The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

This paper describes the research activities at the Laboratory for Control Systems and Automation (LCA) which is a section of the Production Department of Korea Advanced Institute of Science and Technology (KAIST). LCA's research activities are focused on providing fundamental technologies to cope with the current trends in manufacturing automation. The R & D activities at the laboratory cover diverse areas. The research activities at the laboratory are classified into three sections—robotics and automation, fluid servo control, and manufacturing process control. Detailed explanations are given here on robotics and its application to automation. As a laboratory belonging to a nation's leading educational institution, LCA also conducts active educational activities. Such educational programs are also introduced in detail.