A multi-objective procedure for labour assignments and grouping in capacitated cell formation problems

A hierarchical methodology for the design of manufacturing cells is proposed, which includes labour-grouping considerations in addition to part-machine grouping. It is empirically driven and designed for an interactive decision environment, with an emphasis on fast execution times. The method synthesizes the capabilities of neural network methods for rapid clustering of large part-machine data sets, with multi-objective optimization capabilities of mathematical programming. The procedure includes three phases. In Phase I, part families and associated machine types are identified through neural network methods. Phase II involves a prioritization of part families identified, along with adjustments to certain load-related parameters. Phase III involves interactive goal programming for regrouping machines and labour into cells. In machine grouping, factors such as capacity constraints, cell size restrictions, minimization of load imbalances, minimization of intercell movements of parts, minimization of new machines to be purchased, provision of flexibility, etc. are considered. In labour grouping, the functionally specialized labour pools are partitioned and regrouped into cells. Factors such as minimization of hiring and cross-training costs, ensuring balanced loads for workers, minimization of intercell movements of workers, providing adequate levels of labour flexibility, etc. are considered in a pragmatic manner.