Robust Optimization of a Multi-period Production Planning Problem Under Uncertainty
Author: Michal Rozenblit
Publisher:
Published: 2010
Total Pages: 57
ISBN-13:
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Robust Optimization
Author: Aharon Ben-Tal
Publisher: Princeton University Press
Published: 2009-08-10
Total Pages: 565
ISBN-13: 1400831059
DOWNLOAD EBOOKRobust 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.
Robust Discrete Optimization and Its Applications
Author: Panos Kouvelis
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 373
ISBN-13: 1475726201
DOWNLOAD EBOOKThis book deals with decision making in environments of significant data un certainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness ap proach to decision making, which assumes inadequate knowledge of the decision maker about the random state of nature and develops a decision that hedges against the worst contingency that may arise. The main motivating factors for a decision maker to use the robustness approach are: • It does not ignore uncertainty and takes a proactive step in response to the fact that forecasted values of uncertain parameters will not occur in most environments; • It applies to decisions of unique, non-repetitive nature, which are common in many fast and dynamically changing environments; • It accounts for the risk averse nature of decision makers; and • It recognizes that even though decision environments are fraught with data uncertainties, decisions are evaluated ex post with the realized data. For all of the above reasons, robust decisions are dear to the heart of opera tional decision makers. This book takes a giant first step in presenting decision support tools and solution methods for generating robust decisions in a variety of interesting application environments. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making.
Proceedings of the Thirteenth International Conference on Management Science and Engineering Management
Author: Jiuping Xu
Publisher: Springer
Published: 2019-06-19
Total Pages: 790
ISBN-13: 3030212556
DOWNLOAD EBOOKThis book gathers the proceedings of the 13th International Conference on Management Science and Engineering Management (ICMSEM 2019), which was held at Brock University, Ontario, Canada on August 5–8, 2019. Exploring the latest ideas and pioneering research achievements in management science and engineering management, the respective contributions highlight both theoretical and practical studies on management science and computing methodologies, and present advanced management concepts and computing technologies for decision-making problems involving large, uncertain and unstructured data. Accordingly, the proceedings offer researchers and practitioners in related fields an essential update, as well as a source of new research directions.
Multi-objective, Integrated Supply Chain Design and Operation Under Uncertainty
Author: Christopher James Solo
Publisher:
Published: 2009
Total Pages:
ISBN-13:
DOWNLOAD EBOOKThis research involves the development of a flexible, multi-objective optimization tool for use by supply chain managers in the design and operation of manufacturing-distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing supply chain response time. The development of the two-phase mathematical model parallels the supply chain planning process through the formulation of a strategic submodel for infrastructure design followed by a tactical submodel for operational planning. The deterministic strategic submodel, formulated as a multi-period, mixed integer linear programming model, considers an aggregate production planning problem in which long-term decisions such as plant construction, production capacities, and critical raw material supplier selections are optimized. These decisions are then used as inputs in the operational planning portion of the problem. The deterministic tactical submodel, formulated as a multi-period, mixed integer linear goal programming model, uses higher resolution demand and cost data, newly acquired transit time information, and the previously developed infrastructure to determine optimal non-critical raw material supplier selections; revised purchasing, production, inventory, and shipment quantities; and an optimal profit figure. The supply chain scenario is then modified to consider uncertain, long-term demand forecasts in the form of discrete economic scenarios. In this case, a multi-period, mixed integer robust optimization formulation of the strategic submodel is presented to account for the probabilistic demand data. Once the stochastic strategic submodel is presented, short-term, uncertain demand data is assumed to be available in the form of continuous probability distributions. By modifying decision makers' objectives regarding demand satisfaction, the distribution-based demand data is accounted for through the development of a multi-period, mixed integer chance-constrained goal programming formulation of the tactical submodel. In order to demonstrate the flexibility of both the deterministic and stochastic versions of the overall two-phase model, numerical examples are presented and solved. The resulting work provides supply chain managers with a flexible tool that can aid in the design and operation of real-world production-distribution networks, where uncertain demand data is available at different times and in various forms.
Robustness Analysis in Decision Aiding, Optimization, and Analytics
Author: Michael Doumpos
Publisher: Springer
Published: 2016-07-12
Total Pages: 337
ISBN-13: 3319331213
DOWNLOAD EBOOKThis book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies. Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data'” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.
Facility Location Under Uncertainty
Author: Francisco Saldanha-da-Gama
Publisher: Springer Nature
Published:
Total Pages: 535
ISBN-13: 3031559274
DOWNLOAD EBOOKLiner Ship Fleet Planning
Author: Tingsong Wang
Publisher: Elsevier
Published: 2017-05-18
Total Pages: 206
ISBN-13: 0128115033
DOWNLOAD EBOOKLiner Ship Fleet Planning: Models and Algorithms systematically introduces the latest research on modeling and optimization for liner ship fleet planning with demand uncertainty. Container shipping companies have struggled since the financial crisis of 2007-2008, making it critical for them to make informed decisions about their fleet planning and development. Current and future shipping professionals require systematic approaches for investigating and solving their fleet planning problems, as well as methodologies for addressing their other shipping responsibilities. Liner Ship Fleet Planning addresses these needs, providing the most recent quantitative research of liner shipping in maritime transportation. The research and methods provided assist those tasked with optimizing shipping efficiency and fleet deployment in the face of uncertain demand. Suitable for those with any level of quantitative background, the book serves as a valuable resource for both maritime academics, and shipping professionals involved in planning and scheduling departments. Introduces the latest research on maritime transportation problems Analyzes problems of liner ship fleet planning, taking uncertainty into account Promotes the use of mathematics to manage uncertainty, using stochastic programming models, and proposing solution algorithms to solve proposed models Includes case studies that provide detailed examples of real-world examples of fleet optimization Explains how stochastic programming modeling methods and solution algorithms can be applied to other research fields featuring uncertainty, such as container yard planning, berth allocation and vehicle deployment problems
FOCAPD-19/Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, July 14 - 18, 2019
Author: Salvador Garcia Munoz
Publisher: Elsevier
Published: 2019-07-09
Total Pages: 514
ISBN-13: 0128205717
DOWNLOAD EBOOKFOCAPD-19/Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, July 14 - 18, 2019, compiles the presentations given at the Ninth International Conference on Foundations of Computer-Aided Process Design, FOCAPD-2019. It highlights the meetings held at this event that brings together researchers, educators and practitioners to identify new challenges and opportunities for process and product design. Combines presentations from the Ninth International Conference on Foundations of Computer-Aided Process Design, FOCAPD-2019
Handbook of Research on Artificial Intelligence Techniques and Algorithms
Author: Vasant, Pandian
Publisher: IGI Global
Published: 2014-11-30
Total Pages: 873
ISBN-13: 1466672595
DOWNLOAD EBOOKFor decades, optimization methods such as Fuzzy Logic, Artificial Neural Networks, Firefly, Simulated annealing, and Tabu search, have been capable of handling and tackling a wide range of real-world application problems in society and nature. Analysts have turned to these problem-solving techniques in the event during natural disasters and chaotic systems research. The Handbook of Research on Artificial Intelligence Techniques and Algorithms highlights the cutting edge developments in this promising research area. This premier reference work applies Meta-heuristics Optimization (MO) Techniques to real world problems in a variety of fields including business, logistics, computer science, engineering, and government. This work is particularly relevant to researchers, scientists, decision-makers, managers, and practitioners.
