Forming part families by using genetic algorithm and designing machine cells under demand changes
This study develops a methodology which can be used to form manufacturing cells using both a new similarity coefficient based on the number of alternative routes during machine failure and demand changes for multiple periods. The methodology is divided into two phases. A new similarity coefficient, which considers the number of available alternative routes when available during machine failure, is suggested in Phase I. The primary objective of Phase I is to identify part families based on the new similarity coefficient by using a genetic algorithm. One of the major factors contributing to the success of cell implementation is flexibility for demand changes. It is difficult to reorganize the cells according to changes in demand, available machine capacity, and due date. Most of the suggested approaches in the literature tend to use a fixed demand for cellular manufacturing systems. Due to demand changes, cell design should include more than the one period that most researchers of cellular manufacturing systems consider. A new methodology for cell formation, which considers the scheduling and operational aspects in cell design under demand changes, is introduced in Phase II. Machines are assigned to part families by using an optimization technique. This optimization technique employs sequential and simultaneous mixed integer programming models for a given period to minimize the total costs which are related to the scheduling and operational aspects.
genetic algorithm demand changes alternative routes machine failure cellular manufacturing group technology