scholarly journals A Fuzzy Nonlinear Programming Approach for Optimizing the Performance of a Four-Objective Fluctuation Smoothing Rule in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Horng-Ren Tsai ◽  
Toly Chen
Keyword(s):  
Nonlinear Programming ◽  
Mathematical Programming ◽  
Practical Method ◽  
Wafer Fabrication ◽  
Sufficient Evidence ◽  
Programming Approach ◽  
Mathematical Programming Problem ◽  
Simulation Experiments ◽  
Fluctuation Smoothing ◽  
Fuzzy Nonlinear Programming

In theory, a scheduling problem can be formulated as a mathematical programming problem. In practice, dispatching rules are considered to be a more practical method of scheduling. However, the combination of mathematical programming and fuzzy dispatching rule has rarely been discussed in the literature. In this study, a fuzzy nonlinear programming (FNLP) approach is proposed for optimizing the scheduling performance of a four-factor fluctuation smoothing rule in a wafer fabrication factory. The proposed methodology considers the uncertainty in the remaining cycle time of a job and optimizes a fuzzy four-factor fluctuation-smoothing rule to sequence the jobs in front of each machine. The fuzzy four-factor fluctuation-smoothing rule has five adjustable parameters, the optimization of which results in an FNLP problem. The FNLP problem can be converted into an equivalent nonlinear programming (NLP) problem to be solved. The performance of the proposed methodology has been evaluated with a series of production simulation experiments; these experiments provide sufficient evidence to support the advantages of the proposed method over some existing scheduling methods.

scholarly journals Formulating Intuitionistic Fuzzy Regression Models: A Mathematical Programming Approach

2021 ◽  
Author(s):  
Sheng-Hsing Nien ◽  
Liang-Hsuan Chen
Keyword(s):  
Mathematical Programming ◽  
Regression Models ◽  
Fuzzy Regression ◽  
Distance Measures ◽  
Programming Approach ◽  
Mathematical Programming Problem ◽  
Explanatory Variables ◽  
Intuitionistic Fuzzy ◽  
Mathematical Programming Approach ◽  
Ordinary Regression

Abstract This study develops a mathematical programming approach to establish intuitionistic fuzzy regression models (IFRMs) by considering the randomness and fuzziness of intuitionistic fuzzy observations. In contrast to existing approaches, the IFRMs are established in terms of five ordinary regression models representing the components of the estimated triangular intuitionistic fuzzy response variable. The optimal parameters of the five ordinary regression models are determined by solving the proposed mathematical programming problem, which is linearized to make the resolution process efficient. Based on the concepts of randomness and fuzziness in the formulation processes, the proposed approach can improve on existing approaches’ weaknesses with establishing IFRMs, such as the limitation of symmetrical triangular membership (or non-membership) functions, the determination of parameter signs in the model, and the wide spread of the estimated responses. In addition, some numerical explanatory variables included in the intuitionistic fuzzy observations are also allowed in the proposed approach, even though it was developed for intuitionistic fuzzy observations. In contrast to existing approaches, the proposed approach is general and flexible in applications. Comparisons show that the proposed approach outperforms existing approaches in terms of similarity and distance measures.

scholarly journals Mathematical Programming Approach to the Optimality of the Solution for Deterministic Inventory Models with Partial Backordering

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Irena Stojkovska
Keyword(s):  
Mathematical Programming ◽  
Programming Problem ◽  
Optimal Decision ◽  
Alternative Proof ◽  
Programming Approach ◽  
Mathematical Programming Problem ◽  
Inventory Models ◽  
Partial Backordering ◽  
Mathematical Programming Approach

We give an alternative proof of the optimality of the solution for the deterministic EPQ with partial backordering (EPQ-PBO) [Omega, vol. 37, no. 3, pp. 624–636, 2009]. Our proof is based on the mathematical programming theory. We also demonstrate the determination of the optimal decision policy through solving the corresponding mathematical programming problem. We indicate that the same approach can be used within other inventory models with partial backordering, and we consider additional models.

Large Displacement Elastoplastic Analysis of Space Trusses as a Mathematical Programming Problem

1998 ◽  
Vol 13 (3) ◽  
pp. 127-136 ◽  
Author(s):  
S.H. Xia ◽  
F. Tin-Loi
Keyword(s):  
Mathematical Programming ◽  
Programming Problem ◽  
Nonlinear Complementarity Problem ◽  
Geometrical Nonlinearity ◽  
Small Strain ◽  
Large Displacement ◽  
Elastoplastic Analysis ◽  
Programming Approach ◽  
Mathematical Programming Problem ◽  
Space Trusses

A mathematical programming approach is proposed for the large displacement elastoplastic analysis of space trusses. Features of the general methodology include the preservation of static-kinematic duality through the concept of fictitious forces and deformations, exact descriptions of equilibrium and compatibility for arbitrarily large displacements, albeit small strain, that can be specialized to any order of geometrical nonlinearity, and a complementarity description of the elastoplastic constitutive laws. The finite incremental formulation developed takes the form of a special mathematical programming problem known as a nonlinear complementarity problem for which a predictor-corrector type numerical scheme is proposed.

scholarly journals Nonresponse in stratified sampling: a mathematical programming approach

2011 ◽  
Vol 29 (1) ◽  
pp. 40 ◽  
Author(s):  
Ummatul Fatima ◽  
M. J. Ahsan
Keyword(s):  
Nonlinear Programming ◽  
Mathematical Programming ◽  
Stratified Sampling ◽  
Programming Approach ◽  
Nonlinear Programming Problem ◽  
Randomized Response ◽  
Optimization Software ◽  
Sensitive Question ◽  
Integer Nonlinear Programming ◽  
Mathematical Programming Approach

In sampling theory the term nonresponse is used for not being able to obtain from some units selected in the sample. Among other reasons nonresponse may be due to the refusal to answer or due to evasive answers in response to a sensitive question. Warner (1965) presented the Randomized Response (RR) technique to estimate the proportion of respondents to a sensitive question without the knowledge of the respondents' personal status. This paper addresses the problem of optimum allocation in stratified sampling under RR model as an All Integer Nonlinear Programming Problem (AINLPP) in the presence of nonresponse. The solution to the formulated problem is obtained using optimization software.

scholarly journals Enhancing the Long-Term Yield Competitiveness of a Semiconductor Manufacturing Factory Using a Multiobjective Fuzzy Nonlinear Programming Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Toly Chen ◽  
Yi-Chi Wang
Keyword(s):  
Nonlinear Programming ◽  
Correlation Coefficient ◽  
Semiconductor Manufacturing ◽  
Programming Approach ◽  
Manufacturing Plant ◽  
Systematic Procedure ◽  
Fuzzy Correlation ◽  
Proposed Model ◽  
Fuzzy Nonlinear Programming

This study proposes a multiobjective fuzzy nonlinear programming (MOFNP) approach to enhance the long-term yield competitiveness of a semiconductor manufacturing factory. By modeling the long-term competitiveness of every product in a semiconductor manufacturing plant with the fuzzy correlation coefficient (FCC) between time and instantaneous competitiveness, the proposed model considers the various viewpoints when interpreting the overall competitiveness of the semiconductor manufacturing plant in the long-term. All noninferior solutions of the MOFNP solutions are then derived using a systematic procedure. A real example is employed to illustrate the applicability of the proposed methodology.

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