An Algorithm for the Manufacturing Equipment Selection Problem

This paper provides a unified framework in which product and process demands can be related to manufacturing system requirements. A nonlinear cost minimization model is developed that can be used by facility planners to guide the analyses underlying the equipment selection problem. The approach extends current work by accounting for machine flexibility. The objective is to determine how many of each machine type to purchase, as well as what fraction of the time each piece of equipment will be configured for a particular type of operation.

The resultant problem is solved with a depth-first branch and bound routine that employs a greedy set covering heuristic to find good feasible solutions. This permits early fathoming and greatly contributes to the efficiency of the algorithm. A small example is presented to highlight the computations. This is followed by a discussion of me results for a series of test problems designed to evaluate overall algorithmic performance. We show mat 16 process, 25 machine problems can be readily solved in less than 6 minutes on a microcomputer.