• Title/Summary/Keyword: Multi-objective decision

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Response Surface Approximation for Fatigue Life Prediction and Its Application to Compromise Decision Support Problem (피로수명예측을 위한 반응표면근사화와 절충의사결정문제의 응용)

  • Baek, Seok-Heum;Cho, Seok-Swoo;Jang, Deuk-Yul;Joo, Won-Sik
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.1187-1192
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    • 2008
  • In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

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Applying Multi-objective Mathematical Programming Model for Business Planning of Eco-friendly Agrifood Processing Enterprise in Korea (친환경농식품 가공업체의 경영계획 수립을 위한 다목표 수리계획모형의 적용 방안)

  • Cho, Wan-Hyung
    • Korean Journal of Organic Agriculture
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    • v.26 no.2
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    • pp.181-202
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    • 2018
  • Most of eco-friendly agrifood processing enterprises in Korean rural area are small and medium-sized business. For this reason, it's hard for eco-friendly agrifood processing enterprises to neither analyze business performance for efficient business management nor establish their own business plan for rational decision-making. Therefore it's necessary to design effective mathematical programming model and to make practical application which can support rational management decision-making ensuring the stable business activity of eco-friendly agrifood processing enterprises. Accordingly this paper focuses on the designing and its application of multi-objective mathematical programming model using goal programming to support rational decision-making of eco-friendly agrifood processing enterprise. Hansalimanseongmachum Food Inc. which runs soy bean processing business making tofu based on regional-based soybean farms around Anseong City will be the specific case to apply multi-objective mathematical programming model in practice. And it will suggest measures to support rational management decision-making of other eco-friendly agrifood processing enterprises.

Multiobjective Decision-Making applied to Ship Optimal Design

  • Wang, Li-Zheng;Xi, Rong-Fei;Bao, Cong-Xi
    • Journal of Ship and Ocean Technology
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    • v.5 no.1
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    • pp.30-37
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    • 2001
  • Ship optimal design is a multi-objective decision-making process and its optimal solution does not exit in general. It is a problem in which the decision-maker is very interested that an effective solution is how to be found which has good characteristic and is substituted for optimal solution in a sense. In the previous methods of multi-objective decision-making, the weighting coefficients are decided from the point of view of individuals which have a bit sub-jective an unilateral behavior. in order to fairly and objectively decide the weighting coeffi-cients, which are considered to be optimal in all system of multi-objective decision-making and satisfactory solution to the decision-maker, the pater presents a method of applying the Technology of the Biggest Entropy. It is proved that the method described in the paper is very feasible and effective be means of a practical example of ship optimal design.

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Development of Fitness and Interactive Decision Making in Multi-Objective Optimization (다목적 유전자 알고리즘에 있어서 적합도 평가방법과 대화형 의사결정법의 제안 )

  • Yeboon Yun;Dong Joon Park;Min Yoon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.45 no.4
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    • pp.109-117
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    • 2022
  • Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

A Study on Multi-Objective Fuzzy Optimum Design of Truss Structures

  • Mu, Zai-Gen;Ge, Xin;Yan, Mou;Chen, Yun-Zhou
    • Journal of Korean Association for Spatial Structures
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    • v.3 no.2 s.8
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    • pp.77-83
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    • 2003
  • This paper presents decision making method of structural multi-objective fuzzy optimum problem. The data and behavior of many engineering systems are not know precisely and the designer is required to design the system in the presence of fuzziness in the multi-goals, constraints and consequences of possible actions. In this paper, in order to find a satisfactory solution, the membership functions are constructed for the fuzzy objectives subject to the fuzzy constraints, and two approaches are presented by using the different types of fuzzy decision making. Thus, multi-objective fuzzy optimum problem can be converted into single objective non-fuzzy optimum problem and satisfactory solution of the multi-objective fuzzy optimum problem can be found with general optimum programming. Illustrative numerical example of the ten bar truss for minimum weight and minimum deflection is provided to demonstrate the process of finding the solution and the results are discussed.

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Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization

  • Zhang, Wenzhu;Kwak, Kyung-Sup;Feng, Chengxiao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.7
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    • pp.1802-1814
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    • 2012
  • In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

An Interference Avoidance Method Using Two Dimensional Genetic Algorithm for Multicarrier Communication Systems

  • Huynh, Chuyen Khoa;Lee, Won Cheol
    • Journal of Communications and Networks
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    • v.15 no.5
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    • pp.486-495
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    • 2013
  • In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensionalGA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.

Using Machine Learning to Improve Evolutionary Multi-Objective Optimization

  • Alotaibi, Rakan
    • International Journal of Computer Science & Network Security
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    • v.22 no.6
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    • pp.203-211
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    • 2022
  • Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

Acquisition and Refinement of State Dependent FMS Scheduling Knowledge Using Neural Network and Inductive Learning (인공신경망과 귀납학습을 이용한 상태 의존적 유연생산시스템 스케쥴링 지식의 획득과 정제)

  • 김창욱;민형식;이영해
    • Journal of Intelligence and Information Systems
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    • v.2 no.2
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    • pp.69-83
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    • 1996
  • The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

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Fuzzy Preference Based Interactive Fuzzy Physical Programming and Its Application in Multi-objective Optimization

  • Zhang Xu;Huang Hong-Zhong;Yu Lanfeng
    • Journal of Mechanical Science and Technology
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    • v.20 no.6
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    • pp.731-737
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    • 2006
  • Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer.