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Journal articles on the topic 'Container-Drayage'

Author: Grafiati

Published: 1 April 2023

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1

Shiri, Samaneh, Nathan Huynh, Daniel Smith, and Frank Harder. "Impact of Second-Tier Container Port Facilities on Drayage Operation." Logistics 6, no. 4 (September 27, 2022): 68. http://dx.doi.org/10.3390/logistics6040068.

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Background: An increasing number of container and chassis staging, “dray-off”, drop yard, and depot facilities are being established outside of North American marine container terminals. The increased use of these “second-tier” facilities implies that there must be some capacity, delivery time, service, or reliability benefit that offset the additional cost and complexity. Methods: This paper builds on the previously developed integrated drayage scheduling model to determine the impact of second-tier port facilities on drayage operation. It modifies the previously developed model by incorporating the following features: (1) trucks do not have to wait at customers’ locations during the import unloading and export loading operations; (2) drayage operations can include a drop yard (i.e., second-tier facility) for picking up or/and dropping off loaded containers outside the marine container terminal; and (3) a customer is allowed to request any of the following jobs: pick up an empty container, pick up a loaded container, drop off an empty container, and drop off a loaded container. Results: The results indicated that by moving the location of import pickup and export delivery from inside the marine container terminal to a location outside the terminal, the efficiency of drayage operation could increase. Additionally, when import pickup and export delivery take place inside the marine container terminal, the most efficient location for the chassis yard and empty container depot is inside the terminal. However, when the location of import pickup and/or export delivery are outside the terminal, the most efficient location for the chassis yard and empty container depot is also outside the terminal. Conclusions: The modeling results suggest that in addition to adding reserved capacity for marine terminals or as buffers to reconcile the preferred delivery times of importers, the second-tier facilities could also yield operational savings. However, the potential drayage efficiencies depend heavily on shorter queuing and turn time at these less-complex facilities compared to marine container terminals. Lastly, the modeling results suggest that the observed evolution of North American marine container terminals from self-contained entities into multi-tier systems is likely to continue to add additional capacities to accommodate container trade growth. This finding has important implications for regions and communities concerned over the impact of growing container ports.

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2

Xue, Zhaojie, Hui Lin, and Jintao You. "Local container drayage problem with truck platooning mode." Transportation Research Part E: Logistics and Transportation Review 147 (March 2021): 102211. http://dx.doi.org/10.1016/j.tre.2020.102211.

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3

Song, Yujian, Yuting Zhang, Wanli Wang, and Ming Xue. "A Branch and Price Algorithm for the Drop-and-Pickup Container Drayage Problem with Empty Container Constraints." Sustainability 15, no. 7 (March 23, 2023): 5638. http://dx.doi.org/10.3390/su15075638.

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This paper addresses the drop-and-pickup container drayage problem with empty container constraints. In this problem, a truck is allowed to drop off the container at the customer and then leave. After the container has been packed/unpacked, the truck returns to pick it up. The problem is further complicated by the fact that empty containers at the depot are often limited in number. This container drayage problem is of great practical importance but seldom investigated. In this paper, we first formulate the problem as a directed graph and then mathematically model it as a mixed-integer linear program (MILP) with the objective of minimizing total travel costs. To solve the MILP effectively, we devise a branch and price algorithm that incorporates several performance enhancement strategies, including three versions of the bi-directional label setting algorithm, preprocessing of time windows and a heuristic for high-quality upper bounds. The experimental results indicate that (1) the proposed algorithm significantly outperforms CPLEX in terms of efficiency and effectiveness, (2) an average cost saving of 9.95∼12.25% can be achieved from the drop-and-pickup mode and (3) the benefit of drop-and-pickup mode increases when the customer density and the fixed cost increase.

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4

Heggen, Hilde, Yves Molenbruch, An Caris, and Kris Braekers. "Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions." Sustainability 11, no. 6 (March 18, 2019): 1634. http://dx.doi.org/10.3390/su11061634.

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Intermodal logistics service providers decide on the routing of demand through their service network. Long-haul routing decisions determine the selected departure and arrival terminals for containers and imply corresponding drayage tasks. Traditionally, given these long-haul routes and fixed drayage tasks, drayage operations are planned in a second phase by establishing truck routes to transport containers to and from terminals by truck. In this paper, operational decisions on local drayage routing in large-volume freight regions with multiple terminals on the one hand, and intermodal long-haul routing on the other hand are merged into an integrated intermodal routing problem. Different long-haul routing decisions imply different drayage tasks to be performed and thus impact total trucking costs. The approach aims at reducing the number of road kilometres and increases bundling opportunities by maximising the long-haul capacity utilisation. In this way, it contributes to the modal shift towards intermodal transport and a more sustainable transport system. As a weekly planning horizon is used, a maximum daily active time and a minimum overnight’s rest are included for multi-day drayage routing. A large neighbourhood search heuristic is proposed to solve the integrated intermodal routing problem. This integrated planning approach provides decision support for routing customer orders throughout the intermodal network with the aim of minimising total transport costs and maximising capacity utilisation. Experiments show the added value of the integrated approach, which uses more information to make better-informed decisions and increase the capacity utilisation. The largest savings in trucking costs are obtained for clustered instances with demand characteristics closest to real-life cases. Finally, a real-life case study analyses the impact of tactical service network design decisions on the total operational costs.

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5

MALONI, MICHAEL, and ERIC C. JACKSON. "North American Container Port Capacity: A Literature Review." Transportation Journal 44, no. 2 (2005): 16–36. http://dx.doi.org/10.2307/20713596.

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Abstract International marine container volumes have surged over the last several decades, but North American ports and their supporting container distribution networks have struggled to increase capacity to match this expansion. This article seeks to review and organize existing container network capacity literature into a taxonomy based on the interrelated stakeholders of container flows. The article first establishes the industry capacity situation, then examines research of capacity influences from stakeholders, including port authorities, terminal operators, longshore labor, shippers, railroads, drayage carriers, intermediaries, ocean carriers, governments, and local communities. Ultimately, the article attempts to establish the urgency of container network capacity problems and identify areas needing further research.

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MALONI, MICHAEL, and ERIC C. JACKSON. "North American Container Port Capacity: A Literature Review." Transportation Journal 44, no. 2 (2005): 16–36. http://dx.doi.org/10.5325/transportationj.44.2.0016.

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Abstract International marine container volumes have surged over the last several decades, but North American ports and their supporting container distribution networks have struggled to increase capacity to match this expansion. This article seeks to review and organize existing container network capacity literature into a taxonomy based on the interrelated stakeholders of container flows. The article first establishes the industry capacity situation, then examines research of capacity influences from stakeholders, including port authorities, terminal operators, longshore labor, shippers, railroads, drayage carriers, intermediaries, ocean carriers, governments, and local communities. Ultimately, the article attempts to establish the urgency of container network capacity problems and identify areas needing further research.

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7

Chen, Rui, Qiang Meng, and Peng Jia. "Container port drayage operations and management: Past and future." Transportation Research Part E: Logistics and Transportation Review 159 (March 2022): 102633. http://dx.doi.org/10.1016/j.tre.2022.102633.

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8

Yan, Xiaoyuan, Min Xu, and Chi Xie. "Local container drayage problem with improved truck platooning operations." Transportation Research Part E: Logistics and Transportation Review 169 (January 2023): 102992. http://dx.doi.org/10.1016/j.tre.2022.102992.

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9

Torkjazi, Mohammad, Nathan Huynh, and Ali Asadabadi. "Modeling the Truck Appointment System as a Multi-Player Game." Logistics 6, no. 3 (July 22, 2022): 53. http://dx.doi.org/10.3390/logistics6030053.

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Background: Random truck arrivals at maritime container terminals are one of the primary reasons for gate congestion. Gate congestion negatively affects the terminal’s and drayage firms’ productivity and the surrounding communities in terms of air pollution and noise. To alleviate gate congestion, more and more terminals in the USA are utilizing a truck appointment system (TAS). Methods: This paper proposes a novel approach to modeling the truck appointment system problem. Unlike previous studies which largely treated this problem as a single-player game, this study explicitly models the interplay between the terminal and drayage firms with regard to appointments. A multi-player bi-level programming model is proposed, where the terminal functions as the leader at the upper-level and the drayage firms function as followers at the lower-level. The objective of the leader (the terminal) is to minimize the gate waiting cost of trucks by spreading out the truck arrivals, and the objective of the followers (drayage firms) is to minimize their own drayage cost. To make the model tractable, the bi-level model is transformed to a single-level problem by replacing the lower-level problem with its equivalent Karush–Kuhn–Tucker (KKT) conditions and the model is solved by finding the Stackelberg equilibrium in one-shot simultaneous-moves among players. For comparison purposes, a single-player version of the TAS model is also developed. Results: Experimental results indicate that the proposed multi-player model yields a lower gate-waiting cost compared to the single-player model, and that it yields higher cost savings for the drayage firms as the number of appointments per truck increases. Moreover, the solution of the multi-player model is not dependent on the objective function coefficients, unlike the single player model. Conclusions: This study demonstrates that a TAS is more effective if it considers how the assigned appointment slot affects a truck’s drayage cost. It is recommended that terminal operators and port authorities initiate conversations with their TAS providers about incorporating this element into their TAS.

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10

Xue, Zhaojie, Canrong Zhang, Peng Yang, and Lixin Miao. "A Combinatorial Benders’ Cuts Algorithm for the Local Container Drayage Problem." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/134763.

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This paper examines the local container drayage problem under a special operation mode in which tractors and trailers can be separated; that is, tractors can be assigned to a new task at another location while trailers with containers are waiting for packing or unpacking. Meanwhile, the strategy of sharing empty containers between different customers is also considered to improve the efficiency and lower the operation cost. The problem is formulated as a vehicle routing and scheduling problem with temporal constraints. We adopt combinatorial benders’ cuts algorithm to solve this problem. Numerical experiments are performed on a group of randomly generated instances to test the performance of the proposed algorithm.

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11

Namboothiri, Rajeev, and Alan L. Erera. "Planning local container drayage operations given a port access appointment system." Transportation Research Part E: Logistics and Transportation Review 44, no. 2 (March 2008): 185–202. http://dx.doi.org/10.1016/j.tre.2007.07.004.

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12

Shiri, Samaneh, ManWo Ng, and Nathan Huynh. "Integrated drayage scheduling problem with stochastic container packing and unpacking times." Journal of the Operational Research Society 70, no. 5 (April 25, 2018): 793–806. http://dx.doi.org/10.1080/01605682.2018.1457487.

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13

Zhang, Ruiyou, Won Young Yun, and Il Kyeong Moon. "Modeling and optimization of a container drayage problem with resource constraints." International Journal of Production Economics 133, no. 1 (September 2011): 351–59. http://dx.doi.org/10.1016/j.ijpe.2010.02.005.

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14

Chen, Rui, Shukai Chen, Haipeng Cui, and Qiang Meng. "The container drayage problem for heterogeneous trucks with multiple loads: A revisit." Transportation Research Part E: Logistics and Transportation Review 147 (March 2021): 102241. http://dx.doi.org/10.1016/j.tre.2021.102241.

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15

Bjelić, Nenad, Milorad Vidović, Dražen Popović, and Branislava Ratković. "Rolling-horizon approach in solving dynamic multisize multi-trailer container drayage problem." Expert Systems with Applications 201 (September 2022): 117170. http://dx.doi.org/10.1016/j.eswa.2022.117170.

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16

Ihebom, V. I., R. J. O. Ekeocha, and O. S. I. Fayomi. "Alleviation of drayage truck-entry points congestion in container terminals: a review." Journal of Physics: Conference Series 1378 (December 2019): 022080. http://dx.doi.org/10.1088/1742-6596/1378/2/022080.

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17

Song, Yujian, Jiantong Zhang, Zhe Liang, and Chunming Ye. "An exact algorithm for the container drayage problem under a separation mode." Transportation Research Part E: Logistics and Transportation Review 106 (October 2017): 231–54. http://dx.doi.org/10.1016/j.tre.2017.07.010.

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18

Zhang, Ruiyou, Haishu Zhao, and Ilkyeong Moon. "Range-based truck-state transition modeling method for foldable container drayage services." Transportation Research Part E: Logistics and Transportation Review 118 (October 2018): 225–39. http://dx.doi.org/10.1016/j.tre.2018.07.011.

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19

Xue, Zhaojie, Wei-Hua Lin, Lixin Miao, and Canrong Zhang. "Local container drayage problem with tractor and trailer operating in separable mode." Flexible Services and Manufacturing Journal 27, no. 2-3 (January 29, 2014): 431–50. http://dx.doi.org/10.1007/s10696-014-9190-2.

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20

Jia, Shuai, Haipeng Cui, Rui Chen, and Qiang Meng. "Dynamic container drayage with uncertain request arrival times and service time windows." Transportation Research Part B: Methodological 166 (December 2022): 237–58. http://dx.doi.org/10.1016/j.trb.2022.10.010.

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21

Moghaddam, Mahboobeh, Robin H. Pearce, Hamid Mokhtar, and Carlo G. Prato. "A generalised model for container drayage operations with heterogeneous fleet, multi-container sizes and two modes of operation." Transportation Research Part E: Logistics and Transportation Review 139 (July 2020): 101973. http://dx.doi.org/10.1016/j.tre.2020.101973.

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22

NISHIMRA, Etsuko, Koichi SHINTANI, and Stratos PAPADIMITRIOU. "Improvement Effects on Marine Container Drayage by the Optimization Approach with Precedence Constraints." Journal of Japan Institute of Navigation 137 (2017): 66–74. http://dx.doi.org/10.9749/jin.137.66.

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23

Zhang, Ruiyou, Jye-Chyi Lu, and Dingwei Wang. "Container drayage problem with flexible orders and its near real-time solution strategies." Transportation Research Part E: Logistics and Transportation Review 61 (January 2014): 235–51. http://dx.doi.org/10.1016/j.tre.2013.11.009.

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24

Bruglieri, M., S. Mancini, R. Peruzzini, and O. Pisacane. "The Multi-period Multi-trip Container Drayage Problem with Release and Due Dates." Computers & Operations Research 125 (January 2021): 105102. http://dx.doi.org/10.1016/j.cor.2020.105102.

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25

Ghezelsoflu, A., M. Di Francesco, A. Frangioni, and P. Zuddas. "A set-covering formulation for a drayage problem with single and double container loads." Journal of Industrial Engineering International 14, no. 4 (January 25, 2018): 665–76. http://dx.doi.org/10.1007/s40092-018-0256-8.

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26

Ritzinger, Ulrike, Bin Hu, Hannes Koller, and Melitta Dragaschnig. "Synchronizing Trucks and Trailers in a Multiresource Problem with Variable Neighborhood Search." Transportation Research Record: Journal of the Transportation Research Board 2610, no. 1 (January 2017): 28–34. http://dx.doi.org/10.3141/2610-04.

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A real-world container drayage problem in which containers are transported between an intermodal terminal, a container terminal, and customer locations is considered. The problem was modeled as a multi-resource routing problem (MRRP) that included trucks, trailers, and containers. Given a fleet of trucks and trailers, the goal is to use these resources most efficiently to complete a number of given orders. Orders consisted of several tasks with time windows, such as picking up a container at the terminal, delivering it to a customer, and bringing the processed container back. A challenging aspect of this problem is the management of trailers, which are required to transport the containers. Here, the compatibility between container types and trailer types must be considered. Thus, the decision of which trailer should be attached to which truck depends on the containers that must be transported, the day of availability of trailers, and the toll costs of the truck and trailer combination on the highways. This paper presents an efficient way to model this problem and proposes a metaheuristic approach based on a variable neighborhood search. It uses a compact solution representation and tailored neighborhood structures to reduce the search space. Classical MRRP neighborhood structures, as well as problem-specific ones, were used in combination and contributed to the overall success. The results show that the given real-world problem can be solved efficiently, and it can be shown that with proper planning, the utilization of resources can be increased.

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27

Xue, Zhaojie, Canrong Zhang, Wei-Hua Lin, Lixin Miao, and Peng Yang. "A tabu search heuristic for the local container drayage problem under a new operation mode." Transportation Research Part E: Logistics and Transportation Review 62 (February 2014): 136–50. http://dx.doi.org/10.1016/j.tre.2013.12.007.

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28

You, Jintao, Lixin Miao, Canrong Zhang, and Zhaojie Xue. "A generic model for the local container drayage problem using the emerging truck platooning operation mode." Transportation Research Part B: Methodological 133 (March 2020): 181–209. http://dx.doi.org/10.1016/j.trb.2019.12.009.

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29

Benantar, A., M. N. Abourraja, J. Boukachour, D. Boudebous, and C. Duvallet. "On the integration of container availability constraints into daily drayage operations arising in France: Modelling and optimization." Transportation Research Part E: Logistics and Transportation Review 140 (August 2020): 101969. http://dx.doi.org/10.1016/j.tre.2020.101969.

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30

Jovanovic, Raka. "Optimizing Truck Visits to Container Terminals with Consideration of Multiple Drays of Individual Drivers." Journal of Optimization 2018 (September 17, 2018): 1–8. http://dx.doi.org/10.1155/2018/5165124.

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In this paper a new approach for designing a truck appointment system (TAS) at container terminals is proposed. While the vast majority of published research analyzes the TAS from the perspective of drayage companies and terminal operations, in this work this topic is centered around truck drivers. The objective of this approach is to show that it is possible to increase the satisfaction of drivers which will maintain the positive effects that the TAS provides to the port. To be more precise, the focus is on exploiting the fact that individual truck drivers perform multiple visits to the container terminal in a day. Based on this information, a scheduling problem is defined and the corresponding integer programming model is developed. The potential benefits of the proposed approach are evaluated for the ports of Los Angeles and the port of Seattle. This has been done by generating problem instances based on the distribution of the number of daily truck visits to the port, number of daily working hours of drivers, and distances (lengths) of individual dray moves for the corresponding ports. The results of the conducted computational experiments show that using the proposed approach can positively affect gate waiting times and truck driver satisfaction. Further, it is shown that this type of approach favors more experienced drivers, which has a potential to provide additional benefits to the port.

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31

Zhang, Ruiyou, Chao Huang, and Junwei Wang. "A novel mathematical model and a large neighborhood search algorithm for container drayage operations with multi-resource constraints." Computers & Industrial Engineering 139 (January 2020): 106143. http://dx.doi.org/10.1016/j.cie.2019.106143.

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32

Wong, Eugene Yin Cheung, Allen H. Tai, and Stuart So. "Container drayage modelling with graph theory-based road connectivity assessment for sustainable freight transportation in new development area." Computers & Industrial Engineering 149 (November 2020): 106810. http://dx.doi.org/10.1016/j.cie.2020.106810.

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33

Regan, Amelia C., Sreeram Jagannathan, and Xiubin Wang. "Mixed Global and Local Assignment Algorithms for Quasi-Dynamic Local Truckload Trucking Operations with Strict Time Windows." Transportation Research Record: Journal of the Transportation Research Board 1733, no. 1 (January 2000): 49–55. http://dx.doi.org/10.3141/1733-07.

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Examined are the trade-offs associated with local and global, but myopic, assignment heuristics for local truckload trucking operations such as those associated with drayage operations near intermodal facilities. These operations involve a combination of loads that are known at the beginning of the day and those that arrive dynamically throughout the day. Some of the dynamically arriving loads are revenue-generating moves, and others are trailer, chassis, or container repositioning moves. Because a significant fraction of the day’s loads are known a priori, dispatchers would like to be able to construct schedules for the day and then to make minor changes to these schedules as the day progresses. The efficiency of an operation in which new loads are added to or appended to schedules constructed at the start of the day versus one in which the whole system is reoptimized several times during the day is examined. The reoptimization method does not seek to preserve current schedules, but the local optimization techniques do. Solutions were examined with a geographic information system-based simulation model developed for this purpose.

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34

Lee, EunSu. "Spatial analysis for an intermodal terminal to support agricultural logistics." Management Research Review 38, no. 3 (March 16, 2015): 299–319. http://dx.doi.org/10.1108/mrr-06-2013-0131.

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Purpose – The purpose of this study is to address an importance of an intermodal terminal regarding container drayage trips, which have a major concern for agricultural product exporters in the Upper Great Plains. Thus, this study aims to develop a geospatial model considering travel distance and total logistics costs for determining an alternative intermodal terminal location. Design/methodology/approach – This paper develops a spatial model integrating integer linear programming to determine an intermodal facility location that minimizes total logistics costs. This research considers travel distance and total logistics costs including highway, rail and transshipment costs. Findings – The results shows that a Dilworth, Minnesota, terminal reduces vehicle miles of travel on both the highways and rail networks and decreases system-wide total logistics costs compared to the do-nothing scenario while decreasing urban congestion costs in metropolitan areas. Research limitations/implications – The major contribution of the study is that it provides an integrated tool of spatial and economic analyses to support regional decision-making. The paper will be of interest to regional planners and to those in the private business sectors including farmers and manufacturers. The future study should address demand forecasting on the containerized freight in the region. Originality/value – The novel approach of this paper is to use a link blocking constraint, considering the directions of the freight flow in a p-hub intermodal problem.

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35

Zhang, Ruiyou, Decheng Wang, and Junwei Wang. "Multi-Trailer Drop-and-Pull Container Drayage Problem." IEEE Transactions on Intelligent Transportation Systems, 2020, 1–13. http://dx.doi.org/10.1109/tits.2020.2991098.

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36

Huang, Chao, and Ruiyou Zhang. "Container Drayage Transportation Scheduling With Foldable and Standard Containers." IEEE Transactions on Engineering Management, 2021, 1–15. http://dx.doi.org/10.1109/tem.2021.3094994.

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37

Escudero-Santana, Alejandro, Jesús Muñuzuri, Pablo Cortés, and Luis Onieva. "The one container drayage problem with soft time windows." Research in Transportation Economics, July 2020, 100884. http://dx.doi.org/10.1016/j.retrec.2020.100884.

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38

You, Jintao, Canrong Zhang, Zhaojie Xue, Lixin Miao, and Bin Ye. "A bi-objective model for robust local container drayage problem." RAIRO - Operations Research, June 14, 2019. http://dx.doi.org/10.1051/ro/2019063.

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Local Container Drayage Problem (LCDP) refers to the optimization of the process of planning and scheduling container trucks between a terminal and customers to oer door-to-door service to customers in a local area. The time required for (un)packing containers at customers sites are often relatively long and uncertain due to the current (un)packing work level and unexpected deviations in operational situations, which has a significant influence on the planning and scheduling of the container transportation process. This paper examines the LCDP with Separable tractors and trailers, and additionally with consideration of (un)packing time Uncertainties (LCDPSU). A proactive strategy is employed to tackle the uncertainty by proposing a ‘model robust’ bi-objective optimization model to balance the tradeoff between operational cost, which includes traveling cost and tractor deployment setup cost, and robustness, which is represented as an exponential expression of the time buffer between two stages of each individual task. The deterministic version of our problem is proved to be NP hard, and an Ant Colony Optimization (ACO) scheme is therefore proposed to search for feasible solutions in which the Zoutendijk feasible direction algorithm is embedded in order to tackle the nonlinearity brought in by the robustness of the model. Numerical experiments are conducted to validate the effciencies and effectivenesses of the proposed models and methods, and managerial implications are drawn from the numerical results.

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39

Wang, Decheng, Ilkyeong Moon, and Ruiyou Zhang. "Multi-Trip Multi-Trailer Drop-and-Pull Container Drayage Problem." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–17. http://dx.doi.org/10.1109/tits.2022.3156547.

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40

Torkjazi, Mohammad, and Nathan N. Huynh. "Design of a Truck Appointment System Considering Drayage Scheduling and Stochastic Turn Time." Transportation Research Record: Journal of the Transportation Research Board, July 28, 2021, 036119812110296. http://dx.doi.org/10.1177/03611981211029643.

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This paper develops a truck appointment system (TAS) considering variability in turn time at the container terminals. The consideration of this operational characteristic is crucial for optimal drayage scheduling. The TAS is formulated as a stochastic model and solved using the sample averaging approximation (SAA) algorithm. Using turn time distributions obtained from actual data from a U.S. port, a series of experiments is designed to evaluate the effectiveness of the proposed stochastic TAS model compared with the deterministic version where an average turn time is used instead of a distribution. Results of the numerical experiment demonstrate the benefit of the stochastic TAS model given that its drayage cost error was 3.9% lower compared with the deterministic TAS model. This result implies that the schedules produced by the stochastic TAS model are more robust and are able to accommodate a wider range of turn time scenarios. Another key takeaway from the experiment results is that the stochastic TAS model is more beneficial to utilize when the ratio of quotas to requested appointments is lower. Thus, in practice, when this ratio is more likely to be on the lower end, drayage companies would benefit more if the appointment schedule adopts the stochastic approach described in this paper.

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41

You, Jintao, YUAN WANG, and zj xue. "An Exact Algorithm for the Multi-Trip Container Drayage Problem with Truck Platooning." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4006049.

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42

Cui, Haipeng, Shukai Chen, Rui Chen, and Qiang Meng. "A two-stage hybrid heuristic solution for the container drayage problem with trailer reposition." European Journal of Operational Research, June 2021. http://dx.doi.org/10.1016/j.ejor.2021.06.040.

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43

Fazi, Stefano, Sourabh Kumar Choudhary, and Jing-Xin Dong. "The Multi-trip Container Drayage Problem with Synchronization for Efficient Empty Containers Re-usage." European Journal of Operational Research, March 2023. http://dx.doi.org/10.1016/j.ejor.2023.02.041.

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44

Fazi, Stefano, and Sourabh Kumar Choudhary. "The Multi-Trip Container Drayage Problem with Empty Containers Management in a Dry-Port Setting." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.3998748.

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Peng, Wenxiang, and Zhaojie Xue. "Route planning and benefit assessment of container drayage platooning considering truck laden-or-empty state." Computers & Industrial Engineering, December 2022, 108879. http://dx.doi.org/10.1016/j.cie.2022.108879.

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Wang, Naiyu, Qiang Meng, and Canrong Zhang. "A Branch-Price-And-Cut Algorithm for the Local Container Drayage Problem with Controllable Vehicle Interference." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4355431.

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