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A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第2期181卷 903-915页

作者： Yang, Lili Jones, Bryan F. Yang, Shuang-Hua Univ Loughborough Sch Business Loughborough LE11 3TU Leics England Univ Derby Appl Comp Dept Derby DE22 1GB England Univ Loughborough Dept Comp Sci Loughborough LE11 3TU Leics England

Location of fire stations is an important factor in its fire protection capability. This paper aims to determine the optimal location of fire station facilities. The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm. The original fuzzy multiple objectives are appropriately converted to a single unified 'min-max' goal, which makes it easy to apply a genetic algorithm for the problem solving. Compared with the existing methods of fire station location our approach has three distinguish features: (1) considering fuzzy nature of a decision maker (DM) in the location optimization model;(2) fully considering the demands for the facilities from the areas with various fire risk categories;(3) being more understandable and practical to DM. The case study was based on the data collected from the Derbyshire fire and rescue service and used to illustrate the application of the method for the optimization of fire station locations. (c) 2006 Elsevier B.V. All rights reserved.

关键词： location fire stations multi-objective programming genetic algorithm fuzzy programming

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Optimality and duality for a multi-objective programming problem involving generalized d-type-I and related n-set functions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第2期173卷 405-418页

作者： Mishra, S. K. Wang, S. Y. Lai, K. K. City Univ Hong Kong Dept Management Sci Kowloon Hong Kong Peoples R China Govind Ballabh Pant Univ Agr & Technol Coll Basic Sci & Humanities Dept Math Stat & Comp Sci Pantnagar 263145 Uttar Pradesh India Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci Beijing 100080 Peoples R China Hunan Univ Coll Business Adm Changsha Peoples R China

In this paper, we introduce several generalized convexity for a real-valued set function and establish optimality and duality results for a multi-objective programming problem involving generalized d-type-I and relate... 详细信息

关键词： multi-objective programming n-set functions optimality duality generalized convexity

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Lexicographic optimal solution of the multi-objective programming problem subject to max-product fuzzy relation inequalities

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FUZZY SETS AND SYSTEMS 2018年 341卷 92-112页

作者： Yang, Xiao-Peng Yuan, De-Hui Cao, Bing-Yuan Hanshan Normal Univ Dept Math & Stat Chaozhou 521041 Guangdong Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Guangdong Peoples R China Guangzhou Vocat Coll Sci & Technol Guangzhou 510550 Guangdong Peoples R China

It is shown in this paper that the emission base stations in wireless communication can be reduced into a system of fuzzy relation inequalities with max-product composition. For optimal management in such system, we introduce the fuzzy relation multi-objective programming. Concept of feasible index set (FIS) is defined, based on which a novel algorithm, named FIS algorithm, is developed to find the unique lexicographic optimal solution of the proposed problem with polynomial computational complexity. Applying this method, we needn't to find out all the minimal solutions of the constraint. A numerical application example is provided to illustrate the feasibility and efficiency of the FIS algorithm. (c) Elsevier B.V. All rights reserved.

关键词： Fuzzy relation inequality multi-objective programming Lexicographic order Feasible index set Fuzzy relation equation Wireless communication

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Liquefied natural gas importing security strategy considering multi-factor: A multi-objective programming approach

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 87卷 56-69页

作者： Zhang, Xiao-Nan Zhong, Qiu-Yan Qu, Yi Li, Hong-Lei Liaoning Normal Univ Sch Management 850 Huanghe Rd Dalian 116029 Liaoning Peoples R China Dalian Univ Technol Fac Econ & Management 2 Linggong Rd Dalian 116024 Liaoning Peoples R China Agr Bank China Data Ctr 88 Aoni St Shanghai 201200 Peoples R China

LNG importing strategies, in the literature, are primarily studied under a common single-factor framework. However, LNG importing strategies are affected by a variety of factors. To address this existing gap, this paper proposes a multi-objective programming model, which takes into account the cost, the country risk, the shipping risk, and the impact of extreme events. A pure structural change model is used to determine the risk impact coefficient for extreme events. An enhanced Simulated Annealing Algorithm is then used to solve the LNG-importing optimization problem. An experimental study is further conducted to verify the practicability of the proposed approach in the case of China's LNG-importing data. The software implementation of the proposed model is developed in Python. The proposed model provides a decision support tool for LNG importing companies to find an efficient portfolio strategy for LNG importing. The optimization model can be used for analyzing similar scenarios involving such dimensions as economy, energy security, and especially energy diversification. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Liquefied natural gas (LNG) multi-objective programming Extreme events Improved Simulated Annealing Algorithm Software implementation

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Optimality of E-pseudoconvex multi-objective programming problems without constraint qualifications

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OPTIMIZATION LETTERS 2012年第8期6卷 1603-1611页

作者： Yu, GuoLin Lu, Yangyang Jiang, Yan Beifang Univ Nationalities Res Inst Informat & Syst Computat Sci Yinchuan 750021 Peoples R China

In this note, we consider the optimality criteria of multi-objective programming problems without constraint qualifications involving generalized convexity. Under the E-pseudoconvexity assumptions, the unified necessary and sufficient optimality conditions are established for weakly efficient and efficient solutions, respectively, in multi-objective programming problems.

关键词： multi-objective programming E-Convexity E-Pseudoconvex function Efficient solution

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A Synthesizing Effect-Based Solution Method for Stochastic Rough multi-objective programming Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2014年第4期7卷 696-705页

作者： Zhou, Lei Zhang, Guoshan Li, Fachao Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Peoples R China

multi-objective programming with uncertain information has been widely applied in modeling of industrial produce and logistic distribution problems. Usually the expectation value model and chance-constrained model as solution models are used to deal with such uncertain programming. In this paper, we consider the uncertain programming problem which contains random information and rough information and is hard to be solved. A new solution model, called stochastic rough multi-objective synthesis effect (MOSE) model, is developed to deal with a class of multi-objective programming problems with random rough coefficients. The MOSE model contains expectation value model and chance-constrained model by choosing different synthesis effect functions and can be considered as an extension of crisp multi-objective programming model. Combined with genetic algorithm, the optimal solution of the MOSE model can be obtained. It shows that the solutions of the MOSE model are better than that of other solution models. Finally, an illustrative example is provided to show the effectiveness of the proposed method.

关键词： multi-objective programming Random rough variable Stochastic programming Genetic algorithm Synthesis effect

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Smooth tool path generation for five-axis flank milling using multi-objective programming

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2012年第B2期226卷 247-254页

作者： Zheng, G. Bi, Q-Z Zhu, L-M Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Flank milling provides an efficient way to machine turbo-machinery components. However, the trajectory smoothness is seldom considered in the existing tool positioning strategies. In this paper, the tool trajectory is smoothed with a constraint on the resulting geometric error for five-axis flank milling. Unlike existing methods, this new method simultaneously considers the geometric smoothness and geometric deviation. The geometric smoothness is characterized by the strain energy of the cutter axis trajectory surface (S-A). The geometric deviation is measured by the signed maximal orthogonal distance between the design surface (S-D) and the tool envelope surface (S-E). For finish and semi-finish flank millings, smooth tool path optimizations are then modelled as multi-objective programming (MOP) problems. Given a prescribed geometric tolerance, the MOP problems are reformulated as constrained nonlinear programming (NLP) problems. Based on the Taylor expansions of the strain energy and the signed distance on the differential deformation of S-A, the constrained NLP problems are solved efficiently by the sequential quadratic programming (SQP) method. The existence of the optimal solutions is also discussed. The validity of the approach is confirmed by two numerical examples that generate five-axis flank milling tool paths with cylindrical and conical cutters, respectively.

关键词： flank milling tool trajectory smoothness tool orientation machining error multi-objective programming

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A multi-objective programming method for solving network DEA

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APPLIED SOFT COMPUTING 2014年第0期24卷 406-413页

作者： Kao, Han-Ying Chan, Chieh-Yu Wu, Dong-Jyun Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Shoufeng 97401 Hualien Taiwan

This study proposes the multi-objective programming (MOP) method for solving network DEA (NDEA) models. In the proposed method, the divisional efficiencies (within an organization) and the overall efficiency of the organization are formulated as separate objective functions in the multi-objective programming model. Compared with conventional DEA where the intermediate processes and products are ignored, this work measures the organization's overall efficiency without neglecting the efficiencies of its subunits. Two case studies demonstrate the proposed NDEA-MOP's utility in measuring the efficiencies of an organization with concerning interactive internal process. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Network DEA multi-objective programming BCC CCR Divisional efficiency Case studies

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Optimizing system reliability through selective maintenance allocation: A novel multi-objective programming approach using neutrosophic fuzzy concept

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第7期39卷 2784-2806页

作者： Kamal, Murshid Gupta, Srikant Jalil, Syed Aqib Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh India Jaipuria Inst Management Dept Operat & Decis Sci Jaipur India Sohar Univ Fac Business Sohar Oman Sohar Univ Fac Business Sohar 311 Oman

Selective maintenance refers to the problem of decision-making on how to maintain a multi-component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi-objective decision-making model for maximizing the system reliability of a multi-component systems. The objective functions represent the reliability of the sub-systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi-objective selective maintenance model, we have proposed four-valued neutrosophic technique. The proposed solution methodology combines the concept of four-valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi-component system of an aircraft gas turbine engine is studied.

关键词： fuzzy goal programming multi-objective programming neutrosophic fuzzy sets selective maintenance problem system reliability

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A Two-Stage multi-objective programming Model to Improve the Reliability of Solution

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 433-443页

作者： Jin, Chenxia Li, Fachao Feng, Kaixin Guo, Yunfeng Hebei Normal Univ Sch Math Sci Shijiazhuang 050024 Hebei Peoples R China Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Hebei Peoples R China

Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective programming problems under random environment. We first analyze the effects of randomness on multi-objective decision-making results. With the expectation value and the probability of fuzzy events as quantitative index of randomness, we then establish a two-stage random multi-objective programming model based on reliability (i.e., TS-MOPM). Specifically, we give several probability calculation methods of fuzzy events with common distributions, and further present the corresponding calculation procedures for solving TS-MOPM. Finally, a case study is implemented to test the proposed model TS-MOPM. Theoretical analysis and case study indicate that our model has better interpretability and operability. The research results enrich the existing random multi-objective programming methods to some extent. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： multi-objective programming Randomness Expectation value Fuzzy events Reliability

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