- Davis, L. (2016).Improving traffic flow at a 2-to-1 lane reduction with wirelessly connected, adaptive cruise control vehicles. Computers & Industrial Engineering.451 (2), 320–332.
-
Talarico, L.,
Springael, J., &
Sorensen, K. &
Talarico, F.(2015).A large neighborhood metaheuristic for the risk-constrained cash-in-transit vehicle routing problem.
Computers & Industrial Engineering.78(C), 547-556.
-Androutsopoulos, K. N. & Zografos, G. (2012). A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution. Transportation and Logistics.1(1-2), 157-183.
-Blum,C., &Ochoa,G.(2021). A comparative analysis of two matheuristics by means of merged local optima networks.
Operational Research.
290(1), 36-56.
-Bozkaya, B., Salman, F. S. & Telciler, K. (2017). An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit.
Operational Research, 69(3), 256-269.
-Braekers, K., Ramaekers, K. & Nieuwenhuyse, M. (2015).The vehicle routing problem: state of the art classification and review. Computers & Industrial Engineering, 43(1), 238-247.
-Bula, G. A., Prodhon, C., Gonzalez, F. A., Afsar,H. M. & Velasco, N. (2017).Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. Hazardous Materials, 324(44), 472-480.
-Chen, Y., Cowling, P., Polack, F., Remde, S. & Mourdjis, P.(2017).Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system. Operational Operations Research, 62(2), 61-77.
-Davis, L. (2017).Dynamic origin-to-destination routing of wirelessly connected, autonomous vehicles on a congested .Network.Computers & Industrial Engineering, 478(1), 93–102.
-Dorigo, M., & Stutzle, T. (2019). Ant colony optimization: Overview and Recent Advances. Handbook of Metaheuristics, 272.
-Fikarab, C., & Braekers, K. (2022). Bi-objective optimization of e-grocery deliveries considering food quality losses.Computers & Industrial Engineering, 163(4), 238-247.
-Ghanbarpour, F. & Zandiyeh, F. (2020). A new game-theoretical multiple-objective evolutionary approach for cash-in-transit vehicle routing problem with time windows. Operational Research, 93(2), 106-132.
-
He, Y.,
Wang, X.,
Zhou, F. &
Lin, Y. (2019). Dynamic vehicle routing problem considering simultaneous dual services in the last mile delivery.
Operational Research, 49(4), 1267-1284.
-Hoogeboom, M. & Dullaert, W. (2019).Vehicle routing with arrival time diversification in cash distribution.
Operational Research, 275(1), 93-107.
-Jin, Y. Xianlong, G. Zhang, L. (2022). A two-stage algorithm for bi-objective logistics model of cash-in-transit vehicle routing problems with economic and environmental optimization based on real-time traffic data.
Industrial Information Integration26(1), 100-117.
-Kahfi, A. & Tavakkoli-Moghaddam, R.(2021).
Robust Bi-Objective Location-Arc Routing Problem with Time Windows: A Case Study of an Iranian Bank.
Operational Management,8
(1), 1-17.
-Kazantzi, V., Kazantzis, N. & Gerogiannis, C. (2011). Risk informed optimization of hazardous material multi-periodic transportation model. Loss Prevention in the Process Industries, 24(6), 773-67.
-KHaripriya, K., & Kumar Ganesan,V.(2022).Solving Large Scale Vehicle Routing Problems with Hard Time Windows under Travel Time Uncertainty.
IFAC-Papers On Line,
55(10), 233-238.
-Nasr, N. Akhavan Niakib, T.Seifbarghyc, M. ., & Husseinzadeh Kashand, A.(2022).an Agri-Fresh Food Supply Chain Network Design with Routing Optimization: A Case Study of ETKA Company. Advances in Mathematical Finance & Applications, 7(1), 187-198.
-Park, H. Son, D. Koo, B., & Jeong, B.(2021).Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm.
Expert Systems with Applications,
1, 11-39.
-Parsafard, M., Esmaeel, A., Masoud, K., Mohammadreza, N. & Li, X. (2015). Practical approach for finding optimum routes for fuel delivery trucks in large cities. the Transportation Research Board, 2478, 66-74.
-Pradhananga, R., Taniguchi, E., Yamada, T. & Qureshi, G. (2014a).Bi-objective decision support system for routing and scheduling of hazardous materials. Socio-Economic Planning Sciences, 48(2), 135-148.
-Radoji, N. (2018).Fuzzy GRASP with path relinking for the Risk-constrained Cash-in-Transit Vehicle Routing Problem.
Applied Soft Computing,
72, 486-497.
-Rios, B. Xavier, K., & Miyazawa, F.(2021).stochastic multi-depot vehicle routing problem with pickup and delivery. Computer Science and Information Systems, 21, 307–315.
-Roughgarden, T. (2020). Algorithms Illuminated (Part 4): Algorithms for NP-Hard Problems, Soundlike yourself publishing LLC Publications.
-Shangyao, Y., Wang, S. & Wu, S. (2012).A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem. Computers & Industrial Engineering, 63(2), 464–473.
-Soriano, A. Thibaut Vidal, T., & Gansterer, M. (2020). The vehicle routing problem with arrival time diversification on a multigraph.
Operational Research,
286(2), 564-575.
-Talarico, L., Sorensen, K. & Springael, J. (2015). Metaheuristics for the risk-constrained cash-in-transit vehicle routing problem. Operational Research, 244(2), 457–470.
-Talarico, L., Sorensen, K. & Springael, J. (2015). The k-dissimilar vehicle routing problem, Operational Research, 244 (1), 129–140.
-Talarico, L., Sorensen, K. & Springael, J. (2017). A bi-objective decision model to increase security and reduce travel costs in the cash-in-transit sector. Operational Research, 52(2), 24-59.
-Tawakoli Moghadam, R. & Bozorgi Amiri, M.(2021).Multi-objective green routing-location problem in order to improve the money transfer network. Transportation Engineering Quarterly, 13(3), 1559-1586.
-Tikani,H.Setak,M., & Demir, E. (2021). A risk-constrained time-dependent cash-in-transit routing problem in multigraph under uncertainty.
Operational Research,
293(2), 703-730.
-Toumazis, I. & Kwon, C.(2015).Worst-case conditional value-at-risk minimization for hazardous materials transportation. Transportation Science, 50 (4), 1174-1187.
-Wang, J., Wang, C. & Zhang, Z. (2017). Dynamic route choice prediction model based on connected vehicle guidance characteristics. Advanced Transportation, A. 8-15.
-Wang, Z.Ye, K., & Jiang, M. (2022). Solving hybrid charging strategy electric vehicle based dynamic routing problem via evolutionary multi-objective optimization.
Swarm and Evolutionary Computation,
2, 68-75.
-Yaghini. M. (2016), Meta-heuristic optimization algorithms. Academic Jihad Publications.