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Title: Post-Optimizing Individual Activity Plans through Local Search
Authors: Alexiadis, Anastasios
Refanidis, Ioannis
Editors: Salido, Miguel A.
Barták, Roman
Rossi, Francesca
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Issue Date: Jun-2013
Source: Proceedings of ICAPS 2013 Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS-2013), Rome, Italy, June 2013
First Page: 7
Last Page: 15
Volume Title: Proceedings of the 8th Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS-2013)
Abstract: 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.
Appears in Collections:Department of Applied Informatics

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