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

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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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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A generalized behavioral-based goal programming approach for decision-making under imprecision

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OPERATIONS RESEARCH PERSPECTIVES 2024年 13卷

作者： Cherif, Mohamed Sadok King Faisal Univ Sch Business Dept Quantitat Methods PB 400 Al Hasa 31982 Saudi Arabia Univ Monastir Higher Inst Comp Sci & Math Dept Math Ave Corniche Monastir 5000 Tunisia

The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioralbased GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.

关键词： nonlinear goal programming DM's behavioral preferences modeling Behavior-type utility function Decision-making under imprecision Project selection

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A decision support model for risk management of hazardous materials road transportation based on quality function deployment

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2019年 74卷 154-173页

作者： Li, Yan-Lai Yang, Qiang Chin, Kwai-Sang Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 611756 Sichuan Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Tong 83 Tat Chee Ave Hong Kong Peoples R China

Risk management of hazardous materials (hazmats) road transportation has long been a concern because of the potential hazards that poses to society and the environment. In this work, a systematic and semi-quantitative decision support framework for risk management of hazmats road transportation based on the combination of quality function deployment (QFD), fuzzy analytic hierarchy process (F-AHP), fuzzy failure mode and effect analysis (F-FMEA), and nonlinear goal programming is proposed. The QFD is used innovatively to construct the overall framework, which contains three main components of general risk management: risk identification, risk assessment, and risk control. The F-AHP is used to build a hierarchical risk assessment system and determine the importance rating of each risk factor. The F-FMEA is used to evaluate the potential risks of risk control measures and determine the risk adjustment coefficient of each risk measure, which is used subsequently to modify the fulfillment level of risk measure in the nonlinear goal programming model. To address the inherent vagueness and uncertainty contained in the risk management process, the fuzzy set theory is introduced as an effective tool. An empirical case on risk management of a hazmats transportation company is presented to demonstrate the effectiveness and feasibility of the proposed methodology. Some managerial implications on risk management of hazmats road transportation are provided based on the obtained findings.

关键词： Hazardous materials Quality function deployment Failure mode and effect analysis nonlinear goal programming Trapezoidal fuzzy number Fuzzy analytic hierarchy process

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Using mixed integer multi-objective goal programming for stand tending block designation: A case study from Turkey

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FOREST POLICY AND ECONOMICS 2015年 55卷 28-36页

作者： Demirci, Mehmet Bettinger, Pete Minist Forest & Water Affairs Gen Directorate Forestry TR-06560 Ankara Turkey Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA

Sustainable forest management is a key to maintaining the economic, social, environmental and cultural benefits and services of forests for the long term. In Turkey, all forestry activities, such as regeneration and stand tending, are carried out according to forest management plans, which are used as a tool for achieving sustainable forest management goals. An intermediate yield harvest plan, which is a part of management plan, is used for stand tending. Every year, the compartments (stands) within the same stand tending block are thinned. Decision support systems have not been used so far in order to designate the size and location of these stand tending blocks. In this study, we used multi-objective goal programming to designate stand tending blocks for an entire decade. We developed two models: a linear goal programming model and a nonlinear goal programming model. To design these models, we only considered wood flow and distance between the centroids of compartments as the objectives. Then, we used a working circle of the Golcuk forest sub-district, which is a planning unit in Turkey, as a case study. The linear model worked very well, and for reference scenarios, the deviation in volume scheduled for the entire decade was only 16.8 m(3) and the deviation in total distance between compartments was 172 km. Scenario 3, with weights of 02 for distance and 0.8 for volume, produced the best results. The nonlinear model, which in theory would better represent the problem, was not as useful due to a combination of the time required to produce a solution and the quality of the solutions. The linear model can be developed by including other factors and used by forest planners. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Sustainable forest management Forest management plan Thinning Linear goal programming nonlinear goal programming Mixed integer programming

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nonlinear goal programming with -n_i, -p_i and -(n_i+p_i) goals

Nonlinear Goal Programming with -n<sub>i</sub>, -p<sub>i</su...

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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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Generalized Linguistic Ordered Weighted Hybrid Logarithm Averaging Operators and Applications to Group Decision Making

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2015年第3期23卷 421-442页

作者： Liu, Jinpei Chen, Huayou Zhou, Ligang Tao, Zhifu Anhui Univ Sch Business Hefei 230601 Anhui Peoples R China Anhui Univ Sch Math Sci Hefei 230601 Anhui Peoples R China

In this paper, we develop the generalized linguistic weighted logarithm averaging (GLWLA) operator and the generalized linguistic ordered weighted logarithm averaging (GLOWLA) operator in the group decision making under the linguistic surrounding. Then some properties of the families of the GLOWLA operator by different weighting vector are investigated. Furthermore, we present the generalized linguistic ordered weighted hybrid logarithm averaging (GLOWHLA) operator, which extends the GLOWLA operator. We also construct a nonlinear goal programming model to determine GLOWHLA weights from observational linguistic variable values under partial weight information. Finally, a numerical example is given to illustrate the new approach to evaluating university faculty for tenure and promotion, which indicates the feasibility and effectiveness of the new approach.

关键词： Group decision making GLOWHLA operator GLOWLA operator weighting vector nonlinear goal programming

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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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A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations

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COMPUTERS & INDUSTRIAL ENGINEERING 2009年第1期57卷 237-245页

作者： Kovach, Jami Cho, Byung Rae Univ Houston Dept Informat & Logist Technol Houston TX 77204 USA Clemson Univ Dept Ind Engn Adv Qual Engn Lab Clemson SC 29634 USA

The traditional measures of ensuring quality, derived from viewing quality as simply conforming to specifications, does not adequately address today's global markets and value-driven customers. Consequently, process improvement projects are often performed to improve operational performance in order to increase customer satisfaction. Therefore, process improvement methods, such as robust design, are important to industrial quality improvement initiatives. Yet, little of the work in the area of robust design has specifically addressed problems involving physical processing constraints that create an irregularly shape experimental region and the simultaneous consideration of multiple quality characteristics. To address these issues, we propose a new approach to robust design that utilizes D-optimal experimental designs in the context of multiresponse optimization problems in order to overcome the limitations of standard experimental approaches often used in robust design studies. Specifically, we formulate our optimization models as a preemptive nonlinear goal programming problem that focuses on consideration of the mean and variance. We also investigate the extension of optimization models traditionally used in robust design investigations to address multiple responses and compare the outcomes of our proposed approaches using a numerical example. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： D-optimal design Experimental restrictions and constraints Optimal experimental designs Multiresponse robust design problems nonlinear goal programming Mean and variance

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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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