Mass customization, aiming at delivering an increasing product variety that best serves customer needs while keeping mass production e ciency, has recently received numerous attention and popularity in industry and academia alike. This paper presents a methodology of developing product family architecture (PFA) to rationalize product development for mass customization. Systematic steps are developed to formulate a PFA in terms of functional, technical and physical views. The diverse needs of customers are matched with the capabilities of a ®rm through systematic planning of modularity in three consecutive views. The development of a PFA provides a unifying integration platform to synchronize market positioning, commonality employment and manufacturing scale of economy across the entire product realization process. A case study in an electronics company is reported to illustrate the potential and the feasibility of PFA methodology.

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.

This paper addresses the problem of grouping machines in order to design cellular

This paper presents an integrated mathematical programming formulation of the

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.

heuristic to minimize makespan of cell scheduling problem

Cellular manufacturing emerged as a production strategy capable of solving the 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. Keywords: Cellular Manufacturing; Group Technology; Genetic Algorithms; Random Keys AT&T Labs Research Technical Report TD-5FE6RN, October 29, 2002.

Abstract—In recent years, the process of cellular manufacturing and group technology has received much attention and popularity in many developed countries. By applying Group Technology (GT), many benefits of flow-line production can be attained in a batch production system. GT can improve material handling, significantly reduce material flow time and distance, and setup times. In this paper, a two step approach is proposed to solve the GT problem using Genetic Algorithms (GA). The first step assigns parts to the best available machines according to their required specifications. The second step forms manufacturing cells and part families. The proposed GA model has the flexibility of choosing the number of cells required, which is very useful in examining different manufacturing cell configurations; or in case that the workshop or factory prefers a certain number of cells. For example if the workshop or factory doesn't have the workspace required for more than four cells. To compare the performance of the proposed GA model, five part-machine matrices are obtained from the literature are solved using different techniques and their results are compared to the results achieved by the proposed GA model. The GA model results were found satisfactory and superior to other techniques in some cases.

Growing concerns for the environment demands that product design for the life cycle (DFLC) is considered more carefully. Modularity is seen as a means to incorporate life cycle considerations into product architecture design; however, to date, most modular design methods concentrate on generating highly-modular product architectures but fall short in assessing life cycle consequences of these modules. This paper proposes a methodology to find a robust product modular architecture with minimal life cycle costs and environmental impacts at the design configuration stage. The primary objective of the proposed methodology is not to maximize modularity level, but to adopt life cycle costing and life cycle assessment to identify the most beneficial modular structure. In the case study presented, processing facilities are modeled as a closed-loop supply chain, and their influence on life cycle metrics is evaluated. Using the proposed methodology, a designer can identify not only the most beneficial modular structure during configuration design, but also an optimal supply chain network structure.

Cellular manufacturing emerged as a production strategy capable of solving the 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 simultaneous arrangement of part families and machine cells for cellular manufacturing systems. The main feature of the proposed method is, the relevant production data such as process sequences and setup times are taken in to account. It has the ability to select the best solution among the solutions of compactness, group technology efficiency and reducing setup time efficiency for each part before attempting to cluster the machines and parts. The formation of part family and machine cell has been treated as a maximization problem according to a defined performance measure ‘β’. A genetic algorithm has been developed for solving the cell formation problem considering the reduction in setup time. The validation has been done based on a real time manufacturing data. This algorithm is written in the ‘C’ language on Intel Pentium / PIII compatible system.