Inter-terminal Transport Research Articles

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

An efficient and eco-friendly operation mode for container transshipments through optimizing the inter-terminal truck routing problem

To enhance efficiency and reduce carbon emissions of waterway-waterway transshipments for a multi-terminal port group, a crucial strategic planning issue lies in determining whether a centralized or distributed mode of feeder vessel service is more efficient and eco-friendly. This paper proposes a two-stage integer-optimized model to optimize intra- and inter-terminal operations and determine the most effective mode of operation for feeder vessel services. Our model integrates the multi-terminal berth allocation problem and inter-terminal transportation (ITT), considering different cases of transshipment flows. To address this optimization problem, we develop a heuristic approach based on genetic and adaptive large neighborhood search algorithms, which is ideal method for large-scale scenarios. In our case study, the proposed optimization model and heuristic methods were adopted to determine the best operation mode for North Operation Area in Shanghai Yangshan Deep-Water Port (namely Yangshan Phase V). The results indicate that Yangshan Phase V should operate as a feeder vessel service terminal, providing primary feeder vessel services and complementary liner vessel services. This development plan not only minimizes container transshipment delays and overall ITT costs, but also helps reduce approximately 707.46 tons of annual carbon emissions caused by ITT at Yangshan Deep-Water Port.

Inter-terminal transportation for an offshore port integrating an inland container depot

Offshore ports, which are located on islands or away from the hinterland, are usually connected with the hinterland via bridges for truck transportation. With the rapid growth of port throughput, the bridges are likely to become a bottleneck restricting the development of port business. Inland container depots (ICD) have been introduced as satellite terminals to address congestion issues on bridges by allowing a proportion of drayage trucks to pick up and drop off containers at the ICD instead of going to the container terminals of the offshore ports. Containers are transported between the ICD and container terminals by inter-terminal transportation (ITT) trucks owned by port authorities. The ICD is expected to mitigate the bridge congestion by transferring the travel demands of drayage trucks in peak hours to the travel demands of ITT trucks in off-peak hours. We propose a methodology for analyzing the influence of ICDs on port efficiency. An integer linear programming model is developed to optimize the movements of containers, drayage trucks and ITT trucks in an offshore port area with the objective of minimizing the costs of drayage trucks and ITT trucks and the penalty for late delivery of containers. Two model transformation approaches are proposed to reduce the solution space of the model for improving the computational efficiency of the algorithm. The influence of ICDs on port efficiency is evaluated through a real-world case study using the proposed methodology. The results provide insights for port authorities to make strategic decisions.

Optimization of container transportation between multi terminals based on carrier cooperation

To optimize the inter-terminal transportation (ITT) problem, trucks transporting containers from one terminal to another are likely to have the situation of "Full load to empty vehicle return" or "empty vehicle to full load return", a container transportation method proposed in which container carriers. Taking the maximization of the carrier's profit as the goal, considering the terminal operation’s busy degree, the task volume between terminals, and the carrier’s number, a container transportation task allocation model based on carrier cooperation is established. The analysis and comparison between the carrier's "cooperation" strategy and the "non-cooperation" strategy show that if multiple container truck carriers cooperate by sharing trucks and orders, The total profit will increase by 32.3% and the number of trucks will decrease by 25.7%.

Research on scheduling optimization of internal trucks for inter-terminal transportation

Based on the operation mode of inter-terminal transportation in a container port, an optimization method of internal trucks scheduling was proposed to cooperate among multiple terminals in a port. In order to minimize the remaining workload of each terminal, a mixed integer programming model for internal trucks scheduling optimization was established by considering the transportation time and energy consumption of internal trucks. The improved genetic algorithm was used to solve the model, and an example was given to verify the validity of the model. The results show that the scheduling optimization model based on inter-terminal transportation mode can effectively reduce the total operating cost of each terminal and improve the utilization rate of internal trucks.

Dynamic Rolling Horizon Scheduling of Waterborne AGVs for Inter Terminal Transportation: Mathematical Modeling and Heuristic Solution

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

Passenger shuttle service network design in an airport

This study examines the service network design problem (SNDP) for passenger shuttle buses in the airport and nearby places (e.g. train stations, parking, hotels, shopping areas). A time–space service network for bus flows and time–space networks for passenger flows are developed. Based on proposed time–space networks, the studied SNDP is formulated as a mixed integer linear program (MILP) for a single-type bus fleet and deterministic passenger demand, where the objective is to minimize the weighted sum of passenger cost and service operating cost. We then extend the developed SNDP model to the heterogeneous multi-type bus fleet case and the stochastic demand case. To solve the stochastic demand case, a Monte Carlo simulation-based approach is adopted, which is further coupled with the ‘effective demand’ concept (mean demand value plus a margin). The proposed SNDP models and solution approach are applied on the inter-terminal transport network at Sydney Kingsford Smith Airport for illustration.

Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning

Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as interterminal transport (ITT). ITT forms a complex transportation network in a large port, which must be managed efficiently given the economic and environmental implications. The use of trucks in ITT operations leads to the interterminal truck routing problem (ITTRP), which has been attracting increasing attention from researchers. One of the objectives of truck routing optimization in ITT is the minimization of empty-truck trips. Selection of the transport order (TO) based on the current truck location is critical in minimizing empty-truck trips. However, ITT entails not only transporting containers between terminals operated 24 h: in cases where containers need to be transported to a logistics facility within operating hours, empty-truck trip cost (ETTC) minimization must also consider the operational times of the transport origin and destination. Otherwise, truck waiting time might be incurred because the truck may arrive before the opening time of the facility. Truck waiting time seems trivial, but it is not, since thousands of containers move between locations within a port every day. So, truck waiting time can be a source of ITT-related costs if it is not managed wisely. Minimization of empty-truck trips and truck waiting time is considered a multiobjective optimization problem. This paper proposes a method of cooperative multiagent deep reinforcement learning (RL) to produce TO truck routes that minimize ETTC and truck waiting time. Two standard algorithms, simulated annealing (SA) and tabu search (TS) were chosen to assess the performance of the proposed method. The experimental results show that the proposed method represents a considerable improvement over the other algorithms.

Design and Simulation of a Monorail Network for the Inter-terminal Transport

In line with the trend of global transport volume which has increased rapidly over the years, internal transportation in seaports is always conducted with high frequency. Thus, there is always much potential for traffic jams as well as high transportation costs and emissions. Many efforts have been initiated to streamline the inter-terminal container transport (ITT) through the development of automated vehicles and equipment as well as using private transport facilities to overcome these limitations. The purpose of this paper to develop a framework to design, analyze, and validate the efficiency of a new ITT system in a port area based on the monorail network and automatic vehicles. First, the number of shuttles and loaders was determined depending on the transport demand scenario. Next, a simulation model was applied to evaluate the system performance as well as gain more insight into the working process of the ITT system. Finally, by setting goals for the performance indicators, the results showed that the system was highly efficient with 100% of the containers delivered to their destination on time. Besides, a series of other performance tracking was provided to provide insight into the system's capabilities.

Interterminal Truck Routing Optimization Using Deep Reinforcement Learning.

The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.

Applying A* Algorithm in routing for Inter-Terminal Transport System at Busan Port

This paper proposes a heuristic approach named A* algorithm to find shortest route for Inter-Terminal Transport Monorail System (ITT system) that are being applied in Busan Port (Korea). In every transport system, vehicle routing and task assignment are significant problems that affects strongly the overall system’s efficiency, especially the travel cost and the time taking. The proposed ITT system will use shuttles to carry containers along a monorail that links the internal terminals. A* algorithm is used to guide the shuttles in a shortest way automatically from a known loading position to a designated unloading one. In the first part of the paper, we will describe briefly about the ITT system that being considered in Korea. Next, we will explain why and how we implement A* algorithm in dispatching. Finally, we will give some comparisons between performances of new ITT system and traditional transport system through simulation in MATLAB.

Critical Literature Review into Planning of Inter-Terminal Transport: In Port Areas and the Hinterland

Nowadays, the major ports around the world usually consist of multiple terminals and service centers which are often run by different operators. Meanwhile, inland terminals have been also developed to reduce port congestion and improve transport efficiency. The integrated planning of inter-terminal transport (ITT) between the seaport and inland terminals helps in providing frequent and profitable services, but also could lead to higher overall planning complexity. Moreover, the ITT system usually involves multiple stakeholders with different or even conflicting interests. Although an increasing number of studies have been conducted in recent years, few studies have summarized the research findings and indicated the directions for future research regarding ITT. This paper provides a systemic review of ITT planning: we examine 77 scientific journal papers to identify what kind of objectives should be achieved in ITT system planning, which actors should be involved, and what methodologies can be used to support the decision-making process. Based on the analysis of the existing research, several research gaps can be found. For example, the multi-modality ITT systems are rarely studied; cooperation frameworks are needed in the coordination of different actors and quantitative methodologies should be developed to reflect the different actors’ financial interests.

Integration of inter-terminal transport and hinterland rail transport

This paper investigates the problem of inter-terminal movements of containers and vehicles within a port area in order to achieve an integrated and effective transport within the port and towards the hinterland. Containers from different port terminals are first moved to a rail yard and then delivered to the hinterland by rail. To provide insights for stakeholders such as port authority and terminal operators into tactical planning problems, e.g., the coordination between terminals, railway timetable and train sizes, this paper proposes an optimization model describing the movement of containers and various vehicles between and inside terminals. The model aims at improving the container delivery from container terminals to the hinterland considering both railway hinterland transport and terminal handling operations. A network inspired by a real-life port area and its hinterland is used as a test case to test different components, i.e., inter-terminal transport connections, train formation, railway timetable. A rolling horizon framework is used to improve the computation efficiency in large transport demand cases. The result of the optimization helps in identifying the most promising features, namely, that more connections between terminals and a flexible outbound railway timetable could contribute to improving the integrated container transport performance.

Collaborative Inter-Terminal Transportation of Containers

In a port consisting of multiple container terminals, numerous containers are transshipped among the terminals. All the transshipment containers bound for a vessel are generally shifted from one terminal to another within a short time period. However, as each shipping liner issues delivery orders exclusively to a trucking company, it is highly probable that a truck that transports a container from one terminal to another returns back empty, thus resulting in excessive empty travels by trucks among terminals. A scheduling problem for transshipment container deliveries, called Inter- Terminal Transportation Problem (ITTP), is defined, and a mathematical formulation is provided. This study assumes that trucking companies collaborate with each other for minimizing empty travels by sharing delivery orders. A numerical experiment is conducted to investigate the variation in the total cost and profit of each trucking company as a result of the collaboration.

Inter Terminal Transport in Port Areas around the Globe

Inter Terminal Transport in Port Areas around the Globe

A tabu search algorithm to solve the integrated planning of container on an inter-terminal network connected with a hinterland rail network

Transport demand for containers has been increasing for decades, which places pressure on road transport. As a result, rail transport is stimulated to provide better intermodal freight transport services. This paper investigates mathematical models for the planning of container movements in a port area, integrating the inter-terminal transport of containers (ITT, within the port area) with the rail freight formation and transport process (towards the hinterland). An integer linear programming model is used to formulate the container transport across operations at container terminals, the network interconnecting them, railway yards and the railway networks towards the hinterland. A tabu search algorithm is proposed to solve the problem. The practical applicability of the algorithm is tested in a realistic infrastructure case and different demand scenarios. Our results show the degree by which internal (ITT) and external (hinterland) transport processes interact, and the potential for improvement of overall operations when the integrated optimization proposed is used. Instead, if the planning of containers in the ITT system is optimized as a stand-alone problem, the railway terminals may suffer from longer delay times or additional train cancellations. When planning the transport of 4060 TEU containers within one day, the benefits of the ITT planning without considering railway operations account for 17% ITT cost reduction but 93% railway operational cost growth, while the benefits of integrating ITT and railway account for a reduction of 20% in ITT cost and 44% in railway operational costs.

Fast ADMM for Distributed Model Predictive Control of Cooperative Waterborne AGVs

This brief proposes a distributed predictive path following controller with arrival time awareness for multiple waterborne automated guided vessels (waterborne AGVs) applied to interterminal transport (ITT). The goal is to design an efficient cooperative distributed algorithm that solves local problems in parallel and minimizes an overall objective. We model the ITT problem using waterborne AGVs with independent dynamics and objectives but coupling collision avoidance constraints. The problem is then solved by distributed model predictive control (DMPC) of which the parallelism is realized using the alternating direction method of multipliers (ADMM). Successive linearizations are utilized to maintain a tradeoff among computational complexity, optimality, and ease of decomposition. Moreover, we propose a fast ADMM by iteratively incorporating in local problems adaptive global information to improve convergence rates. Simulation results for an ITT case study illustrate the effectiveness of the proposed algorithms for DMPC of time-varying networks in general and cooperative distributed waterborne AGVs in particular.

Global planning in a multi-terminal and multi-modal maritime container port

This paper introduces an optimization model of a multi-terminal, multi-modal maritime container port, such as the ones in the European northern range. The decisions concern the scheduling of ships, trains and trucks on terminals, while limiting inter-terminal transport of containers and minimizing weighted turnaround time. Heuristics based on the decomposition of the resulting mixed-integer program are proposed and tested on realistic generated instances with up to four terminals. The efficiency of the restrict-and-fix heuristic allows to investigate the impact of a global management on port’s performance: an average improvement of 5% was observed.

Planning inland vessel operations in large seaports using a two-phase approach

Inland vessels are often used to transport containers between large seaports and the hinterland. Each time a vessel arrives in such a port, it typically visits several terminals to load and unload containers. In the Port of Rotterdam, the largest port in Europe, there are 77,000 inland vessels that have moored in the port in 2014 for transporting cargo. With the significant growth of containerized cargo transportation over the last decade, large seaports are under pressure to ensure high handling efficiency. Due to this development and the limited capacity at terminals, the inland vessels usually spend longer time in the port that originally planned. This leads to low utilization of terminal resources and congestion in the port. This paper proposes a novel two-phase planning approach that could improve this, taking into account several practical constraints. Specifically, we take into account the restricted opening times of terminals, the priority of sea-going vessels, and the different terminal capacities and sizes. In addition, we also consider the option for inland vessels to carry out additional inter-terminal transport tasks. Our approach is based on the integration of mixed-integer programming (MIP) and constraint programming (CP) to generate rotation plans for inland vessels. In the first phase, a single vessel optimization problem is solved using MIP. In the second phase, a multiple vessel coordination problem is formulated using CP; three large neighborhood search (LNS)-based heuristics are proposed to solve the problem. Simulation experiments show that the proposed LNS-based heuristic outperforms the performance obtained with a state-of-the-art commercial CP solvers both regarding the solution quality and the computation time. Moreover, the simulation results indicate significant improvements with shorter departure times, sojourn times and waiting times.

A Stochastic Model for Interterminal Container Transportation

We propose a novel semiopen queuing network (SOQN) model for the interterminal transportation (ITT) problem where multiple container terminals use a common fleet of vehicles (automated lift vehicles, automated guided vehicles, multitrailers, and barges) to transport containers between terminals. To solve the overall queuing network, our solution approach uses a network decomposition method where the original SOQN is decomposed to a closed and an open queuing network (with bulk-service capacity). To our knowledge, this is the first work that considers bulk service in SOQNs. We develop theoretical upper and lower bounds on the throughput time estimates of our model, and provide an extension for the case when service times at the terminal handling stations depend on the number of containers being loaded/unloaded. We numerically validate our model using simulated data where we find that our model results in errors of less than 5% for vehicle utilization. We also show that our model results in better estimates for the ITT problem when compared to existing approaches in the literature. Finally, we apply our model to real-world data from the Port of Rotterdam and show that it can be used to analyze throughput time trade-offs with alternate dwell point policies, different vehicle types, and variable vehicle capacities. The online appendix is available at https://doi.org/10.1287/trsc.2016.0726 .