06501 Abstracts Collection -- Practical Approaches to Multi-Objective Optimization

From 10.12.06 to 15.12.06, the Dagstuhl Seminar 06501 ``Practical Approaches to Multi-Objective Optimization\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. In the first step, a knee region is determined based on the normalized Euclidean distance from a hyperplane defined by the furthest Pareto solution and the negative unit vector. The size of the knee region depends on the Pareto front’s shape and a design parameter. In the second step, preferences for all objectives formulated by the decision maker, e.g., 50–20–30 for a 3D problem, are translated into a hyperplane which is then used to choose a final solution from the knee region. This way, the decision maker’s preference can be incorporated, while its influence depends on the Pareto front’s shape and a design parameter, at the same time favorizing knee points if they exist. The proposed approach is applied in simulation for the multi-objective model predictive control (MPC) of the two-dimensional rocket car example and the energy management system of a building

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

Interactive Decision Analysis; Proceedings of an International Workshop on Interactive Decision Analysis and Interpretative Computer Intelligence, Laxenburg, Austria, September 20-23, 1983

An International Workshop on Interactive Decision Analysis and Interpretative Computer Intelligence was held at IIASA in September 1983. The Workshop was motivated, firstly, by the realization that the rapid development of computers, especially microcomputers, will greatly increase the scope and capabilities of computerized decision-support systems. It is important to explore the potential of these systems for use in handling the complex technological, environmental, economic and social problems that face the world today. Research in decision-support systems also has another, less tangible but possibly more important, motivation. The development of efficient systems for decision support requires a thorough understanding of the differences between the decision-making processes in different nations and cultures. An understanding of the different rationales underlying decision making is not only necessary for the development of efficient decision-support systems, but it is also an important factor in encouraging international understanding and cooperation. The Proceedings of the Workshop which are contained in this volume are divided in four main sections. The first section consists of an introductory lecture in which a unifying approach to the use of computers and computerized mathematical models for decision analysis and support is described. The second section is concerned with approaches and concepts in interactive decision analysis and section three is devoted to methods and techniques for decision analysis. The final section contains descriptions of a wide range of applications of interactive techniques, covering the fields of economics, public policy planning, energy policy evaluation, hydrology and industrial development

Multiple Criteria Decision Support; Proceedings of an International Workshop, Helsinki, Finland, August 7-11, 1989

Multiple Criteria Decision Making has been an important and active research area for some 20 years. In the 1970's, research focused on the theory of multiple objective mathematical programming and on procedures for solving multiple objective mathematical programming problems. During the 1980's, a shift in emphasis towards multiple criteria decision support was observed. Accordingly, much research has focused on the user interface, the behavioral foundations of decision making, and on supporting the entire decision-making process from problem structuring to solution implementation. Because of the shift in research emphasis the authors decided to make "Multiple Criteria Decision Support" the theme for the International Workshop, which was held at Suomen Saeaestoepankkiopisto in Espoo, Finland. The Workshop was organized by the Helsinki School of Economics, and sponsored by the Helsinki School of Economics and IIASA, Austria. This volume provides an up-to-date coverage of the theory and practice of multiple criteria decision support. The authors trust that it will serve the research community as well as the previously published Conference Proceedings based on IIASA Workshops

A multiple objective optimization approach to quality control

The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios: tuning of process controllers to meet specified performance objectives and tuning of process inputs to meet specified quality objectives. Five case studies are presented

Multiobjective Problems of Mathematical Programming; Proceedings of an International Conference, Yalta, USSR, October 26 - November 2, 1988

IIASA's approach to research in Multiple Objective Decision Support, Multiple Criteria Optimization (MCO) and related topics assumes a high level of synergy between three main components: methodological and theoretical backgrounds, computer implementation and decision support systems and real life applications. This synergy is reflected in the subjects of papers presented at the Conference as well as in the structure of the Proceedings which is divided into three main sections. In the first section, "Theory and Methodology of Multiple Criteria Optimization," 21 papers discussing new theoretical developments in MCO are presented. The second section, "Applications of Multiple Criteria Optimization, " contains nine papers dealing with real-life applications of MCO. Five papers on the application of MCO in the development of Decision Support Systems are included in the final section, "Multiple Criteria Decision Support." Among the important outcomes of this Conference were conclusions regarding further directions of research for Multiple Criteria Optimization, in particular, in the context of cooperation between scientists from Eastern and Western countries

Multi-Objective Building Energy Management Optimization with Model Predictive Control

Today’s goals for the reduction of CO2 emissions are significantly impacting both the civil and the industrial sector. The increasing share of renewable energy sources leads to more volatile and challenging conditions for power consumption. The building sector is responsible for approximately a third of both CO2 emissions and energy consumption in Germany. At the same time, it offers the potential to adapt to the changing conditions by the intelligent use of energy storage systems. These can, e. g., be stationary batteries, electric vehicles at charging stations, heat tanks or the building itself. The control system for the power flow between these elements is called a building energy management (BEM) system. As the control strategy, Model Predictive Control (MPC) is an obvious choice. It allows optimal control while incorporating forecasts of, e. g., power demand, renewable energy production and air temperature. However, in a complex control setting such as BEM, multiple contradicting objectives are to be minimized. For example, next to the reduction of monetary costs, the building’s temperature is supposed to be kept at a comfortable level, electric vehicles have to be charged sufficiently, battery degradation should be kept low and CO2 emissions have to be reduced. To directly optimize real-world objectives such as the examples given above, Economic Model Predictive Control (EMPC) can be utilized, in which the cost function for the optimal control problem (OCP) does not need to be quadratic, but can be of arbitrary form. However, if multiple objectives have to be respected, usually this is done in form of a weighted sum. Thereby, the weights are chosen either from experience or such that all objectives are of the same magnitude. While this is a reasonably simple approach, it neglects that, especially for BEM systems, the OCP varies significantly with the volatile outer conditions. Therefore, the trade-off which is chosen by the fixed weights varies over time, too. The simultaneous optimization of contradicting objectives is called multi- objective optimization (MOO). Usually, the set of all ’optimal’ solutions is approximated and a (human) decision maker (DM) afterwards selects a solution which represents his preferences the most. This is appropriate in the case of one-time optimizations, which is usually the case in MOO. However, we want to use MOO for the permanent control of a BEM system. Therefore, we propose an extended conceptualization of dynamic MOO, which is the systematic combination of MPC and MOO. At every time step, a multi-objective OCP is formulated and an approximation of the Pareto front is derived as its solution, i. e. the set of all optimal compromises. Then, a solution is automatically chosen. To this end, we present two different options. In the metric-based automatized decision making strategy, the Pareto front is first normalized. Then, a metric is calculated for every solution and the solution with the best value is chosen. We present two normalization schemes and three metrics a DM can choose from. In the preference-based automatized decision making strategy, preferences formulated by the DM a priori are utilized. First, a knee region is determined from the normalized Pareto front to exclude solutions which are too extreme. Then, the preferences are used to construct a hyperplane with which a solution from the knee region is finally selected. The applicability of the proposed methods to the BEM problem is shown in long-term simulations. To this end, we show how the most important elements in a BEM system can be modeled while obtaining well-solvable convex optimization problems. Furthermore, we present a new method to determine an approximation of the Pareto front which is more apt for the case of dynamic MOO and its varying conditions

Conflicting Objectives in Decisions

This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem