A Stochastic Search Approach for Tool Requirement Planning

Abstract

In an automated manufacturing unit, tool requirement planning is the primary function of tool management. The issues such as tool procurement, tool life inventory, use of alternative tools etc. can be addressed at a level below aggregate planning and above operational implementation. At this level, it is very essential to choose the optimum set of tools out of the alternative tools available for performing operations on various parts in a production period for minimum cost. In the present work, a model has been developed to consider the above mentioned factors and provide the optimum set of tools, out of the alternative tools available, for performing operations on a batch of parts. The performance of the three stochastic search techniques has been studied. It has been observed that the modified simulated annealing techniques are powerful techniques to provide the results in a very less number of evaluations.