Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling

Author(s):  
Davide Anghinolfi ◽  
Antonio Boccalatte ◽  
Massimo Paolucci ◽  
Christian Vecchiola
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44836-44845 ◽  
Author(s):  
Ammar A. Qamhan ◽  
Aref Ahmed ◽  
Ibrahim M. Al-Harkan ◽  
Ahmed Badwelan ◽  
Ali M. Al-Samhan ◽  
...  

2021 ◽  
pp. 002029402110642
Author(s):  
Dongping Qiao ◽  
Yajing Wang ◽  
Jie Pei ◽  
Wentong Bai ◽  
Xiaoyu Wen

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.


2019 ◽  
Vol 276 (1) ◽  
pp. 79-87 ◽  
Author(s):  
Alessandro Agnetis ◽  
Bo Chen ◽  
Gaia Nicosia ◽  
Andrea Pacifici

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