Time- and Skill-dependent Vehicle Routing Problem in application of facility maintenance
Abstract
With current technological advancements, almost all new buildings require periodic maintenance inspections of their installed facilities. These inspections are performed by technicians with varying experience, and therefore changing work hours and rates. However, such inspections often cannot be performed while the building is in use, which requires scheduling visits based on available time windows. In this paper, the described vehicle routing problem with time windows and varying employee rates is formulated using the MILP model. Four solution methods are proposed: a mathematical solver, an adapted Tabu Search (TS) algorithm, an adapted 1nn algorithm, and the Artificial Bee Colony (ABC) algorithm. The purpose of the study is to examine the impact of time window lengths and the junior-senior ratio of the technician groups on the final solution. The results show that for larger instances of the problem, it is not possible to use Gurobi as a solving method due to long computational time. Two implemented metaheuristics, Tabu Search and Artificial Bee Colony algorithm, are comparable with the note that Tabu Search usually achieves better results, depending on the specific spatial characteristics of the instance. The best results were obtained with InsertMove operator for neighborhood exploration for both metaheuristics. In our case, it is proved that the longer tabu list size is applied, the better are the results. Tuning the ABC algorithm showed that it performs best with a number of bees of 75 and a larger limit parameter. Future research may include hybrid use of the proposed metaheuristics and multi-objective optimization to investigate the influence of individual objective function coefficients.
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