A Study On Yard Truck Scheduling And Storage Allocation Using Modified Brain Storm Optimization Algorithms

With the deep understanding of nature and life,the research on artificial intelligence research has gradually opened up the so-called intelligent optimization path.As an important branch of artificial intelligence,swarm intelligence has shown great superior performance in solving complex and large-scale optimization problems in recent decades.With the advancement of automation and information technology in China,the logistics industry has developed rapidly and is gradually moving toward "intelligence." As an important branch of the logistics industry,port logistics is the node of the global integrated transportation system and has a huge impact on the efficiency and efficiency of the port.It has become a trend that swarm intelligence optimization algorithms are used to solve the scheduling problem of various resources in the port.To improve the effective allocation of port resources and reduce cost,combining the various constraints in port production and the basic theories of intelligent optimization algorithms,the paper proposes a “double trailer” based Yard Truck Scheduling and Storage Allocation Problem.In addition,a novel modified Brain Storm Optimization algorithm is proposed to solve this problem.This thesis was undertaken under the auspices of two National Natural Science Foundation of China,i.e.The Joint Decision-Making of Terminal Layout Design,Berth Allocation & Quay Crane Assignment Based on Complex Adaptive Bacterial Colony(No.71571120)and Study on Comprehensive Learning and Evolutionary Bacteria Foraging Optimization Method(No.71271140).The following innovative work have been carried out as follows:Firstly,in the aspect of model construction,based on the analysis of the port production process,the current situation of port transportation using one-prong and two-pronged connection is introduced into the dispatching of container trucks,and on this basis,the issue of picking and dispatching sub-problems is proposed.In view of the effectiveness of picking dispatch in a complex environment,combined with the classification of the positions,the existence of parking lots,and the operation time of the bridge bridges and bridges,the main factors are the combination of picking,dispatching,and position allocation.Secondly,in the aspect of algorithm design,the thesis is inspired by the phenomenon of "seeking advantages and avoiding disadvantages" and "learning from each other".In this paper,a Brain Storm Optimization algorithm based on "re-updating" mechanism is proposed.In the past,the improvement of Brain Storm Optimization is usually focused on the replacement of clustering method,the improvement of mutation formula and so on.This paper tries to update the individual by adding the strategy of "seeking advantages and avoiding harm" after the iteration of the existing Brain Storm Optimization algorithm.An improved Brain Storm Optimization algorithm is proposed,which improves the optimization ability of Brain Storm Optimization in function optimization,and is more efficient than the original algorithm,which presents a new direction for the improvement of swarm intelligence algorithm.Finally,in the aspect of model solving,this paper makes use of Brain Storm Optimization algorithm to solve the mathematical model of Yard Truck Scheduling and Storage Allocation problem for the first time.In this paper,three kinds of Brain Storm Optimization algorithms and Genetic Algorithm,Differential Evolution Algorithm,Particle Swarm Optimization Algorithm are compared.By comparing the performance of the six algorithms,the best algorithm and the most stable algorithm are selected.