Although long-haul intermodal container transportation combines multiple transportation modes including shipping, the most critical aspect of enabling door-to-door service is container port drayage operation. Container port drayage connects customers and container ports and provides an effective solution to the first/last-mile problem faced by the container logistics industry. Therefore, container port drayage has a significant impact not only on the economic advantages of the long-haul intermodal container transportation chain, but also the management, environment and social benefits of the stakeholders involved. In this paper, we comprehensively review the existing literature on container port drayage studies from different methodological angles (e.g., model development and algorithm design) and technological angles (e.g., autonomous truck and blockchain). Moreover, with the shifting of industrial focus from traditional operations and management of container port drayage towards e-commerce platforms, we identify potential issues for future research directions. 

Container port drayage operations and management: Past and future

This study proposes an autonomous truck (AT) based scheduling problem for container transshipment between two seaport terminals, in which ATs can travel in platoons with short inter-vehicle distance. The following vehicles in a platoon can save fuel consumption because of the reduced air resistance. Based on a fuel consumption function with respect to travel speed and platoon formation, we formulate a mixed-integer second-order-cone programming (MISOCP) model to minimize the total operation cost. To address the extra computational challenges caused by the requirements of platoon coordination, we develop a column-generation-based heuristic method in which two types of columns representing AT schedules and platoon schemes are generated and added into the master problem. According to the practice of container transshipment between two port terminals in Singapore, we conduct extensive numerical experiments and evaluate the performance of the proposed method up to the instance of 50 requests in a two-hour time limit. The results show that the proposed CG heuristic can outperform the state-of-the-art solver by around 10% for the instances with 40–50 requests.

Autonomous truck scheduling for container transshipment between two seaport terminals considering platooning and speed optimization

A two-stage hybrid heuristic solution for the container drayage problem with trailer reposition

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

/pdf/mixed-integer-linear-programming-models-to-solve-a-real-life-1y0m6illp1.pdf

Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

Truck platooning has been identified as a promising technology to save labor force for the local container drayage problem (LCDP). However, existing studies for the platoon-based LCDP assumed that each driver was attached to respective leading truck throughout the service process, and the driverless following trucks dropped off at customer sites cannot further serve the drayage requests until regaining guidance, resulting in low efficiency and utilization of drivers and trucks. To overcome this issue, this paper examines the LCDP under a novel improved platooning operation mode (IPOM), where the drivers are not necessarily attached to their respective leading trucks but can move by alternative transport modes to perform subsequent tasks. The objective is to determine the optimal numbers, routes, and schedules of the drivers and trucks that minimize the total operational cost to complete all the delivery and pickup requests of customers. A mixed-integer linear programming (MILP) model is developed to capture the features pertaining to the new platooning mode. A heuristic construction method incorporating simulated annealing is proposed to solve the problem. Numerical experiments are carried out to evaluate the proposed model and solution method and quantify the advantages of the improved platooning mode. Sensitivity analysis is also conducted to explore the impacts of some major influential factors on the system performance and derive managerial insights. 

Local container drayage problem with improved truck platooning operations

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi-agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies. 

Electric bus charging facility planning with uncertainties: Model formulation and algorithm design

Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond

With the promotion of "ocean power" strategy, more and more scholars have paid attention to the development and application of underwater acoustic communication technology. In order to achieve high-speed and stable underwater acoustic communication and reduce complex data transmission. Based on underwater acoustic communication mechanism, this paper designs and expounds a set of underwater acoustic communication system and signal processing system, data interface module, underwater acoustic transducer, underwater electronic module, power supply module and many other components. This system effectively ensures the stability and simplicity of data transmission process, expands good universality, and is of great significance to the universal system application of underwater acoustic communication machine.

Design and research of underwater acoustic communication system

Container drayage plays a critical role in intermodal global container transportation, as it accomplishes the first- and last-mile shipment of containers. A container drayage operator dispatches a set of tractors and a set of trailers to transport containers within a local area. An important aspect of the operations is that the arrival times of service requests are uncertain, which means that the operator should respond to the requests dynamically. Moreover, since customers usually impose time windows on container pickup and delivery, it would be important to exploit the service flexibilities of requests when allocating resources in order to enhance the resource efficiency. In this paper, we study a dynamic container drayage problem that arises from the practical operations of container drayage. We develop a Markov decision process (MDP) model for the problem to capture the dynamic interactions between the drayage operator and the uncertain environment. For solving the MDP model, we propose a novel integrated reinforcement learning and integer programming method, in which reinforcement learning enables real-time responses to requests by determining whether each request should be served immediately upon arrival or be held for a period of time, while integer programming plans resource allocation periodically for serving the accrued requests. The proposed method aims to identify a fleet management policy that exploits requests’ service flexibilities to maximize the operator’s service capacity and profitability. We also evaluate the performance of the proposed method on instances generated from the operational data of a container drayage operator in Singapore. • A dynamic container drayage problem is studied under uncertain request arrival times. • A new Markov decision model is developed to capture dynamic decisions. • A novel integrated reinforcement learning and integer programming method is proposed. • Our method enables both real-time decision making and periodic resource planning. • Performance of the proposed method is evaluated on real drayage operational data. 

Dynamic container drayage with uncertain request arrival times and service time windows

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