Selective Survey: Most Efficient Models and Solvers for Integrative Multimodal Transport
Volume 32, Issue 2 (2021), pp. 371–396
Pub. online: 31 March 2021
Type: Research Article
Open Access
Open Access
Received
1 November 2020
1 November 2020
Accepted
1 March 2021
1 March 2021
Published
31 March 2021
31 March 2021
Abstract
In the family of Intelligent Transportation Systems (ITS), Multimodal Transport Systems (MMTS) have placed themselves as a mainstream transportation mean of our time as a feasible integrative transportation process. The Global Economy progressed with the help of transportation. The volume of goods and distances covered have doubled in the last ten years, so there is a high demand of an optimized transportation, fast but with low costs, saving resources but also safe, with low or zero emissions. Thus, it is important to have an overview of existing research in this field, to know what has already been done and what is to be studied next. The main objective is to explore a beneficent selection of the existing research, methods and information in the field of multimodal transportation research, to identify industry needs and research gaps and provide context for future research. The selective survey covers multimodal transport design and optimization in terms of: cost, time, and network topology. The multimodal transport theoretical aspects, context and resources are also covering various aspects. The survey‘s selection includes currently existing best methods and solvers for Intelligent Transportation Systems (ITS). The gap between theory and real-world applications should be further solved in order to optimize the global multimodal transportation system.
References
Aifadopoulou, G., Ziliaskopoulos, A., Chrisohoou, E. (2007). Multiobjective optimum path algorithm for passenger pretrip planning in multimodal transportation networks. Transportation Research Record, 2032(1), 26–34. https://doi.org/10.3141/2F2032-04.
Al-Yakoob, S.M., Sherali, H.D. (2018). A mathematical modelling and optimization approach for a maritime facility location transshipment problem. Informatica, 29(4), 609–632. https://doi.org/10.15388/Informatica.2018.184.
Arentze, T.A. (2013). Adaptive personalized travel information systems: a Bayesian method to learn users’ personal preferences in multimodal transport networks. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1957–1966. https://doi.org/10.1109/TITS.2013.2270358.
Ayed, H., Khadraoui, D., Habbas, Z., Bouvry, P., Merche, J.F. (2008). Transfer graph approach for multimodal transport problems. In: International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences. Springer, pp. 538–547. https://doi.org/10.1007/978-3-540-87477-5_57.
Bellman, R. (1962). Dynamic programming treatment of the travelling salesman problem. Journal of the ACM, 9(1), 61–63. https://doi.org/10.1145/321105.321111.
Beresford, A., Pettit, S., Liu, Y. (2011). Multimodal supply chains: iron ore from Australia to China. Supply Chain Managemen, 16(1), 32–42. https://doi.org/10.1108/13598541111103485.
Bielli, M., Boulmakoul, A., Mouncif, H. (2006). Object modeling and path computation for multimodal travel systems. European Journal of Operational Research, 175(3), 1705–1730. https://doi.org/10.1016/j.ejor.2005.02.036.
Bock, S. (2010). Real-time control of freight forwarder transportation networks by integrating multimodal transport chains. European Journal of Operational Research, 200(3), 733–746. https://doi.org/10.1016/j.ejor.2009.01.046.
Bouzir, A., Souissi, B., Benammou, S. (2014). Modeling the passengers’ waiting times at multimodal stations. In: 2014 International Conference on Logistics and Operations Management. IEEE, pp. 139–147. https://doi.org/10.1109/GOL.2014.6887431.
Carlier, K., Catalano, S., Schrijver, J., Van Nes, R. (2005). TRANSFER: a new equilibrium model for analysing multimodal passenger trips. In: Proceedings of ETC 2005 Strasbourg, France. Research to Inform Decision-Making in Transport Innovative Methods in Transport Analysis-Planning and Appraisal Assignment, 13 pp.
Chen, L., Peng, J., Zhang, B. (2017). Uncertain goal programming models for bicriteria solid transportation problem. Applied Soft Computing, 51, 49–59. https://doi.org/10.1016/j.asoc.2016.11.027.
Cheng, C.-B., Mao, C.-P. (2007). A modified ant colony system for solving the travelling salesman problem with time windows. Mathematical and Computer Modelling, 46(9-10), 1225–1235. https://doi.org/10.1016/j.mcm.2006.11.035.
Chiabaut, N. (2015). Evaluation of a multimodal urban arterial: the passenger macroscopic fundamental diagram. Transportation Research Part B-Methodological, 81, 410–420. https://doi.org/10.1016/j.trb.2015.02.005.
Clerc, M. (2004). Discrete particle swarm optimization, illustrated by the traveling salesman problem. In: New Optimization Techniques in Engineering Springer, Berlin, Heidelberg, pp. 219–239. https://doi.org/10.1007/978-3-540-39930-8_8.
Cosma, O., Pop, P., Oliviu, M., Zelina, I. (2018). A hybrid iterated local search for solving a particular two-stage fixed-charge transportation problem. In: Lecture Notes in Computer Science, Vol. 10870, PP. 684–693. https://doi.org/10.1007/978-3-319-92639-1_57.
Cosma, O., Pop, P.C., Dănciulescu, D. (2020). A parallel algorithm for solving a two-stage fixed-charge transportation problem. Informatica, 31(4), 681–706. https://doi.org/10.15388/20-INFOR432.
Crevier, B., Cordeau, J.-F.C., Laporte, G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research, 176(2), 756–773. https://doi.org/10.1016/j.ejor.2005.08.015.
Crisan, G.-C., C.M., Pintea, P., Pop, O., Matei (2016). An analysis of the hardness of novel TSP Iberian instances. In: Lecture Notes in Computer Science, Vol. 9648, pp. 353–364. https://doi.org/10.1007/978-3-319-32034-2_30.
Crisan, G.C., Pintea, C., Palade, V. (2017). Emergency management using geographic information systems: application to the first Romanian traveling salesman problem instance. Knowledge and Information Systems, 50(1), 265–285. https://doi.org/10.1007/s10115-016-0938-8.
Crisan, G.C., Pintea, C.-M., Pop, P.C., Matei, O. (2020a). Economical connections between several European countries based on TSP data. Logic Journal of the IGPL, 28(1), 33–44. https://doi.org/10.1093/jigpal/jzz069.
Crisan, G.C., Pintea, C.-M., Calinescu, A., Pop Sitar, C., Pop, P.C. (2020b). Secure traveling Salesman problem with intelligent transport systems features. Logic Journal of the IGPL. In press. https://doi.org/10.1093/jigpal/jzaa035.
Dorigo, M., Gambardella, L.M. (1997). Ant colonies for the travelling salesman problem. Biosystems, 43(2), 73–81. https://doi.org/10.1016/S0303-2647(97)01708-5.
Dotoli, M., Epicoco, N., Falagario, M. (2016). A technique for efficient multimodal transport planning with conflicting objectives under uncertainty. In: 2016 European Control Conference (ECC), IEEE, pp. 2441–2446. https://doi.org/10.1109/ECC.2016.7810656.
Galvez-Fernandez, C., Khadraoui, D., Ayed, H., Habbas, Z., Alba, E. (2009). Distributed approach for solving time-dependent problems in multimodal transport networks. Advances in Operations Research, 2009. https://doi.org/10.1155/2009/512613.
Ghiani, G., Improta, G. (2000). An efficient transformation of the generalized vehicle routing problem. European Journal of Operational Research, 122(1), 11–17. https://doi.org/10.1016/S0377-2217(99)00073-9.
Golden, B.L., Magnanti T.L. Nguyen H.Q. (1977). Implementing vehicle routing algorithms. Networks, 7(2), 113–148. https://doi.org/10.1002/net.3230070203.
Greulich, C., Edelkamp S., Gath M. (2013). Agent-based multimodal transport planning in dynamic environments. In: Annual Conference on Artificial Intelligence. Springer, pp. 74–85. https://doi.org/10.1007/978-3-642-40942-4_7.
Hao, C., Yue, Y. (2016). Optimization on combination of transport routes and modes on dynamic programming for a container multimodal transport system. Procedia Engineering, 137(1), 382–390. https://doi.org/10.1016/j.proeng.2016.01.272.
Helsgaun, K. (2015). Solving the equality generalized traveling salesman problem using the Lin-Kernighan Helsgaun Algorithm. Mathematical Programming Computation, 7, 269–287. https://doi.org/10.1007/s12532-015-0080-8.
Holzinger, A., Plass, M., Kickmeier-Rust, M.D., Holzinger, K., Crisan, G.C., Pintea, C.-M., Palade, V. (2019). Interactive machine learning: experimental evidence for the human in the algorithmic loop – a case study on Ant Colony Optimization. Applied Intelligence, 49(7), 2401–2414. https://doi.org/10.1007/s10489-018-1361-5.
Hu, Z.-H. (2011). A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Systems with Applications, 38(3), 2632–2639. https://doi.org/10.1016/j.eswa.2010.08.053.
Islam, D.M.Z., Dinwoodie, J., Roe, M. (2006). Promoting development through multimodal freight transport in Bangladesh. Transport Reviews, 26(5), 571–591. https://doi.org/10.1080/01441640600576902.
Jing, X., Y., Liu, W., Cao (2012). A hybrid genetic algorithm for route optimization in multimodal transport. In: 2012 Fifth International Symposium on Computational Intelligence and Design, IEEE, pp. 261–264. https://doi.org/10.1109/ISCID.2012.73.
Kai, K., Haijiao, N., Yuejie, Z. Weicun, Z., (2010). Research on improved integrated optimization model for mode and route in multimodal transportation basing on the PSO-ACO. In: International Conference on Logistics Systems and Intelligent Management (ICLSIM). IEEE, Los Alamitos, CA, pp. 1445–1449. https://doi.org/10.1109/ICLSIM.2010.5461206.
Kamel, I., Shalaby, A., Abdulhai, B. (2019). Integrated simulation-based dynamic traffic and transit assignment model for large-scale network. Canadian Journal of Civil Engineering, 999, 1–10. https://doi.org/10.1139/cjce-2018-0706.
Karballaeezadeh, N., Zaremotekhases, F., Shamshirband, S., Mosavi, A., Nabipour, N., Csiba, P., Várkonyi-KÓczy, A.R. (2020). Intelligent road inspection with advanced machine learning; hybrid prediction models for smart mobility and transportation maintenance systems. Energies, 13(7), 1718. https://doi.org/10.3390/en13071718.
Kengpol, A., Tuammee, S., Tuominen, M. (2014). The development of a framework for route selection in multimodal transportation. International Journal of Logistics Management, 25(3), 581–610. https://doi.org/10.1108/IJLM-05-2013-0064.
Khan, M.F., Asghar, S., Tamimi, M.I., Noor, M.A. (2019). Multi-objective transport system based on regression analysis and genetic algorithm using transport data. IEEE Access, 7, 81121–81131. https://doi.org/10.1109/ACCESS.2019.2918217.
Kozan, E., Preston, P. (1999). Genetic algorithms to schedule container transfers at multimodal terminals. International Transactions in Operational Research, 6(3), 311–329. https://doi.org/10.1111/j.1475-3995.1999.tb00158.x.
Kumar, P.P., Parida, M., Swami, M. (2013). Performance evaluation of multimodal transportation systems. Procedia – Social and Behavioral Sciences, 104(2), 795–804. https://doi.org/10.1016/j.sbspro.2013.11.174.
Lancinskas, A., Fernández, P., Pelegrín, B., Žilinskas, J. et al.(2016). Solution of discrete competitive facility location problem for firm expansion. Informatica, 27(2), 451–462. https://doi.org/10.15388/Informatica.2016.94.
Lee, K.-Y., Lim, J.-S., Ko, S.-S. (2019). Endosymbiotic evolutionary algorithm for an integrated model of the vehicle routing and truck scheduling problem with a cross-docking system. Informatica, 30(3), 481–502. https://doi.org/10.15388/Informatica.2019.215.
Leuveano, R.A.C., Ab Rahman, M.N., Wan Mahmood, W.M.F., Saleh, C. et al.(2019). Integrated vendor–buyer lot-sizing model with transportation and quality improvement consideration under just-in-time problem. Mathematics, 7(10), 1–25. article no. 944. https://doi.org/10.3390/math7100944.
Li, X., Tian, P., Aneja, Y. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E Logistics and Transportation Review, 46(6), 1111–1127. https://doi.org/10.1016/j.tre.2010.02.004.
Lin, B.L., Sun, X., Salous, S. (2016). Solving travelling salesman problem with an improved hybrid genetic algorithm. Journal of Communications and Computers, 4(15), 98–106. https://doi.org/10.4236/jcc.2016.415009.
Luo, X., Zhang, H.-B., Zhang, Z.-L., Yu, Y., Li, K. (2019). A new framework of intelligent public transportation system based on the internet of things. IEEE Access, 7, 55290–55304. https://doi.org/10.1109/ACCESS.2019.2913288.
Malik, W., Rathinam, S., Darbha, S. (2007). An approximation algorithm for a symmetric generalized multiple depot, multiple travelling salesman problem. Operations Research Letters, 35(6), 747–753. https://doi.org/10.1016/j.orl.2007.02.001.
Matei, O., Pop, P.C., Sas, J.L., Chira, C. (2015). An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem. Neurocomputing, 150, 58–66. https://doi.org/10.1016/j.neucom.2014.02.074.
Matei, O., Pop, P. (2010). An efficient genetic algorithm for solving the generalized traveling salesman problem. In: ICCP, pp. 87–92. IEEE. https://doi.org/10.1109/ICCP.2010.5606458.
Mavrovouniotis, M., Yang, S. (2011). A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Computing, 15(7), 1405–1425. https://doi.org/10.1007/s00500-010-0680-1.
Mes, M., Iacob, M.-E. (2016). Synchromodal transport planning at a logistics service provider. In: Logistics and Supply Chain Innovation. Springer, Cham, pp. 23–36. https://doi.org/10.1007/978-3-319-22288-2_2.
Moreno, A., Alem, D., Ferreira, D. (2016). Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics. Computers and Operations Research, 69, 79–96. https://doi.org/10.1016/j.cor.2015.12.002.
Moslem, S., Gul, M., Farooq, D., Celik, E., Ghorbanzadeh, O., Blaschke, T. (2020). An integrated approach of best-worst method (bwm) and triangular fuzzy sets for evaluating driver behavior factors related to road safety. Mathematics, 8(3), 1–20. article no. 414. https://doi.org/10.3390/math8030414.
Natvig, M.K., Westerheim, H., Skylstad, G.F., Haugset, B. (2006). ARKTRANS. The Norwegian system framework architecture for multimodal transport systems supporting freight and passenger transport Version 5.0. SINTEF Rapport A146, 312 pp. https://core.ac.uk/reader/52133062.
Natvig, M.K., Vennesland, A. (2010). Flexible organisation of multimodal travel information services. IET Intelligent Transport Systems, 4(4), 401–412. https://doi.org/10.1049/iet-its.2009.0134.
Onwubolu, G., Clerc, M. (2004). Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. International Journal of Production Research, 42(3), 473–491. https://doi.org/10.1080/00207540310001614150.
Ouaarab, A., Ahiod, B., Yang, X.-S. (2014). Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing & Applications, 24(7–8), 1659–1669. https://doi.org/10.1007/s00521-013-1402-2.
Panayides, P.M., Song, D.-W. (2008). Evaluating the integration of seaport container terminals in supply chains. International Journal of Physical Distribution & Logistics Management, 38(7), 562–584. https://doi.org/10.1108/09600030810900969.
Pedro, O., Saldanha, R., Camargo, R. (2013). A tabu search approach for the prize collecting traveling salesman problem. Electronic Notes in Discrete Mathematics, 41, 261–268. https://doi.org/10.1016/j.endm.2013.05.101.
Peplowska-Dabrowska, Z., Nawrot, J. (2019). Codification of Maritime Law: Challenges, Possibilities and Experience. Taylor & Francis, London. https://doi.org/10.4324/9780429351792.
Petrovan, A., Erdei, R., Pop-Sitar, P., Matei, O. (2019). A self-adapting immigrational genetic algorithm for solving a real-life application of vehicle routing problem. In: Advances in Intelligent Systems and Computing, Vol. 1047. Springer. pp. 144–156. https://doi.org/10.1007/978-3-030-31362-3_15.
Pintea, C.-M. (2015). A unifying survey of agent-based approaches for equality-generalized traveling salesman problem. Informatica, 26(3), 1–14. https://doi.org/10.15388/INFORMATICA.2015.61.
Pintea, C.-M., Chira, C., Dumitrescu, D. (2011). Sensitive ants in solving the generalized vehicle routing problem. International Journal of Computers Communications & Control, 6(4), 731–738. https://doi.org/10.15837/ijccc.2011.4.2098.
Pintea, C.-M., Calinescu, A., Pop, P.C., Sabo, C. (2016). Towards a secure two-stage supply Chain network: a transportation-cost approach. In: Advances in Intelligent Systems and Computing, Vol. 527. Springer, Cham, pp. 547–554. https://doi.org/10.1007/978-3-319-47364-2_53.
Pintea, C.-M., Crisan, G.C., Pop, P.C. (2018). Towards secure transportation based on intelligent transport systems. Novel approach and concepts. Advanced Intelligent Systems, 771, 469–477. https://doi.org/10.1007/978-3-319-94120-2_45.
Pintea, C.M., Calinescu, A., Sitar, C.P., Pop, P.C. (2019). Towards secure & Green two-stage supply Chain networks. Logic Journal of the IGPL, 27, 137–148. https://doi.org/10.1093/jigpal/jzy028.
Pop, P.C., Pintea, C., Zelinac, I., Dumitrescu, D. (2009a). Solving the generalized vehicle routing problem with an ACS-based algorithm. In: Enachescu, C., et al. (Eds.), BICS 2008, Vol. 1117. AIP, Springer, Melville, NY, pp. 157–162. https://doi.org/10.1063/1.3130618.
Pop, P.C., Zelina, I., Lupşe, V., Sitar, C.P., Chira ,C., (2011). Heuristic algorithms for solving the generalized vehicle routing problem. International Journal of Computers Communications & Control, VI, 158–165. https://doi.org/10.15837/ijccc.2011.1.2210.
Pop, P., Matei, O., Sitar, C.P. (2013). An improved hybrid algorithm for solving the generalized vehicle routing problem. Neurocomputing, 109, 76–83. https://doi.org/10.1016/j.neucom.2012.03.032.
Pop, P.C., Pintea, C.-M., Sitar, C.P., Hajdu-Măcelaru, M. (2014). An efficient reverse distribution system for solving sustainable supply Chain network design problem. Journal of Applied Logic, 2(13), 105–113. https://doi.org/10.1016/j.jal.2014.11.004.
Pop, P., Oliviu, M., Sabo, C. (2017). A hybrid diploid genetic based algorithm for solving the generalized traveling salesman problem. Lecture Notes in Computer Science, Vol. 10334, Springer, pp. 149–160. https://doi.org/10.1007/978-3-319-59650-1_13.
Redondo, J.L., Ortigosa, P.M., Žilinskas, J., (2012). Multimodal evolutionary algorithm for multidimensional scaling with city-block distances. Informatica, 23(4), 601–620. https://doi.org/10.15388/Informatica.2012.377.
Reniers, G.L.L., Dullaert, W. (2013). A method to assess multi-modal Hazmat transport security vulnerabilities: Hazmat transport SVA. Transport Policy, 28, 103–113. https://doi.org/10.1016/j.tranpol.2012.05.002.
Repoussis, P.P., Tarantilis, C.D., Ioannou, G. (2006). A hybrid metaheuristic for a real life vehicle routing problem. In: Lecture Notes in Computer Science, Vol. 4310. Springer, 247–254. https://doi.org/10.1007/978-3-540-70942-8_29.
Roy, S.K., Maity, G., Weber, G.-W. (2017). Multi-objective two-stage grey transportation problem using utility function with goals. Central European Journal of Operations Research, 25(2), 417–439. https://doi.org/10.1007/s10100-016-0464-5.
Saharan, S., Bawa, S., Kumar, N. (2020). Dynamic pricing techniques for intelligent transportation system in smart cities: a systematic review. Computer and Communications, 150, 603–625. https://doi.org/10.1016/j.comcom.2019.12.003.
Schöharting, J., Schmidt, A., Frank, A., Bremer, S. et al.(2003). Towards the multimodal transport of people and freight: interconnective networks in the RheinRuhr Metropolis. Journal of Transport Geography, 11(3), 193–203. https://doi.org/10.1016/S0966-6923(03)00030-9.
Sharma, G., Sharma, V., Pardasani, K.R., Alshehri, M. (2020). Soft set based intelligent assistive mOdel for multiobjective and multimodal transportation problem. IEEE Access, 8, 102646–102656. https://doi.org/10.1109/ACCESS.2020.2997302.
Stank, T.P., Keller, S.B., Daugherty, P.J. (2001). Supply Chain collaboration and logistical service performance. Journal of Business Logistics, 22(1), 29–48. https://doi.org/10.1002/j.2158-1592.2001.tb00158.x.
Stanković, M., Stević, Ž., Kumar Das, D., Subotić, M., Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 1–18. Article no. 457. https://doi.org/10.3390/math8030457.
SteadieSeifi, M., Dellaert, N.P., Nuijten, W., Van Woensel, T., Raoufi, R. et al.(2014). Multimodal freight transportation planning: a literature review. European Journal of Operational Research, 233(1), 1–15. https://doi.org/10.1016/j.ejor.2013.06.055.
Subramanian, A., Vaz Penna, P.H., Uchoa, E., Ochib, L.S. (2012). A hybrid algorithm for the heterogeneous fleet vehicle routing problem. European Journal of Operational Research, 221(2), 285–295. https://doi.org/10.1016/j.ejor.2012.03.016.
Sumalee, A., Uchidab, K., Lam, W.H.K., (2011). Stochastic multi-modal transport network under demand uncertainties and adverse weather condition. Transportation Research Part C Emerging Technologies, 19(2), 338–350. https://doi.org/10.1016/j.trc.2010.05.018.
Szyliowicz, J.S., Zamparini L., Reniers, G.L.L., Rhoades, D.L. et al.(2016). Multimodal Transport Security: Frameworks and Policy Applications in Freight and Passenger Transport. Edward Elgar Publishing, 1783474823. https://doi.org/10.4337/9781783474820.
Tarantilis, C., Kiranoudis, C., Vassiliadis, V. (2004). A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem. European Journal of Operational Research, 152(1), 148–158. https://doi.org/10.1016/S0377-2217(02)00669-0.
Wang, C., Lin, M., Zhong, Y.-W., Zhang, H. (2015). Solving travelling salesman problem using multiagent simulated annealing algorithm with instance-based sampling. International Journal of Computational Science and Mathematics, 6(4), 336–353. https://doi.org/10.1504/IJCSM.2015.071818.
Wang, K.-P., Huang, L. Zhou, C.-G., Pang, W. (2003). Particle swarm optimization for traveling salesman problem. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, Vol. 3. IEEE, pp. 1583–1585. https://doi.org/10.1109/ICMLC.2003.1259748.
Wang, Q.-Z., Chen, J.-M., Tseng, M.-L., Luan, H.-M., Ali, M.H. (2020a). Modelling green multimodal transport route performance with witness simulation software. Journal of Cleaner Production, 248, 119–245. https://doi.org/10.1016/j.jclepro.2019.119245.
Wang, Z., Zhang, M., Chu, R., Zhao, L. (2020b). Modeling and planning multimodal transport paths for risk and energy efficiency using AND/OR graphs and discrete ant colony optimization. IEEE Access, 8, 132642–132654. https://doi.org/10.1109/ACCESS.2020.3010376.
Wiseman, Y., Giat, Y. (2016). Multimodal passenger transportation security in Israel. In: Multimodal Transport Security. Edward Elgar Publishing, pp. 246–260. https://doi.org/10.4337/9781783474820.00026.
Xu, M., Gao, Z. (2009). Multi-class multi-modal network equilibrium with regular choice behaviors: a general fixed point approach. In: Transportation and Traffic Theory. Springer, Boston, MA, pp. 301–325. https://doi.org/10.1007/978-1-4419-0820-9_15.
Yamada, T., Febri, Z. (2015). Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium. Transportation Research Part E: Logistics and Transportation Review, 75, 164–187. https://doi.org/10.1016/j.tre.2015.01.001.
Yamada, T., Russ B.F., Castro J. (2007). Optimal planning of multimodal freight transport network. Doboku Gakkai Ronbunshuu D, 63(2), 103–109. https://doi.org/10.2208/jscejd.63.103.
Yamada, T., Russ, B.F., Castro, J., Taniguchi, E. (2009). Designing multimodal freight transport networks: A heuristic approach and applications. Transportation Science, 43(2), 129–143. https://doi.org/10.1287/trsc.1080.0250.
Yuen, K.F., Thai, V. (2017). Barriers to supply chain integration in the maritime logistics industry. Maritime Economics & Logistics, 19(3), 551–572. https://doi.org/10.1057/mel.2016.10.
Zamanifar, M., Hartmann, T. (2020). Literature review of optimization based decision model for disaster recovery planning of transportation network. DepositOnce TU Berlin, 8 pp. https://doi.org/10.14279/depositonce-9077.
Zeng, Y.-C., Wang, Y., Lai, Z.-Z. (2009). Research on model and algorithm of multimodal transportation with time windows. https://en.cnki.com.cn/.
Zhang, J., Liao, F., Arentze, T., Timmermans, H. (2011). A multimodal transport network model for advanced traveler information systems. Procedia – Social and Behavioral Sciences, 20, 313–322. https://doi.org/10.1016/j.sbspro.2011.08.037.
Zhang, M., Wiegmans, B., Tavasszy, L. (2013). Optimization of multimodal networks including environmental costs: a model and findings for transport policy. Computers in Industry, 64(2), 136–145. https://doi.org/10.1016/j.compind.2012.11.008.
Zhang, S., Lin, K. (2020). Short-term traffic flow forecasting based on data-driven model. Mathematics, 8(2), 1–17. Article no. 152. https://doi.org/10.3390/math8020152.
Zhang, T., Yang, Y., Cheng, G., Jin, M. (2020). A practical traffic assignment model for multimodal transport system considering low-mobility groups. Mathematics, 8(3), 1–19. Article no.351. https://doi.org/10.3390/math8030351.
Zheng, N., Geroliminis, N. (2016). Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing. Transportation Research Part B-Methodological, 83, 36–58. https://doi.org/10.1016/j.trb.2015.10.008.
Zidi, S., Maouche, S. (2006). Ant colony optimization for the rescheduling of multimodal transport networks. In: The The Proceedings of the Multiconference on “Computational Engineering in Systems Applications”, Vol. 1. IEEE, pp. 965–971. https://doi.org/10.1109/CESA.2006.4281789.
Zidi, S., Maouche S., Hammadi S. (2006). Real-time route planning of the public transportation system. In: 2006 Intelligent Transportation Systems Conference, IEEE, pp. 55–60. https://doi.org/10.1109/ITSC.2006.1706718.
Zografos, K.G., Androutsopoulos, K.N. (2008). Algorithms for itinerary planning in multimodal transportation networks. IEEE Transactions on Intelligent Transportation Systems, 9(1), 175–184. https://doi.org/10.1109/TITS.2008.915650.
TSPLIB: TSP library, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/.
TSP data instances, http://www.math.uwaterloo.ca/tsp/data/index.html.
GTSP Library, http://www.cs.nott.ac.uk/~pszdk/gtsp.html.
TSP world-wide countries instances, http://www.math.uwaterloo.ca/tsp/world/countries.html.
NEOS-Concorde integer TSP solver, https://neos-server.org/neos/solvers/co:concorde/TSP.html.
Biographies
Matei Oliviu
O. Matei received MS in artificial intelligence from the Vrije Universiteit of Amsterdam and his PhD from the Technical University of Cluj-Napoca. He is a professor at the Technical University of Cluj-Napoca and holds five patents. His research interests include artificial intelligence with evolutionary computation, evolutionary ontologies, optimization, robotics, transportation, operational research and related fields.
Erdei Rudolf
R. Erdei is a researcher and developer with the HOLISUN S.R.L. R.&D. department. His research interests include software development for cloud & serverless deployment optimization, energy optimization and logistic operations software. He is a member of several international research programs including Horizon 2020 and CHIST-ERA.
Pintea Camelia-M.
C-M. Pintea is an associate professor habil. at the Technical University of Cluj-Napoca. She is a senior member of IEEE, SMC-IEEE and CIS-IEEE. Her research interests include applied mathematics, optimization, artificial intelligence, and operational research.
