Scheduling truck arrivals for efficient container flow management in port logistics

A decision support system for a capacity management problem at a container terminal

Due to mega-ships, increasing container throughput, and nonuniform truck arrivals, many container terminals face challenges of unbalanced workloads of yard equipment, shortage of equipment resources in peak hours, and congestion problem. To solve such issues, we propose a mixed-integer bilevel programming model to optimize the vessel-dependent time windows for inbound trucks and yard crane deployment simultaneously. In the proposed bilevel model, the upper level aims to minimize the total truck waiting time at the container terminal gate and yard, while the lower level is formulated to minimize the total workload overflow to next shift in the whole container yard. The optimal yard crane deployment obtained in the lower level will transfer to the upper level problem to determine the waiting time of trucks in the yard and then affect the truck arrivals pattern. To solve the model, a hybrid algorithm—called hybrid genetic algorithm, based on collective decision optimization—is put forward by combining the genetic algorithm and the collective decision optimization algorithm. Numerical experiments are conducted to validate the proposed approach is effective to simultaneously flatten truck arrivals and improve the efficiency of yard cranes. The proposed approach can significantly reduce container terminals’ truck waiting time.

Truck congestion is one of the most critical problems that affect many port realities around the world. To alleviate the truck congestion problem in container terminals, several approaches have been proposed and implemented. One of them refers to Truck Appointment Systems (TASs), which are technological platforms aiming to coordinate truck flows at ports, by supporting the scheduling of truck arrivals and truck operations. In this paper an integer programming model is formulated to schedule the operations of trucks that have to pick up or deliver containers in a container terminal operating with a TAS. The mathematical model seeks to assign appointments to the different time slots composing the gate opening time, with the goal of optimizing terminal resources, guaranteeing a predefined service level to haulers (i.e. maximum turnaround times in the container terminal), while minimizing congestion inside the terminal. The validity of the proposed optimization approach is highlighted by an experimental campaign based on real data sets provided by some Italian and Mexican container terminals. The obtained results highlight that the proposed model is efficient in reducing waiting times of trucks, improving both the operation efficiency and the accuracy of resource allocation in container terminals. Particularly, it can be observed that the terminal vehicle capacity and the truck arrival pattern are the most significant factors that affect the total truck delay.

Drayage is a critical link in the intermodal supply chain. In the past decade, a sharp growth in drayage activity has occurred at major container ports in the United States. Because of the high volume of dray-age trucks arriving at container terminals, the trucks often experience significant delays at the terminal gates. Reducing this delay is necessary to improve the efficiency of drayage operations and the entire freight supply chain and to reduce the emissions from diesel-fueled trucks. This study developed a planning-level tool that can be used by design engineers, terminal operators, port authorities, and transportation planners to assess the effectiveness of gate layouts and determine the optimal layout for marine container terminals. Specifically, the analytical tool can be used to determine the average truck queuing time for a given gate con-figuration and to determine how many service gates and queuing lanes are needed to achieve a desired level of service for a given truck arrival rate and truck service rate. This tool accounts for the non deterministic nature of truck arrivals and service times. Specifically, the tool takes into account that trucks arrive as a Poisson stream, a truck is served by one of n gates, and gate service times are independent and identically distributed random variables. In this paper, example applications and results are presented and discussed. In addition, the setup of the online tool is described.

Due to the globalization, international logistics becomes to rely more on the maritime transportation than ever before. This presentation discusses various research issues in field of maritime logistics and various cases of applications of IT to container terminals. The issues include port logistics, container transportation, and maritime transportation systems. Various research topics on the optimization of maritime logistics are introduced in more detail, including topics on the design and operation container terminals, empty container management, the design and operation of container transportation systems. Some approaches to solve the problems, which have been developed in Korea, will be introduced. We also introduce the case of investigating new pervasive computing technologies of RFID, RTLS, and mesh networking for improving the efficiency of container port terminals. Unfortunately, container terminal is a harsh environment in which wireless communication would be unstable. To address these challenges, a case of developing the mesh network protocol based real time locating system is introduced for building smart container terminal management. For collecting real-time information of RFID tags, RTLS tags, and sensor tags, it is required to implement an integrated middleware platform which is mainly used to realize synchronization of port logistics. It will be shown that a new breakthrough of optimizing the port logistics can be achieved for realizing smart container terminals with the help of the integrated middleware platform for supporting RFID, RTLS, and sensor tags altogether.

Managing truck arrivals with time windows to alleviate gate congestion at container terminals

This paper quantifies the benefits to drayage trucks and container terminals from a data-sharing strategy designed to improve operations at the drayage truck–container terminal interface. This paper proposes a simple rule for using truck information to reduce container rehandling work and suggests a method for evaluating yard crane productivity and truck transaction time. Various scenarios with different levels of information quality are considered to explore how information quality affects system efficiency (i.e., truck wait time and yard crane productivity). Different block configurations and truck arrival rates are also investigated to evaluate the effectiveness of truck information under various system configurations. The research demonstrates that a small amount of truck information can significantly improve crane productivity and reduce truck delay, especially for those terminals operating near capacity or using intensive container stacking, and that complete truck arrival sequence information is not necessary for system improvement.

The truck scheduling problem in container terminals has become quite challenging due to the continuous growth of the world seaborne trade. Proper planning of the arrival of trucks to the terminal gate is necessary to limit truck congestion, not only inside the terminal but also in the surrounding area. One of the methodologies that are implemented to manage the chaotic arrival of trucks is the implementation of a Truck Appointment System (TAS). A TAS fosters the endeavors of solving the random arrival of trucks that causes higher truck densities, long possible turnaround times and excess penalties. In this paper, an integer programming model is formulated to minimize the truck turnaround time manifested by the total delay of the trucks arriving in the terminal to pick up/deliver containers from/to the terminal. Various scenarios are investigated, and the obtained results show that the TAS reduces the delay of the trucks and the turnaround time. According to the inputs of the scenarios, the tardiness per truck ranges from 0 to 3.5 hours. All the scenarios were solved in less than one seconds.

Purpose In Industry 4.0 era, many existing port logistics systems are inconsistent, old and ineffective and it restricts the effective operations of port logistics. The study aims to understand the issues faced by the players/actors of port logistics in the Industry 4.0 era for emerging economies and to develop a conceptual framework for managing the port logistics issues associated with it and by providing their possible solutions.Design/methodology/approach The study is divided into two parts, first part deals with identifying the major port logistics issues in Industry 4.0 era for emerging economies. It is achieved by conducting a semi-structured interview during the field visit to one of the major container handling ports in India. Second, the study adopts Soft System Methodology (SSM) to understand the issues and challenges faced by various actors of port logistics in the Industry 4.0 era and uses CATWOE analysis to identify the root causes.Findings Issues related to loading/unloading, transit, storage (warehouse), customs clearance, regulatory authorities, port management unit and inland transport connection providers are considered in the study and using SSM a final implementable model has been developed. This study focuses on analyzing and understanding the complete communication and organization structure of the port logistics system. The study identifies the major issues, various inefficiencies and root causes faced by various actors of port logistics during information sharing, cargo movement, the arrangement of the cargo shipments, etc. Further, the study develops a final implementable model by combining the delivery system, criteria system and Industry 4.0-enabled system.Research limitations/implications The study enables concerned authorities like state government, central government and policymakers to have a profound understanding of the issues faced by the actors of the port logistics system. The study brings out valuable insights that help managers and stakeholders to make informed decisions for managing the port logistics issues and develop necessary policies aimed to deliver the cargoes in right place at right time. The current study also has some limitations because of sensitivity associated with concerned areas, due to its confidentiality, lack of availability of complete data and the nonsharing attitude of respondents. Further, the study was conducted only for private container shipping terminals and public container terminals were not included.Originality/value This research analyzes the port logistics sector as a whole system through SSM to identify issues and challenges faced by various actors of port logistics for emerging economies in the Industry 4.0 era. The study develops a comprehensive and integrated framework for reducing the unpredictability of costs and time for key processes. Further, the framework creates a transparent platform and helps in bringing standardization to ports.

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice.

Recent trends in port performance improvement include the coordination of intermodal transport logistics to reduce congestion and inefficiencies generated at the gates of the terminals. Congestion at the gate of a terminal generates several problems such as pollution and long waiting times for truck carriers. As part of the strategies and best practices to reduce congestion, some ports worldwide have implemented advanced booking systems in order to coordinate truck arrivals and deliveries at the gate of their container terminals. We will refer to these systems as truck appointment systems. In general terms, a truck appointment system provides a mechanism where truck carriers coordinate their time of arrival at the container terminal based on an advanced booking. In this way, gate managers are able to better plan their port operations and equipment allocation, to reduce the waiting times of trucks and improve the turnaround time for container deliveries. In order to account for the real benefits of such systems, the particular conditions of each container terminal need to be considered. In this paper, a case study of a Chilean port terminal is analyzed. The aim is to provide recommendations that may reduce congestion and improve the container terminal´s gate control of truck arrivals, turnaround times and container deliveries by means of efficient lane segmentation policies. Several scenarios were examined under which different booking levels are considered for an environment in which the arrival of containers can vary significantly from day to day and on a seasonal basis. As a basis for our study a fractional factorial design is performed in order to analyze the impact of controllable factors on two service levels measures, which reduce the number of scenarios needed to obtain robust conclusions.

Truck appointment systems (TAS) are a well-recognized method to smooth the peaks in truck arrivals at seaport container terminals and thereby reduce operation costs for the terminals and waiting times for trucking companies. This study analyzes the influence of different drayage patterns on the success of a TAS at seaport container terminals by means of a discrete event simulation model. These drayage patterns vary in the percentages of transports between container terminals and container terminals and other logistics nodes. Past studies mainly focus on the general impact of TAS on container terminals or on trucking companies. Rarely different TAS' characteristics are analyzed regarding their impact on the performance. To the authors' knowledge, the influence of varying shares of different drayage origins and destinations have not been studied so far. The results show that the pattern of drayage transports has a considerable influence on the success of TAS. As the transport from terminals to logistics nodes and vice versa is bound to the opening hours of the logistics nodes, trucking companies often need to execute inter terminal transports at night.

Despite the number of sailings canceled in the past few months, as demand has increased, the utilization of ships has become very high, resulting in sudden peaks of activity at the import container terminals. Ship-to-ship operations and yard activity at the container terminals are at their peak and starting to affect land operations on truck arrivals and departures. In response, a Truck Appointment System (TAS) has been developed to mitigate truck congestion that occurs between the gate and the yard of the container terminal. The vehicle booking system is developed and operated in-house at large-scale container terminals, but efficiency is low due to frequent truck schedule changes by the transport companies (forwarders). In this paper, we propose a new form of TAS in which the transport companies and the terminal operator cooperate. Numerical experiments show that the efficiency of the cooperation model is better by comparing the case where the transport company (forwarder) and the terminal operator make their own decision and the case where they cooperate. The cooperation model shows higher efficiency as there are more competing transport companies (forwarders) and more segmented tasks a truck can reserve.

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.

This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine learning algorithms with discrete-event simulation to predict ship turnaround times using historical data. The proposed approach is empirically validated with data from the Algiers Port Container Terminal, achieving an exceptionally high predictive precision of 0.9991. Simulating terminal operations with both real and predicted data offers valuable insights into improving performance. The results demonstrate that minimizing empty trips and reducing the waiting times for handling equipment significantly enhance turnaround time. Additionally, optimizing terminal operations reduces carbon emissions, aligning with sustainable development objectives in port logistics. This study proposes a novel integration of machine learning and simulation, demonstrating its effectiveness in optimizing ship turnaround times and reducing carbon emissions. By integrating machine learning and discrete-event simulation, this research offers new perspectives on port logistics, contributing to the advancement of sustainable and efficient terminal operations.