Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

2020 ◽  
Vol 16 (4) ◽  
pp. 2809-2819 ◽  
Author(s):  
Rui Zhang ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Hot Rolling ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Robust Scheduling ◽  
Rolling Production

scholarly journals Ant-Set: A Subset-Oriented Ant Colony Optimization Algorithm for the Set Covering Problem

2020 ◽  
Vol 26 (2) ◽  
pp. 293-316
Author(s):  
Murilo Falleiros Lemos Schmitt ◽  
Mauro Mulati ◽  
Ademir Constantino ◽  
Fábio Hernandes ◽  
Tony Hild
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Ant Colony Optimization Algorithm ◽  
The Impact

This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

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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