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Due to market competition, manufacturers typically produce their products with different customized features, leading to the production of product variants (or a product family). Since the market trend can change swiftly, the demands of individual product variants can be difficult to predict. Two flexible manufacturing layouts are commonly considered: functional and cellular layouts. While the functional layout is more resilient to demand changes due to better resource pooling, the cellular layout can be more productive on some occasions due to better routing efficiency. In this context, the purpose of this paper is to quantify and study the criticality of product variants. The criticality score of a product variant can estimate and rank which product variants can sensitively cause bottlenecks in the functional and cellular layouts. The proposed criticality analysis starts with the estimation of bottleneck machines. Through the dependency information of machines and parts, we can estimate the criticality of product variants. The criticality analysis is demonstrated and examined through a simulation study with a study case involving the production of five furniture products with 16 unique parts using 11 machines. The simulation results show that the productions with more critical product variants tend to deteriorate the completion time of the cellular layout more severely. In practice, manufacturers can use the proposed criticality analysis to evaluate the criticality of product variants and support their facility layout decision. For example, if more demand for critical products is expected, the layout should support more resource pooling (e.g., functional layouts). [ABSTRACT FROM AUTHOR]

Abstract: Consider nondeterministic polynomial-time Turing machine that on input x outputs a 3×3 matrix with entries from {−1,0,1} on each of its paths. Define the function f where f (x) is the upper left entry in the product of all these matrices (in an order of the paths to be made precise below). We show that the class of functions f computable as just described is exactly the class FPSPACE of integer-valued functions computable by polynomial-space Turing machines. Along the way we obtain characterizations of FPSPACE in terms of arithmetic circuits and straight-line programs. [Copyright &y& Elsevier]

Cellular Manufacturing (CM) is an important application of Group Technology (GT) in which families of parts are produced in manufacturing cells or a group of various machines, which are physically close together and can entirely process a part family. The manufacturing system established based on such an idea is called Cellular Manufacturing System (CMS). A major problem associated with many CMSs is the existence of Exceptional Elements (EEs), i.e. bottleneck machines and exceptional parts. These are machines/parts that cannot be exclusively assigned to a machine cell/part family. In this paper a new model is presented for dealing with the EEs in the form of a Multi-objective Optimization Problem (MOP). This model aims to minimize: (1) intercellular parts movements, (2) total cost needed for machine duplication and part subcontracting, (3) the system's under-utilization, and (4) deviations among the cells' utilization. Attaining an ideal solution, which is optimal to all of the objectives is prohibited, as they conflict with each other. Hence, a Multi-Objective Genetic Algorithm (MOGA) is developed to provide the decision-maker with a set of non-dominated or Pareto-optimal solutions. Comparisons between the developed MOGA and three other MOGAs show its viability in three performance aspects, namely: quality, diversity and CPU time. [ABSTRACT FROM AUTHOR]