Efficient heuristics for robot acquisition planning for a CIM system

In this paper, a planning model and three efficient heuristics are developed for equipment acquisition planning for a CIM system using multiple-type robots. The present planning model considers selection of a proper mix of multiple-type robots such that operational requirements (i.e., time and space) from a given number of work stations are satisfied at minimal system cost. In specific, each robot is characterized by its fixed charge and subject to two capacity constraints on machine time and work space; and each work station has known demands for both machine time and work space, and is to be served by only one robot. The model is formulated as a pure 0-1 mathematical program and is shown to be harder than two-dimensional bin packing, a well-known NP-hard problem. The three heuristics developed are: a greedy heuristic, tabu thresholding, and simulated annealing. All heuristics are tested by solving 450 randomly genrated problems. Computational results indicate that all three heuristics are effective and efficient in solivng problems of a practical size (i.e., 50 work stations and a maximum of 20 robots). However, none of the heuristics are over-whelmingly better than the others in terms of both solution time and quality. Fugure research issues are also discussed.