Edited By : Nicola Policella and Nilufer Onder Rome , Italy - June 12 , 2013

Post-optimization through local search is known to be a powerful approach for complex optimization problems. In this paper we tackle the problem of optimizing individual activity plans, i.e., plans that concern activities that one person has to accomplish independently of others, taking into account complex constraints and preferences. Recently, this problem has been addressed adequately using an adaptation of the Squeaky Wheel Optimization Framework (SWO). In this paper we demonstrate that further improvement can be achieved in the quality of the resulting plans, by coupling SWO with a post-optimization phase based on local search techniques. Particularly, we present a bundle of transformation methods to explore the neighborhood using either hill climbing or simulated annealing.We present several experiments that demonstrate an improvement on the utility of the produced plans, with respect to the seed solutions produced by SWO, of more than 6% on average, which in particular cases exceeds 20%. Of course, this improvement comes at the cost of extra time. The paper is published in the Proceedings of the workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS). ICAPS 2013 Application Showcase 1 Autonomous Search and Tracking via Temporal Planning Sara Bernardini, Maria Fox, Derek Long Department of Informatics King’s College London Strand London WC2R 2LS [email protected] John Bookless Advanced Technology Centre BAE Systems Bristol, UK, BS34 7QW [email protected]