A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem

Ming Wei; Congxin Yang; Bo Sun; Binbin Jing; Ming Wei; Congxin Yang; Bo Sun; Binbin Jing

doi:10.3934/era.2023322

Electronic Research Archive

2023, Volume 31, Issue 10: 6363-6383. doi: 10.3934/era.2023322

Previous Article Next Article

Research article Special Issues

A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem

1.
CAAC Key Laboratory of Civil Aviation Wide Surveillance and Safety Operation Management & Control Technology, Civil Aviation University of China, Tianjin 300300, China
2.
School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
3.
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China

Received: 17 July 2023 Revised: 14 September 2023 Accepted: 19 September 2023 Published: 25 September 2023

We proposed a multi-objective optimization framework for green demand responsive airport shuttle scheduling, which simultaneously aims at assigning demand points to selected stops and routing airport shuttles to visit these stops in their overlapping time windows to transport all passengers from their homes or workplaces to the airport. Our objectives were to minimize total travel time for passengers, the punishment expense of violating the time-window as well as carbon emissions for all shuttles. Since such issues belongs to the NP-problem, a two-stage Multi-objective ant lion optimizer (MOALO)-based algorithm incorporating dynamic programming search method was developed to acquire the optimal scheduling schemes. Finally, a case study of airport shuttle service in Tianjin Airport, China, was used to demonstrate the validity of the model and algorithm.
- green demand responsive airport shuttle scheduling,
- stop location,
- two-stage heuristic approach
Citation: Ming Wei, Congxin Yang, Bo Sun, Binbin Jing. A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem[J]. Electronic Research Archive, 2023, 31(10): 6363-6383. doi: 10.3934/era.2023322

Related Papers:

Abstract

We proposed a multi-objective optimization framework for green demand responsive airport shuttle scheduling, which simultaneously aims at assigning demand points to selected stops and routing airport shuttles to visit these stops in their overlapping time windows to transport all passengers from their homes or workplaces to the airport. Our objectives were to minimize total travel time for passengers, the punishment expense of violating the time-window as well as carbon emissions for all shuttles. Since such issues belongs to the NP-problem, a two-stage Multi-objective ant lion optimizer (MOALO)-based algorithm incorporating dynamic programming search method was developed to acquire the optimal scheduling schemes. Finally, a case study of airport shuttle service in Tianjin Airport, China, was used to demonstrate the validity of the model and algorithm.

References

[1]	R. Q. Zhao, W. Liu, F. N. Zhang, T. T. R. Koo, G. Lodewijks, Passenger shuttle service network design in an airport, Transportmetrica B: Transport Dyn., 10 (2022), 1099–1125. https://doi.org/10.1080/21680566.2021.2008279 doi: 10.1080/21680566.2021.2008279
[2]	X. Li, T. Wang, W. Xu, H. Li, Y. Yuan, A novel model and algorithm for designing an eco-oriented demand responsive transit (DRT) system, Transp. Res. Part E Logist. Transp. Rev., 157 (2022), 102556. https://doi.org/10.1016/j.tre.2021.102556 doi: 10.1016/j.tre.2021.102556
[3]	Y. Yu, S. Wang, J. Wang, M. Huang, A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows, Transp. Res. Part B Methodol., 122 (2019), 511–527. https://doi.org/10.1016/j.trb.2019.03.009 doi: 10.1016/j.trb.2019.03.009
[4]	X. Li, M. Wei, J. Hu, Y. Yuan, H. Jiang, An agent-based model for dispatching real-time demand-responsive feeder bus, Math. Probl. Eng., 2018 (2018). https://doi.org/10.1155/2018/6925764 doi: 10.1155/2018/6925764
[5]	B. Sun, M. Wei, W. Wu, An optimization model for demand-responsive feeder transit services based on ride-sharing car, Information, 10 (2019), 370. https://doi.org/10.3390/info10120370 doi: 10.3390/info10120370
[6]	Y. Xiao, A. Konak, The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion, Transp. Res. Part E Logist. Transp. Rev., 88 (2016), 146–166. https://doi.org/10.1016/j.tre.2016.01.011 doi: 10.1016/j.tre.2016.01.011
[7]	A. K. Beheshti, S. R. Hejazi, A novel hybrid column generation-metaheuristic approach for the vehicle routing problem with general soft time window, Inf. Sci., 316 (2015), 598–615. https://doi.org/10.1016/j.ins.2014.11.037 doi: 10.1016/j.ins.2014.11.037
[8]	M. Wei, B. Jing, J. Yin, Y. Zang, A green demand-responsive airport shuttle service problem with time-varying speeds, J. Adv. Transp., 2020 (2020). https://doi.org/10.1155/2020/9853164 doi: 10.1155/2020/9853164
[9]	Y. K. Xia, Z. Fu, Improved tabu search algorithm for the open vehicle routing problem with soft time windows and satisfaction rate, Cluster Comput., 22 (2019), S8725–S8733. https://doi.org/10.1007/s10586-018-1957-x doi: 10.1007/s10586-018-1957-x
[10]	J. P. Xu, F. Yan, S. Li, Vehicle routing optimization with soft time windows in a fuzzy random environment, Transp. Res. Part E Logist. Transp. Rev., 47 (2011), 1075–1091. https://doi.org/10.1016/j.tre.2011.04.002 doi: 10.1016/j.tre.2011.04.002
[11]	J. Zhang, Y. Zhao, W. Xue, J. Li, Vehicle routing problem with fuel consumption and carbon emission, Int. J. Prod. Econ., 170 (2015), 234–242. https://doi.org/10.1016/j.ijpe.2015.09.031 doi: 10.1016/j.ijpe.2015.09.031
[12]	J. Li, D. P. Wang, J. H. Zhang, Heterogeneous fixed fleet vehicle routing problem based on fuel and carbon emissions, J. Cleaner Prod., 201 (2018), 896–908. https://doi.org/10.1016/j.jclepro.2018.08.075 doi: 10.1016/j.jclepro.2018.08.075
[13]	M. Wen, W. Sun, Y. Yu, J. Tang, K. Ikou, An adaptive large neighborhood search for the larger-scale multi depot green vehicle routing problem with time windows, J. Cleaner Prod., 374 (2022), 133916. https://doi.org/10.1016/j.jclepro.2022.133916 doi: 10.1016/j.jclepro.2022.133916
[14]	D. Zhang, X. Wang, S. Li, N. Ni, Z. Zhang, Joint optimization of green vehicle scheduling and routing problem with time-varying speeds, PLoS One, 13 (2018), e0192000. https://doi.org/10.1371/journal.pone.0192000 doi: 10.1371/journal.pone.0192000
[15]	E. Dernir, T. Bektas, G. Laporte, A review of recent research on green road freight transportation, Eur. J. Oper. Res., 237 (2014), 775–793. https://doi.org/10.1016/j.ejor.2013.12.033 doi: 10.1016/j.ejor.2013.12.033
[16]	M. Adelzadeh, V. M. Asl, M. Koosha, A mathematical model and a solving procedure for multi-depot vehicle routing problem with fuzzy time window and heterogeneous vehicle, Int. J. Adv. Manuf. Technol., 75 (2014), 793–802. https://doi.org/10.1007/s00170-014-6141-8 doi: 10.1007/s00170-014-6141-8
[17]	J. Brito, F. J. Martínez, J. A. Moreno, J. L. Verdegay, An ACO hybrid metaheuristic for close-open vehicle routing problems with time windows and fuzzy constraints, Appl. Soft Comput., 32 (2015), 154–163. https://doi.org/10.1016/j.asoc.2015.03.026 doi: 10.1016/j.asoc.2015.03.026
[18]	N. Norouzi, M. Sadegh-Amalnick, R. Tavakkoli-Moghaddam, Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption, Optim. Lett., 11 (2017), 121–134. https://doi.org/10.1007/s11590-015-0996-y doi: 10.1007/s11590-015-0996-y
[19]	D. Wu, J. Li, J. Cui, D. Hu, Research on the time-dependent vehicle routing problem for fresh agricultural products based on customer value, Agriculture, 13 (2023), 681. https://doi.org/10.3390/agriculture13030681 doi: 10.3390/agriculture13030681
[20]	H. Luo, M. Dridi, O. Grunder, A branch-price-and-cut algorithm for a time-dependent green vehicle routing problem with the consideration of traffic congestion, Comput. Ind. Eng., 177 (2023), 109093. https://doi.org/10.1016/j.cie.2023.109093 doi: 10.1016/j.cie.2023.109093
[21]	F. Zhao, B. Si, Z. Wei, T. Lu, Time-dependent vehicle routing problem of perishable product delivery considering the differences among paths on the congested road, Oper. Res., 23 (2023). https://doi.org/10.1007/s12351-023-00751-3 doi: 10.1007/s12351-023-00751-3
[22]	B. Ma, D. Hu, Y. Wang, Q. Sun, L. He, X. Chen, Time-dependent vehicle routing problem with departure time and speed optimization for shared autonomous electric vehicle service, Appl. Math. Modell., 113 (2023), 333–357. https://doi.org/10.1016/j.apm.2022.09.020 doi: 10.1016/j.apm.2022.09.020
[23]	S. Zhang, Z. Zhou, R. Luo, R. Zhao, Y. Xiao, Y. Xu, A low-carbon, fixed-tour scheduling problem with time windows in a time-dependent traffic environment, Int. J. Prod. Res., 61 (2023), 6177–6196. https://doi.org/10.1080/00207543.2022.2153940 doi: 10.1080/00207543.2022.2153940
[24]	C. Ye, F. Liu, Y. K. Ou, Z. Xu, Optimization of vehicle paths considering carbon emissions in a time-varying road network, J. Adv. Transp., 2022 (2022). https://doi.org/10.1155/2022/9656262 doi: 10.1155/2022/9656262
[25]	O. Jabali, T. Van Woensel, A. G. D. Kok, Analysis of travel times and CO2 emissions in time-dependent vehicle routing, Prod. Oper. Manage., 21 (2012), 1060–1074. https://doi.org/10.1111/j.1937-5956.2012.01338.x doi: 10.1111/j.1937-5956.2012.01338.x
[26]	M. Wei, C. Yang, T. Liu, An integrated multi-objective optimization for dynamic airport shuttle bus location, route design and departure frequency setting problem, Int. J. Environ. Res. Public Health, 19 (2022), 14469. https://doi.org/10.3390/ijerph192114469 doi: 10.3390/ijerph192114469
[27]	M. Wei, T. Liu, B. Sun, B. Jing, Optimal integrated model for feeder transit route design and frequency-setting problem with stop selection, J. Adv. Transp., 2020 (2020). https://doi.org/10.1155/2020/6517248 doi: 10.1155/2020/6517248
[28]	X. Luo, L. Dong, Y. Dou, Y. Li, K. Liu, J. Ren, et al., Factor decomposition analysis and causal mechanism investigation on urban transport CO2 emissions: Comparative study on Shanghai and Tokyo, Energy Policy, 107 (2017), 658–668. https://doi.org/10.1016/j.enpol.2017.02.049 doi: 10.1016/j.enpol.2017.02.049
[29]	C. Dhifaoui, O. Kahouli, H. H. Abdallah, Multi-objective ant lion optimizer to solve the dynamic economic dispatch problem with valve point effect, in 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, (2019), 564–571. https://doi.org/10.1109/STA.2019.8717301
[30]	S. Mirjalili, P. Jangir, S. Saremi, Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems, Appl. Intell., 46 (2017), 79–95. https://doi.org/10.1007/s10489-016-0825-8 doi: 10.1007/s10489-016-0825-8
[31]	X. B. Li, L. C. Yang, L. C. Huang, C. Wang, Dynamic multiobjective optimization and multivariate analysis for power generation scheduling of the diesel generators in dynamically positioned vessels, Appl. Ocean Res., 122 (2022), 103132. https://doi.org/10.1016/j.apor.2022.103132 doi: 10.1016/j.apor.2022.103132
[32]	R. Akbari, R. Hedayatzadeh, K. Ziarati, B. Hassanizadeh, A multi-objective artificial bee colony algorithm, Swarm Evol. Comput., 2 (2012), 39–52. https://doi.org/10.1016/j.swevo.2011.08.001 doi: 10.1016/j.swevo.2011.08.001
[33]	K. Kasturi, C. K. Nayak, M. R. Nayak, Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO, Int. Trans. Electr. Energy Syst., 29 (2019), e12013. https://doi.org/10.1002/2050-7038.12013 doi: 10.1002/2050-7038.12013
[34]	R. J. Kuo, M. F. Luthfiansyah, N. A. Masruroh, F. E. Zulvia, Application of improved multi-objective particle swarm optimization algorithm to solve disruption for the two-stage vehicle routing problem with time windows, Expert Syst. Appl., 225 (2023), 120009. https://doi.org/10.1016/j.eswa.2023.120009 doi: 10.1016/j.eswa.2023.120009
[35]	A. P. Guerreiro, C. M. Fonseca, L. Paquete, The hypervolume indicator: Computational problems and algorithms, ACM Comput. Surv., 54 (2021). 119. https://doi.org/10.1145/3453474 doi: 10.1145/3453474

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)