Cellular manufacturing emerged as a production strategy capable of solving the certain problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach for obtaining machine cells and product families. The approach combines a local search heuristic with a genetic algorithm. Computational experience with the algorithm on a set of group technology problems available in the literature is also presented. The approach produced solutions with a grouping efficacy that is at least as good as any results previously reported in literature and improved the grouping efficacy for 59 % of the problems.

The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

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Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation cond...

Technology and a Nested Genetic Algorithm *

This paper presents a new model that integrates design and planning to prescribe a cost-effective cellular configuration that is responsive to real world considerations. The model incorporates practical engineering features such as the finite capacity of machines, use of alternative machines, multiple "copies" of a machine type, and limitations on cell size. It integrates design decisions, locating machines in each cell and identifying product families, with planning considerations, assuring that machine capacities are sufficient to produce required volumes and dealing with between cell movement to use alternative machines. Computational experience using a commercially available optimization package demonstrates that run time required to resolve problems of realistic size and scope can be quite reasonable.

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Abstract. This research aims to develop a new team building method based on competency modelling in the field of project management. This method is divided into three main stages. First, a competency matrix based on a task-actor compatibility indicator helps to characterise the competency levels. Second, we apply a clustering algorithm in order to reduce the problem complexity and favour the employees ’ expertise. The clustering algorithm will decompose large sets of tasks and actors into smaller task groups related to different actor groups. It facilitates the project leaders to organise the actors into teams. Finally, the proposed task assignment model incorporates a learning curve in order to take the competency dynamics into account. Our computational experiments suggest that incorporating a clustering algorithm as a step of the method results in preserving expertise and thus helps project managers to find better tradeoffs between project cost (short term goal) and competency dynamics (long term goal).

Abstract: A key issue in the design of a cellular manufacturing system is the formation of the machines and parts into groups or production cells. The production cells are designed to minimize the costs of inter-cell part movements and intra-cell processing, while balancing the workload within each cell. Most of the prior research represents the cell formation problem as a binary machine-part incidence matrix. The workload balance within each production cell may be precisely calculated only if the processing times and demand rates are included in the analysis. For this reason, a heuristic-based procedure that uses processing times and demand rates to form the production cells is proposed. The procedure considers the cell imbalance costs as well as the costs associated with the inter-cell part movements and intra-cell processing. The efficiency and effectiveness of the heuristic is compared to other methods, and an industrial application of the proposed heuristic is presented.