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| Author | : Ritu Arora |
| Publisher | : CRC Press |
| Total Pages | : 184 |
| Release | : 2023-05-12 |
| Genre | : Business & Economics |
| ISBN | : 1000859851 |
Download Combinatorial Optimization Under Uncertainty Book in PDF, ePub and Kindle
This book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimal production evaluation of cotton in different soil and water conditions, the healthcare sector, intuitionistic fuzzy quadratic programming problem, and multi-objective optimization problem. This book may serve as a valuable reference for researchers working in the domain of optimization for solving combinatorial problems under uncertainty. The contributions of this book may further help to explore new avenues leading toward multidisciplinary research discussions.
| Author | : Ritu Arora |
| Publisher | : CRC Press |
| Total Pages | : 221 |
| Release | : 2023-05-12 |
| Genre | : Business & Economics |
| ISBN | : 1000859819 |
Download Combinatorial Optimization Under Uncertainty Book in PDF, ePub and Kindle
This book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimal production evaluation of cotton in different soil and water conditions, the healthcare sector, intuitionistic fuzzy quadratic programming problem, and multi-objective optimization problem. This book may serve as a valuable reference for researchers working in the domain of optimization for solving combinatorial problems under uncertainty. The contributions of this book may further help to explore new avenues leading toward multidisciplinary research discussions.
| Author | : Marco Dorigo |
| Publisher | : MIT Press |
| Total Pages | : 324 |
| Release | : 2004-06-04 |
| Genre | : Computers |
| ISBN | : 9780262042192 |
Download Ant Colony Optimization Book in PDF, ePub and Kindle
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
| Author | : Directeur de Recherches Du Fnrs Marco Dorigo |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 445 |
| Release | : 2004-08-19 |
| Genre | : Computers |
| ISBN | : 3540226729 |
Download Ant Colony Optimization and Swarm Intelligence Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, held in Brussels, Belgium in September 2004. The 22 revised full papers, 19 revised short papers, and 9 poster abstracts presented were carefully reviewed and selected from 79 papers submitted. The papers are devoted to theoretical and foundational aspects of ant algorithms, ant colony optimization and swarm intelligence and deal with a broad variety of optimization applications in networking and operations research.
| Author | : A. De Filippo |
| Publisher | : IOS Press |
| Total Pages | : 126 |
| Release | : 2022-04-12 |
| Genre | : Computers |
| ISBN | : 1643682636 |
Download Hybrid Offline/Online Methods for Optimization Under Uncertainty Book in PDF, ePub and Kindle
Balancing the solution-quality/time trade-off and optimizing problems which feature offline and online phases can deliver significant improvements in efficiency and budget control. Offline/online integration yields benefits by achieving high quality solutions while reducing online computation time. This book considers multi-stage optimization problems under uncertainty and proposes various methods that have broad applicability. Due to the complexity of the task, the most popular approaches depend on the temporal granularity of the decisions to be made and are, in general, sampling-based methods and heuristics. Long-term strategic decisions that may have a major impact are typically solved using these more accurate, but expensive, sampling-based approaches. Short-term operational decisions often need to be made over multiple steps within a short time frame and are commonly addressed via polynomial-time heuristics, with the more advanced sampling-based methods only being applicable if their computational cost can be carefully managed. Despite being strongly interconnected, these 2 phases are typically solved in isolation. In the first part of the book, general methods based on a tighter integration between the two phases are proposed and their applicability explored, and these may lead to significant improvements. The second part of the book focuses on how to manage the cost/quality trade-off of online stochastic anticipatory algorithms, taking advantage of some offline information. All the methods proposed here provide multiple options to balance the quality/time trade-off in optimization problems that involve offline and online phases, and are suitable for a variety of practical application scenarios.
| Author | : Massimiliano Vasile |
| Publisher | : Springer Nature |
| Total Pages | : 573 |
| Release | : 2021-02-15 |
| Genre | : Science |
| ISBN | : 3030601668 |
Download Optimization Under Uncertainty with Applications to Aerospace Engineering Book in PDF, ePub and Kindle
In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.
| Author | : Lap Chi Lau |
| Publisher | : Cambridge University Press |
| Total Pages | : 255 |
| Release | : 2011-04-18 |
| Genre | : Computers |
| ISBN | : 1139499394 |
Download Iterative Methods in Combinatorial Optimization Book in PDF, ePub and Kindle
With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.
| Author | : Lukas Nölke |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Download Matching and Packing Problems - Optimization Under Uncertainty in Theory and Practice Book in PDF, ePub and Kindle
When solving optimization problems that arise from real-world decision-making processes, uncertainty is a ubiquitous phenomenon that poses a significant obstacle. More often than not, lack of (full) knowledge about certain input parameters requires us to make decisions without knowing what their full effects will be. This thesis investigates how to algorithmically deal with such uncertainties when solving matching and packing problems. Both matching and packing problems are well-studied and among the most fundamental problems in combinatorial optimization. In matching problems, the task is to find a set of disjoint pairs of items, a matching, while in packing problems, items need to be assigned to containers with limited capacities. Additionally, there is an optimization objective, such as minimizing the cost of the matching or maximizing the value of packed items. Both problems have numerous practical applications in which uncertainty plays an important role. It may manifest itself, for instance, in the form of unknown items that are revealed only over time. It should come as no surprise that missing information about crucial problem parameters generally prevents us from reaching the same quality as an optimal offline solution. We consider several matching and packing problems and design algorithms that compute provably good solutions despite uncertainty in the input. Specifically, we consider the following models of uncertainty: online, recourse, and dynamic.
| Author | : Michael Z. Zgurovsky |
| Publisher | : Springer |
| Total Pages | : 518 |
| Release | : 2018-09-24 |
| Genre | : Technology & Engineering |
| ISBN | : 3319989774 |
Download Combinatorial Optimization Problems in Planning and Decision Making Book in PDF, ePub and Kindle
The book focuses on the next fields of computer science: combinatorial optimization, scheduling theory, decision theory, and computer-aided production management systems. It also offers a quick introduction into the theory of PSC-algorithms, which are a new class of efficient methods for intractable problems of combinatorial optimization. A PSC-algorithm is an algorithm which includes: sufficient conditions of a feasible solution optimality for which their checking can be implemented only at the stage of a feasible solution construction, and this construction is carried out by a polynomial algorithm (the first polynomial component of the PSC-algorithm); an approximation algorithm with polynomial complexity (the second polynomial component of the PSC-algorithm); also, for NP-hard combinatorial optimization problems, an exact subalgorithm if sufficient conditions were found, fulfilment of which during the algorithm execution turns it into a polynomial complexity algorithm. Practitioners and software developers will find the book useful for implementing advanced methods of production organization in the fields of planning (including operative planning) and decision making. Scientists, graduate and master students, or system engineers who are interested in problems of combinatorial optimization, decision making with poorly formalized overall goals, or a multiple regression construction will benefit from this book.
| Author | : Aharon Ben-Tal |
| Publisher | : Princeton University Press |
| Total Pages | : 576 |
| Release | : 2009-08-10 |
| Genre | : Mathematics |
| ISBN | : 1400831059 |
Download Robust Optimization Book in PDF, ePub and Kindle
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
