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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.

Numerous studies have proposed the use of a Truck Appointment System (TAS) to alleviate traffic congestion at logistics sites. Unfortunately, the implementation of such a system was often optimized based on the interest of a single stakeholder. Meanwhile, long truck queues have been observed in many chemical plants. This study aims to evaluate the TAS performances to mitigate traffic congestion in chemical plants from the multi-stakeholder perspective. We proposed a joint-optimization model to accommodate various interests on the site. An improved fluid-flow approximation was developed to estimate the time-dependent performance of the system. The results suggest that the benefit of TAS is mostly enjoyed by the site manager through the reduction of site overtime, while the benefits for trucking companies are found to be marginal. Through numerical experiments, we show that the proposed joint-optimization model is effective in redistributing the benefits of TAS across the stakeholders, while keeping the total logistics costs to a minimum.

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.

In Container Terminals (CTs), many containers are daily delivered by a large number of External Trucks (ETs) which lead to several issues such as long waiting times at gates and yards, harmful emis...

This paper proposes a novel approach for designing a Truck Appointment System (TAS) intended to serve both the maritime container terminal operator and drayage operators. The aim of the proposed TAS is to minimize the impact to both terminal and drayage operations. In regard to terminal operations, the TAS seeks to distribute the truck arrivals evenly throughout the day to avoid gate and yard congestion. In regard to drayage operations, the TAS explicitly considers the drayage truck tours and seeks to provide appointment times such that trucks do not have to deviate greatly from their original schedule. The proposed TAS is formulated as a mixed integer nonlinear program (MINLP) and the model is solved using the Lingo commercial software. Experimental results indicate that the proposed TAS reduces the drayage operation cost by 11.5% compared to a TAS where its aim is only to minimize gate queuing time by making truck arrivals uniform throughout the day.

Aiming at the truck scheduling problem between the outer yard and multi-terminals, the appointment optimization model of truck is established. In this model, the queue time and the operation time of truck during the appointment period of different terminals are different. Under the restriction of given appointment quotas of each appointment period, determine the arrival amount of trucks in each appointment period. The goal is to reduce carbon emissions and total costs, improve the efficiency of truck scheduling. To solve this model, hybrid genetic algorithm with variable neighborhood search was designed. Firstly, generate chromosomes, and the front part of the chromosome represents the demand for 40 ft containers and the back part represents the demand for 20 ft containers. Then, the route is generated according to the time constraint and appointment quotas of each appointment period. Finally, the neighborhood search strategy is adopted to improve the solution quality. The validity of the model and algorithm were verified by an example. A low-carbon scheduling scheme was obtained under truck appointment system. The results show that the scheduling scheme under truck appointment system uses fewer trucks, improves the efficiency of delivery, reduces the total costs, and it takes into account the requirements of low carbon.

Port terminals consist of two interfaces for transferring cargo among transport modes: (1) the seaside or quayside interface and (2) the landside interface. At the seaside interface, cargo is loaded and unloaded from the vessels and stored temporarily at the yard. Landside operations consist of receiving and dispatching cargo from external trucks and rail. The increasing volumes of international trade are demanding more efficient cargo handling throughout the port logistic chain and coordination with the hinterland, hence attracting more attention from both practitioners and researchers on the landside interface of ports. Due to the high variability of truck arrivals with a significant concentration at peak hours, congestion at the access gates of ports and an unbalanced utilization of the resources occur. Truck appointment systems (TAS) have already been implemented in some ports as a coordination mechanism to reduce congestion at ports, balance demand and capacity, and reduce truck turnaround times. Based on the current situation faced by the Port of Arica, Chile, this paper aims to analyze potential configurations of a TAS and evaluate its impacts on yard operations, specifically in the reduction of container rehandles, as well as truck turnaround times. For this, a discrete-event simulation model and a heuristic procedure are proposed and experimentation is performed using historical data from the port terminal. Results indicate that implementing a TAS may significantly benefit yard operations in terms of reducing container rehandles as well as truck waiting times.

Appointment systems for truck arrivals at container terminals have been applied in many ports to reduce truck congestion. This study suggests a new appointment process by which trucking companies and terminals collaboratively determine truck operation schedules and truck arrival appointments. This study formulates a mathematical model involving a sub-problem for each trucking company to determine the optimal dispatching schedules for trucks and the other sub-problem for the terminal to estimate the expected truck system time in each time interval. An iterative collaboration process is proposed based on a decomposed mathematical formulation. Numerical experiments are conducted to investigate the performance of the decision process and the robustness of the process in practical operation conditions.

Optimization of freight logistics is crucial for economic development. Technology intrusion in logistics sector leads it to a new height. However, the major problem persists with freight trucks, either business-to-business (B2B) or owner-driver, is running back empty after they deliver their primary freight to destination. For enhancing earning, freight driver or managing company can leverage the use of an information system for finding out the customer in the return trip as well. Hence, in this paper, we first examine different aspects related to state of art and practice, for return trip truck appointment system. Further, we provided a modular framework for the system. The model of the system is so adapted that freight truck and customer, both behave as a resource to each other, equally likely so that ethics of trip-deal remain protected. The notification is generated according to involve intricacies like internet data connection fluctuation and driver illiteracy.

Disruption often occurs in both land-side and sea-side operations at a container terminal, causing substantial interference of scheduled operations and poor green performance. The land-side disruption is rarely researched, and this paper addresses the ordinary level of such disruption where typically some truck arrivals deviate from their schedule in the appointment system. The aim is to find a response strategy that can maintain high resilience ability of the system in neutralizing the impact of disruptions. First, we consider different levels of late or early arrivals, as well as non-appointed arrivals at a container terminal that is running an appointment system. Second, we propose some response strategies to cope with different levels of disruptions, and evaluate their resilience ability with two Key Performance Indicators (KPIs): total waiting time of on-time trucks and total idling emissions of all trucks, in order to balance the service quality to punctual arrivals and green performance of the whole system. Third, we conduct a sensitivity analysis using a discrete event simulation to understand the performance of the proposed strategies. Considering both KPIs, the best strategy in most scenarios is a combined one based on priority and yard-crane moving distance; its performance depends primarily on the concentration level of container locations and secondly on the system utilization. In the other scenarios that have low arrival punctuality, the best strategy could focus purely on yard-crane moving distance, especially when the first KPI is given lower weight than the second one.

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.

In Container Terminals, managing the external truck arrivals is one of the essential managerial activities that, typically, the terminal operators conduct continuously on the short term using a Truck Appointment System (TAS). However, the trucking companies are influenced by the developed appointments and schedules which affect their operations and resources. This paper introduces a collaborative TAS to enable the trucking companies and container terminals to share making the decisions on scheduling the external truck appointments. This paper proposes a Dynamic Collaborative Truck Appointment System (DCTAS) which adopts a simulation optimization approach which integrates a discrete event simulation model with a mixed integer programming model. The DCTAS considers some realistic parameters that enhance the development of reliable appointments that fit the dynamic and stochastic circumstances of container terminals environment. The performance of the developed DCTAS is investigated by solving a numerical instance. The results show that both the container terminals and trucking companies can gain benefits from using the DCTAS. Yard efficiency performance indicators such as the maximum queue lengths, average waiting times, maximum waiting times and average truck turn times are improved using the developed appointment system.

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.

Port-related emissions are a growing problem for urban areas often located directly next to maritime container terminals highly frequented by container trucks. Empty truck trips, caused by a lack of coordination among truckers, are responsible for a significant share of these emissions, and collaboration among truckers is seen as the major opportunity to address this issue. Truck appointment systems (TASs) schedule truck activities and enable collaboration for transportation between terminals and client locations. The aim of this work is to introduce a collaborative planning model to be operated within a TAS and to investigate its impact on emission and cost objectives. Starting with a review of requirements for a TAS with collaboration, an optimization model based on the multiple traveling salesman problem with time windows is developed, leveraging collaboration to reduce costs and emissions. The results for a real-world case demonstrate that the developed approach provides appropriately coordinated truck schedules and effectively reduces truck emissions and costs.

This study discusses the landside operation and presents the application of Truck Appointment System (TAS) and lane segmentation strategy through discrete event simulation in Terminal 3 Ocean-going PT Pelabuhan Tanjung Priok. This study aims to analyze the operation planning system in Terminal 3 Ocean-going and identify problems that exist on terminal 3. This study also seeks to examine the landside operation process and offer alternative scenarios to solve the congestion problem in the terminal. Alternative scenarios provided are a combination of Truck Appointment System (TAS) and lane segmentation strategy. Truck Appointment Strategy (TAS) with 100% utilization is proved to reduce average total time in the system by 76% and reduce average total time in peak days by 88%, from 151.5 minutes to 38.4 minutes per truck. Truck Appointment System (TAS) is also proved to reduce queue length by 97%, from 162 trucks to 5 trucks.

Container terminals pay more and more attention to the service quality of inland transport modes such as tucks, trains and barges. Truck appointment systems are a common approach to reduce truck turnaround times. This paper provides a tool to use the truck appointment system to increase not only the service quality of trucks, but also of trains, barges and vessels. We propose a mixed integer linear programming model to determine the number of appointments to offer with regard to the overall workload and the available handling capacity. The model is based on a network flow representation of the terminal and aims to minimize overall delays at the terminal. It simultaneously determines the number of truck appointments to offer and allocates straddle carriers to different transport modes. Numerical experiments, conducted on actual data, quantify the benefits of this combined solution approach. Discrete-event simulation validates the results obtained by the optimization model in a stochastic environment.