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Showing papers on "Multi-objective optimization published in 1990"


Book
01 Jul 1990
TL;DR: In this article, the authors present methods and computer procedures for solving multicriteria optimum design problems including interactive methods and knowledge-based systems, and an extensive range of applications of these methods to design processes in the following fields.
Abstract: Interest in the field of multicriteria optimization and its application to design processes has grown quickly in recent years. After an introduction to multicriteria optimization and the advantages of using multicriteria techniques, the first part of this book presents methods and computer procedures for solving multicriteria optimum design problems including interactive methods and knowledge-based systems. The second part presents an extensive range of applications of these methods to design processes in the following fields: mechanisms and dynamic systems, aircraft and space technology, machine tool design, metal forming and cast metal technology, civil and architectural engineering, and structures made of advanced materials.

210 citations


Journal ArticleDOI
TL;DR: Generalized DP avoids the potential pitfalls created by this absence of monotonicity, thereby guaranteeing optimality in a prototypical multicriteria DP problem, namely a multicritical version of the shortest path problem.

141 citations


Book
21 Dec 1990
TL;DR: Part 1 Introduction: a preliminary survey on solution algorithms in one-parametric optimization some motivations abstracts of the chapters 2-6 and applications: on globally convergent algorithms on global optimization on multiobjective optimization.
Abstract: Part 1 Introduction: a preliminary survey on solution algorithms in one-parametric optimization some motivations abstracts of the chapters 2-6. Part 2 Theoretical background: unconstrained optimization problems constraint sets critical points, stationary points, stability generic singularities in one-parametric optimization problems the approach via piecewise differentiability. Part 3 Pathfollowing of curves of local minimizers: the estimation of the radius of convergence an active index set strategy the ALGORITHM PATH I and numerical results. Part 4 Pathfollowing along a connected component in the Karush-Kuhn-Tucker set and in the critical set: pathfollowing in the Karush-Kuhn-Tucker set the ALGORITHM PATH II and numerical results pathfollowing in the critical set the ALGORITHM PATH III. Part 5 Pathfollowing in the set of local minimizers and in the set of critical points: jumps in the set of local minimizers and the ALGORITHM JUMP I jumps in the critical set and the ALGORITHM JUMP II. Part 6 Applications: on globally convergent algorithms on global optimization on multiobjective optimization.

122 citations


Journal ArticleDOI
TL;DR: In this paper a probabilistic index is employed as a measure of structural redundancy that is compatible with structural system reliability analysis and shows that optimal searchings balancing weight, system reliability and system redundancy produce more rational structural designs.

100 citations


Journal ArticleDOI
TL;DR: The application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments are discussed.
Abstract: Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. The literature relevant to optimization in simulation is scattered, and no comprehensive and up-to-date treatment of the subject is presently available. To that end, this article brings together numerous concepts related to t he problem of optimization in simulation. Specifically, it discusses the application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments. The article provides a user with an overview of the available optimization techniues and identifies future research possibilities.

91 citations


Journal ArticleDOI
TL;DR: The application of multiobjective optimization techniques to the selection of system parameters and large scale structural design optimization problems is the main purpose of this paper.
Abstract: SUMMARY The use of multiobjective optimization techniques, which may be regarded as a systematic sensitivity analysis, for the selection and modification of system parameters is presented. A minimax multiobjective optimization model for structural optimization is proposed. Three typical multiobjective optimization techniques-goal programming, compromise programming and the surrogate worth trade-off method-are used to solve such a problem. The application of multiobjective optimization techniques to the selection of system parameters and large scale structural design optimization problems is the main purpose of this paper.

54 citations


Journal ArticleDOI
TL;DR: A pseudopolynomial approximation algorithm for bicriteria linear programming using the lower and upper approximation of the optimal value function is given and Numerical results for the bikriteria minimum cost flow problem on NETGEN-generated examples are presented.
Abstract: A subsetS⊂X of feasible solutions of a multicriteria optimization problem is called e-optimal w.r.t. a vector-valued functionf:X→Y $$ \subseteq $$ ℝ K if for allx∈X there is a solutionz x∈S so thatf k(z x)≤(1+e)f k (x) for allk=1,...,K. For a given accuracy e>0, a pseudopolynomial approximation algorithm for bicriteria linear programming using the lower and upper approximation of the optimal value function is given. Numerical results for the bicriteria minimum cost flow problem on NETGEN-generated examples are presented.

51 citations


Proceedings Article
27 Jul 1990

40 citations


Proceedings ArticleDOI
03 Jul 1990
TL;DR: Optimization criteria for obstacle avoidance, manipulability, least torque norm and maximum actuator torque are first discussed and emphasis is placed on optimization methods for problems involving multi-requirements and multicriteria optimization.
Abstract: An important characteristic of practical mobile manipulators, i.e. manipulators mounted on mobile platforms, is their particular kinematic redundancy created by the addition of the degrees of freedom of the platform to those of the manipulator. This paper is concerned with the resolution of this kinematic redundancy, and in particular with the local optimization of the position and configuration of the system during task commutations when changes occur in both task requirements and task constraints. Optimization criteria for obstacle avoidance, manipulability, least torque norm and maximum actuator torque are first discussed. Emphasis is then placed on optimization methods for problems involving multi-requirements and multicriteria optimization. Sample results of the methods for a system including a three-link manipulator mounted on a mobile platform are presented and discussed. >

38 citations


Journal ArticleDOI
TL;DR: In this paper, the combined problem of grouping and loading in a flexible manufacturing system is formulated as a multistage multiobjective optimization model and the min-max approach to multi-objective optimisation is used to obtain a compromise solution.
Abstract: SUMMARY The combined problem of grouping and loading in a flexible manufacturing system is formulated as a multistage multiobjective optimization model. The min-max approach to multiobjective optimization is used to obtain a compromise solution. The application of the model is illustrated by an example.

36 citations


Journal ArticleDOI
01 May 1990
TL;DR: The interactive step tradeoff method (ISTM) is composed of three basic steps: an efficient solution and the corresponding local tradeoff information are provided by the analyst, and the decision maker determines the preference direction and step size.
Abstract: The interactive step tradeoff method (ISTM) is composed of three basic steps. First, an efficient solution and the corresponding local tradeoff information are provided by the analyst. Then, the decision maker determines the preference direction and step size. Again the analyst looks for a new efficient solution according to the preference information; the new solution should dominate the previous one. In ISTM, the efficient solution and the local tradeoff information, the current values of objective functions, and the tradeoff rates between them are obtained by solving an auxiliary problem. The auxiliary problem is defined, and relationships between the optimal solutions of the auxiliary problem and the efficient solutions of the original problem are explored. The relationships between the Kuhn-Tucker multipliers (or simplex multipliers) of the auxiliary problems and the tradeoff rates are analyzed. The particular steps of the ISTM algorithm are given. An example is discussed to illustrate the use of the algorithm. >

Journal ArticleDOI
TL;DR: A general multiobjective algorithm which accommodates uncertainty is proposed which is appropriate for use in a multiple criteria framework with a discrete number of states of nature.
Abstract: Uncertainty presents unique difficulties in multiobjective optimization problems, because decision makers are faced with risky situations requiring analysis of multiple outcomes in differing states of nature. Very few direct choice interactive multiobjective methods are capable of addressing problems with probabilistic outcomes. We thus propose a general multiobjective algorithm which accommodates uncertainty. The method is appropriate for use in a multiple criteria framework with a discrete number of states of nature. Without loss of generality, and in the interest of simplicity of exposition, our method is explored and developed in the context of a bicriterion optimization problem using a two stage mathematical programming model. Simulation and behavioral experiments are conducted which verify that the method is viable for problems with greater dimensionality.

Journal ArticleDOI
TL;DR: In this paper, the authors extended the traditional decision tree analysis to incorporate multiple non-commensurate objective functions and use of the conditional expected value of the risk of extreme and catastrophic events.
Abstract: Single-objective-based decision-tree analysis has been extensively and successfully used in numerous decision-making problems since its formal introduction by Howard Raiffa more than two decades ago This paper extends the traditional methodology to incorporate multiple noncommensurate objective functions and use of the conditional expected value of the risk of extreme and catastrophic events The proposed methodology considers the cases where (a) a finite number of actions are available at each decision node and (b) discrete or continuous states of nature can be presented at each chance node The proposed extension of decision-tree analysis is introduced through an example problem that leads the reader step-by-step into the methodological procedure The example problem builds on flood warning systems Two noncommensurate objectives—the loss of lives and the loss of property (including monetary costs of the flood warning system)–are incorporated into the decision tree In addition, two risk measures—the common expected value and the conditional expected value of extreme and catastrophic events—are quantified and are also incorporated into the decision-making process Theoretical difficulties associated with the stage-wise calculation of conditional expected values are identified and certain simplifying assumptions are made for computational tractibility In particular, it is revealed that decisions concerning experimentation have a very interesting impact on the noninferior solution set of options—a phenomenon that has no equivalence in the single-objective case

Journal ArticleDOI
01 May 1990
TL;DR: A deterministic bi objective model of the stochastic short-term scheduling of thermoelectric plants is introduced and the corresponding biobjective optimization problem is solved with the relaxation-projection approach.
Abstract: Multicriteria optimization problems are addressed in the context of relaxation-projection techniques. Theoretical and practical aspects of a methodology for solving (interactively) decision problems with multiple objective functions are discussed. Relaxation and projection are shown to constitute a suitable basis through which a decision maker can develop his or her apprenticeship about the behavior of the problem under consideration. As an application, a deterministic biobjective model of the stochastic short-term scheduling of thermoelectric plants is introduced and the corresponding biobjective optimization problem is solved with the relaxation-projection approach. >

Journal ArticleDOI
TL;DR: In this paper, a decision support tool for maximizing profit and minimizing yield risk simultaneously in upland-rice production, subject to the constraints imposed on the three decision variables, namely, sowing date, fertilizer treatment and plant population, is presented.

Journal ArticleDOI
TL;DR: A novel decomposition strategy for multicriteria optimization of large-scale systems with heuristic character and contains four stages that are suitable for designing machine tool spindle systems with hydrostatic bearings.

Journal ArticleDOI
TL;DR: It is argued that in a truly pre-emptive situation, direct lexicographical optimization of the objectives, without introduction of goals, has a number of advantages and when applied to special structure models such as transportation or assignment, this approach enables one to maintain the structure and hence the efficiency of those algorithms.
Abstract: A decision-maker, using mathematical programming optimization models, is often faced with a choice of many alternative solutions optimizing the objective function. The decision may be based on secondary, tertiary or higher-order objectives. Such problems are usually handled using goal programming (GP) with pre-emptive priorities. Pre-emptive prioritization is discussed in the literature in the context of GP. This paper suggests that the two are separable, and presents algorithms to accomplish this. It argues that in a truly pre-emptive situation, direct lexicographical optimization of the objectives, without introduction of goals, has a number of advantages. In addition, when applied to special structure models such as transportation or assignment, this approach enables one to maintain the structure and hence the efficiency of those algorithms.

Journal ArticleDOI
TL;DR: The concept of a Pareto-optimal solution in the context of multiple objective helicopter design problems is introduced and results obtained using seven different multi-objective optimization techniques are presented.
Abstract: The design of a complex engineering system involves several competing objectives which cannot be combined into a single objective function. This paper introduces the concept of a Pareto-optimal solution in the context of multiple objective helicopter design problems. Commonly used techniques for generating Pareto-optimal solutions are discussed. The use and effectiveness of multiple objective optimization techniques in the formulation and solution of design problems are developed and demonstrated via an application to two types of helicopter design problems. The first problem deals with the determination of optimum flight parameters to accomplish a specified mission in the presence of three competing objectives. The second example addresses an optimum design of the main rotor blades of a helicopter involving eight objective functions. Results obtained using seven different multi-objective optimization techniques are presented. A comparison is made between the relative efficiency of these techniques in ter...

Proceedings ArticleDOI
01 Dec 1990
TL;DR: Investigates the finite-time behavior of two specific simulation optimization algorithms: a Robbins-Monro procedure applied in a conventional way and a more recently proposed single-run optimization algorithm and provides some basic insight into the behavior of such algorithms.
Abstract: Investigates the finite-time behavior of two specific simulation optimization algorithms: a Robbins-Monro procedure applied in a conventional way and a more recently proposed single-run optimization algorithm. By applying these algorithms to simple systems it is shown that, in practice, convergence of the former algorithm can be slow while that of the latter is very fast. The authors also provide evidence that the choice of projection operator (to deal with constraints in the optimization problem) has a significant effect on the finite-time performance of the latter algorithm. These results provide some basic insight into the behavior of such algorithms. >

Book ChapterDOI
01 Jan 1990
TL;DR: MO-procedures treated in Chapter 2 will be developed into interactive procedures which integrate the decision making process into optimization algorithms and improve the implicit preferences of the decision maker.
Abstract: In this chapter MO-procedures treated in Chapter 2 will be developed into interactive procedures which integrate the decision making process into optimization algorithms. The interactive procedures provide the Decision Maker (DM) with a selection of Pareto-optimal solutions which to some extent are representative for the whole set of available solutions. This procedure consists of a sequence of decision and computation phases. In the decision phase the DM decides whether or not a solution is optimal with respect to his implicit preferences. In the latter case he must give some information about the direction in which he expects to obtain a better solution. In the computation phase the new solution is generated for the next decision phase. The procedure is stopped when the optimal solution which reflects the DM’s preferences is found. Such a dialogue does not only improve the implicit preferences of the decision maker but also supports and simplifies the process of decision making.

Journal Article
TL;DR: In this paper, an interactive procedure for finding a "satisfactory" solution to the multiobjective optimization problems using Nash Bargaining Principle has been presented, where the concept of measure of conflict has been introduced to elicit tradeoffs between the objectives.
Abstract: This paper outlines an interactive procedure for finding a ‘satisfactory’ solution to the multiobjective optimization problems using Nash Bargaining Principle. The concept of ‘measure of conflict’ has been introduced to elicit tradeoffs between the objectives. The suggested procedure has been implemented on a personal computer and its performance has been compared with the GDF procedure reported in the literature.

Journal ArticleDOI
01 Dec 1990
TL;DR: The technique called PARETO OPTIMAL SERIAL DYNAMIC PROGRAMMING is presented here as a tool for rational mine planning that enables the identification of a set of decision alternatives considered superior to the remaining feasible, usually numerous, decision alternatives, when a number of conflicting, noncommensurable, objectives are simultaneously optimized.
Abstract: Recent advances in mathematical optimization have resulted in the development of superior techniques for solving realistic decision making problems. The technique called PARETO OPTIMAL SERIAL DYNAMIC PROGRAMMING is presented here as a tool for rational mine planning. This approach always enables the identification of a set of decision alternatives considered superior to the remaining feasible, usually numerous, decision alternatives, when a number of conflicting, noncommensurable, objectives are simultaneously optimized. It is further noted that the decision makers' truly preferred decision is always one of the members identified as the superior set.

Journal ArticleDOI
TL;DR: A process-control problem, originally solved by the desirability-function approach, is solved using this new model, which incorporates prediction-interval constraints into a goal-programming model.
Abstract: A challenging problem in process control is the selection of input levels which will produce desirable output quality. This problem is complicated by the unsure relationships of cause and effect and by the trade-offs between meeting conflicting output specifications. This paper proposes a new approach, which incorporates prediction-interval constraints into a goal-programming model. A process-control problem, originally solved by the desirability-function approach, is solved using this new model. Comparisons between the two approaches are discussed.

Journal Article
TL;DR: In this paper, a multiobjective optimization approach to deal with structural reliability-based design under multiple limit states is developed, where keeping track of the reliability level of each limit state of interest becomes an intrinsic part of the optimization method used to find the objective solution set.
Abstract: A multiobjective optimization approach to deal with structural reliability-based design under multiple limit states is developed. This approach resolves the limit states reliability interaction problem. In fact, keeping track of the reliability level of each limit state of interest becomes an intrinsic part of the optimization method used to find the objective solution set. It is concluded that the proposed approach produces a more balanced optimum solution.

Journal ArticleDOI
TL;DR: An attempt has been made to integrate the two approaches to spatial planning by developing a hybrid multicriteria alternative selectiion module (HYDAS-HYbrid Discrete Alternative Selection) which will work as a postprocessor for both modules.
Abstract: During the development of an expert system for regional planning (The Shanxi Province Decision Support System) by IIASA's Advanced Computer Applications (ACA) group, two spatial planning systems evolved. PDAS, a system for the optimization of the industrial structure of an area, which is based on numerical multicriteria optimization techniques; and REPLACE, a site-selection system, implemented in PROLOG, which is based on a qualitative, constraint-satisfaction method. Although both approaches partially overlap, each approach has certain advantages over the other. As a natural extension of the Shanxi DSS, within the framework of a project on Hybrid Decision Support Tools sponsored by the U.S. Bureau of Reclamation, an attempt has been made to integrate the two approaches by developing a hybrid multicriteria alternative selectiion module (HYDAS-HYbrid Discrete Alternative Selection) which will work as a postprocessor for both modules. In HYDAS, artificial intelligence (AI) paradigms and numeric multicriteria optimization techniques are combined to arrive at a hybrid approach to discrete alternative selection. These techniques include: (1) qualitative analysis, (2) various statistical checks and recommendations, (3) robustness and sensitivity analysis, and (4) help for defining acceptable regions for analysis. HYDAS is implemented on high performance workstations using C, PROLOG, and the X graphics library and window system.

Journal ArticleDOI
01 Aug 1990
TL;DR: An interactive procedure for finding a ‘satisfactory’ solution to the multiobjective optimization problems using Nash Bargaining Principle and the concept of ‘measure of conflict’ has been introduced to elicit tradeoffs between the objectives.
Abstract: This paper outlines an interactive procedure for finding a ‘satisfactory’ solution to the multiobjective optimization problems using Nash Bargaining Principle. The concept of ‘measure of conflict’ has been introduced to elicit tradeoffs between the objectives. The suggested procedure has been implemented on a personal computer and its performance has been compared with the GDF procedure reported in the literature.

Proceedings ArticleDOI
12 Aug 1990
TL;DR: In this article, a linear programming-based multi-criteria model is developed based on the described system constraints for the optimization of the hydropower generation versus irrigation releases applied to the Egyptian network.
Abstract: The problem of multiobjective optimization of the hydropower generation versus irrigation releases applied to the Egyptian Network is addressed. A linear programming-based multi-criteria model is developed based on the described system constraints. Two objective functions are considered in the analysis. The first is to maximize the total sum of potential hydropower production of the whole system. The second is to minimize the total water release to the system, while satisfying the different demands imposed at various nodes. Using the generation technique for solution, the results have revealed several trade-off curves of both hydropower and irrigation uses. These scenarios are shown to be very effective planning tools that aid the decision maker in selecting the best suitable compromise for his need. >

Proceedings ArticleDOI
Soemon Takakuwa1
01 Dec 1990
TL;DR: A method of multiple objective optimization is proposed in an attempt to determine the optimal job-assignment and conveyor systems for a digital picking/conveyance system.
Abstract: A method of multiple objective optimization is proposed in an attempt to determine the optimal job-assignment and conveyor systems for a digital picking/conveyance system. A system is considered which consists of operators, conveyor lines, and tracks containing items to be picked. Four particular types of conveyor systems are analyzed to examine their performance under various picking conditions. In addition, a multiple objective optimization problem is formulated in terms of goal programming by using a simulation technique. A procedure for obtaining the solution for multiple objective systems is presented with a numerical example. >

Book ChapterDOI
01 Jan 1990
TL;DR: In this paper, the authors investigate the use of two controllers: a QDMC controller which is tuned based on a nominal plant and a nonlinear programming-based controller which considers the model uncertainty explicitly.
Abstract: This report develops a solution to the Shell Standard Control Problem which embodies the key elements of all control problems: multiple objective criteria, inequality and equality constraints, and model uncertainty. The problem consists of developing a suitable controller which meets the performance objectives over the entire uncertainty region. Here we investigate the use of two controllers: a QDMC controller which is tuned based on a nominal plant and a Nonlinear Programming-based controller which considers the model uncertainty explicitly. Three future areas of research are outlined: the use of Embedded Optimization technology for accomplishing multiobjective optimization, the development of a Design Validation problem for discerning inherent limitations to control prior to control system design, and the development of a Design Analysis problem for analyzing various controllers.

Journal ArticleDOI
TL;DR: In this paper, the authors introduce three estimation problems for parabolic systems: the estimation of the intensity of sources on the basis of available measurements, the state of a distributed process through given observations, and an inverse problem of multiobjective optimization whose solution allows to estimate the largest input levels that ensure a guaranteed system output performance.