Figure - uploaded by Veterina Nosadila Riaventin
Content may be subject to copyright.
Source publication
In container terminals at hub ports, each trucking company delivers a large number of containers every day. The truck drivers for the delivery operation may experience long waiting times when they arrive at peak hours. To reduce congestion, this study assumes that the container terminal charges a congestion fee (appointment charge), which depends o...
Context in source publication
Context 1
... a task for outbound containers may be assigned to more than one yard block in the space plan. More specifically, for a task of outbound containers, more than one block may have cost parameters other than M. Table 5 and Figure 3 illustrate the scheduling scheme in a tabular and network format, where each task may be assigned to either Block A or B. ...Citations
... Huiyun et al. [19] described this method as a two-dimensional decision-making system that balances truck arrivals over time to reduce spatial pressure at the terminal. Truck scheduling entails selecting time slots for truck arrivals, which can be determined by the trucking company and the port [25][26][27]. ...
Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.
... Pengambilan keputusan juga perlu dilakukan dalam waktu yang pendek, menyesuaikan rentang waktu yang tersedia, misalnya selama sistem tidak sedang beroperasi dalam permasalahan pengambilan keputusan yang statis . Analisa untuk pengambilan tersebut dapat dilakukan menggunakan berbagai pendekatan seperti aturan sederhana (Kim dkk., 2000), pemodelan matematis (Wicaksono dkk., 2019), algoritma (Riaventin & Kim, 2019), dan metaheuristik (Yu dkk., 2017). Penelitian terbaru menunjukkan potensi metode kecerdasan buatan untuk memahami hubungan kompleks antara variabel keputusan dengan nilai fungsi tujuan saat menyelesaikan permasalahan optimasi (Arnold & Sörensen, 2019). ...
Developing the new capital city of Indonesia is one of the government's main concerns. The development plan until 2045 also includes the logistics sector with detailed performance measures, such as the maximum length of travel for access to transportation modes, the use of low-emission transportation such as electric and hydrogen-based vehicles, etc. The readiness of the nation's capital in terms of disaster logistics and health is essential, given the great danger of disasters in Indonesia and current pandemic events such as COVID. Optimization of disaster and health logistics systems can be done using an operations research approach, but it needs to be combined with artificial intelligence to process large-scale data. These two approaches are combined in a digital twin concept that allows real-time analysis and decision-making whenever required. This study proposes a strategy for implementing operations research and artificial intelligence in disaster and health logistics in the digital twin concept, which is in line with the target of the new capital city of Indonesia that the government has formulated. This strategy can provide input for selecting research topics that will answer the real needs of the current government.
... (1) adapting and managing the infrastructure at the terminal gate and to the yard (new lanes, allocation of trucks to lanes, automation technologies) (Maguire, et al., 2012;Kulkarni, et al., 2017;Moszyk, Deja and Dobrzynski, 2021), (2) informing trucking companies/truck drivers of potential congestion through cameras, web pages, traffic light systems, information boards (Heilig and Voß, 2017;Riaventin and Kim, 2019) and ...
Purpose: Rising handling volumes and increasingly profound disruptions of global transport chains are placing severe stresses on container terminal processes. This affects landside handling in particular. In order to relieve this burden, more and more truck appointment systems have been introduced over the past 20 years, but they have only partially fulfilled the hopes placed in them. This study identifies the potential for improvement but also shows the limitations of this approach.
Methodology: In order to highlight the different approaches used both in academia and in practice to adapt truck appointment systems to the respective requirements and to arm them against disruptions, a structured literature review was conducted. A total of 136 scientific publications were classified and the results were evaluated in detail.
Findings: The developed solution approaches often only refer to individual sub-problems of container terminals instead of including the entire terminal or even the entire port with all its stakeholders. Furthermore, combinations of different methods are rarely used, where the weaknesses of individual methods could be compensated.
Originality: The massive disruption of the global transportation chain has created new challenges for truck appointment systems. A structured analysis of the possibilities and limits has not yet taken place from this point of view.
... After submitting an appointment request by a truck driver, the TAS is used to receive the workload levels from the terminal, then calculate the best schedule for drivers to come. Based on this step, the trucking companies organize their plans regarding the expected waiting times in the terminal [24]. The TAS enables the terminal managers to balance the workload level and reduce the congestion, emissions, and total costs resulting from excessive waiting times. ...
Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost.
... DES Exact Approx. DES (Schulte et al., 2017) x (Azab et al., 2017b) x x (Azab et al., 2017a) x x (Ramírez-Nafarrate et al., 2017) x x (Torkjazi et al., 2018) x (Zhang et al., 2018) x x (Azab et al., 2019) x x (Riaventin & Kim, 2019) x (Yi et al., 2019) x (Abdelmagid et al., 2020) x (Caballini et al., 2020) x (Azab & Morita, 2021) x Note: Approx.: approximate methods (i.e. heuristics, meta-heuristics, etc.), ABS: Agent-based simulation, DES: Discrete event simulation, ST: Stationary queuing system, and NST: Non-stationary queuing system. ...
The performance of a container terminal is typically evaluated using two main indicators: the vessel berthing time and the truck turnaround time. These two indicators are significantly influenced by the truck’s arrivals and departures to/from the container terminal. In the truck appointment scheduling problem, it is desired to designate a time slot for each truck to pick up/deliver a container from/to the container terminal while considering the objectives and constraints of the terminal and the trucking companies. Truck appointment systems are considered as a solution to control the truck arrivals and improve terminal efficiency. This paper presents a comprehensive review and analysis of the literature addressing the external truck appointment scheduling problem and presents possible directions for future research. After explaining the problem and its importance, the reviewed literature is classified based on three main categories: control and decision perspectives, modelling methodologies, and collaboration between stakeholders. Furthermore, the impact of implementing information and communication technologies on the external truck appointment scheduling problem is addressed. The paper covers the most recent publications and discusses their contributions. Besides, research gaps and future research directions are concluded.
... The authors used mathematical modeling, queuing models, simulation models, or combined approaches to solve the truck scheduling problem. Some authors obtained an exact solution to the problem, such as Abdelmagid, et al. [28] and Torkjazi, et al. [29], while others used approximate methods to find near-optimal solutions and benefit from reducing the computational time consumed to find optimal solutions, such as Yi, et al. [27] and Riaventin and Kim [30]. ...
... Also, the workload pattern of the terminal is not considered. Yi, et al. [27], Riaventin and Kim [30] proposed two recent studies aimed at reducing the total cost related to the trucking companies. However, they disregard the gate operations, which are an essential aspect that affects the whole terminal operational efficiency. ...
The rapid growth in the worldwide shipping industry has made the process of exchanging all kinds of goods easier. Containerized shipping has become the standard model of trading goods in global supply chains. The core advantages of depending on a sustainable freight means of transport can be summed up as cost-effectiveness, time-saving, and higher reliability. According to the International Chamber of Shipping, almost 90% of raw materials, foods, vehicles, manufacturing equipment, and products are shipped by sea around the world. Containerized trade using vessels is considered the lifeblood of the worldwide economy. Therefore, Container Terminals (CTs) have received a great deal of attention from researchers and responsible authorities who seek to manage their activities. CTs constitute a complicated network aiming to move goods among the world countries. Trucks are mainly responsible for the transportation operations from/to the container terminal. At the landside area, external trucks are inspected, and handling containers takes place.
Due to the increasing demand for transporting containers to/from the terminals, trucking companies devote their trucks to perform the loading and dropping off tasks. Having various trucking companies sending their trucks during the same time slots in a random fashion results in high arrival rates of trucks. Therefore, congestion levels rise at the terminal gates, causing excessive waiting times of the trucks and resulting in harmful emissions that increase global warming, as well as high operating costs for the trucking companies and reduced utilization of the terminal resources.
In light of the problem of overcrowded trucks in front of the terminal gate and in the yard area, container terminals implement Truck Appointment Systems (TAS). The terminal
managers are responsible for setting such appointments according to various considerations
(e.g., terminal workload, vessel berthing time, and quay cranes operation schedule, etc.). The idea is to alleviate the workload in high load time slots. Although truck appointment systems are beneficial for the trucking companies, there are some anticipated drawbacks. The main drawback is assigning the trucks to inconvenient appointments for the trucking companies. This is due to the overlap with other tasks that should be performed by the same trucking company. On the other hand, if the container terminal allows the trucks to arrive at the terminal randomly, the terminal managers will lose control of the terminal workload, and equipment utilization will be low.
By investigating the previous studies, it can be concluded that many operational constraints, time, and cost parameters related to the truck arrival scheduling problem in container terminals are not considered. This thesis introduces a comprehensive model to balance the trade-off between the objectives of the trucking companies and the CT considering the different constraints related to both stakeholders. Firstly, this thesis considers the synchronization between the yard and hinterland area by investigating the effect of scheduling trucks on both areas. Also, the formulated model aims at minimizing the total cost incurred by trucking companies to transport containers from/to the terminal. In addition, it presents a method for trucking companies to control and exploit their fleets of trucks optimally, which confer higher utilization. From the terminal’s perspective, the proposed model results in better workload distribution due to less truck densities inside the terminal. The obtained results show that applying the proposed model will allow the trucking companies to achieve the best use of their trucks by limiting their fleets’ volume. Despite the increase in the total costs and total truck turnaround times, the startup costs and cost per miles incurred by the trucking company are decreased. It can be concluded that the trade-off between the total cost and the fleet volume could be balanced to achieve the ultimate benefit for trucking companies. By performing a sensitivity analysis, the proposed model is found to be responsive to changes in the fleet size of the trucking companies. The trucking companies can dispense with some of their resources while performing the required tasks.
... Trucking companies pay the appointment charge for each arriving truck, which is proportional to the time for a truck remaining in the terminal. Riaventin and Kim (2018) first addressed the problem of scheduling appointments. They proposed a two-phase algorithm, in which the transportation problem is solved first and the resulting solution is modified to satisfy one additional constraint. ...
... Unlikely from previous studies which have dealt with modelling the waiting times considering appointments or collaboration among trucking companies by using appointment systems, this study addresses a scheduling method for appointments considering various practical constraints and cost terms from the viewpoint of a trucking company. Even though there are other previous studies on the scheduling of appointments Kim 2015a, 2016;Riaventin and Kim 2018), this study has contributions in the following aspects: firstly, this study attempts to provide an efficient algorithm to schedule a large number of appointments in a short time for trucking companies; secondly, this study considers various practical constraints and cost terms such as the available number of trucks, appointment quotas set by the terminal, detail situation of each yard block at each time window, and the flexibility of space allocation of outbound containers; thirdly, considering that the number of appointments is large, the effect of the number of appointments on the waiting time at the yard is directly considered during the scheduling, which has not been considered in the previous study (Riaventin and Kim 2018). ...
... Unlikely from previous studies which have dealt with modelling the waiting times considering appointments or collaboration among trucking companies by using appointment systems, this study addresses a scheduling method for appointments considering various practical constraints and cost terms from the viewpoint of a trucking company. Even though there are other previous studies on the scheduling of appointments Kim 2015a, 2016;Riaventin and Kim 2018), this study has contributions in the following aspects: firstly, this study attempts to provide an efficient algorithm to schedule a large number of appointments in a short time for trucking companies; secondly, this study considers various practical constraints and cost terms such as the available number of trucks, appointment quotas set by the terminal, detail situation of each yard block at each time window, and the flexibility of space allocation of outbound containers; thirdly, considering that the number of appointments is large, the effect of the number of appointments on the waiting time at the yard is directly considered during the scheduling, which has not been considered in the previous study (Riaventin and Kim 2018). ...
Trucking companies deliver a large number of containers every day to container terminals at hub ports. Truck drivers for the delivery operation can experience long waiting times when they arrive at peak hours. This study proposes a scheduling method for appointments that considers the cost of trucks staying in the terminal, demurrage cost, container delivery cost, number of appointments allowed at each time window and block, and number of trucks available during each time window. Unlike previous studies, this study considers the effects of the appointments on the waiting time at the terminal when the appointment schedule is constructed. This paper introduces a mathematical formulation and a heuristic algorithm based on the Frank–Wolfe algorithm to solve the problem within a reasonable computational time. Numerical experiments are conducted to compare the proposed algorithm with the other heuristic approaches and analyze the effects of the appointments using empirical data. In addition, the impact of appointments by multiple trucking companies is examined.
Purpose: Truck appointment systems (TAS) are a widely used method to alleviate peaks in truck arrivals at container terminals in seaports and in the hinterland. One big advantage is the opportunity to reduce operation costs for the terminals and the truck queue length in front of the terminal gate. This study aims to analyze and clas-sify different approaches used in science and industry to determine the quota of al-lowed trucks per time window.
Methodology: A comprehensive systematic literature analysis is applied to identify the different approaches to determine the quota of time windows in science and in industry.
Findings: The results of the study show that many approaches have been based on experience and are mostly used to improve individual terminals rather than the port as a whole. Methods used to improve and analyze interrelationships are mainly methods of mathematical optimization and simulation.
Originality: The question under consideration was mostly only marginally consid-ered in existing investigations, even though it has a major impact on the success of a TAS. Furthermore, only individual solutions have been examined so far and not the suitability of the approaches compared.
Truck arrivals at distribution centres (DCs) follow two main working practices: either working on a first-come-first-served principle or, alternatively, using time slots. While the former results in time losses due to unanticipated waiting time, the latter should generate less time losses as more time is spent scheduling beforehand. Yet, although a schedule is made, in reality ad-hoc changes still require that sometimes orders are served on a first-come-first-served basis, making the use of slots appear as superfluous. This research looks into the reasons why using time slots does not bring the expected benefits and collects data to show the time spent on scheduling (planning and dispatching) procedures for both working practices. To do so, time measurements are carried out to reflect both the duration of each procedure and the operational delay generated to trucks. An in-depth analysis is carried out on data from three distribution centres and five trucking companies that call those DCs. This research shows that, although slot booking systems are in use, they do not bring the expected time savings. This shortcoming happens due to the incompatibility of the multiple ICT systems in use for slot bookings at different DCs and the variety of rules that need to be met when rebooking slots. As follow-up, this research proposes an overarching solution defined as a Dynamic Slot booking System (DSS) that can address these issues. The conceptual design of a DSS that is proposed makes use of data and algorithms to anticipate ad-hoc changes. This DSS closes the gap between fragmented information available at DCs for slot use, planning of trucking companies, real-time time delay databases and the operational planning needs. Further results are given with regard to the time savings that can be made when a DSS is rolled out. Average-size carriers, running on average 75 trucks, can save between 885 and 992 min per day due to automatic rescheduling of trucks that currently arrive earlier or later than the planned slots. Moreover, for trucks that arrive later than the planned slots, DCs that handle 72 trucks a day can save on a daily basis around 50 min of labour. This labour is currently used for the manual follow up of the planning and for processing ad-hoc changes.
