Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times

This study addresses a part selection problem for flexible manufacturing systems in which part processing times are controllable to optimize the total cost associated with energy consumption, operational performance, and so on. The problem is to determine the set of parts to be produced, part processing times and the number of tools for each tool type in each period of a planning horizon while satisfying processing time capacity, tool magazine capacity and tool life restrictions. The objective is to minimize the sum of part processing, earliness/tardiness, tool and subcontracting costs. Tool sharing among part types is also considered. After an integer programming model is developed, two types of solution algorithms are proposed, that is, fast heuristics useful when decision time is critical and variable neighborhood search algorithms when solution quality is important. Computational experiments were conducted on a number of test instances and the best fast heuristics are specified, together with reporting how much the variable neighborhood search algorithms improve the fast heuristics.