Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

2016 ◽  
Vol 49 (1) ◽  
pp. 84-97 ◽  
Author(s):  
Xingong Zhang ◽  
Yunqiang Yin ◽  
Chin-Chia Wu
2013 ◽  
Vol 690-693 ◽  
pp. 3007-3013
Author(s):  
Chou Jung Hsu ◽  
Hung Chi Chen

This paper explored a single-machine scheduling deterioration jobs with multi-maintenance activities. The non-resumable case and simple linear deterioration effect were taken into account as well. We assumed that after a maintenance activity, the machine will revert to its initial condition and the deterioration effect will start anew. The objective was to minimize the makespan in the system. The problem was proven to be NP-hard in the strong sense. Therefore, a heuristic and a lower bound were introduced and tested numerically. Computational results showed that the proposed algorithm performed well.


2013 ◽  
Vol 40 (8) ◽  
pp. 2116-2127 ◽  
Author(s):  
Wen-Hsiang Wu ◽  
Jianyou Xu ◽  
Wen-Hung Wu ◽  
Yunqiang Yin ◽  
I-Fan Cheng ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hongming Zhou ◽  
Ya-Chih Tsai ◽  
Ful-Chiang Wu ◽  
Shenquan Huang ◽  
Fuh-Der Chou

This paper addresses a single-machine scheduling problem with periodic preventive maintenance activities that are predeterministic so that the machine is not available all the time, and jobs have to be processed between two consecutive maintenance periods. We propose a mixed integer programming (MIP) model and two heuristics to minimize the makespan. With more constraints in our model, the model is more efficient than the recent model of Perez-Gonzalez and Framinan , and our model could solve problems with up to fifty jobs. Two heuristic algorithms, namely, H (MW) and H (LB∗), are also proposed, in which two bin-packing policies of the minimum waste and minimum lower bound are used, respectively. Furthermore, we also proposed an improvement procedure. The results showed that the heuristic H (MW) outperformed other heuristics of the paper, indicating that the bin-packing policy of the minimum waste is more effective than well-known ones such as full batch and best fit. Additionally, all the heuristic algorithms addressed in this paper combined with the improvement procedure could achieve a similar and high quality of solutions with a very tiny increase in computational expense.


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