimization of Disjointly Linear Piecewise Bilinear Functions
• 5 Empirical Analysis
• 6 Conclusion and Future Work
• References
• Local Branching Relaxation Heuristics for Integer Linear Programs
• 1 Introduction
• 2 Background
• 2.1 ILP and Its LP Relaxation
• 2.2 LNS for ILP Solving
• 2.3 LB Heuristic
• 3 Related Work
• 3.1 LNS for ILPs
• 3.2 LNS-Based Primal Heuristics in BnB
• 3.3 LNS for Other COPs
• 4 The Local Branching Relaxation Heuristic

This book constitutes the proceedings of the 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, held in Nice, France, during May 29-June 1, 2023. The 26 full papers and the 6 short papers presented in this book were carefully reviewed and selected from a total of 71 submissions. The content of the papers present new techniques or new applications, and provide an opportunity for researchers in one area to learn about techniques in the others. Besides they give researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems.

• A Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs - Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.
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This book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization".
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• 1. A cooperative optimization approach for distributing service points in mobility applications / Thomas Jatschka, Tobias Rodemann, and Günther R. Raidl
• 2. A binary algebraic differential evolution for the multidimensional two-way number partitioning problem / Valentino Santucci, Marco Baioletti, Gabriele Di Bari, and Alfredo Milani
• 3. A new representation in genetic programming for evolving dispatching rules for dynamic flexible job shop scheduling / Fangfang Zhang, Yi Mei, and Mengjie Zhang
• 4. An iterated local search algorithm for the two-machine flow shop problem with buffers and constant processing times on one machine / Hoang Thanh Le, Philine Geser, and Martin Middendorf
• 5. Route planning for a fleet of electric vehicles with waiting times at charging stations / Baoxiang Li, Shashi Shekhar Jha, and Hoong Chuin Lau
• 6. Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani
• 7. Rigorous performance analysis of state-of-the-art TSP heuristic solvers / Paul McMenemy, Nadarajen Veerapen, Jason Adair, and Gabriela Ochoa
• 8. Runtime analysis of discrete particle swarm optimization applied to shortest paths computation / Alexander Raß, Jonas Schreiner, and Rolf Wanka
• 9. Quasi-optimal recombination operator / Francisco Chicano, Gabriela Ochoa, Darrell Whitley, and Renato Tinós
• 10. Insights into the feature selection problem using local Optima networks / Werner Mostert, Katherine M. Malan, Gabriela Ochoa, and Andries P. Engelbrecht

• 11. Clarifying the difference in local Optima network sampling algorithms / Sarah L. Thomson, Gabriela Ochoa, and Sébastien Verel
• 12. A unifying view on recombination spaces and abstract convex evolutionary search / Marcos Diez García and Alberto Moraglio
• 13. Program trace optimization with constructive heuristics for combinatorial problems / James McDermott and Alberto Moraglio
• C1. Correction to: Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani.

This book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications. The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.
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• Intro; Preface; Organization; Invited Talks; Symmetry in Integer Programming; IntSat: From SAT to Integer Linear Programming; Constraint Programming for Infeasibility Diagnosis with BARON; Abstracts of Fast Tracked Journal Papers; Lagrangian Bounds from Decision Diagrams; A Constraint-Based Local Search Backend for MiniZinc (Summary); New Filtering for ATMOSTNVALUE and ItsWeighted Variant: A Lagrangian Approach; A Hybrid Exact Method for a Scheduling Problem with a Continuous Resource and Energy Constraints; A Column-Generation Approach for Joint Mobilization and Evacuation Planning; Contents

• A Time-Dependent No-Overlap Constraint: Application to Urban Delivery Problems1 Introduction; 2 Definition of the TDTSP; 3 A New Benchmark for the TDTSP; 4 Related Work; 5 Classical CP Model for the TDTSP and Its Limitations; 5.1 CP Model; 5.2 Model Limitations; 6 Time-Dependent No-Overlap Constraint; 6.1 Propagation of Next Arcs; 6.2 Propagation of Successor Arcs; 6.3 Time-Dependent Disjunctive Propagation; 6.4 Complexity; 7 Experimental Evaluation; 8 Discussion; References; Rectangle Placement for VLSI Testing; 1 Introduction and Background; 1.1 Placement Algorithms and Tools

• 2 Constraint Programming (CP) Formulation2.1 Variables and Domains; 2.2 Constraints; 3 Heuristics for Test-site Placement; 3.1 Cluster Handling; 3.2 Merging IC Macros; 3.3 Value Ordering; 3.4 Variable Ordering; 3.5 Retries; 4 Experimental Results; 4.1 Soft Retries; 5 Discussion; References; A Constraint-Based Local Search for Edge Disjoint Rooted Distance-Constrained Minimum Spanning Tree Problem; 1 Introduction; 2 Formal Specification and Complexity; 3 Constraint Optimisation Formulation; 4 Iterated Constraint-Based Local Search; 4.1 Move-Operators; 4.2 Operations and Complexities

• 4.3 Algorithm5 Long-Reach Passive Optical Networks; 6 Empirical Evaluation; 6.1 Experimental Protocol; 6.2 Experimental Results; 7 Conclusions and Future Work; References; A Benders Approach to the Minimum Chordal Completion Problem; 1 Introduction; 2 Graph Notation; 3 Problem Description; 4 Integer Programming Approach; 5 Finding Chordless Cycles; 6 Benders Cuts; 7 Minimum/Minimal Chordal Completion Algorithms; 8 Computational Results; 9 Conclusion; References; MaxSAT-Based Scheduling of B2B Meetings; 1 Introduction; 2 The B2B Problem; 3 Encodings; 3.1 MaxSAT Base Encoding for the B2BSOP-d

• 3.2 Extended Encoding3.3 Encoding of Global Constraints; 4 Experiments, Conclusions and Future Work; References; Embedding Decision Trees and Random Forests in Constraint Programming; 1 Introduction; 2 Background; 3 Embedding Decision Trees and Random Forests in CP; 3.1 Rule Based Encoding; 3.2 Table Based Encoding; 3.3 MDD Based Encoding; 3.4 Embedding Random Forests in CP; 4 Experimentation; 4.1 Comparing the Different Encodings; 4.2 Comparison with a State of the Art Local Search Approach; 5 Other Related Work; 6 Concluding Remarks; References

This book constitutes the proceedings of the 12th International Conference on the Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming, CPAIOR 2015, held in Barcelona, Spain, in May 2015. The 29 papers presented together with 8 short papers in this volume were carefully reviewed and selected from 90 submissions. The purpose of the conference series is to bring together researchers in the fields of Constraint Programming, Artificial Intelligence and Operations Research to explore ways of solving hard and large scale combinatorial optimization problems that emerge in various industrial domains. Pooling the skills and strengths of this diverse group of researchers has proved extremely effective and valuable during the past decade leading to improvements and cross-fertilization between the three fields as well as breakthrough for actual applications.

• On Monte Carlo Tree Search for Weighted Vertex Coloring.- A RNN-based Hyper-heuristic for combinatorial problems.- Algorithm Selection for the Team Orienteering Problem.- Performance evaluation of a parallel ant colony optimization for the real-time train routing selection problem in large instances.- Deep Infeasibility Exploration Method for Vehicle Routing Problems.- Evolutionary Algorithms for the Constrained Two-Level Role Mining Problem.- Simplifying Dispatching Rules in Genetic Programming for Dynamic Job Shop Scheduling.- Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.- A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation.- Modeling the Costas Array Problem in QUBO for Quantum Annealing.- Penalty Weights in QUBO formulations: Permutation Problems.- PUBOi: a tunable benchmark with variable importance.- Stagnation Detection meets Fast Mutation.
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This book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP. The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
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• Fairer comparisons for Travelling Salesman Problem solutions using Hash Functions
• Application of Adapt-CMSA to the Two-Echelon Electric Vehicle Routing Problem with Simultaneous Pickup and Deliveries
• Real-World Vehicle Routing using Adaptive Large Neighborhood Search
• A Multilevel Optimization Approach for Large Scale Battery Exchange Station Location Planning
• A memetic algorithm for deinterleaving pulse trains
• Application of Negative Learning Ant Colony Optimization to the Far From Most String Problem
• Monte Carlo Tree Search with Adaptive Simulation: a Case Study on Weighted Vertex Coloring
• Evolutionary Strategies for the Design of Binary Linear Codes
• A Policy-Based Learning Beam Search for Combinatorial Optimization
• Cooperative Coevolutionary Genetic Programming Hyper Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing
• OneMax is not the Easiest Function for Fitness Improvements
• The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits
• Multi-objectivization Relaxes Multi-funnel Structures in Singleobjective NK-landscapes
• Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation
• On the e_ect of solution representation and neighborhood definition in AutoML Fitness Landscapes.

This book constitutes the refereed proceedings of the 23rd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2023, held as part of Evo*2023, in Brno, Czech Republic in April 2023, co-located with the Evo*2023 events: EvoMUSART, EvoApplications, and EuroGP. The 15 revised full papers presented in this book were carefully reviewed and selected from 32 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.

This book constitutes the refereed proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2000, held in Saarbrucken, Germany in September 2000. The 22 revised full papers presented together with four invited contributions were carefully reviewed and selected from 68 submissions. The topics dealt with include design and analysis of approximation algorithms, inapproximibility results, on-line problems, randomization techniques, average-case analysis, approximation classes, scheduling problems, routing and flow problems, coloring and partitioning, cuts and connectivity, packing and covering, geometric problems, network design, and various applications.
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This book constitutes the refereed proceedings of the International Workshop on Approximation Algorithms for Combinatorical Optimization, APPROX'98, held in conjunction with ICALP'98 in Aalborg, Denmark, in July 1998. The volume presents 14 revised full papers together with three invited papers selected from 37 submissions. The papers address the design and analysis of approximation algorithms, inapproximability results, on-line problems, randomization techniques, average-case analysis, approximation classes, scheduling problems, routing and flow problems, coloring and partitioning, cuts and connectivity, packing and covering, geometric problems, network design, and various applications.
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• Routing among Convex Polygonal Obstacles in the Plane.- Target Coverage with Minimized Number of Camera Sensors.- Two-Stage Submodular Maximization under Curvature.- An Improved Approximation Algorithm for Capacitated Correlation Clustering Problem.- The Selection of COVID-19 Epidemic Prevention and Control Programs Based on Group Decision Making.- Which Option is a Better Way to Improve Transfer Learning Performance ?.- On Maximizing the Difference between an Approximately Submodular Function and a Linear Function Subject to a Matroid Constraint.- On Various Open-End Bin Packing Game.- A Linear-Time Streaming Algorithm for Cardinality-Constrained Maximizing Monotone Non-submodular Set Functions.- Approximation Algorithms for Two Parallel Dedicated Machine Scheduling with Conflict Constraints.- Computing the One-Visibility Cop-Win Strategies for Trees.- Complexity and Approximation Results on the Shared Transportation Problem.- The complexity of finding optimal subgraphs to represent spatial correlation.- New Approximation Algorithms for the Rooted Budgeted Cycle Cover Problem.- Evolutionary Equilibrium Analysis for Decision on Block Size in Blockchain Systems.- Efficient Algorithms for Scheduling Parallel Jobs with Interval Constraints in Clouds.- Two-Stage Stochastic Max-Weight Independent Set Problems.- Routing and Scheduling Problems with Two Agents on a Line-Shaped Network.- The Price of Anarchy of Generic Valid Utility Systems.- Single Machine Scheduling with Rejection and Generalized Parameters.- Approximation Algorithm and Hardness Results for Defensive Domination in Graphs.- An Improved Physical ZKP for Nonogram.- Finding All Leftmost Separators of Size $\leq k$.- Maximize the Probability of Union-Influenced in Social Networks.- A Novel Algorithm for Max Sat Calling MOCE to Order.- The Smallest Number of Vertices in a 2-Arc-Strong Digraph without Pair of Arc-Disjoint in- and out-Branchings.- Generalized Self-Profit Maximization in Attribute Networks.- Parameterized Complexity Classes Defined by Threshold Circuits: Using Sorting Networks to Show Collapses with W-Hierarchy Classes.- Maximization of Monotone Non-Submodular Functions with a Knapsack Constraint over the Integer Lattice.- Sublinear-time Reductions for Big Data Computing.- Capacitated Partial Inverse Maximum Spanning Tree under the Weighted $l_{\infty}$-Norm.- Approximation Algorithms for Some Min-Max and Minimum Stacker Crane Cover Problems.- Succinct Data Structures for SP, Block-Cactus and 3-Leaf Power Graphs.- Streaming Submodular Maximization under Differential Privacy Noise.- Online Bottleneck Semi-Matching.- Optimal Due Date Assignment without Restriction and Convex Resource Allocation in Group Technology Scheduling.- Constrained Stable Marriage with Free edges or Few Blocking Pairs.- Backgammon is Hard.- Two-Facility Location Games with a Minimum Distance Requirement on a Circle.- Open Shop Scheduling Problem with a Non-Resumable Flexible Maintenance Period.- Parallel Algorithm For Minimum Partial Dominating Set In Unit Disk Graph.- An Improved Approximation Algorithm for Squared Metric k-Facility Location.- Parameterized Algorithms for Linear Layouts of Graphs with Respect to the Vertex Cover Number.- The Fractional k-Truncated Metric Dimension of Graphs.- On Structural Parameterizations of the Offensive Alliance Problem.- On the k-colored Rainbow Sets in Fixed Dimensions.- Cycle-Connected Mixed Graphs and Related Problems.- Directed width parameters on semicomplete digraphs.- Improved Parameterized Approximation for Balanced k-Median.- A LP-Based Approximation Algorithm for Generalized Traveling Salesperson Path Problem.- Hardness Results of Connected Power Domination for Bipartite Graphs and Chordal Graphs.- Approximation Algorithm for Min-Max Correlation Clustering Problem with Outliers.- Delay-Constrained Minimum Shortest Path Trees and Related Problems.- On the Feedback Number of 3-Uniform Linear Extremal Hypergraph.- A Multi-Pass Streaming Algorithm for Regularized Submodular Maximization.
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This book constitutes the refereed proceedings of the 15th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2021, which took place in Tianjin, China, during December 17-19, 2021.The 55 papers presented in this volume were carefully reviewed and selected from 122 submissions. They deal with combinatorial optimization and its applications in general, focusing on algorithms design, theoretical and experimental analysis, and applied research of general algorithmic interest.
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• Cognitive radio networks
• Wireless sensor networks
• Cyber-physical systems
• Distributed and localized algorithm design and analysis
• Information and coding theory for wireless networks
• Localization
• Mobile cloud computing
• Topology control and coverage
• Security and privacy
• Underwater and underground networks
• Vehicular networks
• Information processing and data management
• Programmable service interfaces
• Energy-efficient algorithms
• System and protocol design
• Operating system and middleware support
• Experimental test-beds, models and case studies.

This volume constitutes the proceedings of the 13th International Conference on Combinatorial Optimization and Applications, COCOA 2019, held in Xiamen, China, in December 2019. The 49 full papers presented in this volume were carefully reviewed and selected from 108 submissions. The papers cover the various topics, including cognitive radio networks, wireless sensor networks, cyber-physical systems, distributed and localized algorithm design and analysis, information and coding theory for wireless networks, localization, mobile cloud computing, topology control and coverage, security and privacy, underwater and underground networks, vehicular networks, information processing and data management, programmable service interfaces, energy-efficient algorithms, system and protocol design, operating system and middleware support, and experimental test-beds, models and case studies.