scholarly journals Optimization in Determining Routes of Goods Distribution Vehicle Using the Ant Colony Optimization Algorithm Method at PT XYZ

2020 ◽  
Vol 2 (1) ◽  
pp. 202-215
Author(s):  
Ranti Dwi Djayanti ◽  
Yani Iriani
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Transportation Cost ◽  
Transportation Costs ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Total Distance ◽  
Ant Behavior ◽  
Total Transportation Cost

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    

scholarly journals Prediction of Flow Characteristics in the Bubble Column Reactor by the Artificial Pheromone-Based Communication of Biological Ants

2020 ◽  
Author(s):  
Shahab Shamshirband ◽  
Meisam Babanezhad ◽  
Amir Mosavi ◽  
Narjes Nabipour ◽  
Eva Hajnal ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Bubble Column ◽  
Flow Characteristics ◽  
Ant Colony ◽  
Biological Behavior ◽  
Bubble Column Reactor ◽  
Ant Colony Optimization Algorithm ◽  
Column Reactor ◽  
Ant Behavior

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor (BCR). This artificial ant algorithm is mimicking real ant behavior. This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings. The results show a great agreement between ant colony prediction and CFD outputs in different sections of the BCR. The combination of ant colony system and neural network framework can provide the smart structure to estimate biological and nature physics base phenomena. The ant colony optimization algorithm (ACO) framework based on ant behavior can solve all local mathematical answers throughout 3D bubble column reactor. The integration of all local answers can provide the overall solution in the reactor for different characteristics. This new overview of modelling can illustrate new sight into biological behavior in nature.

scholarly journals Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haiou Xiong
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Transportation Cost ◽  
Economic Benefits ◽  
Path Selection ◽  
Ant Colony ◽  
Cold Chain ◽  
Cost Calculation ◽  
Ant Colony Optimization Algorithm ◽  
Logistics Distribution

The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

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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