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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Werth, Bernhard Pitzer, Erik Karder, Johannes Wagner, Stefan Affenzeller, Michael Josef Ressel Center for Adaptive Optimization in Dynamic Environments Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria Institute for Formal Models and Verification Johannes Kepler University Linz4040 Austria

ISBN: (纸本)9783031253119

Dynamic optimization problems are of significant practical relevance, but suffer from a lack of analysis. The characteristics of time-linked problems are especially difficult to capture as future problem states depend on the optimizer’s performance. By tracking numeric features along the optimization process, information about the problems characteristics can be obtained, which can then be used to analyse algorithmic performance. We demonstrate this approach on a dynamic vehicle routing problem combining domain specific and fitness landscape features that can be applied to any optimization problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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Fitness Landscape Analysis on Binary Dynamic optimization Problems

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Procedia Computer Science 2022年 200卷 1004-1013页

作者： Bernhard Werth Andreas Beham Johannes Karder Stefan Wagner Michael Affenzeller Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria

Fitness Landscape Analysis (FLA) denotes the task of analyzing black-box optimization problems and capturing their characteristic features with the goal of providing additional information, that may help in algorithm selection, parametrization or guidance. Many real-world optimization tasks require dynamic on-going optimization and a plethora of meta-heuristic algorithms has been introduced for this task. However, most analysis focuses on static problems or dynamic optimization tasks without time-linkage, where the dynamic changes of the problem are independent of the decisions taken by the optimizer, but many real-world optimization problems display very heavy dependence on previous states and decisions. In this paper, the techniques of the static FLA are combined with dynamic and domain specific measures and applied to two dynamic problems. A time-linked dynamic OneMax problem and a dynamic multi-objective knapsack problem are presented and the impact of time-linkage on their FLA features is analyzed.

关键词： fitness landscape analysis dynamic optimization time-linkage multi-dimensional knapsack

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Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service Times

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Procedia Computer Science 2022年 200卷 1031-1040页

作者： Johannes Karder Andreas Beham Bernhard Werth Stefan Wagner Michael Affenzeller Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Hagenberg Austria Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

Many real-world processes are of dynamic nature and therefore subject to change. In this paper, dynamic warehouse operations are taken care of, more specifically crane operations that involve moving steel coils between storage locations within a large warehouse. An open-ended optimization approach is employed to create an optimal schedule of crane moves given a set of requested crane operations. Conventionally, the problem model defines static crane speeds and service times, the time needed to pickup and dropoff coils from/to locations. In a dynamic environment, these properties can depend on a variety of factors, including the proficiency of the crane operator or the storage locations that are accessed. Therefore, an open-ended genetic algorithm is enhanced with integrated machine learning (IML) tasked with learning crane speeds and service times from historical data and adapting said properties in the underlying problem model in order to provide the optimizer with a more realistic view on the current world state. To understand the performance gain achieved by this enhancement, experimental setups with and without IML are evaluated. The results show that IML improves the optimizer’s performance, as the algorithm gains better understanding of the current world state and is therefore able to create more suitable schedules, considering the crane’s current performance.

关键词： open-ended optimization dynamic optimization crane scheduling machine learning genetic algorithm

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Dynamic Warehouse Environments for Crane Stacking and Scheduling

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Procedia Computer Science 2022年 200卷 1461-1470页

作者： Andreas Beham Sebastian Raggl Johannes Karder Bernhard Werth Stefan Wagner Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Hagenberg Austria Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

Dynamic optimization is of high practical relevance for many production and logistics processes. Often however, in research, the dynamics are neglected and an algorithm or optimization is presented for a static decision scenario. The effects that occur with implementing decisions one by one in a dynamic environment subject to other dynamic events have not received as much attention as static problem scenarios. We think this is in part due to the increased complexity of describing a dynamic environment and parameterizing it in a meaningful way. In this publication we present three dynamic environments in the context of warehouse logistics where manipulation occurs by gantry cranes. Our scenarios are heavily inspired by real-world steel logistics operations. We provide free open source implementations of these dynamic environments.

关键词： gantry crane stacking scheduling steel industry dynamic optimization simulation

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Hyper-parameter handling for Gaussian processes in efficient global optimization 19

Hyper-parameter handling for Gaussian processes in efficient...

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19th International Conference on Modeling and Applied Simulation, MAS 2020

作者： Werth, Bernhard Karder, Johannes Beham, Andreas Wagner, Stefan Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 13 Hagenberg4232 Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Softwarepark 13 Hagenberg4232 Austria Institute for Formal Models Verification Johannes Kepler University Altenberger Straße 69 Linz4040 Austria

ISBN: (纸本)9788885741492

In simulation-based optimization, a common issue with many meta-heuristic algorithms is the limited computational budget. Performing a simulation is usually considerably more time-consuming than evaluating a closed mathematical function. Surrogate-assisted algorithms alleviate this problem by using representative models of the simulation which can be evaluated much faster. One of the most promising surrogate-assisted optimization approaches is Efficient Global optimization, which uses Gaussian processes as surrogate-models. In this paper, the importance of carefully chosen hyper-parameters for Gaussian process kernels and a way of self-configuration is shown. Based on properties of the training set, e.g. distances between observed points, observed target values, etc., the hyper-parameters of the used kernels are initialized and bounded accordingly. With these initial values and bounds in mind, hyper-parameters are then optimized, which results in improved Gaussian process models that can be used for regression. The goal is to provide an automated way of hyper-parameter initialization, which can be used when building Kriging models in surrogate-assisted algorithms, e.g. Efficient Global optimization (EGO). Obtained results show that applying the proposed hyper-parameter initialization and bounding can increase the performance of EGO in terms of either convergence speed or achieved objective function value. © 2020 The Authors.

关键词： Gaussian distribution

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Performance, Quality, and Control in Steel Logistics 4.0

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Procedia Manufacturing 2020年 42卷 429-433页

作者： Andreas Beham Sebastian Raggl Viktoria A. Hauder Johannes Karder Stefan Wagner Michael Affenzeller Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Softwarepark 13 4232 Hagenberg AUSTRIA Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 13 4232 Hagenberg AUSTRIA Institute for Formal Models and Verification Johannes Kepler Universit¨at Linz Altenberger Straße 69 4040 Linz AUSTRIA Institute of Production and Logistics Management Johannes Kepler Universit¨at Linz Altenberger Straße 69 4040 Linz AUSTRIA

In the steel industry, logistics is very often part of the value chain since storage processes and therefore cooling processes contribute to the product quality to a very larger degree. As a result, steel logistics is concerned with the storage and movement of – in our case – work in process (WIP) materials. Thousands of tons of steel are transported with cranes and heavy-duty vehicles and stored in stacks at large yards every day. The whole industry is under pressure to reduce costs, which strongly influences logistics operations. The efficiency of transport and storage processes is a crucial success factor and is challenged by highly dynamic processes and environments. In this article we focus on slab logistics with respect to logistics performance measurement, quality assurance, and operational control in the processes that directly follow the continuous caster. Closely related to this, we concentrate on selected aspects of the steel production value chain, especially concerning the logistics part. We evaluate the performance measurement and simultaneously show how quality assurance may be supported. Finally, methods from the domain of prescriptive analytics are employed to automate or support human resources in handling complex logistics operations.

关键词： steel industry material flow performance measurement stacking

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Approximation algorithms for 2-Peripathetic Salesman Problem with edge weights 1 and 2

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Electronic Notes in Discrete Mathematics 2006年 27卷 35-36页

作者： Glazkov, Yury Baburin, Alexei Gimadi, Edward Della Croce, Federico Vangelis, Paschos Discrete Optimization and Operations Research Sobolev Insitute of Mathematics Novosibirsk Russian Federation Discrete Optimization and Operations Research Sobolev Institute of Mathematics Novosibirsk Russian Federation Optimization: algorithms and models Laboratory of Analysis and Modeling of Decision Support Systems Paris France

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Artificial agents for discovering business strategies for network industries

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 2000年第1期5卷 9-36页

作者： Wu, DJ Abstract This study explores the use of artificial agents to discover "good" pricing investment and operating strategies for network industries. It models the first-best pricing investment and operating problems for general network industries applies this theoretical framework to the electric power industry and uses artificial agents to obtain computational results on realistic problems. Artificial agents can discover optimal or near-optimal pricing investment and operating strategies when the optimal solution is known. For problems with unknown optimal solutions they can match the "best-known solutions." The near-optimal solutions provided by artificial agents can sometimes only be tested by pushing the limits of currently available nonlinear optimization software. Artificial agents if carefully designed and controlled seem very promising for solving difficult problems that are intractable by traditional analytic methods such as discovering business strategies for network industries. Keywords ARTIFICIAL AGENTS ELECTRIC POWER NETWORKS FIRST-BEST MODEL GENETIC ALGORITHMS NETWORK INDUSTRIES Institutional LoginWelcome! To use the personalized features of this site please log in or register. If you have forgotten your username or password we can help.advanced search FindQuery BuilderClose|Clear Fields Title (ti)Summary (su)Author (au)ISSN (issn)ISBN (isbn)DOI (doi)Keyword (kw)Operators And Or Not ( ) * (wildcard) Within all contentWithin this journalWithin this issueExport Citation RIS | Text &reg 2015 M.E.Sharpe Metapress Privacy Policy Remote Address:183.67.53.225?Server:MPSHQWBRDR02PHTTP User Agent:Mozilla/4.0 (compatible MSIE 8.0 Windows NT 5.0 .NET CLR 1.1.4322 .NET CLR 2.0.50215 )

This study explores the use of artificial agents to discover "good" pricing, investment, and operating strategies for network industries. It models the first-best pricing, investment, and operating problems for general network industries, applies this theoretical Framework to the electric power industry and uses artificial agents to obtain computational results on realistic problems. Artificial agents can discover optimal or near-optimal pricing, investment, and operating strategies when the optimal solution is known. For problems with unknown optima I solutions, they can match the "best-known solutions." The near-optimal solutions provided by artificial agents can sometimes only be tested by pushing the limits of currently available nonlinear optimization software. Artificial agents, if carefully designed and controlled, seem very promising for solving difficult problems that are intractable by traditional analytic methods, such as discovering business strategies for network industries.

关键词： artificial agents electric power networks first-best model genetic algorithms network industries

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PUTTING AN OIL-SPILL CLEANUP COMPUTER-MODEL TO WORK FOR THE NAVY

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NAVAL ENGINEERS JOURNAL 1983年第3期95卷 165-172页

作者： NYHART, JD PSARAFTIS, HN YAROSCHAK, PJ Mr. J. D. Nyhart:is professor of management at the Sloan School of Management and the Department of Ocean Engineering at the Massachusetts Institute of Technology. His current research and writing focus on the use of scientific and technical material in formal judicial and administrative proceedings as well as the development of economic and regulatory models appropriate for deep ocean mining and oil-spill control. Harilaos N. Psaraftis:is Assistant Professor of Marine Systems at the Department of Ocean Engineering at the Operations Research Center at M.I.T. Professor Psaraftis received two M.Sc. degrees in 1977 (in Naval Architecture and Marine Engineering and in Shipping and Shipbuilding Management) and a Ph.D. in 1979 (in Ocean Systems Operations Research) all from M.I.T. Professor Psaraftis has been conducting research in problem areas such as the probabilistic modeling of underwater detection and optimal sensor allocation (project sponsored by ONR) the optimization of oil spill cleanup operations (project sponsored by a consortium of government and industry organizations) the development of routing and scheduling algorithms in transportation problems (project sponsored by DOT) and the analysis and solution algorithms of sealift routing and scheduling problems (project sponsored by ONR and the Military Sealift Command). Professor Psaraftis has published in various journals and is currently the Chairman of the Ocean Systems Management Program at M.I.T. Mr. Paul J. Yaroschak:received his Bachelors Degree in Civil Engineering from Villanova University and his Masters Degree in Environmental Engineering from Northeastern University. He has served in the Civil Engineer Corps U.S. Navy in Public Works and Construction Management and as a Project Engineer for Navy-wide Water and Wastewater Treatment Projects for the Naval Facilities Engineering Command. He is currently Head of the Environmental Engineering Branch of the Naval Facilities Engineering Command. Mr. Yaroschak is a Registered Professional Engineer i

A research group at the Massachusetts Institute of Technology has completed the first phase of the development of a computer assisted model for analyzing complex decisions and policies regarding oil spill cleanup. The model is the product of an ongoing MIT Sea Grant project, sponsored by a consortium of government and industry organizations, including the National Oceanic and Atmospheric Administration, the U.S. C oast G uard , the U.S. N avy , the Commonwealth of Massachusetts, the Spill Control Association of America, JFB Scientific Corporation, the Doherty Foundation, Petro-Canada and Texaco. The model can be used, among other things, in strategic planning for the long-term oil spill response needs of a region, in assisting On Scene Coordinators in responding to a specific spill (tactical/operational setting), in evaluating the environmental and economic damages of a spill versus the cost of cleanup, in simulation and training, and in the analysis of various policy and regulatory issues such as the effects of delays, the use of dispersants and the investigation of liability and compensation issues. The paper describes the model in detail, focuses on its potential uses and presents experience with its application in conjunction with pollution control efforts of the U.S. Navy. Specifically, we outline the application of the model in the Port of Charleston, South Carolina, an ongoing project sponsored by the Naval Facilities Engineering Command. The difficulty of gathering data for such an application is discussed.

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