Integrated of Berth Schedule and Quay Crane Allocation in Container Terminal

2014 ◽  
Vol 513-517 ◽  
pp. 1437-1444
Author(s):  
Ji Feng Qian ◽  
Xiao Ning Zhu ◽  
Yong Jun Liu
Keyword(s):  
Genetic Algorithm ◽  
Container Terminal ◽  
Planning Horizon ◽  
Optimization Method ◽  
Planning Model ◽  
Mathematical Planning ◽  
Integrated Optimization ◽  
Quay Cranes ◽  
Container Handling ◽  
Set Up

In order to shorten the total time of the ships in port at a planning horizon, an integrated optimization method for berth schedule and quay crane allocation was developed. Interrelation between berth and quay crane allocation was analyzed, sharing quay cranes with the adjacent berths was put forward, dynamically allocating the limited quay cranes in the scheduling of handling tasks on each ship, an integer mathematical planning model was set up, and a corresponding genetic algorithm was designed to solve the problem. The result suggests that the method can improve container handling efficiency effectively compared with that of separate allocation.

scholarly journals Optimization Method for Container Quay Crane Operation Based on Energy Saving

2018 ◽  
Vol 53 ◽  
pp. 01011
Author(s):  
Liu Leilei ◽  
Sun Lei ◽  
Zhao Dadi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Container Terminal ◽  
Optimization Method ◽  
Container Terminals ◽  
Energy Utilization ◽  
Quay Cranes ◽  
Actual Operation ◽  
Loading And Unloading ◽  
Reducing Energy

This paper studies the energy utilization of the quay crane, one of the major energy using equipment for the container terminal. We establish an optimization model for the loading and unloading of the quay crane, use the genetic algorithm to solve it, and verify the validity of the model by actual operation data of container terminals. The results show that the model has a significant effect on reducing energy consumption of quay cranes.

scholarly journals Automatic Procedures as Help for Optimal Cam Design

2006 ◽  
Author(s):  
Massimo Antonini ◽  
Alberto Borboni ◽  
Roberto Bussola ◽  
Rodolfo Faglia
Keyword(s):  
Genetic Algorithm ◽  
Soft Computing ◽  
Design Process ◽  
Model Identification ◽  
Optimization Method ◽  
Realistic Model ◽  
Design Problems ◽  
Soft Computing Techniques ◽  
Cam Follower ◽  
Set Up

In this work we suggest a synthesis of recent results obtained on the application of soft-computing techniques to solve typical automatic machines design problems. Particularly, here we show an optimization method based on the application of a specialized algorithms ruled by a generalized software procedures, which appears able to help the mechanical designer in the first part of the design process, when he has to choose among different wide classes of solutions. In this frame, among the different problems studied, we refer here about the choice of the best class of motion profiles, to be imposed to a cam follower, which must satisfy prefixed design specifications. A realistic behaviour of the system is considered and the parameter model identification is set up by a soft computing procedure. The design, based on theoretical knowledge, sometimes is not sufficient to fulfil desired dynamical performances, in this situation, a residual optimization is achieved with the help of another optimizing method. The problem of a cam-follower design is presented. A class of motion profiles and the best theoretical motion profile is selected by an evolutionary algorithm. A realistic model is considered and its parameter identification is achieved by a genetic algorithm. The residual optimization is achieved by a servomotor optimized by another genetic algorithm. Evolutionary approach is used during all the design process and, as was shown, it allows really interesting performance in terms of simplicity of the design process and in terms of performance of the product.

scholarly journals Integrated Optimization of Mixed Cargo Packing and Cargo Location Assignment in Automated Storage and Retrieval Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Bin Lei ◽  
Zhaoyuan Jiang ◽  
Haibo Mu
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Retrieval System ◽  
Packing Problem ◽  
Optimization Method ◽  
Integrated Optimization ◽  
Storage And Retrieval ◽  
Location Assignment ◽  
Automated Storage ◽  
Delivery Efficiency

To improve the delivery efficiency of automated storage and retrieval system, the problem of the integrated optimization of mixed cargo packing and cargo location assignment is addressed. An integrated optimization model of mixed cargo packing and location assignments with the shortest time for the stacker in a certain historical period is established and is transformed into a conditional packing problem. An improved hybrid genetic algorithm based on a group coding method is designed to solve the problem. When the initial population is generated, a new heuristic algorithm is designed to improve the convergence speed of the genetic algorithm considering the correlation and frequency of the goods outbound. A heuristic algorithm for a two-dimensional rectangular-packing problem is designed to determine whether a variety of goods can be mixed in packing. Taking actual data from an automated storage and retrieval system for an aviation food company as an example, the established model and design algorithm are verified and the influence of changes in the outbound delivery orders on the optimization result is analyzed. The results show that compared to the method of separate storage of goods based on cube-per-order index rules and a phased optimization method of mixed storage of goods, an integrated optimization method of mixed cargo packing and location assignment can improve the outbound delivery efficiency of the stacking machine by 11.43–25.98% and 1.73–5.51%, respectively, and reduce the cargo location used by 50–55% and 0–10%, respectively. The stronger the correlation of the goods leaving a warehouse, the greater the potential of the design method in this paper to improve the efficiency of the stacker.

scholarly journals Container Terminal Berth-Quay Crane Capacity Planning Based on Markov Chain

2021 ◽  
Vol 33 (2) ◽  
pp. 267-281
Author(s):  
Meixian Jiang ◽  
Guoxing Wu ◽  
Jianpeng Zheng ◽  
Guanghua Wu
Keyword(s):  
Markov Chain ◽  
Capacity Planning ◽  
Queuing Theory ◽  
Container Terminal ◽  
Transition Probability ◽  
Transfer Model ◽  
Planning Model ◽  
Steady State Probability ◽  
Quay Cranes ◽  
Time Operation

This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning.

A New Genetic Algorithm for Continuous Berth and Quay Crane Allocation Problem

2011 ◽  
Vol 97-98 ◽  
pp. 619-622 ◽  
Author(s):  
Na Li ◽  
Zhi Hong Jin ◽  
Erick Massami
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Container Terminal ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Improved Genetic Algorithm ◽  
Berth Allocation Problem ◽  
The Real ◽  
Quay Cranes ◽  
Quay Crane Assignment

The combined optimization of continuous berth allocation problem and quay crane assignment problem are solved. Considering the real constraints of container terminal, an improved genetic algorithm is proposed. The chromosome is composed of berthing time, berthing location and number of quay cranes. While in the following, specific quay cranes are fixed to assign to ships. Through comparisons with the former two literatures, the results are improved averagely by 33.78% and 28.57% respectively by the proposed genetic algorithm, which shows its effectiveness.

Integration and Optimization of Quay Cranes and Trucks at Container Terminal

2014 ◽  
Vol 505-506 ◽  
pp. 922-926 ◽  
Author(s):  
Qing Yu Zhao ◽  
Jin Xin Cao ◽  
Zhi Jun Gao
Keyword(s):  
Network Theory ◽  
Container Terminal ◽  
Optimization Method ◽  
Container Port ◽  
Queuing Network ◽  
Logistics System ◽  
Mathematical Algorithm ◽  
Quay Cranes ◽  
Port Logistics ◽  
Container Ports

Along with the rapid growth of world trade, new requirement for throughput efficiency of modern container port are put forward. The existing optimization method to research the transport of the container terminal is single and complex. So in this article, the quantitative integrated optimization of the quay cranes and the trucks is made and the research scale is enlarge, in order to improve the efficiency of the research results. A model is built based on queuing network theory, and the mathematical algorithm is used to solve the model. Some experiments are used to demonstrate the feasibility and effectiveness of this study. The result shows that developed a genetic algorithm get the most optimal ratio of resource, and through a series of numerical examples . Are given The method to match the quay cranes and the trucks at container port logistics system, obtained by the techniques and algorithms in this study, can improvs the comprehensive efficiency of container ports.

AUTOMOTIVE PARTS' LOADING OPTIMIZATION BASED ON IMPROVED QUADRATIC PARTICLE SWARM OPTIMIZATION

2010 ◽  
Vol 07 (04) ◽  
pp. 699-712
Author(s):  
JIAN LIU ◽  
CHUNYAN WU ◽  
XIANGYIN WANG ◽  
DEJIE YU
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Present Article ◽  
Particle Swarm ◽  
Optimization Method ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Automotive Parts ◽  
Set Up ◽  
Decision Elements

The present article designed a genetic quadratic particle swarm optimization (GQPSO). Aiming at the low particle diversity at the early searching stage of quadratic particle swarm optimization (QPSO), the method adopts mutation and exchanging and regenerating mechanisms from genetic algorithm so as to avoid premature convergence and improves optimization. Meanwhile, the present article gave a comprehensive consideration to decision elements such as cost, resources, and service in the process of automotive parts' logistics, transportation, and loading; a model of automotive parts' logistics, transportation, and loading optimization was set up. GQPSO was introduced for solutions. Simulation examples show that GQPSO improves the computational efficiency, significantly, and there is a higher probability of searching the global optimum. It provides optimization method for the automobile parts' logistics and transportation plans.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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