scholarly journals Truck Appointment System for Cooperation between the Transport Companies and the Terminal Operator at Container Terminals

2020 ◽  
Vol 11 (1) ◽  
pp. 168
Author(s):  
Hyeonu Im ◽  
Jiwon Yu ◽  
Chulung Lee
Keyword(s):  
Large Scale ◽  
Container Terminal ◽  
Container Terminals ◽  
The Past ◽  
Cooperation Model ◽  
Appointment System ◽  
New Form ◽  
Truck Appointment System ◽  
Import Container ◽  
Very High

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.

scholarly journals Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion

2021 ◽  
Vol 13 (3) ◽  
pp. 1181
Author(s):  
Bowei Xu ◽  
Xiaoyan Liu ◽  
Yongsheng Yang ◽  
Junjun Li ◽  
Octavian Postolache
Keyword(s):  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Urban Traffic ◽  
Container Terminals ◽  
Mixed Integer ◽  
Novel Approach ◽  
Appointment System ◽  
Truck Appointment System ◽  
Evening Peak ◽  
Operation Cost

Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.

scholarly journals Optimising truck arrival management and number of service gates at container terminals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chu Cong Minh ◽  
Nguyen Van Noi
Keyword(s):  
Time Slot ◽  
The United States ◽  
Container Terminals ◽  
Queuing Model ◽  
Content Type ◽  
Total Cost ◽  
Container Ports ◽  
Appointment System ◽  
Optimisation Model ◽  
Truck Appointment System

PurposeTruck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.Design/methodology/approachThe approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.FindingsThe results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.Originality/valueThe proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

scholarly journals Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Bin Li ◽  
Yuqing He
Keyword(s):  
Deep Learning ◽  
Container Terminal ◽  
Container Terminals ◽  
Hardware Platform ◽  
Logistics Network ◽  
Carbon Efficiency ◽  
The Past ◽  
Global Logistics ◽  
High Degree ◽  
Deep Learning Model

Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

Dispatching Strategies of Drayage Trucks at Seaport Container Terminals with Truck Appointment System

2018 ◽  
pp. 162-166 ◽  
Author(s):  
Ann-Kathrin Lange ◽  
Fredrik Branding ◽  
Tilmann Schwenzow ◽  
Constantin Zlotos ◽  
Anne Kathrina Schwientek ◽  
...  
Keyword(s):  
Container Terminals ◽  
Drayage Trucks ◽  
Appointment System ◽  
Truck Appointment System

scholarly journals Modeling and Design of Container Terminal Operations

2020 ◽  
Vol 68 (3) ◽  
pp. 686-715 ◽  
Author(s):  
Debjit Roy ◽  
René De Koster ◽  
René Bekker
Keyword(s):  
Large Scale ◽  
Container Terminal ◽  
Heavy Traffic ◽  
Building Blocks ◽  
Transport Processes ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Design Parameters ◽  
Container Terminal Operations ◽  
Terminal Operations

The design of container terminal operations is complex because multiple factors affect operational performance. These factors include numerous choices for handling technology, terminal topology, and design parameters and stochastic interactions between the quayside, stackside, and vehicle transport processes. In this research, we propose new integrated queuing network models for rapid design evaluation of container terminals with automated lift vehicles and automated guided vehicles. These models offer the flexibility to analyze alternate design variations and develop insights. For instance, the effect of different vehicle dwell point policies and efficient terminal layouts are analyzed. We show the relation among the dwell point–dependent waiting times and also show their asymptotic equivalence at heavy traffic conditions. These models form the building blocks for design and analysis of large-scale terminal operations. We test the model efficacy using detailed simulation experiments and real-terminal validation.

Design of a Truck Appointment System Considering Drayage Scheduling and Stochastic Turn Time

2021 ◽  
pp. 036119812110296
Author(s):  
Mohammad Torkjazi ◽  
Nathan N. Huynh
Keyword(s):  
Numerical Experiment ◽  
Stochastic Model ◽  
Stochastic Approach ◽  
Container Terminals ◽  
Actual Data ◽  
Operational Characteristic ◽  
Appointment Schedule ◽  
Appointment System ◽  
Series Of Experiments ◽  
Truck Appointment System

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.

scholarly journals Agent-based Truck Appointment System for Containers Pick-up Time Negotiation

2020 ◽  
Vol 14 (1) ◽  
pp. 81
Author(s):  
Fakhri Ihsan Ramadhan ◽  
Meditya Wasesa
Keyword(s):  
Container Terminal ◽  
Time Estimation ◽  
Arrival Rate ◽  
Turnaround Time ◽  
Arrival Times ◽  
Agent Based ◽  
Appointment System ◽  
Truck Appointment System ◽  
Trucking Companies ◽  
Truck Arrival

Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times.

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