Application of Weight, Quality, Cost, Delivery, and Productivity Optimization Techniques as an Aerospace Design for Manufacturing and Assembly Guideline in the Selection of Alternate Aerospace Commercial Off-the-Shelf Consumables

2020 ◽  
Author(s):  
Mani Rathinam Rajamani ◽  
Eshwaraiah Punna
Keyword(s):  
Design For Manufacturing ◽  
Optimization Techniques ◽  
Quality Cost ◽  
Commercial Off The Shelf ◽  
Selection Of

Fuzzy COTS Selection for Modular Software Systems Based on Cohesion and Coupling under Multiple Applications Environment

2012 ◽  
Vol 3 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Pankaj Gupta ◽  
Shilpi Verma ◽  
Mukesh Kumar Mehlawat
Keyword(s):  
Functional Performance ◽  
Fuzzy Optimization ◽  
Optimization Techniques ◽  
Software Systems ◽  
Small Scale ◽  
Software System ◽  
Modular Software ◽  
Commercial Off The Shelf ◽  
Small Scale Industries ◽  
Selection Of

Due to the rapid growth of development of component based software systems, the selection of optimal commercial-off-the-shelf (COTS) components has become the key of optimization techniques used for the purpose. In this paper, the authors use fuzzy mathematical programming (FMP) for developing bi-objective fuzzy optimization models that aims to select the best-fit COTS components for a modular software system under multiple applications development task. The proposed models maximize the functional performance and minimize the total cost of the software system satisfying the constraints of minimum threshold on intra-modular coupling density and reusability of COTS components. The efficiency of the models is illustrated using a real-world scenario of developing two financial applications for two small-scale industries.

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

scholarly journals Selection of Commercial-Off-the-Shelf Antistatic Coating and Its Applicability for Space-Use Solar Arrays

2014 ◽  
Vol 57 (3) ◽  
pp. 153-159
Author(s):  
Minoru IWATA ◽  
Akitoshi TAKAHASHI ◽  
Musashi SAKAMOTO ◽  
Mengu CHO ◽  
Ryo MURAGUCHI
Keyword(s):  
Space Use ◽  
Solar Arrays ◽  
Antistatic Coating ◽  
Commercial Off The Shelf ◽  
Selection Of

ECONOMIC SELECTION OF MEAN VALUE FOR A FILLING PROCESS UNDER QUADRATIC QUALITY LOSS

2004 ◽  
Vol 11 (01) ◽  
pp. 81-90 ◽  
Author(s):  
MIN-KOO LEE ◽  
SANG-BOO KIM ◽  
HYUCK-MOO KWON ◽  
SUNG HOON HONG
Keyword(s):  
Quadratic Function ◽  
Mean Value ◽  
Optimum Process ◽  
Selling Price ◽  
Filling Process ◽  
Important Ingredient ◽  
Quality Cost ◽  
Economic Selection ◽  
Production Inspection ◽  
Selection Of

Consider a filling process where containers are filled with an important ingredient in a character. All containers are inspected, and the containers satisfying to meet the predetermined specification limits are sold in a regular market for a fixed price, and failing to meet them are emptied and refilled by the same filling process after some reprocessing. We assume that reprocessing cost is proportional to the quantity of the ingredients in a container that is not changed after reprocessing. An economic model is constructed on the basis of the selling price and the costs of production, inspection, reprocessing, and quality. We assume that the quality cost function is a quadratic function of the deviation from target and the quantity of the ingredients in a container is normally distributed with a known variance. Method for finding the optimum process mean is presented and a numerical example is given.

scholarly journals Selection Criteria for Six Sigma Projects in Slovenian Manufacturing Companies

Organizacija ◽  
2009 ◽  
Vol 42 (4) ◽  
pp. 137-143 ◽  
Author(s):  
Dušan Gošnik ◽  
Matej Hohnjec
Keyword(s):  
Customer Satisfaction ◽  
Cost Analysis ◽  
Six Sigma ◽  
Business Strategy ◽  
Selection Criteria ◽  
Manufacturing Companies ◽  
Quality Cost ◽  
Financial Benefits ◽  
Tools And Techniques ◽  
Selection Of

Selection Criteria for Six Sigma Projects in Slovenian Manufacturing CompaniesResearches reveal that successful six sigma implementation is related to proper six sigma project prioritisation and selection. This research is limited to the selection of six sigma projects in some manufacturing companies in Slovenia. The purpose of this study is to identify what criteria are considered for prioritisation and selection of six sigma projects and how six sigma projects are selected. A research sample is limited by the number of companies which have implemented six sigma so far. The results indicate that Slovenian manufacturing organisations tend to select six sigma projects based on criteria such as customer satisfaction, connection with a business strategy financial benefits, and growth of the organisation. Several tools and techniques such as quality cost analysis, brainstorming and interviews are used to identify and prioritise projects. Identification of the most commonly used criteria to select six sigma projects can help practitioners to select projects based on multiple criteria by using tools and techniques identified in this study. This topic has not been applied in the field of Slovenian manufacturing companies and thus it presents the first study in this field in Slovenia.

scholarly journals Prediction and analysis of penetration rate in drilling operation using deterministic and metaheuristic optimization methods

2021 ◽  
Author(s):  
Ibrahim Sobhi ◽  
Abdelmadjid Dobbi ◽  
Oussama Hachana
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Computational Time ◽  
Objective Functions ◽  
Memory Space ◽  
Drilling Operation ◽  
Drilling Parameters ◽  
Selection Of

AbstractThe rate of penetration (ROP) optimization is one of the most important factors in improving drilling efficiency, especially in the downturn time of oil prices. This process is crucial in the well planning and exploration phases, where the selection of the drilling bits and parameters has a significant impact on the total cost and time of the drilling operation. Thus, the optimization and best selection of the drilling parameters are critical. Optimization of ROP is difficult due to the complexity of the relationship between the drilling variables and the ROP. For this reason, the development of high-performance computer systems, predictive models, and algorithms will be the best solution. In this study, a new investigation approach for ROP optimization has been done regarding different ROP models (Maurer, Bingham, Bourgoyne and Young models), algorithms (Multiple regression, ant colony optimization (ACO), fminunc, fminsearch, fsolve, lsqcurvefit, lsqnonlin), and different objective functions. The well-known data from the Louisiana field in an offshore well have been used to compare the used parameter estimation approach with other techniques. Indeed, datasets from an onshore well in the Hassi Messaoud Algerian field are explored. The results confirmed the superiority and the effectiveness of B&Y models compared to Bingham and Maurer models. Fminsearch, lsqcurvefit, ACO, and Excel (GRG) algorithms give the best results in ROP prediction while the application of the MNLR approach. Using the mean squared error (MSE) and the determination coefficient (R$$^{2}$$ 2 ) as objective functions significantly increases the accuracy prediction where the results given are ($$R=0.9522$$ R = 0.9522 , $$RMSE=2.85$$ R M S E = 2.85 ) and ($$R= 0.9811$$ R = 0.9811 , $$RMSE=4.08$$ R M S E = 4.08 ) for Wells 1 and 2, respectively. This study validates the application of B&Y model in both onshore and offshore wells. The findings reveal to deal with data limitation problems in ROP prediction. Simple and effective optimization techniques that require less memory space and computational time have been provided.

Components for Composite Material Customization System

2009 ◽  
Author(s):  
Soumitra Nandi ◽  
Zahed Siddique
Keyword(s):  
Composite Materials ◽  
Material Selection ◽  
Selection Process ◽  
Material Design ◽  
Optimization Techniques ◽  
Conventional Approach ◽  
Wide Range ◽  
Properties Of Materials ◽  
Selection Of

With the advancements of composite materials and research in nano-composites, designers have the flexibility to select materials from a wide range of properties to meet their specific design needs. Even with all these advancements, the material selection process during design follows a very conventional approach. The conventional approach to material design is to select a certain material from a given pre-set material list that allows the attainment of nearest properties required for the product. One of the disadvantages of this approach is that the trade-off inherent in the selection of material, when multiple properties are targeted, can be cumbersome to achieve or addressed at all. In this paper we present an approach to select and design composite materials, where the designer will have flexibility to select multiple properties of materials during the design of a new product. This approach employs an index for selection combined with heuristic optimization techniques to select the optimized combination of composite materials that could meet closest possible property goals. In the case study presented in this paper, we did not perform any optimization; rather, emphasize is given to the explanation of material selection technique, and an RMS value is introduced as an index for the selection.

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