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Optimizing individual activity personal plans through local search

机译：通过本地搜索优化个人活动个人计划

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摘要

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.
机译：通过本地搜索进行优化是解决复杂优化问题的有效方法。在本文中，我们解决了优化个人活动个人计划的问题，即涉及一个人必须独立于其他人完成的活动的计划，同时考虑了复杂的约束和偏好。近来，使用吱吱作响的车轮优化框架（SWO）的改编已充分解决了这个问题。在本文中，我们证明，通过将SWO与基于本地搜索技术的优化后阶段相结合，可以进一步改善最终计划的质量。特别是，我们提出了一套转换方法，以探索使用爬山法或模拟退火法由SWO产生的溶液的邻域。从一个空的计划开始，仅采用本地搜索就可以获得类似的结果，从而证明了所提出的本地搜索技术的实力。我们提出了几个实验，这些实验表明，相对于仅由SWO生产的解决方案，所生产计划的实用性平均提高了6％，在特定情况下超过了20％。当然，这种改进是以增加时间为代价的。

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