Research optimization on logistics distribution center location based on adaptive particle swarm algorithm

Xiang, Hua; Hu, Xiao; Yuan, Wuwei

doi:10.1016/j.ijleo.2016.06.032

Cited by 49 publications

(30 citation statements)

References 22 publications

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“…Whether the weight distribution obtained above is reasonable or not, it is necessary to perform consistency check on the judgment matrix. Test formula CI CR = RI (5) CR is the random consistency ratio of the judgment matrix; CI is the general consistency index of the judgment matrix .…”

Section: Construction Of Evaluation Index Systemmentioning

confidence: 99%

“…Li Lei [2] used GI method and entropy method to construct the smallest total distribution cost including the population of demand points. Site model; Xiao Jianhua [3] found that timeliness and responsiveness are two very important factors in the location of fresh agricultural products; So Zhilin [4] Hua [5] established a site with the lowest logistics cost Optimize the model and study the global balance search and local search distribution center location issues; Pawel [6] proposed that the transportation cost depends on the purchase price of the vehicle. In terms of agricultural product quality and safety research, Zhou Wencan [7] used the binary Logit model to identify the significant factors affecting farmers' safe supply of agricultural products, such as farmers' awareness of the national ban on pesticides.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Location Selection of Agricultural Products Distribution Center based on Cost Optimal in E-commerce Environment --Taking Aba Prefecture as an Example

Guo¹,

Siyang²,

Yao³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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In recent years, people pay more and more attention to food safety. In order to study the relationship between agricultural product safety and location selection of distribution centers, this paper studies the establishment mechanism of agricultural product distribution centers based on food safety in the e-commerce environment. Based on the cost optimization perspective, the analytic hierarchy process is used to qualitatively analyze the four influencing factors of transportation cost, storage cost, processing cost and human resource cost, and the relevant weights are obtained. Then the gray correlation degree is used to evaluate the four candidate towns. The selection of the optimal distribution center site is selected to provide reference for the research of fresh and perishable products such as yak meat, and the research on the safety mechanism of agricultural products is put forward.

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Section: Construction Of Evaluation Index Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Location Selection of Agricultural Products Distribution Center based on Cost Optimal in E-commerce Environment --Taking Aba Prefecture as an Example

Guo¹,

Siyang²,

Yao³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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“…Nevertheless, when the optimal number of deports is identified it can be not conformed to the best way between stores and final destinations, their location should be taken into account in this case. Xiang Hua, Xiao Hu and Wuwei Yuan [13] present a mean to solve this problem by applying the Adaptive particle swarm optimization (APSO). The APSO is a supplement of nonlinear inertia weight as well as time-varying acceleration coefficients as opposite to the Particle swarm optimization (PSO), which is a common method of decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of urban freight transportation based on evolutionary modelling

Gladchenko

Saprykin

Тихонов

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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Logistics problems require special attention, because every year they become more complicated and multivariable. On the one hand, a supply chain management includes incessant monitoring of such issues as requests elaboration, paths determination, routing of shipments, multimodal choice, set up of transhipments, fleet choice and maintenance, warehousing, packaging and others. On the other hand, dozens of people are involved in the logistics process. All these moments complicate the decision-making that is why data driven decisions are required nowadays. As well as shipment problems are NP-hard, the heuristic methods should be applied to resolve them. In this article we propose a genetic algorithm to solve the complex problem that consists of the Travelling Salesman Problem combined with the Knapsack Problem. We have developed an urban freight transportation model which is focused on the minimization of the underway time as well as on the maximization of the truck’s loading. A significant contribution in our method is the census of traffic frequency by using traffic zoning. The developed approach has been implemented using the Python programming language in the Zeppelin environment. The first version of the system has been approved in the city of Samara (Russia) with test demand dataset.

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“…Nature of the Problem. Much of the literature has studied the problem of selecting DCs' location under a certain and a deterministic environment [9,11,12,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. This kind of problem was characterized as static and deterministic, and parameters are known and fixed [33].…”

Section: Related Literaturementioning

confidence: 99%

“…In this category of methods, there is more work compared to the other two categories (cited above). Among these approaches, we cite the conceptual framework based on Adjusted Kuehn-Hamburger model, the method based on Grid model and ELECTRE [13], the Fixed-Charge Facility Location model [14], the Genetic algorithm [15][16][17], the Bilevel Programming model [18], the method based on Center of Gravity principle [19], the Binary Integer Programming [20], the Particle Swarm Optimization algorithm [21][22][23], the DNA Artificial Fish Swarm algorithm [24], the Firefly algorithm [25], the method based on the Genetic algorithm, and AHP [26].…”

Section: The Metaheuristics For the Multiobjective Decision-mentioning

confidence: 99%

ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

Agrebi

Abed

Omri

2017

Journal of Advanced Transportation

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The location selection of distribution centers is one of the important strategies to optimize the logistics system. To solve this problem, under certain environment, this paper presents a new multicriteria decision-making method based on ELECTRE I. The proposed method helps decision-makers to select the best location from a given set of locations for implementing. After having identified decision-makers, the criteria, and the set of locations, the factors influencing the selection are analyzed in order to identify the best location. A sensitivity analysis is then performed to determine the influence of criteria weights on the selection decision. The strength of the proposed method is to incorporate decision-makers’ preferences into the decision-making process. In addition, the proposed method considers both quantitative and qualitative criteria. Finally, the selected solution is validated by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method.

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Research optimization on logistics distribution center location based on adaptive particle swarm algorithm

Cited by 49 publications

References 22 publications

Research on Location Selection of Agricultural Products Distribution Center based on Cost Optimal in E-commerce Environment --Taking Aba Prefecture as an Example

Research on Location Selection of Agricultural Products Distribution Center based on Cost Optimal in E-commerce Environment --Taking Aba Prefecture as an Example

Optimization of urban freight transportation based on evolutionary modelling

ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

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