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Alternatives and challenges in optimizing industrial safety using genetic algorithms

RELIABILITY ENGINEERING & SYSTEM SAFETY 2004年第1期86卷 25-38页

作者： Martorell, S Sánchez, A Carlos, S Serradell, V Univ Politecn Valencia Dept Chem & Nucl Engn Valencia 46071 Spain Univ Politecn Valencia Dept Chem & Nucl Engn Valencia 46071 Spain Univ Politecn Valencia Dept Stat & Operat Res Valencia 46071 Spain

Safety (S) improvement of industrial installations leans on the optimal allocation of designs that use more reliable equipment and testing and maintenance activities to assure a high level of reliability, availability and maintainability (RAM) for their safety-related systems. However, this also requires assigning a certain amount of resources (C) that are usually limited. Therefore, the decision-maker in this context faces in general a multiple-objective optimization problem (MOP) based on RAMS + C criteria where the parameters of design, testing and maintenance act as decision variables. Solutions to the MOP can be obtained by solving the problem directly, or by transforming it into several single-objective problems. A general framework for such MOP based on RAMS + C criteria is proposed in this paper. Then, problem formulation and fundamentals of two major groups of resolution alternatives are presented. Next, both alternatives are implemented in this paper using genetic algorithms (GAs), named single-objective GA and multi-objective GA, respectively, which are then used in the case of application to solve the problem of testing and maintenance optimization based on unavailability and cost criteria. The results show the capabilities and limitations of both approaches. Based on them, future challenges are identified in this field and guidelines provided for further research. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： reliability availability maintainability and safety (RAMS) safety optimization genetic algorithms testing and maintenance multi-criteria decision-making

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On regular and parametric data envelopment analysis

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2004年第1期60卷 15-28页

作者： Neralic, L Stein, O Univ Zagreb Fac Econ Zagreb 10000 Croatia Univ Aachen Dept Math C D-52056 Aachen Germany

We give a generic regularity condition under which each weakly efficient decision making unit in the CCR model of data envelopment analysis is also CCR-efficient. Then we interpret the problem of finding maximal parameters which preserve efficiency of CCR-efficient DMUs under directional perturbations as a general semi-infinite optimization problem and use a recently suggested numerical method for this problem class to calculate maximal directionally efficient DMUs. As a practical example we investigate the efficiency of Croatian banks under additive perturbations.

关键词： semi-infinite programming bilevel optimization constraint qualification multi-objective programming production planning

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Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2004年第3期20卷 225-236页

作者： Liu, M Wu, C Tsinghua Univ Dept Automat Natl CIMS Engn Res Ctr Beijing 100084 Peoples R China

Re-entrant production lines, such as those which occur in micro-electronic wafer fabrication, are characterized by a product routing that consists of multiple visits to a facility during the manufacturing process. With the development of micro-electronic technology, the research on the scheduling and control problem of re-entrant micro-electronic production line has attracted more and more people from both academia and industry to study and has become a challenging research subject. Some results of the scheduling of re-entrant micro-electronic production line based on heuristic sequence rules have been obtained. However, performances of these sequence rules are not good enough in re-entrant micro-electronic production line because of their sensitivity to the variation of types of production line. A genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line is proposed in this paper. Comparisons between the proposed algorithm and some other typical sequence rules have been made through the simulations of a practical micro-electronic production line. The static and dynamic simulation results show that the algorithm has considerable improvements on performances of the micro-electronic production such as mean cycle time, mean number of work-in-process, production rate. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： genetic algorithm rule chain heuristic re-entrant production line scheduling multi-objective optimization micro-electronic production

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Ideal evaluation from coevolution

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EVOLUTIONARY COMPUTATION 2004年第2期12卷 159-192页

作者： de Jong, ED Pollack, JB Brandeis Univ DEMO Lab Volen Natl Ctr Complex Syst Waltham MA 02454 USA

In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in game-playing. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult problem in itself, and approximations are likely to introduce human biases into the search process. Coevolution evolves the set of tests used for evaluation, but has so far often led to inaccurate evaluation. We show that for any set of learners, a Complete Evaluation Set can be determined that provides ideal evaluation as specified by Evolutionary multi-objective optimization. This provides a principled approach to evaluation in coevolution, and thereby brings automatic ideal evaluation within reach. The Complete Evaluation Set is of manageable size, and progress towards it can be accurately measured. Based on this observation, an algorithm named DELPHI is developed. The algorithm is tested on problems likely to permit progress on only a subset of the underlying objectives. Where all comparison methods result in overspecialization, the proposed method and a variant achieve sustained progress in all underlying objectives. These findings demonstrate that ideal evaluation may be approximated by practical algorithms, and that accurate evaluation for test-based problems is possible even when the underlying objectives of a problem are unknown.

关键词： coevolution pareto-coevolution accurate evaluation evolutionary multi-objective optimization underlying objectives pareto-hillclimber over-specialization

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On the design and optimization of waveguides using evolutionary approaches

On the design and optimization of waveguides using evolution...

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IEEE Antennas-and-Propagation-Society International Symposium

作者： Hussein, YA El-Ghazaly, SM Stanford Univ Stanford Linear Accelerator Ctr Menlo Pk CA 94025 USA

ISBN: (纸本)0780383028

This paper presents a new and efficient CAD-oriented algorithm for the design and optimization of high frequency coupled coplanar waveguides (CCPW's). The technique is based on genetic algorithms to obtain the global optimal solution of the problem. The proposed algorithm optimizes a multi-objective, highly non-linear problem having multiple local minima with one constraint. The new approach obtains the optimal structure dimensions that minimize the attenuation and at the same time be as close as possible to the circuit matching condition. After validation, the proposed technique is compared to a global search optimizer and then successfully applied to the design of practical monolithic implementations.

关键词： Dielectric waveguides

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An optimization strategy for nonlinear simulated moving bed chromatography: multi-level optimization procedure MLOP)

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KOREAN JOURNAL OF CHEMICAL ENGINEERING 2004年第4期21卷 836-852页

作者： Lim, YI Hankyong Natl Univ Dept Chem Engn Anseong 456749 Kyonggi Do South Korea

This paper presents a multi-level optimization strategy to obtain optimum operating conditions (four flow-rates and cycle time) of nonlinear simulated moving bed chromatography. The multi-level optimization procedure (MLOP) approaches systematically from initialization to optimization with two objective functions (productivity and desorbent consumption), employing the standing wave analysis, the true moving bed (TMB) model and the simulated moving bed (SMB) model. The procedure is constructed on a non-worse solution property advancing level by level and its solution does not mean a global optimum. That is, the lower desorbent consumption under the higher productivity is successively obtained on the basis of the SMB model, as the two SMB-model optimizations are repeated by using a standard SQP (successive quadratic programming) algorithm. This approach takes advantage of the TMB model as well as surmounts shortcomings of the TMB model in the general case of any nonlinear adsorption isotherm using the SMB model. The MLOP is evaluated on two nonlinear SMB cases characterized by i) quasi-linear/non-equilibrium and ii) nonlinear/nonequilibrium model. For the two cases, the MLOP yields a satisfactory solution for high productivity and low desorbent consumption within required purities.

关键词： simulated moving bed (SMB) chromatography simulation and optimization multi-level optimization procedure (MLOP) nonlinear adsorption isotherms productivity desorbent consumption

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Convexity and upper semicontinuity of fuzzy sets

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2004年第1-2期48卷 117-129页

作者： Syau, YR Lee, ES Jia, LX Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA Da Yeh Univ Dept Ind Engn Changhua 51505 Taiwan Chicago State Univ Dept Math & Comp Sci Chicago IL 60628 USA

Since almost all practical problems are fuzzy and approximate, fuzzy decision making becomes one of the most important practical approaches. One of the important aspects for formulating and for solving fuzzy decision problems is the concept of convexity. In this paper, we investigate the interrelationships of several concepts of generalized convex fuzzy sets. We also prove that, in the upper semicontinuous case, the class of semistrictly quasi-convex fuzzy sets lies between the convex and quasi-convex classes. Aggregation or composition is an essential part for optimization or modeling, and some important composition rules for upper semicontinuous fuzzy sets are developed. We prove that a convex combination of upper semicontinuous fuzzy sets is an upper semicontinuous fuzzy set and the intersection of finitely many upper semicontinuous fuzzy sets is an upper semicontinuous fuzzy set. Finally, the criteria for the existence of fuzzy decision under upper semicontinuity conditions are derived and two examples in multiple objective programming are used to illustrate the approach. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： convex fuzzy sets fuzzy decision making fuzzy multiobjective optimization upper semicontinuity

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Nonlinear multi-criteria optimization for the integrated design and control of bioprocesses 9th

Nonlinear multi-criteria optimization for the integrated des...

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9th IFAC International Symposium on Computer Applications in Biotechnology, CAB 2004

作者： Sendin, Oscar H. Moles, Carmen G. Alonso, Antonio A. Banga, Julio R. Eduardo Cabello 6 Vigo36208 Spain

The simultaneous design and control of continuous bioprocesses is considered here as a multi-objective optimization problem subject to non-linear differential-algebraic constraints. This type of problem is very challenging to solve due to its non-convexity (multi-modality). We present two novel solution approaches based on extensions of an stochastic global optimization method. The robustness and efficiency of these approaches are illustrated with a wastewater treatment plant case study. Copyright © IFAC Computer Applications in Biotechnology, Nancy, France, 2004.

关键词： Global optimization

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Genetic algorithm approaches for conceptual design of spacecraft systems including multi-objective optimization and design under uncertainty

Genetic algorithm approaches for conceptual design of spacec...

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作者： Hassan, Rania A. Purdue University

学位级别：Ph.D.

In the design of complex large-scale spacecraft systems that involve a large number of components and subsystems, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-architecting process. Currently, spacecraft design is heavily based on the heritage of the industry. Old spacecraft designs are modified to adapt to new mission requirements, and feasible solutions—rather than optimal ones—are often all that is achieved. During the conceptual phase of the design, the choices available to designers are predominantly discrete variables describing major subsystems' technology options and redundancy levels. The complexity of spacecraft configurations makes the number of the system design variables that need to be traded off in an optimization process prohibitive when manual techniques are used. Such a discrete problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. This research presents a systems engineering framework that places design requirements at the core of the design activities and transforms the design paradigm for spacecraft systems to a top-down approach rather than the current bottom-up approach. To facilitate decision-making in the early phases of the design process, the population-based search nature of the Genetic Algorithm is exploited to provide computationally inexpensive—compared to the state-of-the-practice—tools for both multi-objective design optimization and design optimization under uncertainty. In terms of computational cost, those tools are nearly on the same order of magnitude as that of standard single-objective deterministic Genetic Algorithm. The use of a multi-objective design approach provides system designers with a clear tradeoff optimization surface that allows them to understand the effect of their decisions on all the design objectives under consid

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An empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP

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4th International Workshop on Ant Colony optimization and Swarm Intelligence

作者： García-Martínez, C Cordón, O Herrera, F Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

ISBN: (纸本)3540226729

The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and experimentally tested in several instances of the bi-objective traveling salesman problem, comparing their performance with that of two well-known multi-objective genetic algorithms.

关键词： Ant colony optimization

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