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This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers.

The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space.

The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques.

The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations.

Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

This study aims to propose a decision support system (DSS) that performs a decision-based part-fixture assignment and fixture flow control in planned production periods.

The principal approaches were fuzzy case-based reasoning (FCBR) and discrete-event simulation (DES). Besides, the fuzzy analytic hierarchy process (FAHP), an object-oriented (OO) method and a fuzzy weighted Euclidean distance were used to support the decision-making process.

It shows that integrating FCBR and DES systems is a promising approach to address part-fixture planning problems. The FCBR subsystem proposed various stable numbers of fixtures as scenarios. The DES model analyzed the future performances of these scenarios and identified the best alternative.

The DSS was tested in laboratory environments using a numerical analysis; however, it was not validated in industrial situations.

The synergy of integrating FCBR and DES systems was not exploited in the past in part-fixture assignment and fixture flow control problems.

The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed.

The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS.

The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP.

Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems.

The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world.

Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.

This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.

The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.

Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.

The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.

The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

The purpose of this paper is to investigate the relationship between quality management practice and labor productivity in labor-intensive manufacturing companies in a developing nation and benchmark with the world average.

Primary and secondary data were collected from 34 selected companies. The primary data were obtained using a questionnaire survey to determine the quality management adoption level of each company using the European Business Excellence Model. Secondary data also collected in order to compute labor productivity of each organization and benchmark with international norms.

In this research, labor productivity is measured by revenues per employee and total assets per employee and found that adopting quality management has strong relationships with revenue per employee unlike total asset per employee that is weakly related.

Several authors suggest a positive relationship between adoption quality management principles and productivity in large organizations located in developed countries. However, this paper particularly focuses on labor productivity of labor-intensive companies.