In the present study an effort has been made to optimize the machining conditions for electric discharge machining of LM25 Al (7 Si, 0.33 Mg, 0.3 Mn, 0.5 Fe, 0.1 Cu, 0.1 Ni,.2 Ti) reinforced with green bonded SiC part...
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In the present study an effort has been made to optimize the machining conditions for electric discharge machining of LM25 Al (7 Si, 0.33 Mg, 0.3 Mn, 0.5 Fe, 0.1 Cu, 0.1 Ni,.2 Ti) reinforced with green bonded SiC particles with approximate size of 25 μm. Polynomial models were developed for the various EDM characteristics such as metal removal rate, tool wear rate and surface roughness in terms of the process parameters such as volume fraction of SiC, current and pulse time. The models were used to optimize the EDM characteristics using nonlinear goal programming.
Multiplicative preference relations can be expanded into Neutrosophic multiplicative preference relations (NMPR) and Interval Neutrosophic Multiplicative Preference Relations (INMPR). They are appropriate for capturin...
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Multiplicative preference relations can be expanded into Neutrosophic multiplicative preference relations (NMPR) and Interval Neutrosophic Multiplicative Preference Relations (INMPR). They are appropriate for capturing the experts' assessments' uncertainty, ambiguity, and indeterminacy. This work aims to provide a consistency and consensus-based approach for dealing with group decision-making using NMPRs and INMPRs, as well as many goal programming models to manage the consistency and consensus of NMPRs and INMPRs. To define and measure acceptable consistency for NMPRs and INMPRs, the study first offers a consistency index. Several consistency-based programming approaches are designed to address the inconsistency and provide an appropriate consistent NMPR and INMPR for an NMPR and INMPR that are not consistent enough. We provide a consistency-based approach to NMPR and INMPR decision-making. Then, considering the consensus in GDM, a consensus index is suggested for determining the level of agreement between specific NMPRs and INMPRs. Thereafter, a group NMPR and INMPR are created by combining individual NMPRs and INMPRs using an aggregation operator that ensures the consistency of the group NMPRs and INMPRs. With a collection of NMPRs and INMPRs, a consistency- and consensus-based GDM approach is built on single-valued neutrosophic sets (SVNS) and interval-valued neutrosophic sets (IVNS). Finally, two real-world numerical examples are shown, along with a comparison. The proposed method checked the individual consistency level and the group consensus, which is less than the existing method. The ranking of the alternatives was given, which was more convincing than the existing methods. It is also clear that it is much simpler than the previous methods.
In this paper we present an iterative goal programming approach for solving multiobjective integer linear programming problems. After illustrating the approach we give the definition of, and an algorithm to determine ...
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In this paper we present an iterative goal programming approach for solving multiobjective integer linear programming problems. After illustrating the approach we give the definition of, and an algorithm to determine the stability set of the first kind for multiobjective problems of all-integer variables and with parameters in the right-hand side of the constraints. Finally, the paper is concluded together with some points for further research. (C) 2003 Elsevier Inc. All rights reserved.
Wireless sensor networks (WSNs) have become an important technology for execution of sensitive applications requiring real-time sensing and data acquisition for decision-making purposes. Apart from many challenges fac...
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ISBN:
(纸本)9781479910786
Wireless sensor networks (WSNs) have become an important technology for execution of sensitive applications requiring real-time sensing and data acquisition for decision-making purposes. Apart from many challenges facing WSNs deployment and operations, security of wireless sensor networks is a an important challenging issue. There is always a potential threat of various types of malicious attacks against the security of WSNs. Due to the unreliable environments in which WSNs operate, the threat of distributed attacks against sensory resources such as power consumption, communication, and computation capabilities cannot be ignored. In this paper, a goal programming based approach is proposed and empirically analyzed in the context of distributed denial of service attacks in WSNs. The problem was analyzed using fuzzy logic approach in previous studies, but reflected some deficiencies in the proposed approach. The current goal programming based approach proposed herein is formulated as a multi-criteria decision-making problem, with attack detection rate and energy decay rate as the two decision criteria. A goal programming based mechanism is developed to achieve the best trade-off between the two aforementioned conflicting criteria. Empirical analysis proves the effectiveness of the proposed approach.
in recent years, the companies concerned with Flexible Manufacturing Systems(FMS), because of requirements about low volume production, short product life, availability of the new products for the market in a short ti...
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ISBN:
(纸本)0780366522
in recent years, the companies concerned with Flexible Manufacturing Systems(FMS), because of requirements about low volume production, short product life, availability of the new products for the market in a short time and flexibility of design and production management. A Flexible Manufacturing System is a set of computer-numerically controlled machine tools connected by an automatic material handling system and all controlled by a central computer system. In this study, a goal programming model is developed for flexible manufacturing systems that consist of several machine and work parts and has automatic material handling system. Every work part has different routes for which alternative machines can be used for part production. This model is tested for problems with different sizes. Multiple- criteria programming approach is used to consider the distance factor between machines in loading and routing problems in FMS.
This study presents a new robust estimation method that can produce a regression median hyperplane for any data set. The robust method starts with dual variables obtained by least absolute value estimation. It then ut...
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This study presents a new robust estimation method that can produce a regression median hyperplane for any data set. The robust method starts with dual variables obtained by least absolute value estimation. It then utilizes two specially designed goal programming models to obtain regression median estimators that are less sensitive to a small sample size and a skewed error distribution than least absolute value estimators. The superiority of new robust estimators over least absolute value estimators is confirmed by two illustrative data sets and a Monte Carlo simulation study. [ABSTRACT FROM AUTHOR]
Several authors have proposed a social choice function based upon distance-consensus between different committee rankings. Under this framework, the total absolute disagreement between committees is minimised. The pur...
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Several authors have proposed a social choice function based upon distance-consensus between different committee rankings. Under this framework, the total absolute disagreement between committees is minimised. The purpose of this paper is to formulate the underlying optimisation problem as a goal programming (GP) model. To do this, the following three GP formulations are proposed: (a) a linear weighting GP model, where consensus is established by the minimisation of the weighted aggregated disagreement, (b) a MINMAX GP model, where the consensus is defined as the minimisation of the maximum disagreement and (c) an extended GP model, which subsumes the two previous formulations as particular cases. (C) 1999 Elsevier Science Ltd. All rights reserved.
Countries generally aim to attain several different or sometimes contrary goals. To this end, a goal programming (GP) model integrated with an Input-Output (I-O) one was implemented in a number of previous studies. Th...
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Countries generally aim to attain several different or sometimes contrary goals. To this end, a goal programming (GP) model integrated with an Input-Output (I-O) one was implemented in a number of previous studies. This paper attempts to integrate a GP with a Social Accounting Matrix (SAM) framework to allocate economic resources of an economy among alternative sectors in order to achieve a number of goals. To do so, although the goals and limitations of regions are determined with respect to their situations, the supply and demand for products of economic sectors, as well as Gross Regional Products (GRP);employment for different educational groups of human forces;and income distribution inequality in addition to the personal income mean are formulated as the first to third goals of the research, respectively. The model has been examined by using the SAM Table for the Golestan Province of Iran. The result of implementing the model demonstrates that integrating SAM instead of I-O with GP will increase the capability of the integrated model.
Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Paretooptimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfact...
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Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Paretooptimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. In this paper, a new method for solving multi-objective programming problems is developed that unlike other methods in the literature, provides compromise solutions satisfying both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. This method combines the advantages of the intuitionistic fuzzy sets concept, goal programming, and interactive procedures, and supports the decision maker in the process of solving programming problems with crisp, fuzzy, or intuitionistic fuzzy objectives and constraints. A characteristic of the proposed method is that it provides a well-structured approach for determining satisfaction and the dissatisfaction degrees that efficiently uses the concepts of violation for both objective functions and constraints. Another feature of the proposed method comes from its continuous interaction with the decision maker. In this situation, through adjusting the problem's parameters, the decision maker would have the ability of revisiting the membership and non-membership functions. Therefore, despite the lack of information at the beginning of the solving process, a compromise solution that satisfies the decision maker's preferences can be obtained. A further feature of the proposed method is the introduction of a new two-step goal programming approach for determining the compromise solutions to multi-objective problems. This approach ensures that the compromise solution obtained during each iterative step satisfies both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The application of the proposed model is also discussed in this paper. (C) 2016 Elsevier Ltd. All rights reserved.
In this paper, the a-satisfactory goal programming (GP) method is proposed for multi-objective optimization (MOO) with priorities and fuzzy parameters. Fuzzy parameters are treated as fuzzy numbers, and all objectives...
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In this paper, the a-satisfactory goal programming (GP) method is proposed for multi-objective optimization (MOO) with priorities and fuzzy parameters. Fuzzy parameters are treated as fuzzy numbers, and all objectives are modeled into fuzzy goals over alpha-level sets. The order of alpha-satisfactory degrees is applied to preemptive priority requirement, where the objectives with higher priority can achieve the higher alpha-satisfactory degree. In order to guarantee the feasibility and seek the preferred solution, a priority variable is introduced to relax the strict order. For three fuzzy relations, GP is combined with the relaxed order constraint to formulate the different alpha-satisfactory optimization models. By regulating optimization parameters lambda and alpha, the most satisfactory solution over fuzzy parameters can be obtained, and the balance between optimization and priority can be realized. The reformulated alpha-GP models are proved to be feasible, and their solutions are guaranteed to be M-alpha-Pareto optimal by the test model. In order to decrease optimization burden, the algorithm to compute the alpha-maximum regulating parameter is proposed, by which the bound of lambda can be determined. The effectiveness of the proposed method is well demonstrated by numerical examples.
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