The Research on Location of Multi-Level Network Distribution Centers

2011 ◽  
Vol 97-98 ◽  
pp. 653-658
Author(s):  
Ying Wu ◽  
Yi Lu ◽  
Yang Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Strategy ◽  
Service Level ◽  
Distribution Center ◽  
Location Model ◽  
Practical Case ◽  
Distribution Centers ◽  
Advantages And Disadvantages ◽  
Multi Level

This paper studies the time matter in distribution center’s service level, states a model’s preconditions and constructs a location model of multi-level disribution center which concerns the time matter. In solution, it studies the advantages and disadvantages of genetic algorithm, proposes a mixed strategy, designs the HGA program and studies the application of HGA in the location model of distribution center. In the end , it shows the practicality and correctness the proposed methods and models by the application in the practical case.

Integrated Production, Inventory, and Location-Allocation Decisions in Designing Supply Chain Networks

2016 ◽  
Vol 9 (4) ◽  
pp. 22-42 ◽  
Author(s):  
Sepideh Alavi ◽  
Nader Azad ◽  
Mojtaba Heydar ◽  
Hamid Davoudpour
Keyword(s):  
Supply Chain ◽  
Search Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Service Level ◽  
Distribution Center ◽  
Neighborhood Search ◽  
Distribution Centers ◽  
Location Allocation ◽  
Supply Chain Networks ◽  
Allocation Decisions

This paper studies the design and development of an inventory model for manufacturers with constant production rates considering location and allocation decisions in a three-level supply chain. In this supply chain, the demands of customers and the lead times are assumed to be uncertain. Therefore, each distribution center retains some amount of safety stock to provide suitable service level for customers. The proposed non-linear model aims to minimize location and inventory costs of distribution centers, manufacturers and transportation costs subject to relevant constraints. To solve the model, an efficient imperialist competitive algorithm and a Tabu search algorithm, each using variable neighborhood search, are proposed. The model outputs are decisions such as which distribution centers and manufacturers are opened, the allocation of customers to distribution centers, and distribution centers to manufacturers. Results are also the ordering quantity of each opened distribution center and the production rate of each opened manufacturer. The computational results for several instances of the problem are represented to show the efficiency of proposed algorithm.

scholarly journals Location Model and Optimization of Seaborne Petroleum Logistics Distribution Center Based on Genetic Algorithm

2013 ◽  
Vol 6 (1) ◽  
pp. 70-74
Author(s):  
Chu-Liangyong Chu-Liangyong ◽  
Wang-Hongpeng Wang-Hongpeng ◽  
Zhou-Jianpin Zhou-Jianpin ◽  
Yang-Weihong Yang-Weihong ◽  
Xu-Xiaowei Xu-Xiaowei
Keyword(s):  
Genetic Algorithm ◽  
Distribution Center ◽  
Location Model ◽  
Logistics Distribution

Multi-Node Distribution Center Location Model under Manufacturing and Remanufacturing System

2010 ◽  
Vol 39 ◽  
pp. 140-145 ◽  
Author(s):  
Ya Peng Zhao
Keyword(s):  
Distribution Center ◽  
Location Model ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Dea Model ◽  
Logistics Distribution ◽  
Center Location ◽  
Node Distribution ◽  
Effective Decision ◽  
Distribution Center Location

This study establishes the logistics distribution center location model under manufacturing and remanufacturing system and modifies alternative expenses and time- restricting condition s of logistics distribution center location model according to the specific feature of model under manufacturing and remanufacturing system. It’s necessary to consider all the factors during the course of establishing the logistics distribution center location model. And it is also necessary to consider effectiveness of distribution center in model and ask for the experts’ opinion and advice. A location model by combining DEA model and mixed integer programming is presented. In this model, in terms of the results of the evaluation of the efficiency of the distribution center by the DEA model, this result is regarded as a constraint of the model. Finally, An example demonstrates that the model can solve the problem of distribution centers location and provide an effective decision tool for decision-maker.

Hyperspectral Multi-level Image Thresholding using Qutrit Genetic Algorithm

2021 ◽  
pp. 115107
Author(s):  
Tulika Dutta ◽  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Somnath Mukhopadhyay ◽  
Prasun Chakrabarti
Keyword(s):  
Genetic Algorithm ◽  
Image Thresholding ◽  
Multi Level

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Holes Machining Process Optimization with Genetic Algorithm

2011 ◽  
Vol 460-461 ◽  
pp. 117-122 ◽  
Author(s):  
Guang Yu Zhu ◽  
Lian Fang Chen
Keyword(s):  
Genetic Algorithm ◽  
Cutting Tool ◽  
Machining Process ◽  
Processing Route ◽  
Optimal Route ◽  
Optimal Processing ◽  
Global Optimal ◽  
Level Method ◽  
Multi Level ◽  
Changing Times

In this paper, a multi-level method has been adopted to optimize the holes machining process with genetic algorithm (GA). Based on the analyzing of the features of the part with multi-holes, the local optimal processing route for the holes with the same processing feature is obtained with GA, then try to obtain the global optimal route with GA by considering the obtained local optimal route and the holes with different features. That is what the multi-level method means. The optimal route means the minimum moving length of the cutting tool and the minimum changing times of the cutting tool. The experiment is carried out to verify the algorithm and the proposed method, and result indicates that with GA and using the multi-level method the optimal holes machining route can be achieved efficiently.

scholarly journals Fluid Genetic Algorithm (FGA)

2017 ◽  
Vol 4 (2) ◽  
pp. 158-167 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Brian K. Smith
Keyword(s):  
Genetic Algorithm ◽  
Success Rate ◽  
Optimization Problems ◽  
Engineering Problem ◽  
Review Of The Literature ◽  
Learning Rates ◽  
New Concepts ◽  
Convergence Control ◽  
Multi Level ◽  
Paper Method

Abstract Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP), is shown and experienced. Highlights This work presents a novel Genetic Algorithm alteration. Chromosome concept and structure in FGA is more similar to the real genetic world. FGA comprises global and individual learning rates. We show FGA enjoys higher success rate, and better convergence control.

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