Tool requirement planning in stochastic job shops: A simulated annealing approach

Tool management in shop floor control has been discussed widely in the recent literature. The need for an effective tool management system has been underscored by the impact of tool availability in flexible manufacturing systems and cellular manufacturing shops. With the understanding that most job shops face a budgetary constraint on the units of tools to be maintain, this paper presents an optimization model based on queueing theory for determining the number of units of tools to be maintained in stochastic job shop production systems. The optimization model seeks to minimize total tool related cost under some managerial and operational constraints. To plan tool requirement based on the optimization model, an improved simulated annealing algorithm is presented. Starting with an arbitrary initial solution, the algorithm searches for a solution among the combinatorial alternatives in a controlled random fashion. Unlike the iterative improvement algorithms, the simulated annealing algorithm is applicable to multimodal objective function due to its capability of probabilistic hill-climbing. To demonstrate the performance of the model and the algorithm, a medium-scale numerical example is also discussed in detail. © 1993.