scholarly journals Integrated Yard Space Allocation and Yard Crane Deployment Problem in Resource-Limited Container Terminals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Caimao Tan ◽  
Junliang He

Yard storage space and yard crane equipment are the core resources in the container terminal yard area. This paper studies the integrated yard space allocation (outbound container space) and yard crane deployment problem in resource-limited container terminals where yard space and yard cranes are extremely scarce. Two corresponding counterstrategies are introduced, respectively, and the integrated problem is solved as mixed integer programming. The model this paper formulated considers the container volume fluctuation of the service line, and the objective is a trade-off between yard sharing space and terminal operation cost. In numerical experiments, this paper tries to reveal the management meaning in practical operation of container terminal and provides decision support for terminal managers; therefore a series of scenarios are presented to analyze the relations among the yard sharing space, the number of yard cranes, the size of yard subblock, and the cost of terminal operation.

2021 ◽  
Vol 11 (15) ◽  
pp. 6922
Author(s):  
Jeongmin Kim ◽  
Ellen J. Hong ◽  
Youngjee Yang ◽  
Kwang Ryel Ryu

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.


Author(s):  
Gamal Abd El-Nasser A. Said ◽  
El-Sayed M. El-Horbaty

Seaport container terminals are essential nodes in sea cargo transportation networks. In container terminal, one of the most important performance measures in container terminals is the service time. Storage space allocation operations contribute to minimizing the vessel service time. Storage space allocation problem at container terminals is a combinatorial optimization NP-hard problem. This chapter proposes a methodology based on Genetic Algorithm (GA) to optimize the solution for storage space allocation problem. A new mathematical model that reflects reality and takes into account the workload balance among different types of storage blocks to avoid bottlenecks in container yard operations is proposed. Also the travelling distance between vessels berthing positions and storage blocks at container yard is considered in this research. The proposed methodology is applied on a real case study data of container terminal in Egypt. The computational results show the effectiveness of the proposed methodology.


2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Luo ◽  
Hongying Fei ◽  
Dana Sailike ◽  
Tingyi Xu ◽  
Fuzhi Huang

“Double-Line Ship Mooring” (DLSM) mode has been applied as an initiative operation mode for solving berth allocation problems (BAP) in certain giant container terminals in China. In this study, a continuous berth scheduling problem with the DLSM model is illustrated and solved with exact and heuristic methods with an objective to minimize the total operation cost, including both the additional transportation cost for vessels not located at their minimum-cost berthing position and the penalties for vessels not being able to leave as planned. First of all, this problem is formulated as a mixed-integer programming model and solved by the CPLEX solver for small-size instances. Afterwards, a particle swarm optimization (PSO) algorithm is developed to obtain good quality solutions within reasonable execution time for large-scale problems. Experimental results show that DLSM mode can not only greatly reduce the total operation cost but also significantly improve the efficiency of berth scheduling in comparison with the widely used single-line ship mooring (SLSM) mode. The comparison made between the results obtained by the proposed PSO algorithm and that obtained by the CPLEX solver for both small-size and large-scale instances are also quite encouraging. To sum up, this study can not only validate the effectiveness of DLSM mode for heavy-loaded ports but also provide a powerful decision support tool for the port operators to make good quality berth schedules with the DLSM mode.


2013 ◽  
Vol 446-447 ◽  
pp. 1334-1339 ◽  
Author(s):  
Seyed Hamidreza Sadeghian ◽  
Mohd Khairol Anuar Bin Mohd Ariffin ◽  
Say Hong Tang ◽  
Napsiah Binti Ismail

Automation of the processes at the quays of the world's large container ports is one of the answers to the required ever-increasing transshipment volumes within the same timeframe. For such purpose, using new generation of vehicles is unavoidable. One of the automatic vehicles that can be used in container terminals is Automated Lifting Vehicle (ALV). Integrated scheduling of handling equipments with quay cranes can increase the efficiency of automated transport systems in container. In this paper, an integrated scheduling of quay cranes and automated lifting vehicles with limited buffer space is formulated as a mixed integer linear programming model. This model minimizes the makespan of all the loading and unloading tasks for a pre-defined set of cranes in a scheduling problem.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wang ◽  
Xiaoning Zhu ◽  
Zhengyu Xie

Efficient storage strategy of railway container terminals is important in balancing resource utilization, reducing waiting time, and improving handling efficiency. In this paper, we consider the formulation and solution algorithm for storage space allocation problem of inbound containers in railway container terminal. The problem is formulated as two-stage optimization models, whose objectives are balancing the workload of inbound containers and reducing the overlapping amounts. An algorithm implement process based on rolling horizon approach is designed to solve the proposed models. Computational experiments on an actual railway container terminal show that the proposed approach is effective to solve space allocation problem of inbound container and is significant for the operation and organization of railway container terminals.


Author(s):  
Lingxiao Wu ◽  
Shuaian Wang

This paper discusses tactical joint quay crane (QC) and yard crane (YC) deployment in container terminals. The deployments of QCs and YCs are critical for the efficiency of container terminals. Although they are closely intertwined, the deployments of QCs and YCs are usually sequential. This paper proposes a mixed-integer programming model for the joint deployment of QCs and YCs in container terminals. The objective of the model is to minimize the weighted vessel turnaround time and the weighted delayed workload for external truck service in yard blocks, both of great importance for a container terminal but rarely considered together in the literature. This paper proves that the studied problem is NP-hard in the strong sense. Case studies demonstrate that the proposed model can obtain better solutions than the sequential method. This paper also investigates the most effective combinations of QCs and YCs for a container terminal at various demand levels.


2021 ◽  
Vol 9 (5) ◽  
pp. 527
Author(s):  
Armi Kim ◽  
Hyun-Ji Park ◽  
Jin-Hyoung Park ◽  
Sung-Won Cho

The rapid increase in international trade volume has caused frequent fluctuation of the vessels’ arrival time in container terminals. In order to solve this problem, this study proposes a methodology for rescheduling berth and quay cranes caused by updated information on the arrival time of vessels. A mixed-integer linear programming model was formulated for the berth allocation and crane assignment problem, and we solved the problem using a rolling-horizon approach. Numerical experiments were conducted under three scenarios with empirical data from a container terminal located in Busan, Korea. The experiments revealed that the proposed model reduces penalty costs and overall delayed departure time compared to the results of the terminal planner.


Author(s):  
Baicheng Yan ◽  
Xiaoning Zhu ◽  
Li Wang ◽  
Yimei Chang

In this paper, the integrated scheduling of handling equipment at the railway handling area in container terminals is studied, where rail-mounted gantry cranes, internal trucks, and reach stackers are deployed. In the course of the handling operation, loading and unloading containers are handled simultaneously. The handling process is first studied and some scheduling schemes are put forward. Based on the analysis, the problem is formulated as a mixed-integer programming model, with the objectives of minimizing the makespan and the total waiting time of all equipment. Then, to solve the problem, a genetic algorithm is employed, where the first available machine rule is applied in the selection of trucks and reach stackers. Sets of numerical experiments are conducted to verify the effect of the proposed algorithm. Based on the results of experiments, some key indicators are calculated and the effects of different equipment configuration schemes are studied.


2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Meisu Zhong ◽  
Yongsheng Yang ◽  
Yamin Zhou ◽  
Octavian Postolache

With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.


2021 ◽  
Vol 13 (3) ◽  
pp. 1181
Author(s):  
Bowei Xu ◽  
Xiaoyan Liu ◽  
Yongsheng Yang ◽  
Junjun Li ◽  
Octavian Postolache

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.


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