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 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.

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

A novel optimisation model in the collaborative supply chain with production time capacity consideration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Ridwan Andi Purnomo ◽  
Adhe Rizky Anugerah ◽  
Salvia Fatma Aulia ◽  
Abdullah ‘Azzam
Keyword(s):  
Supply Chain ◽  
Low Cost ◽  
Transportation Cost ◽  
Production Costs ◽  
Furniture Industry ◽  
Production Time ◽  
Content Type ◽  
Total Cost ◽  
Penalty Cost ◽  
Optimisation Model

Purpose This study aims to propose an optimal procurement model of the collaborative supply chain in the furniture industry. The final output is the total cost minimisation to produce a furniture product that covers material cost, processing cost, transportation cost and holding cost. Therefore, if companies can give the best value to customers at a low cost, then competitive advantages can be achieved. Design/methodology/approach A genetic algorithm (GA) as a metaheuristic approach was used to solve problems in this research. The optimisation was initiated by developing a mathematical model to formulate the objective function. Findings Based on the case study, the proposed GA model was able to reduce the total cost of production. The cost was reduced by 73.09% compared to the existing system. Besides, the production time of the proposed model is within the capacity of both companies; hence, no penalty cost is imposed. Practical implications The proposed GA model has been implemented and tested to minimise production costs in the Indonesian furniture industry. Originality/value To the best of author knowledge, there is no research has proposed an optimisation model that incorporates production cost, transportation cost and production time capacity together in the collaborative supply chain. This research is the first to collaborate these factors using GA in the furniture industry.

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.

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohsen Abdoli ◽  
Mostafa Zandieh ◽  
Sajjad Shokouhyar
Keyword(s):  
Waiting Time ◽  
Queuing System ◽  
System Capacity ◽  
Outpatient Clinics ◽  
Effective Parameters ◽  
Queuing Model ◽  
Public And Private ◽  
Content Type ◽  
Total Cost ◽  
Patient Waiting Time

Purpose This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized. Design/methodology/approach In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model. Findings In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment. Originality/value Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

Model for economic replacement time of mining production rigs including redundant rig costs

2015 ◽  
Vol 21 (2) ◽  
pp. 207-226 ◽  
Author(s):  
Hussan Saed Al-Chalabi ◽  
Jan Lundberg ◽  
Majid Al-Gburi ◽  
Alireza Ahmadi ◽  
Behzad Ghodrati
Keyword(s):  
Regression Analysis ◽  
Mining Industry ◽  
Content Type ◽  
Total Cost ◽  
Effect Regression ◽  
Negative Effect ◽  
Optimisation Model ◽  
Artificial Neural Network Ann ◽  
Drilling Rigs ◽  
Replacement Time

Purpose – The purpose of this paper is to present a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine. Design/methodology/approach – In total, four years of cost data are collected. Data are analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs. Findings – The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT. Practical implications – The proposed approach can be used by the decision maker in determining the ERT of production machines which used in mining industry. Originality/value – The research proposed in this paper provides and develops an optimisation model for ERT of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.

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 Lane Allocation Optimization in Container Seaport Gate System Considering Carbon Emissions

2021 ◽  
Vol 13 (7) ◽  
pp. 3628
Author(s):  
Zhihong Jin ◽  
Xin Lin ◽  
Linlin Zang ◽  
Weiwei Liu ◽  
Xisheng Xiao
Keyword(s):  
Branch And Bound ◽  
Carbon Emissions ◽  
Integer Program ◽  
Arrival Times ◽  
Proposed Model ◽  
Gate System ◽  
Appointment System ◽  
Truck Appointment System ◽  
Gate Congestion ◽  
Allocation Decisions

Long queues of arrival trucks are a common problem in seaports, and thus, carbon emissions generated from trucks in the queue cause environmental pollution. In order to relieve gate congestion and reduce carbon emissions, this paper proposes a lane allocation framework combining the truck appointment system (TAS) for four types of trucks. Based on the distribution of arrival times obtained from the TAS, lane allocation decisions in each appointment period are determined in order to minimize the total cost, including the operation cost and carbon emissions cost. The resultant optimization model is a non-linear fractional integer program. This model was firstly transformed to an equivalent integer program with bilinear constraints. Then, an improved branch-and-bound algorithm was designed, which includes further transforming the program into a linear program using the McCormick approximation method and iteratively generating a tighter outer approximation along the branch-and-bound procedure. Numerical studies confirmed the validity of the proposed model and algorithm, while demonstrating that the lane allocation decisions could significantly reduce carbon emissions and operation costs.

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