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3283. Learning Priority Indices for Energy-Aware Scheduling of Jobs in Flow Shops with Batch Processing Machines
Invited abstract in session MD-24: Sustainable Operations, stream Sustainable Supply Chains.
Monday, 14:30-16:00Room: 83 (building: 116)
Authors (first author is the speaker)
| 1. | Daniel Schorn
|
| Enterprise-wide Software Systems, FernUniversität in Hagen | |
| 2. | Lars Moench
|
| FernUniversität in Hagen |
Abstract
A scheduling problem for a two-stage flexible flow shop with parallel batch processing machines (BPMs) is considered. Here, a batch is a group of jobs that are processed at the same time on a single machine. This problem is motivated by process conditions found in semiconductor wafer fabrication facilities (wafer fabs). Jobs having unequal ready times are assumed. Moreover, critical queue time constraints, i.e. maximum time lags, are taken into account. A blended objective function combining the total weighted tardiness (TWT) and the total electricity cost (TEC) under a time-of-use (TOU) tariff is considered. A genetic programming (GP) procedure is designed to automatically discover priority indices for a heuristic scheduling framework. Results of computational experiments based on randomly generated problem instances are reported that demonstrate that the learned priority indices lead to high-quality schedules in a short amount of computing time.
Keywords
- Scheduling
Status: accepted
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