2019

Conférence

Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM. Presented at the GECCO ’19: Genetic and Evolutionary Computation Conference, ACM New York (pp. 77-78). Prague Czech Republic. doi:10.1145/3319619

This paper proposes a new approach to solve the Bike Rebalancing Problem (BRP) based on the Honey-Bee Mating Optimization (HBMO) algorithm. The aim is to reduce the overall traveling cost of redistribution operations under various constraints. The performance of the proposed algorithm is evaluated using a set of benchmark instances for the BRP. Preliminary results are obtained and showed that the proposed approach is promising.

@inproceedings{10.1145/3319619.3326790, author = {Sebai, Mariem and Fatnassi, Ezzeddine and Rejeb, Lilia}, title = {A honeybee mating optimization algorithm for solving the static bike rebalancing problem}, year = {2019}, isbn = {9781450367486}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3319619.3326790}, doi = {10.1145/3319619.3326790}, abstract = {This paper proposes a new approach to solve the Bike Rebalancing Problem (BRP) based on the Honey-Bee Mating Optimization (HBMO) algorithm. The aim is to reduce the overall traveling cost of redistribution operations under various constraints. The performance of the proposed algorithm is evaluated using a set of benchmark instances for the BRP. Preliminary results are obtained and showed that the proposed approach is promising.}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, pages = {77–78}, numpages = {2}, keywords = {vehicle routing problem, honey bee mating optimization, heuristics, bike rebalancing problem}, location = {Prague, Czech Republic}, series = {GECCO '19} }