Name
Title | ||
|---|---|---|
A new solution approach for flow shop scheduling with an exponential time-dependent learning effect |
Abstract | ||
|---|---|---|
This paper addresses a flow shop scheduling problem with a sum-of-process-times based learning effect. The objective is to find schedules that can minimize the maximum completion time. For constructing a solution framework, we propose a new random-sampling-based solution procedure called Bounds-based Nested Partition (BBNP). In order to enhance the effectiveness of BBNP, we develop a composite bound for guidance. Two heuristic algorithms are conducted with worst-case analysis as benchmarks. Numerical results show that the BBNP algorithm outperforms benchmark algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COASE.2019.8843150 | 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) |
Keywords | Field | DocType |
exponential time-dependent learning effect,flow shop scheduling problem,heuristic algorithms,worst-case analysis,numerical analysis,bounds-based nested partition algorithm,benchmark algorithms,random-sampling model,minimisation,maximisation | Heuristic,Learning effect,Mathematical optimization,Job shop scheduling,Exponential function,Computer science,Flow shop scheduling,Schedule,Partition (number theory) | Conference |
ISSN | ISBN | Citations |
2161-8070 | 978-1-7281-0357-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingxuan Liu | 1 | 0 | 0.34 |
| Hongyu He | 2 | 5 | 1.10 |
| Leyuan Shi | 3 | 361 | 51.32 |