A model of the assessment and optimisation of production process quality using the fuzzy sets and genetic algorithm approach

Nestić, Snežana; Stefanović, Miladin; Đorđević, Aleksandar; Arsovski, Slavko; Tadić, Danijela

doi:10.1504/ejie.2015.067453

Cited by 15 publications

(20 citation statements)

References 36 publications

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“…Antony (2001) has utilised the design of experiments (DOE) for improving the manufacturing process. Nestic et al (2015) have developed a model based on the fuzzy sets and genetic algorithm approach for the assessment and optimisation of production process quality. Low et al (2015) have identified and categorised the existing improvement models used in process improvement.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle

Attri

Grover

2017

IJPMB

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Abstract:The main purpose of this paper is to develop a framework for the process design stage of production system life cycle (PSLC) using weighted interpretive structural modelling (ISM) and MICMAC analysis. In the present study, various quality enabled factors (QEFs) of process design stage has been identified through the extensive literature analysis and opinions of industrial and academic experts. In order to validate the identified QEFs, a questionnaire survey has been utilised. Then, QEFs are analysed by using ISM approach in order to develop the relationship among the considered QEFs. Afterwards, Matriced Impacts Croises Multiplication Appliqueeaun Classement (MICMAC) analysis is used to identify the driving and dependence power of QEFs. Lastly, a method of effectiveness index (EI) has been used for identifying the key areas of process design stage of PSLC. This index can be effectively utilised by the manufacturing organisations to benchmark their process design phase through effectively utilising the QEFs reported in this work.Keywords: process design; production system; quality enabled factors; QEFs; interpretive structural modelling; ISM; effectiveness index; EI; Matriced Impacts Croises Multiplication Appliqueeaun Classement; MICMAC.Reference to this paper should be made as follows: Attri, R. and and Grover, S. Developing the weighted ISM-MICMAC framework 95 journals and conferences. He guides research at post-graduate and doctorate levels, and his research areas include total quality management, manufacturing management, and human resource management. With more than 20 years of teaching and research experience, currently he is working as a Professor in the

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Section: Literature Reviewmentioning

confidence: 99%

Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle

Attri

Grover

2017

IJPMB

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“…These managers do not have equal importance in the decision making process. The importance of each manager w e , e " 1, ..., E is determined based on the results of good practice in the SMEs of the process industry (by analogy to [23]). …”

Section: The Formal Problem Statementmentioning

confidence: 99%

“…It is known that the Genetic Algorithm (GA) method uses a set of tools based on natural selection and population genetics mechanisms [21,22] to find a near optimal solution for specific objective functions, with respect to a set of constraints. It has been shown that a model based on GA can be used for the ranking of SMEs, sub processes of the production process and key performance indicators (KPIs) of the production process [23]. The proposed GA model introduces two functions: total sum of the overall weighted coefficient of SMEs, sub processes, and KPIs, and the second function is variance of the existing variables.…”

Section: Introductionmentioning

confidence: 99%

“…MATLAB-GA toolbox is used for the development of this software solution. Choosing the appropriate strategy for organizational resilience enhancement may be treated as an optimization issue where GA, in recent years, has found many applications [23,24]. With respect to the obtained results, an improvement strategy is developed by using GA in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and Enhancement of Organizational Resilience Potential in Process Industry SMEs

et al. 2015

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Abstract:The business environment is rapidly changing and puts pressure on enterprises to find effective ways to survive and develop. Since it is almost impossible to identify the multitude of complex conditions and business risks, an organization has to build its resilience in order to be able to overcome issues and achieve long term sustainability. This paper contributes by establishing a two-step model for assessment and enhancement of organizational resilience potential oriented towards Small and Medium Enterprises (SMEs) in the process industry. Using a dynamic modelling technique and statistical tools, a sample of 120 SMEs in Serbia has been developed as a testing base, and one randomly selected enterprise was used for model testing and verification. Uncertainties regarding the relative importance of organizational resilience potential factors (ORPFs) and their value at each level of business are described by pre-defined linguistic expressions. The calculation of the relative importance of ORPFs for each business level is stated as a fuzzy group decision making problem. First, the weighted ORPFs' values and resilience potential at each business level are determined. In the second step, near optimal enhancement of ORPFs' values is achieved by applying a genetic algorithm (GA).

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“…First, in aspect of SMEs, the collaboration with large firms accelerates commercialization of high-tech products. In particular, in the biotechnology industry, strategic alliances between SMEs and large companies are common because R&D expenditure is high and commercialization cycles are long for SMEs [3,4]. Second, each company obtains external information and knowledge resources by strategic R&D cooperation [5].…”

Section: Introductionmentioning

confidence: 99%

An Approach for R&D Partner Selection in Alliances between Large Companies, and Small and Medium Enterprises (SMEs): Application of Bayesian Network and Patent Analysis

2016

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Abstract:The enhanced R&D cooperative efforts between large firms and small and medium-sized enterprises (SMEs) have been emphasized to perform innovation projects and succeed in deploying profitable businesses. In order to promote such win-win alliances, it is necessary to consider the capabilities of large firms and SMEs, respectively. Thus, this paper proposes a new approach of partner selection when a large firm assesses SMEs as potential candidates for R&D collaboration. The first step of the suggested approach is to define the necessary technology for a firm by referring to a structured technology roadmap, which is a useful technique in the partner selection from the perspectives of a large firm. Second, a list of appropriate SME candidates is generated by patent information. Finally, a Bayesian network model is formulated to select an SME as an R&D collaboration partner which fits in the industry and the large firm by utilizing a bibliography with United States patents. This paper applies the proposed approach to the semiconductor industry and selects potential R&D partners for a large firm. This paper will explain how to use the model as a systematic and analytic approach for creating effective partnerships between large firms and SMEs.

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A model of the assessment and optimisation of production process quality using the fuzzy sets and genetic algorithm approach

Cited by 15 publications

References 36 publications

Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle

Developing the weighted ISM-MICMAC framework for process design stage of production system life cycle

Modelling and Enhancement of Organizational Resilience Potential in Process Industry SMEs

An Approach for R&D Partner Selection in Alliances between Large Companies, and Small and Medium Enterprises (SMEs): Application of Bayesian Network and Patent Analysis

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