Name
Abstract | ||
|---|---|---|
Learning effect is a phenomenon in industrial processes that a machine (plant, worker, etc) can improve its productivity continuously with time, that is the actual processing time of a job decreases after the machine (plant, worker, etc) processes other jobs and gains some experiences. We study single machine scheduling problems with sum-of-processing-time based and job-dependent learning effect. The objectives are to minimize the maximum lateness, the number of tardy jobs, and total weighted completion time. By performing reductions from equal cardinality partition problem, we prove that these problems under investigation are all NP-hard. Two special cases that can be solved in polynomial time are also presented. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1057/jors.2016.40 | JORS |
Keywords | Field | DocType |
Scheduling, Single machine, Learning effect, NP-hardness | Partition problem,Mathematical optimization,Single-machine scheduling,Learning effect,Scheduling (computing),Computer science,Cardinality,Time complexity,Operations management | Journal |
Volume | Issue | ISSN |
68 | 7 | 1476-9360 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyi Jiang | 1 | 7 | 1.79 |
| Fangfang Chen | 2 | 0 | 0.34 |
| Xiandong Zhang | 3 | 8 | 2.27 |