Optimizing individual activity personal plans through local search

Optimization through local search is known to be a powerful approach to confront complex optimization problems. In this article we tackle the problem of optimizing individual activity personal plans, that is, plans involving activities 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 article 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 of the solution produced by SWO using either hill climbing or simulated annealing. Similar results can be obtained by employing local search only, starting from an empty plan, thus demonstrating the strength of the proposed local search techniques. We present several experiments that demonstrate an improvement on the utility of the produced plans, with respect to the solutions produced by SWO only, of more than 6% on average, which in particular cases exceeds 20%. Of course, this improvement comes at the cost of extra time.