A fast ant-colony algorithm for single-machine scheduling to minimize the sum of weighted tardiness of jobs

2005 ◽  
Vol 56 (8) ◽  
pp. 947-953 ◽  
Author(s):  
O Holthaus ◽  
C Rajendran
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.


2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Do Gyun Kim ◽  
Jin Young Choi

We consider a two-agent single-machine scheduling problem that minimizes the total weighted tardiness of one agent under the restriction that the second agent is prohibited from having tardy jobs. The actual processing times of all jobs are affected by a sum-of-processing-times-based aging effect. After showing the NP-hardness of the problem, we design a branch-and-bound (B&B) algorithm to find an optimal solution by developing dominance properties and a lower bound for the total weighted tardiness to increase search efficiency. Because B&B takes a long time to find an optimal solution, we propose a genetic algorithm as an efficient, near optimal solution approach. Four methods for generating initial populations are considered, and edge recombination crossover is adopted as a genetic operator. Through numerical experiments, we verify the outstanding performance of the proposed genetic algorithm.


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