scholarly journals An Optimization Model of Integrated AGVs Scheduling and Container Storage Problems for Automated Container Terminal Considering Uncertainty

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1904
Author(s):  
Wentao Jian ◽  
Jishuang Zhu ◽  
Qingcheng Zeng
Keyword(s):  
Optimization Model ◽  
Container Terminal ◽  
Operation Time ◽  
Triangular Fuzzy Number ◽  
Automated Guided Vehicles ◽  
Improved Genetic Algorithm ◽  
Running Time ◽  
Location Allocation ◽  
Storage Location ◽  
Maximum Completion Time

The running path of automated guided vehicles (AGVs) in the automated terminal is affected by the storage location of containers and the running time caused by congestion, deadlock and other problems during the driving process is uncertain. In this paper, considering the different AGVs congestion conditions along the path, a symmetric triangular fuzzy number is used to describe the AGVs operation time distribution and a multi-objective scheduling optimization model is established to minimize the risk of quay cranes (QCs) delay and the shortest AGVs operation time. An improved genetic algorithm was designed to verify the effectiveness of the model and algorithm by comparing the results of the AGVs scheduling and container storage optimization model based on fixed congestion coefficient under different example sizes. The results show that considering the AGVs task allocation and container storage location allocation optimization scheme with uncertain running time can reduce the delay risk of QCs, reduce the maximum completion time and have important significance for improving the loading and unloading efficiency of the automated terminal.

scholarly journals A Multilayer Genetic Algorithm for Automated Guided Vehicles and Dual Automated Yard Cranes Coordinated Scheduling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qianru Zhao ◽  
Shouwen Ji ◽  
Wenpeng Zhao ◽  
Xinling De
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Operating Time ◽  
Operation Time ◽  
Complex Model ◽  
Automated Guided Vehicles ◽  
Total Energy Consumption ◽  
Equipment Operation ◽  
Storage Location ◽  
Calculation Results

At present, a lot of studies on automatic terminal scheduling are aimed at the shortest operating time. An effective way to reduce the operating time is to increase the amount of operating equipment. However, people often ignore the additional costs and energy consumption caused by increasing the amount of equipment. This paper comprehensively considers the two aspects of the equipment operation time and equipment quantity matching. With the minimum total energy consumption of the operating equipment as the objective function, a cooperative scheduling model of Automated Guided Vehicles (AGVs) and dual Automated Yard Cranes (AYCs) is established. In the modelling process, we also considered the interference problem between dual Automated Yard Cranes (AYCs). In order to solve this complex model, this paper designs an improved multilayer genetic algorithm. Finally, the calculation results from CPLEX and a multilayer genetic algorithm are compared, and the effectiveness of the model and algorithm is proved by experiments. In addition, at the same time, it is proved that it is necessary to consider the interference problem of dual Automated Yard Cranes (AYCs), and the optimal quantity matching scheme for the equipment and the optimal temporary storage location is given.

scholarly journals Optimization of Storage Location Assignment in Tier-To-Tier Shuttle-Based Storage and Retrieval Systems Based on Mixed Storage

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bin Lei ◽  
Fangxin Hu ◽  
Zhaoyuan Jiang ◽  
Haibo Mu
Keyword(s):  
Optimization Model ◽  
Job Scheduling ◽  
Historical Period ◽  
Design Algorithm ◽  
Location Allocation ◽  
Storage And Retrieval ◽  
Retrieval Systems ◽  
Coding Method ◽  
Storage Location ◽  
Two Stages

To improve the efficiency of tier-to-tier shuttle-based storage and retrieval system (SBS/RS), the optimization problem of the location allocation based on the mixed storage of goods is proposed. Considering the effect of warehouse operation scheduling and batch outbound allocation on the location allocation, an optimization model with the shortest outbound time of all outbound orders in a certain historical period is established. The optimization model consists of two stages: location allocation and job scheduling. A two-layer genetic algorithm is designed to solve the model. The first layer is used to solve the location allocation, and the coding method is group coding; the second layer is used for job scheduling, and the coding mode is real number coding. When the population is initialized during the location allocation phase, the BFD algorithm is used to improve the convergence velocity of the algorithm. Taking the actual data of a tier-to-tier SBS/RS of an aviation food company as an example, the established model and design algorithm were verified, and the different batch intervals of each cargo space for storing different types of goods and outbound were analyzed. The optimization effects of the algorithm are compared, and the effects of considering the job scheduling and not considering the job scheduling on the location allocation are compared. The results show that based on the cargo allocation strategy of cargo mixed storage, the outbound efficiency can be improved by about 20%. Considering job scheduling, the efficiency of warehousing is improved by about 5% compared with the optimization of warehouse allocation without job scheduling, where the efficiency of delivery is increased by about 6%.

Optimization of Restoration Paths of Power System after Blackout

2013 ◽  
Vol 347-350 ◽  
pp. 1467-1472
Author(s):  
Wen Wei Huang ◽  
Gang Yao ◽  
Xiao Yan Qiu ◽  
Nian Liu ◽  
Guang Tang Chen
Keyword(s):  
Power System ◽  
Transmission Lines ◽  
Reactive Power ◽  
Minimum Spanning Tree ◽  
Operation Time ◽  
Combinatorial Problem ◽  
Weather Conditions ◽  
Recovery Process ◽  
Point Of View ◽  
Improved Genetic Algorithm

Optimization of restoration paths of power system after blackout is a multi-stage, multi-target, multi-variable combinatorial problem in the power system restoration. This paper presents a reasonable model and effectually method. The proposed model is considered as a typical partial minimum spanning tree problem from the mathematical point of view which considering all kinds of constraints. Improved data envelopment analysis (DEA) was used to get the weight which considering line charging reactive power, weather conditions, operation time and betweenness of transmission lines. The improved genetic algorithm method is employed to solve this problem. Finally, an example is given which proves the strategy of the line restoration can effectively handle the uncertainty of the system recovery process, to guarantee the system successfully restored after the catastrophic accidents.

scholarly journals A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongrui Chu ◽  
Yahong Chen
Keyword(s):  
Optimization Model ◽  
Complex Model ◽  
Theory And Practice ◽  
Logistics System ◽  
Logistics Network ◽  
Location Allocation ◽  
Emergency Logistics ◽  
Ga Algorithm ◽  
Relief Distribution ◽  
Deprivation Costs

Increased frequency of disasters keeps reminding us of the importance of effective resource distribution in postdisaster. To reduce the suffering of victims, this paper focuses on how to establish an effective emergency logistics system. We first propose a multiobjective optimization model in which the location and allocation decisions are made for a three-level logistics network. Three objectives, deprivation costs, unsatisfied demand costs, and logistics cost, are adopted in the proposed optimization model. Several cardinality and flow balance constraints are considered simultaneously. Then, we design a novel effective IFA-GA algorithm by combining the firefly algorithm and genetic algorithm to solve this complex model effectively. Furthermore, three schemes are proposed to improve the effectiveness of the IFA-GA algorithm. Finally, the numerical results provide several insights on the theory and practice of relief distribution, which also illustrate the validity of the proposed solution algorithm.

A Genetic Algorithm for Minimizing Relocations at Container Yard in Container Terminal

2014 ◽  
Vol 931-932 ◽  
pp. 1683-1688
Author(s):  
Phatchara Sriphrabu ◽  
Kanchana Sethanan ◽  
Somnuk Theerakulpisut
Keyword(s):  
Genetic Algorithm ◽  
Current Practice ◽  
Container Terminal ◽  
Experimental Result ◽  
Container Ship ◽  
Storage Assignment ◽  
Storage Location ◽  
Location Assignment

This paper focuses on storage location assignment and exported container relocation in container yard of container terminal with the objective of minimizing the number of container lifting. On the lifting steps, the truck with yard crane should be chosen in order to deliver a container from container yard to container ship, and this action can reduce container ship's docking time and increase effectiveness in container terminal service. In this paper, a genetic algorithm (GA) in container storage assignment and a heuristic for the container relocation determination are adopted. Also, the current practice including first-in-first-stored (FIFS) and simple relocation (SR) is used to compare the effectiveness of the GA and the proposed heuristic (RH). The experimental result presented that the proposed method is able to construct the effective solutions of storage location assignment of exported containers, and it reduces the number of relocations of exported container effectively.

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