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IEEE 16th International Conference on Industrial Engineering and Engineering Management

作者： Zhang, Zhiyong Yang, Shanlin Liu, Xinbao Shang, Jen S. Yang, Jianbo Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Logist Sch Beijing AH-101149 Peoples R China Univ Pittsburgh Joseph M Katz Grad Sch Business Pittsburgh PA 15260 USA Univ Manchester Manchester Business Sch Manchester M156PB Lancs England

ISBN: (纸本)9781424436705

The (minimizing) achievement function of the traditional goal programming (GP) model has five basic forms: n(i), p(i), (n(i)+p(i)), (n(i)-p(i)), and (p(i)-n(i)), where n(i) and p(i) are nonnegative under and over achievement variables in the i(th) goal-constraint. Zhang and Shang (2001) proposed the theory of Coal Programs with -n(i), -p(i), and -(n(i)+p(i)) goals, which has many interesting and practical applications. This paper extends the theory further into the nonlinear situation and proposes a new algorithm for solving the ensuing nonconvex nonlinear program. Results obtained in this paper shows that the basic conclusions for the linear GP model still hold for the nonlinear case.

关键词： nonlinear goal programming Nonconvex goal programming Multiobjective Algorithm

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Evolution program for nonlinear-goal programming

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COMPUTERS & INDUSTRIAL ENGINEERING 1996年第3-4期31卷 907-911页

作者： Zheng, DW Gen, M Ida, K Department of Industrial Systems Engineering Ashikaga Institute of Technology Ashikaga 326 Japan

goal programming (GP) is one of powerful techniques for solving multi-objective optimization and has been applied to various real-life problems. This paper presents an evolution program for solving nonlinear goal prog... 详细信息

关键词： nonlinear goal programming evolution program genetic

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Method for solving nonlinear goal programming with interval coefficients using genetic algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 1997年第3-4期33卷 597-600页

作者： Taguchi, T Ida, K Gen, M Ashikaga Inst Technol Dept Ind & Syst Engn Ashikaga 326 Japan

Traditional formulations on reliability optimization problems have assumed that the coefficients of models are known as fixed quantities and reliability design problem is treated as deterministic optimization problems. Because that the optimal design of system reliability is resolved in the same stage of overall system design, model coefficients are highly uncertainty and imprecision during design phase and it is usually very difficult to determine the precise values for them. However, these coefficients can be roughly given as the intervals of confidence. In this paper, we formulated reliability optimization problem as nonlinear goal programming with interval coefficients and develop a genetic algorithm to solve it. The key point is how to evaluate each solution with interval data. We give a new definition on deviation variables which take interval relation into account. Numerical example is given to demonstrate the efficiency of the proposed approach. (C) 1997 Elsevier Science Ltd.

关键词： nonlinear goal programming genetic algorithm interval coefficients

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nonlinear goal programming with -ni-pi and -(ni+pi) goals

Nonlinear Goal Programming with -ni-pi and -(ni+pi) Goals

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2009 IEEE 16th International Conference on Industrial Engineering and Engineering Management(IEEE第16届工业工程与工程管理国际学术会议)

作者： Zhiyong Zhang Shanlin Yang Xinbao Liu Jen S.Shang Jianbo Yang Management School Hefei University of TechnologyHefei 230009Anhui ProvinceP.R.China Logistics Sch Management School Hefei University of TechnologyHefei 230009Anhui ProvinceP.R.China Joseph M.Katz Graduate School of Business University of PittsburghPittsburghPA 15260USA Manchester Business School (East) University of ManchesterManchester M15 6PBUK

ISBN: (纸本)9781424436712

The (minimizing) achievement function of the traditional goal programming (GP) model has five basic forms: ni,pi,(ni+pi),(n-pi),and (pi-ni),where ni and pi are nonnegative under and over achievement variables in the ith goal-constraint. Zhang and Shang (2001) proposed the theory of goal Programs with -ni,-pi and -(ni+pi) goals, which has many interesting and practical applications. This paper extends the theory further into the nonlinear situation and proposes a new algorithm for solving the ensuing nonconvex nonlinear program. Results obtained in this paper shows that the basic conclusions for the linear GP model still hold for the nonlinear case.

关键词： nonlinear goal programming Nonconvez goal programming Multiobjective Algorithm

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Intelligent search methods for nonlinear goal programming problems

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INFOR 2005年第2期43卷 79-92页

作者： Kuriger, G Ravindran, AR Univ Oklahoma Sch Ind Engn Norman OK 73019 USA Penn State Univ Dept Ind Engn University Pk PA 16802 USA

Three methods were developed to solve nonlinear goal programming (NLGP) problems by adapting and extending the Nelder-Mead method, the Complex search method, and the Hooke and Jeeves Pattern Search method to account for multiple criteria. These modifications were largely accomplished by using goal programming, lexicographic ordering, and partitioning concepts. The three resulting methods were the Lexicographic Nelder-Mead (LNM) method, the Partitioning Nelder-Mead-Complex (PNMC) method, and the Partitioning Pattern Search (PPS) method. Each method is analyzed based on results and compared with the other methods. Each of the methods appears to function effectively and generate a good solution. In general the PPS method did well in respect to computational time and number of iterations, however, none of the methods clearly outperformed or was outperformed by the other methods.

关键词： nonlinear goal programming goal programming search methods

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Method for solving nonlinear goal programming tasks with interval coefficients using GA

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 2003年第4期86卷 55-65页

作者： Taguchi, T Yokota, T Gen, M Ashikaga Inst Technol Dept Management & Informat Engn Ashikaga 3268558 Japan

In this study, the authors proposed a solution for directly using genetic algorithms without linear conversion for solving nonlinear goal programming problems involving interval coefficients. The proposed technique involves directly computing left-side interval numbers in the goal constraint equation from the genetic algorithm solution and comparing their size with those of the right-side numbers, for obtaining new difference variables. These difference variables vary depending on the goal type;they are defined using weights which express interval order evaluation criteria. In addition, as an example of application field for this method, the authors studied a large-scale problem for optimal design of system reliability involving interval coefficients, in order to clarify localization of this method. (C) 2002 Wiley Periodicals, Inc.

关键词： nonlinear goal programming genetic algorithm interval coefficients

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A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining

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NEURAL COMPUTING & APPLICATIONS 2016年第7期27卷 2065-2081页

作者： He, Yong Gao, Siwei Liao, Nuo Liu, Hongwei Guangdong Univ Technol Sch Management Guangzhou 510520 Guangdong Peoples R China Eastern Kentucky Univ Sch Business Richmond KY 40475 USA

This study proposes a combined 'nonlinear goal-programming'-based 'differential evolution' (DE) and 'artificial neural networks' (ANN) methodology for grade optimization in iron mining production processes. The nonlinear goal-programming model has decision variables of 'cutoff grade,' 'dressing grade' and 'concentrate grade,' with the goals being 'concentrate output,' 'resource utilization rate' and 'economic benefit (profit).' The model, which contains three unknown functions, the 'loss rate,' the 'ore-dressing metal recovery rate' and the 'total cost,' is subsequently converted into an unconstrained optimization problem, to be solved by our integrated DE-ANN approach. DE is used to search for the optimum combination of the cutoff, dressing and concentrate grades, with the crossover rate in the DE analysis being dynamically adjusted within the evolutionary process. The loss rate is calculated by a regression model, whilst the ore-dressing metal recovery rate and the total cost functions are, respectively, calculated using 'back-propagation' and 'radial basis function' neural networks. We subsequently go on to analyze a case study of the Daye iron mine in China to demonstrate the reliability and efficiency of our proposed approach. Our study provides a novel approach for decision makers to guide production and management in iron mining.

关键词： nonlinear goal programming Grade optimization Differential evolution Artificial neural networks

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A nonlinear goal programming model for the loading problem in a flexible manufacturing system

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Engineering Optimization 1987年第4期12卷 315-323页

作者： Kumar, Prevendra Singh, N. Tewari, N.K. Department of Mechanical Engineering Indian Institute of Technology New Delhi-110016 Hauz Khas India

A nonlinear goal programming model is developed for the loading problem in a flexible maunfacturing system. A sequential search approach is used to obtain the solution. An example is presented to illustrate the applic... 详细信息

关键词： Flexible manufacturing Loading problem nonlinear goal programming

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EXTREMAL AND GAME-THEORETIC CHARACTERIZATIONS OF THE PROBABILISTIC APPROACH TO INCOME-REDISTRIBUTION

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1984年第3期44卷 435-451页

作者： CHARNES, A DUFFUAA, S INTRILIGATOR, M TEXAS A&M UNIV DEPT MATHCOLLEGE STNTX 77843 UNIV CALIF LOS ANGELES ECON & POLIT SCILOS ANGELESCA 90024

In this paper, we cast the problem of income redistribution in two different ways, one as a nonlinear goal programming model and the other as a game theoretic model. These two approaches give characterizations for the probabilistic approach suggested by Intriligator for this problem. All three approaches reinforce the linear income redistribution plan as a desirable mechanism of income redistribution.

关键词： Probabilistic approach linear income system nonlinear goal programming Kullback-Leibler information function core of a game Shapley value

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Probabilistic Linguistic Group Decision-Making Method Based on Attribute Decision and Multiplicative Preference Relations

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2021年第7期23卷 2200-2217页

作者： Zhao, Huiyan Li, Boquan Li, Yongyi Anhui Normal Univ Sch Math & Stat Wuhu 241003 Anhui Peoples R China Nanjing Tech Univ Sch Transportat Engn Nanjing 210009 Peoples R China

In some cases, decision makers (DMs) need to use multiplicative linguistic terms to express evaluations, and probabilistic linguistic preference relation (PLPR) cannot meet the needs of decision makers. Multiplicative probabilistic linguistic preference relations (MPLPRs) can solve this problem. The consistency of preference relations is crucial to ensure reliable decision results. We define the ordinal consistency of MPLPRs, and improve the ordinal consistency of MPLPRs. In order to facilitate calculation, the mapping from probabilistic linguistic term set (PLTS) to linguistic term is defined. In probabilistic linguistic information decision-making methods, some methods only collect the information of the decision matrix, and some methods only collect the information of the preference matrix, and almost no decision method that uses the decision matrix and the preference relation information at the same time, but the optimal ranking of alternatives is obtained according to the comprehensive attribute of alternatives. Based on the above considerations, first, we construct a nonlinear objective optimization model containing alternative attributes and alternative preferences. Then, a probabilistic linguistic group decision-making method based on attribute decision and multiplicative preference relations is further proposed. Finally, the feasibility of the method is illustrated by an example of hospital evaluation, and the advantages of the proposed method are verified by comparing the differences between the proposed method and other methods.

关键词： Probabilistic linguistic term set nonlinear goal programming Ordinal consistency Probabilistic linguistic preference relation Multiplicative probabilistic linguistic preference relation

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