scholarly journals Research on green single machine scheduling based on improved ant colony algorithm

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.

2020 ◽  
Vol 39 (4) ◽  
pp. 5329-5338
Author(s):  
Yan Zheng ◽  
Qiang Luo ◽  
Haibao Wang ◽  
Changhong Wang ◽  
Xin Chen

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.


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 ◽  
...  

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