Metaheuristics approach for solving personalized crew rostering problem in public bus transit
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Journal of Heuristics, Jahrgang 23, Nr. 5, 10.2017, S. 321-347.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Metaheuristics approach for solving personalized crew rostering problem in public bus transit
AU - Xie, Lin
AU - Merschformann, Marius
AU - Kliewer, Natalia
AU - Suhl, Leena
PY - 2017/10
Y1 - 2017/10
N2 - The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.
AB - The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.
KW - Business informatics
KW - Ant colony optimization
KW - Simulated annealing
KW - Tabu search
KW - Crew rostering problem
KW - Personalized/non-cyclic rostering
KW - Public transport
UR - http://www.scopus.com/inward/record.url?scp=85021732871&partnerID=8YFLogxK
U2 - 10.1007/s10732-017-9348-7
DO - 10.1007/s10732-017-9348-7
M3 - Journal articles
VL - 23
SP - 321
EP - 347
JO - Journal of Heuristics
JF - Journal of Heuristics
SN - 1381-1231
IS - 5
ER -