Sani Susanto

A new fuzzy-c-means and assignment technique-based cell formation algorithm to perform part- type clusters and machine-type clusters separately

The incorporation of fuzziness in the cell formation problem by Chu and Hayya (1991 International Journal of Production Research, 29, 1475-1487) is a notable contribution, in which non-binary classification logic is used. However, despite this development, numerical illustrations performed in this research demonstrate that the Chu and Hayya approach can result in solutions with empty party-type cluster(s) and/or empty machine-type cluster(s). Further, it is noted that solutions based on the Chu and Hayya approach can contain non-empty part-type cluster(s) being assigned to empty machine-type cluster(s) and vice versa. Three strategies are offered in this research to overcome these inadequacies: the separate formation of part-type and machine-type clusters; modification of the stopping criterion; and the adoption of the assignment technique in the formation of the final manufacturing cell solutions. A new algorithm has resulted from these modifications and has been rigorously compared to the performance of...

Optimised Cell Formation Algorithm Considering Sequence of Operations, Alternative Routing and Part-Volume

A novel cell formation algorithm has been introduced in this paper. The motivation of the research is the lack of attention in important information, e.g., production demand (or part-volume), in the most of the cell formation techniques. For example, one of the crucial steps in the design of a cellular manufacturing system is the cell formation problem, which involves grouping the parts into part families and machines into machine groups. It has been found that 80% of the cell formation problems consider only 0-1 (binary) machine-part incidence matrix that ignores the sequence of operations in manufacturing parts. Further, literature reveals that only a few studies consider alternative routing. In order to cope with this situation a new cell formation algorithm, considering the factors sequence of operation, alternative routing and part-volume, is introduced.

Fuzzy Multi-object Linear Programming Application in Product-Mix Decision-Making

This paper focuses on optimizing product-mix problem using real-world data of a food processing industry. The extensively used LP model has been remodeled using fuzzy sets and applied to the real-world problem. The developed LP model is able to consider the fuzziness in the parameters and to tackle the presence of multiple objective functions. The degree of satisfaction of the product-mix decision-maker in terms of (i) the total profit obtained and (ii) the waste resulted, is at least 70%, which is encouraging

Fuzzy Clustering-Based Optimised Cell Formation Algorithm Considering Sequence of Operations, Alternative Routing and Part-Volume

CM is the grouping of discrete multi-machines that produce part families with similar geometry or sequence of process and aims to improve manufacturing systems productivity. The methods/models used are classified into: array-based, clustering, mathematical programming based (viz., integer programming), graph theoretic, Multi-Criteria Decision-Making (MCDM) and artificial intelligence techniques. Fuzzy c-means clustering approach has been used to manufacturing cell formation problem by Chu and Hayya, this differs from a previous ones proposed by Xu and Wang, and later by Li and Ding, in the sense that Chu and Hayya use manufacturing routing data rather than design features in part family formation. Optimised cell formation, taking into account sequence of operations, alternative routing and part volume together is not present in Chu and Hayya. Thus this paper focuses on stages selection of parts and part families generation, and selection of machines and process and grouping of these into cells in the design of a new manufacturing system considering sequence of operation, alternative routing and part-volume. One of the crucial steps in the design of a cellular manufacturing system is the cell formation problem, which involves grouping the parts into part families and machines into machine groups.

Fuzzy Multi-objective Linear Programming Having Probabilistic Constraints: Application in Product-Mix Decision-Making

A fuzzy multi-objective linear programming model having probabilistic constraints is demonstrated in order to make product-mix decision. The proposed model considers fuzziness in presence of multiple objective functions. The most important aspect of the model is that it is able to tackle constraints which are probabilistic in nature. A product-mix problem having real-world data of a food processing industry is illustrated focusing the application of the proposed model.

Fuzzy LP with a Non-Linear MF for Product-Mix solution: A Case-Based Re-modelling and Solution

This paper deals with re-modelling of a fuzzy linear programming (FLP) for an optimal product-mix decision problem and its solution. Database of a chocolate exporting company has been used here to show the practicability of using the proposed model. The proposed model includes a non-linear membership function (MF), a logistic function, which resemblances the realistic behaviour of the solution. A software platform LINGO® has been utilized to find the optimal solution.