Journal ArticleDOI
Soft-computing-based approaches to the group technology problem: A state-of-the-art review
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A state-of-the-art review synthesizing the literature on the use of soft-computing-based approaches, e.g. neural networks and fuzzy models to the group technology problem, to identify the gaps for performing future research in this area.Abstract:
This paper presents a state-of-the-art review synthesizing the literature on the use of soft-computing-based approaches, e.g. neural networks and fuzzy models to the group technology problem. The objectives of this paper are to discuss the trend and to identify the gaps for performing future research in this area.read more
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BookDOI
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Stefan Voß,David L. Woodruff +1 more
TL;DR: Optimization Modeling starts with an mrp Model and extends to an MRP II Model, a Better Model and Extensions to the Model, and Implementation Examples.
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A simulated annealing approach for manufacturing cell formation with multiple identical machines
TL;DR: In this paper a mathematical programming model for the cell formation problem with multiple identical machines, which minimises the intercellular flow is presented and a simulated annealing algorithm was developed to solve it.
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CF-GGA: A grouping genetic algorithm for the cell formation problem
TL;DR: In this paper, a grouping genetic algorithm (GGA) for solving the machine-part cell formation (MPCF) problem is presented and tested, and the quality of the GGA's solutions is evaluated using the measurements of efficiency and efficacy.
Journal ArticleDOI
Evaluating performance advantages of grouping genetic algorithms
TL;DR: Empirical tests of the performance of GA and GGA in three domains which have substantial, practical importance, and which have been the subject of considerable academic research are described.
Journal ArticleDOI
Concurrent design of cellular manufacturing systems: a genetic algorithm approach
TL;DR: In this article, a hierarchical genetic algorithm (HGA) is developed to solve the integrated cell design problem, which integrates cell formation and group layout decisions and incorporates some important factors, such as operation sequence, part demand, transfer batch, machine capacity, and layout type.