A Two-Phase Soft Optimization Method for the Uncertain Scheduling Problem in the Steelmaking Industry
| Autor: | Jinghua Hao, Min Liu, Shenglong Jiang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Rate-monotonic scheduling
0209 industrial biotechnology Schedule Mathematical optimization Job shop scheduling Least slack time scheduling Computer science Constrained optimization Scheduling (production processes) 02 engineering and technology Flow shop scheduling Dynamic priority scheduling Fair-share scheduling Computer Science Applications Scheduling (computing) Human-Computer Interaction Ordinal optimization 020901 industrial engineering & automation Estimation of distribution algorithm Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Software |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47:416-431 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2015.2503388 |
| Popis: | In this paper, an uncertain scheduling problem arising from the steelmaking-continuous casting (SCC) production system is investigated. For the practical SCC production system, it is difficult to obtain a schedule with better performance using traditional deterministic scheduling methods since there exists uncertainty in processing times. According to the analysis on characteristics of the uncertain SCC scheduling problem (SCCSP), we construct a soft-form schedule which includes slack ratios as characteristic indexes and the job sequence at the casting stage as key decision variables to cope with the uncertainty in processing times, and propose a two-phase soft optimization method to solve the uncertain SCCSP with the just-in-time and the waiting time objectives under the break probability. In the first phase, the continuous estimation distribution algorithm (EDA) with the ordinal optimization policy is proposed to optimize slack ratios under the chance constraint, in which the optimal computing budget allocation with constrained optimization is applied to reduce the computational burden. In the second phase, based on the above optimized characteristic indexes, the discrete EDA with a local search procedure is proposed to optimize the job sequence at the casting stage. Finally, computational experiments with various scales and noise levels are performed to validate the effectiveness of the proposed algorithm. |
| Databáze: | OpenAIRE |
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