Container Drayage Research Articles

Environmental challenge of vehicle dispatching in marine container drayage

AbstractIn marine container drayage (CD), which refers to the transport of goods over a short distance, empty containers (ECs) are required to be transported long distances between a terminal and a shipper/consignee by conventional transportation. This causes truck traffic congestion in harbor districts that increases CO2 emissions (CO2EM). This study addresses the vehicle dispatch problem with respect to the environmental impact of marine CD, considering the tractor dwell time is long at the customer site due to cumbersome container loading/unloading (L/U). We model this problem as an extended version of the vehicle routing problem with precedence constraints to minimize the total CO2 weighted travel distance. To improve CD operation in long L/U time, we propose a new practice for CD operations. Additionally, we develop a mixed integer programing model (MIP) for the new practice representation, and propose a Simulated Annealing (SA) based heuristic approach to solving the new practice instance. By implementing our proposed new operation, in a long L/U time, the CO2EM, EC move and number of tractors required can also be reduced by allowing uncoupled tractor move.

Container drayage problem integrated with truck appointment system and separation mode

Growing demand for seaborne trade has exacerbated the challenges faced by drayage operators in inland container transportation. In some cases, drayage operators must make truck scheduling decisions and book periods for trucks to visit terminals daily. This paper considers the appointment system and separation mode in container drayage problem, which consists of delivery and pickup transportation tasks for empty and full containers. A mixed-integer linear programming model is developed to minimize the total truck operating time while solving the truck scheduling problem, the empty container allocation problem, and the appointment problem. Another key feature of the model is considering the temporal constraints between tasks from a more realistic perspective. Due to the complexity of the problem, it is not practical to obtain exact solutions for large instances. Therefore, we complete the implementation of a new genetic algorithm (GA) that introduces a topological sort tailored to this problem. Several experiments and analyses are used to demonstrate the correctness of the proposed model as well as the efficiency of the algorithm. The findings indicate that, when compared to traditional mode, separation mode can reduce total truck operating time by an average of 29.4%. The total truck operating time difference between the two modes shows an overall positive trend if the task size increases. In addition, a rational setting of the appointment system will have a beneficial effect on truck scheduling. The decision on the selection of periods and the increment of each quota is crucial in optimizing the setting of the appointment system to reduce the total truck operating time for the mutual benefit of both terminals and drayage operators.

Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Container drayage involves the transportation of containers by trucks. Although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs and significantly contributes to increased fuel consumption and carbon emissions. Thus, the modeling of the container drayage problem (CDP) has received a lot of attention in the last two decades. However, the three fundamental modeling factors, including the combination of trucking operation modes and empty container relocation strategies, as well as empty container constraints and multiple inland depots, have not been simultaneously investigated. Hence, this study addressed a comprehensive CDP that simultaneously incorporates the three modeling factors. The problem was formulated as a novel mixed integer linear programming (MILP) model based on the DAOV graph. Given the complexity of this problem, it was not realistic to find an exact solution for large instances. Therefore, an improved genetic algorithm (GA) was designed by integrating the “sequential insertion” method and “solution re-optimization” operation. The performance of Gurobi and GA was validated and evaluated through randomly generated instances. The results indicate that (1) the proposed algorithm can provide near-optimal solutions for large-scale instances within a reasonable running time, (2) the greatest cost savings from combining trucking operation modes and empty container relocation strategies range from 10.45% to 31.86%, and (3) the three modeling factors significantly influence the fuel consumption and carbon emissions, which can provide managerial insights for sustainable container drayage practices.

The dynamic stochastic container drayage problem with truck appointment scheduling

The dynamic stochastic container drayage problem with truck appointment scheduling

Drop-and-pull container drayage with flexible assignment of work break for vehicle drivers

Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is of extraordinary importance for safety driving and guaranteeing the interests of vehicle drivers. Drop-and-pull transportation is an efficient mode in container drayage services and creates interdependencies between the routes of vehicle drivers. The situation becomes even more complicated when the drivers must comply with requirements of the mandatory work break. This paper addresses a drop-and-pull container drayage problem with flexible assignment of work break. The time and location of work break for drivers can be flexibly chosen based on scheduling, routing and optimization requirements. The transportation vehicle is comprised of two parts as a tractor and a trailer. The tractor can drag at most one trailer at a time, and is separable from the trailer when executing drayage activities. The problem is formulated as a mixed-integer programming model, which is then strengthened by proposing several families of valid inequalities. To efficiently solve realistic-sized instances, a backtracking adaptive threshold accepting algorithm with a mixed-integer programming model specially coping with the assignment of work break is proposed. Mechanisms of tabu search are incorporated to enhance the searching ability of the algorithm. Experiments indicate that the proposed algorithm can efficiently handle the vehicle scheduling and routing as well as the work break assignment and outperforms the mathematical programming approach. Sensitivity analyses about different policies and varied length of the work break are also conducted with some managerial insights presented.

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

A branch-price-and-cut algorithm for the local container drayage problem with controllable vehicle interference

This paper investigates a local container drayage problem with controllable vehicle interference (LCDP&CVI) under the tractor-and-trailer separation mode in which a tractor can be coupled or decoupled with a trailer at a customer location or a terminal. The container drayage requests proposed by customers should be fulfilled by a set of vehicle fleets, and each vehicle fleet comprises of a certain number of tractors and trailers that should be matched to each other. The controllable vehicle interference (CVI) means that the routes of any tractors from different vehicle fleets are independent of each other, implying that the interference between tractors is restricted only to the tractors from the same vehicle fleets. A mixed-integer linear programming (MILP) model is proposed for the LCDP&CVI. To exactly solve the problem, we develop a branch-price-and-cut algorithm based on the reformation of the MILP model, including a master problem and a pricing sub-problem. The pricing sub-problem that deals with the temporal synchronization constraints is formulated as the multi-vehicle shortest path problem with interference. By adding the tractor to the temporal-spatial network, a new representation of the pricing sub-problem is constructed, which enables us to solve the sub-problem by the multi-label method. In addition, the symmetrical elimination and dominance rules are incorporated into the multi-label method to enhance its effectiveness. Extensive numerical experiments are conducted to validate the proposed model and solution algorithm. For small-scale instances, the proposed solution algorithm is able to solve instances with up to 24 requests to optimality within two hours, which outperforms significantly the commercial MILP solver in terms of efficiency and effectiveness. For large-scale instances, the proposed algorithm can yield competitive solutions that are superior to the heuristic algorithms reported in the literature. Our experimental results also show that the controllable vehicle mechanism can strike a good balance between computational efficiency and operational costs by meticulously designing the structure of vehicle fleets.

Container Drayage Transportation Scheduling With Foldable and Standard Containers

Traditional container drayage scheduling with standard containers has been studied for years. In this article, we focus on a novel container drayage transportation problem involving foldable and standard containers simultaneously. Four types of container tasks are required to be scheduled among a terminal, a depot, and a number of customers in an export-dominant area. The optimizing objective is to minimize the total working time of trucks in operation, which reveals the total cost for trucking companies to some certain extents. An improved truck-state transition method is designed to analyze the state of trucks. A mathematical model based on this method is built to describe the problem. A large neighborhood search algorithm considering time window constraints and the state transition is used to solve the drayage problem. The mathematical model and the algorithm are evaluated and validated based on a large number of randomly generated instances. Experimental results show that the developed algorithm is effective in solving the problem. With the increase of the maximum number of empty foldable containers that a truck can carry at a time, the total working time of trucks in operation decreases.

Dynamic container drayage booking and routing decision support approach for E-commerce platforms

E-commerce drayage platforms are relatively new to the container drayage industry. Their revenue management poses an enormous logistical challenge owing to the unpredictable demand coupled with complex operations. In this paper, we propose a novel dynamic container drayage booking and routing problem with uncertain demand that incorporates important features of this emerging business model from the perspective of an e-commerce drayage platform. We formulate a multi-stage stochastic programming model for problem followed by developing an effective two-phase heuristic method based on a multi-scenario approach to derive both service booking and fleet routing solutions. In the two-phase heuristic, a container drayage service booking sub-problem is solved at the first phase to determine whether each request should be accepted for service or rejected, as well as the time period for serving the accepted requests, while a fleet routing sub-problem is tackled at the second phase to generate work schedules for the tractors and trailers to transport the containers. We conduct comprehensive computational experiments to demonstrate the quality and value of the proposed two-phase heuristic, and reveal insights into the practical operations management of e-commerce drayage platforms.

An exact algorithm for the multi-trip container drayage problem with truck platooning

Container Drayage Problem (CDP) refers to the optimization problem of routing and scheduling a set of container trucks around a container terminal. Conventionally, a driver is stuck to one container truck and allowed to perform multiple trips (multi-trip) to the terminal within their working time. The recent development of automation technologies enables semi-autonomous trucks to follow the leading human-driven truck as a platoon on the road; therefore, truck platooning can save human labor and reduce the fuel cost of following trucks through aerodynamic drag reduction. In this paper, we study a multi-trip container drayage problem with truck platooning (MT-CDP-TP), where multi-trip, truck platooning, and fuel cost reduction are simultaneously considered in a CDP. Despite the operational benefits brought by the MT-CDP-TP, the problem is challenging to solve due to its NP-hardness when formulated as a multi-trip pickup and delivery problem with load-dependent cost. We propose a Branch-and-Price-and-Cut (BPC) algorithm, with a route-based set partitioning model and tight linear relaxations, to yield the exact solutions. Valid inequalities are generated based on a graph structure, where each node represents a feasible route, and each arc stands for the conflict between two routes. Moreover, we design a tailored pulse propagation algorithm with novel pruning procedures based on the dual information from the master problem and valid inequalities to solve the pricing problem efficiently. Extensive numerical experiments are conducted for performance validation, and the computational results show that the proposed exact algorithm can solve instances with up to 100 task nodes (i.e., 50 customers) and facilitate reducing the labor cost and fuel consumption by a wide margin in container drayage operations.

A Branch and Price Algorithm for the Drop-and-Pickup Container Drayage Problem with Empty Container Constraints

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.

The multi-trip container drayage problem with synchronization for efficient empty containers re-usage

We study a typical daily drayage problem concerning the last-mile logistics at seaports for inland container supply chains. A set of trucks available at an inland container terminal must fulfil shippers’ requests of transporting containers within time windows and, to do so, can perform multiple daily trips. A request may entail picking up or delivering containers either at the shippers’ premises, the inland terminal or the seaport. Demand for empty containers can be satisfied by either using the available limited stock at the inland terminal, by street-turning or, ultimately, by retrieving them at a local depot for empties resulting in extra mileage. Hence, the minimization of routing costs also entails synchronizing trucks’ trips that retrieve and add empty containers to the inland terminal stock to avoid unnecessary visits to the empty depot. After modelling the problem mathematically, we develop an exact column-and-row generation approach embedded in a branch-and-price framework. To accelerate the solving process of the pricing problem, we propose effective strategies by combining a set of tailored pricing algorithms. These strategies perform well on a set of adapted Solomon’s instances up to 100 nodes and against a standard branch-and-cut solver. Finally, experiments on real-world instances, inspired by a case study of an inland terminal at the Port of Rotterdam region, provide insights into current planning practices.

Local container drayage problem with improved truck platooning operations

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.

Route planning and benefit assessment of container drayage platooning considering truck laden-or-empty state

This study investigates the local container drayage problem in semi-automated platooning mode, focusing on the effect of laden-or-empty state of trucks on platoon fuel consumption. The fuel economy characteristics of platoon trucks are described according to the change in the laden-or-empty state of the truck after the platoon serves the customer while considering the sequence of the truck in the platoon. Accordingly, a mixed integer linear programming model for the truck and driver route planning problem is developed, and the lower bound of the problem is analyzed. A simulated annealing algorithm is designed to solve the problem efficiently. Finally, a series of numerical experiments are conducted to evaluate the performance of the proposed algorithm, assess the benefits of the platooning mode considering truck laden-or-empty state, and analyze the influences of some key parameters.

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

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.

Multi-Trip Multi-Trailer Drop-and-Pull Container Drayage Problem

This paper addresses a novel and interesting container drayage problem in which a tractor can pull multiple trailers and can perform multiple trips during a working day. A state-based method and an IF-THEN method are proposed to model the multi-trip feature. The problem is mathematically formulated as two mixed-integer programming models. Three families of valid inequalities are introduced to strengthen the models. An adaptive large neighborhood search (ALNS) algorithm with an embedded decoding mathematical model is proposed to solve realistic-sized instances of the problem. Numerical experiments based on a large number of benchmark instances indicate that the inequalities are valid and that the model based on IF-THEN constraints shows excellent performance. The ALNS algorithm outperforms the strengthened mathematical model considering large-sized instances. The multi-trailer mode is more suitable if customers are clustered in the area far away from the depot when compared to the single-trailer mode.

Rolling-horizon approach in solving dynamic multisize multi-trailer container drayage problem

We consider the multisize multi-trailer drayage problem in which dynamism is treated by the re-optimization concept. Information on vehicles' current positions and their distances to all task locations are available in real-time. All vehicles in the fleet can be of longer combination sizes. The results of this study provide an efficient solution approach for solving real-size problem instances in a dynamic surrounding of container drayage processes. The solving approach is based on the implementation of a variable neighborhood search based heuristic algorithm. The testing results confirmed the benefits of using longer combination size vehicles by indicating potential reductions in overall traveled distances for up to approximately 30%, and in a total number of required trips for up to approximately 40%. Eventually, we tested the effects of implementing an assignment strategy in which an active task can be taken away from the assigned vehicle before it starts container (un)loading at the task's premise. That strategy proved to reduce tasks' lateness aspect of the problem but on the cost of increased traveling distances.

The Multi-Trip Container Drayage Problem with Empty Containers Management in a Dry-Port Setting

The Multi-Trip Container Drayage Problem with Empty Containers Management in a Dry-Port Setting

An Exact Algorithm for the Multi-Trip Container Drayage Problem with Truck Platooning

An Exact Algorithm for the Multi-Trip Container Drayage Problem with Truck Platooning

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

Container drayage operations with tractor-trailer distinction and trailer reposition exist widely in real-world logistics yet have seldom been reported in the literature. This paper studies a container drayage problem with trailer reposition (CDPTR), in which trailers might be provided at pickup locations and kept at delivery locations. A two-stage solution framework is adopted to solve the problem. The first stage generates tractor schedules to maximize profit, and the second stage aims to ensure sufficient trailers at each trailer depot during the container drayage operation. A hybrid large neighborhood search and tabu search (LNS-TS) heuristic is developed to solve the problem. We construct 72 instances with different characteristics, for example, time window duration, initial trailer resource and distribution, and the number of requests. Results show that our solutions outperform the solutions obtained from the solver. A detailed analysis of the results also reveals several insights for container drayage operators.