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作者： TAI PHAM THAMMASAT UNIVERSITY

学位级别：硕士

Supply chain management has long been a compass for every successful business. Its long-term decision plays an important role in shaping the supply chain structure, or design of supply chain network, which significantly affects supply chain performance for prolong period. Since each industry has a unique set of characteristics which evidently drive the design of supply chain network, a number of various models have been formulated to meet the needs of such business contexts. Even though many models have been proposed for manufacturing industry context, most of them are based on the facility location model. The model tends to lead the supply chain network design model to be complicated. Therefore, the purpose of this research is to propose an alternative approach to formulate manufacturing network design problem. Features, such as multi-echelon, multi-commodity, product's structure, and manufacturing process, are taken into consideration as characteristics of the studied environment. Moreover, uncertainty factors are also integrated to the model by employing possibilistic theory. Eventually in addition to the methodology, a case study in a consumer product firm is used to demonstrate applicability of the suggested method. Two models, deterministic and fuzzy, has been explored in the study and both of them has demonstrated the validity of the proposed formulation method. Moreover, it is shown that the fuzzy model outperforms its deterministic counterpart in term of cost effectiveness.

关键词： strategic supply chain planning supply chain network design network flow production network fuzzy programming optimization mixed-integer linear programming (MILP) uncertainty

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Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2013年第9期26卷 816-829页

作者： Peric, Tunjo Babic, Zoran Veza, Ivica Univ Zagreb Fac Econ & Business Zagreb 10000 Croatia Univ Split Fac Econ Split 21000 Croatia Univ Split Fac Elect Engn Mech Engn & Naval Architecture Split 21000 Croatia

One of the possible conceptions for the survival in a turbulent world market is a chain cooperation among the producer, his suppliers and customers, according to the concept of supply chain management. The problem of vendor selection and determination of material quantities supplied is one of the most important activities in the supply chain. The purpose of this article is to propose a model for vendor selection and determination of supply quotas in the area of flour supply by using the analytic hierarchy process (AHP) and multi-objective linear programming (LP) model solved by fuzzy LP method, based on the decision-making in a fuzzy environment, and to point to the advantages of the proposed model in comparison to some other vendor selection and supply quantity determination models. The proposed model applied in the concrete problem of vendor selection and determination of supply quotas proved to be very efficient in determining the priority of alternatives, taking into account all the criteria and sub-criteria, as well as in defining criteria weights that express the decision-maker's preferences, which allows a simple sensitivity analysis of the obtained solutions.

关键词： vendor selection supply quantity determination multi-criteria AHP fuzzy programming bakery

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Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach

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APPLIED MATHEMATICAL MODELLING 2010年第9期34卷 2377-2387页

作者： Baky, Ibrahim A. Benha Univ Dept Basic Sci Benha Higher Inst Technol El Kalyoubia Egypt

In this paper, two new algorithms are presented to solve multi-level multi-objective linear programming (ML-MOLP) problems through the fuzzy goal programming (FGP) approach. The membership functions for the defined fuzzy goals of all objective functions at all levels are developed in the model formulation of the problem: so also are the membership functions for vectors of fuzzy goals of the decision variables, controlled by decision makers at the top levels. Then the fuzzy goal programming approach is used to achieve the highest degree of each of the membership goals by minimizing their deviational variables and thereby obtain the most satisfactory solution for all decision makers. The first suggested algorithm groups the membership functions for the defined fuzzy goals of the objective functions at all levels and the decision variables for each level except the lower level of the multi-level problem. The second proposed algorithm lexicographically solves MOLP problems of the ML-MOLP problem by taking into consideration the decisions of the MOLP problems for the upper levels. An illustrative numerical example is given to demonstrate the algorithms. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Multi-objective linear programming Multi-level programming problems Goal programming fuzzy programming fuzzy goal programming

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A hybrid fuzzy-GA algorithm for the integrated machine allocation problem with fuzzy demands

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APPLIED SOFT COMPUTING 2014年 23卷 417-431页

作者： Nasab, Hasan Hosseini Yazd Univ Dept Ind Engn Paghohesh St Yazd Iran

The integrated machine allocation and facility layout problem (IMALP) is a branch of the general facility layout problem in which, besides selecting machine locations, the processing route of each product is determined. Most research in this area suppose that the flow of material is certain and exact, which is an unrealistic assumption in today's dynamic and uncertain business environment. Therefore, in this paper the demand volume has been assumed as fuzzy numbers with different membership functions. To solve this problem, the deterministic model is first integrated with a fuzzy implication via the expected value model, and thereafter an intelligent hybrid algorithm, including a genetic algorithm and a fuzzy simulation approach has been applied. Finally, the efficiency of the proposed algorithm is evaluated with a set of numerical examples. The results show the effectiveness of the hybrid algorithm in finding the IMALP solutions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： fuzzy programming Hybrid algorithm Genetic algorithm

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Models for inverse minimum spanning tree problem with fuzzy edge weights

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2014年第5期27卷 2691-2702页

作者： Zhang, Jingyu Zhou, Jian Zhong, Shuya Philips Res North Amer Dept Clin Decis Support Solut New York NY USA Shanghai Univ Sch Management Shanghai Peoples R China

An inverse minimum spanning tree problem is to make the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, a type of fuzzy inverse minimum spanning tree problem is introduced from a LAN reconstruction problem, where the weights of edges are assumed to be fuzzy variables. The concept of fuzzy alpha-minimum spanning tree is initialized, and subsequently a fuzzy a-minimum spanning tree model and a credibility maximization model are presented to formulate the problem according to different decision criteria. In order to solve the two fuzzy models, a fuzzy simulation for computing credibility is designed and then embedded into a genetic algorithm to produce some hybrid intelligent algorithms. Finally, some computational examples are given to illustrate the effectiveness of the proposed algorithms.

关键词： Minimum spanning tree inverse optimization fuzzy programming genetic algorithm

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Coupling fuzzy-chance constrained program with minimax regret analysis for water quality management

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2014年第7期28卷 1769-1784页

作者： Li, Y. P. Zhang, N. Huang, G. H. Liu, J. North China Elect Power Univ Sino Canada Resources & Environm Res Acad Beijing 102206 Peoples R China Univ Regina Environm Syst Engn Program Fac Engn & Appl Sci Regina SK S4S 0A2 Canada

Mathematical modeling technique plays an important role for regionalization assessment of integrated economy and environment problems, resulting in provision of decision makers with break-through insights and risk-informed strategies. However, such a planning effort is complicated with a variety of uncertain and dynamic factors as well as their interactions. In this study, a fuzzy-chance constrained programming (FCP) method is firstly developed for addressing uncertainties characterized as fuzzy sets and random variables and, then, minimax regret (MMR) analysis technique is advanced to determine desired alternative that can reflect compromises between maximized system benefit and minimized system-failure risk. FCP coupled with MMR is applied to a real-case study of water quality management through optimizing chemical industry activities of the New Binhai District, an economically and industrially fast growing region in the center of northern China. Modeling formulation can analyze interactions among criteria of industry layout, economic benefit, pollution mitigation, and water quality security. Solutions for planning the water quality management have been generated, reflecting that there is trade-off among industrial structure, environmental protection, and economic development.

关键词： Chance-constrained fuzzy programming Management Minimax regret Water quality Uncertainty

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Interactive TOPSIS algorithms for solving multi-level non-linear multi-objective decision-making problems

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APPLIED MATHEMATICAL MODELLING 2014年第4期38卷 1417-1433页

作者： Baky, Ibrahim A. Benha Univ Fac Engn Dept Basic Sci El Qalyoubia Egypt

This paper extended the concept of the technique for order preference by similarity to ideal solution (TOPSIS) to develop a methodology for solving multi-level non-linear multi-objective decision-making (MLN-MODM) problems of maximization-type. Also, two new interactive algorithms are presented for the proposed TOPSIS approach for solving these types of mathematical programming problems. The first proposed interactive TOPSIS algorithm includes the membership functions of the decision variables for each level except the lower level of the multi-level problem. These satisfactory decisions are evaluated separately by solving the corresponding single-level MODM problems. The second proposed interactive TOPSIS algorithm lexicographically solves the MODM problems of the MLN-MOLP problem by taking into consideration the decisions of the MODM problems for the upper levels. To demonstrate the proposed algorithms, a numerical example is solved and compared the solutions of proposed algorithms with the solution of the interactive algorithm of Osman et al. (2003)141. Also, an example of an application is presented to clarify the applicability of the proposed TOPSIS algorithms in solving real world multi-level multi-objective decision-making problems. (C) 2013 Elsevier Inc. All rights reserved.

关键词： TOPSIS Multi-objective decision-making Non-linear programming Multi-level programming fuzzy programming

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MULTI-OBJECTIVE GEOMETRIC programming PROBLEM WITH KARUSH-KUHN-TUCKER CONDITION USING ε-CONSTRAINT METHOD

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RAIRO-OPERATIONS RESEARCH 2014年第4期48卷 429-453页

作者： Ojha, A. K. Ota, Rashmi Ranjan Indian Inst Technol Sch Basic Sci Bhubaneswar 751013 Odisha India SOA Univ ITER Dept Math Bhubaneswar 751030 Odisha India

Optimization is an important tool widely used in formulation of the mathematical model and design of various decision making problems related to the science and engineering. Generally, the real world problems are occurring in the form of multi-criteria and multi-choice with certain constraints. There is no such single optimal solution exist which could optimize all the objective functions simultaneously. In this paper, epsilon-constraint method along with Karush-Kuhn-Tucker (KKT) condition has been used to solve multiobjective Geometric programming problems(MOGPP) for searching a compromise solution. To find the suitable compromise solution for multi-objective Geometric programming problems, a brief solution procedure using epsilon-constraint method has been presented. The basic concept and classical principle of multi-objective optimization problems with KKT condition has been discussed. The result obtained by epsilon-constraint method with help of KKT condition has been compared with the result so obtained by fuzzy programming method. Illustrative examples are presented to demonstrate the correctness of proposed model.

关键词： Geometric programming Karush-Kuhn-Tucker (KKT) condition epsilon-constraint method fuzzy programming duality theorem Pareto optimal solution

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Agricultural farming planning and water resources management under fuzzy uncertainty

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ENGINEERING OPTIMIZATION 2014年第2期46卷 270-288页

作者： Xu, Y. Huang, G. H. Shao, L. G. North China Elect Power Univ Sino Canada Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing Peoples R China Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Planning Res Inst China Ctr Informat Ind Dev Beijing Peoples R China

In this study, a rank-based fuzzy optimization (RBFO) approach was provided for supporting agricultural farming planning under complex uncertainties. RBFO was built by linking feasibility-degree-based fuzzy programming with fuzzy ranking. First, the model was solved under different levels of violation risks to environmental or capacity constraints, leading to a group of cost-effective solutions;then, the ranking technique was used to help identify the most desirable solution from different alternatives, based on the trade-off analysis between the system benefit and risk. A farm planning case with water resources management consideration was used to demonstrate the applicability of RBFO. The study results indicate that the provided systems analysis approach could help decision makers to gain a deep insight into the complex interrelationships among agricultural economic development, pollution control and water resources conservation in a farm planning system, and to seek cost-effective production patterns with favourable deliberation of various factors.

关键词： fuzzy programming fuzzy ranking farm planning water resources management uncertainty

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Multi-objective geometric programming problem with ε-constraint method

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APPLIED MATHEMATICAL MODELLING 2014年第2期38卷 747-758页

作者： Ojha, A. K. Biswal, K. K. Indian Inst Technol Bhubaneswar Sch Basic Sci Bhubaneswar 751013 Orissa India NIIS Inst Business Adm Dept Math Bhubaneswar 752054 Orissa India

In multi-objective geometric programming problem there are more than one objective functions: There is no single optimal solution which simultaneously optimizes all the objective functions. Under these conditions the decision makers always search for the most "preferred" solution, in contrast to the optimal solution. A few mathematical programming methods namely fuzzy programming, goal programming and weighting methods have been applied in the recent past to find the compromise solution. In this paper epsilon-constraint method has been applied to find the non-inferior solution. A brief solution procedure of epsilon-constraint method has been presented to find the non-inferior solution of the multi-objective programming problems. Further, the multi-objective programming problems is solved by the fuzzy programming technique to find the optimal compromise solution. Finally, two numerical examples are solved by both the methods and compared with their obtained solutions. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Geometric programming Vector minimization epsilon-Constraint method fuzzy programming Duality theory Pareto optimal solution

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