In Distribution System Minimum Loss Reconfiguration using Ant Colony Optimization Algorithm

Author(s):  
Prabha Uikey ◽  
Anil Kori
2020 ◽  
Vol 2 (1) ◽  
pp. 202-215
Author(s):  
Ranti Dwi Djayanti ◽  
Yani Iriani

PT XYZ is one of freight forwarding companies in Indonesia, which is located in the city of Bandung. This company has managerial functions related to Collecting, Processing, Transporting, Delivery, and Reporting. However, the fact is in the process of Transporting this company still uses a zoning system which is a shipping system that still divides tertiary areas and each of these areas uses one vehicle. One problem that arises is that companies want effective and efficient performance in the distribution system of goods with the minimum total transportation costs. However, the company does not know yet whether the company's shipping routes have been effective and efficient or not. The company has tertiary network distribution route that are 2 routes with a total distance of 143.4 Km and a total transportation cost of  Rp 5,681,484 /month. This research aims to determine the optimal goods distribution route using the Ant Colony Optimization Algorithm method, which is the method of finding the shortest path following ant behavior in taking food to its nest. Based on the results of the research, it is obtained a total distance of 109.2 Km because it becomes 1 route and total transportation costs Rp 3,337,992 /month, then it is obtained optimal results with a difference in distance is 34.2 Km and a total transportation cost of  Rp 2,343,492 /month using one vehicle. Keywords: Optimization, Distribution, Ant Colony Optimization Algorithm    


2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.


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