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- W2522917304 abstract "s 221 Developing test instances was done by adopting an extension to the speed model of [5] for TDVRP, which simulates congestion patterns by using road categories and time slots. The travel time is determined by defining a speed level for every combination of a time slot and road category, requiring that every arc (link between two locations) is assigned to a road category. The datasets used for the TDVRP solution methods don’t resemble a real live road network as the arcs were randomly assigned to a road category. Therefore we transformed the existing datasets of the orienteering problem to time dependent instances by manually inserting congestion patterns, imitating a real live road network (e.g. realistic morning and evening peaks between city centers and living areas). Comparing the results between the three solution procedures proves that obtaining solutions in realtime is feasible as the maximum computation time for the largest datasets (102 vertices) is still smaller than 6 seconds and the average runtime is 2 seconds. Currently, the AIS and ACS clearly outperform the ILS framework, especially on larger instances, but require more computation time. The reason might be that, in these test instances, a lot of good solution characteristics are lost due to incremental vertex deletions as an infeasible solution needs to be repaired after a shake step. Another reason might be that too few vertices can be included (since the travel time limit is too constraining) in the developed test instances, significantly hampering the delete and insert procedure of the ILS. Moreover, the average gap between the best solution found and the metaheuristic is marginally lower for the AIS than the ACS (1.6% versus 1.8%). However, ACS performs better on the largest instances. A preliminary analysis yields insights about the performance of each component, AIS relies heavily on the iterative insert move while the backbone of the ACS is formed by the ant construction procedure together with the pheromone memory effect. Furthermore, both AIS & ACS tend to get stuck in local optima as there is little improvement in performance when the number of iterations is increased. Based on these preliminary results, adjustments need to be made to design an appropriate metaheuristic that strikes the optimal balance between the quality of the results and the computation time. To enhance the test procedure, instances will be developed so that the optimal solution is known, providing more insight into the obtained results." @default.
- W2522917304 created "2016-09-30" @default.
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- W2522917304 date "2012-01-01" @default.
- W2522917304 modified "2023-09-27" @default.
- W2522917304 title "Metaheuristics for the time-dependent orienteering problem" @default.
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