287. A quantitative analysis of the benefits of lot streaming in hybrid flow shop manufacturing
Invited abstract in session MC-12: Advanced analytics in manufacturing, stream Scheduling and Project Management.
Monday, 12:30-14:00Room: Clarendon SR 1.02
Authors (first author is the speaker)
| 1. | Janis Sebastian Neufeld
|
| Operations Management, Otto von Guericke University Magdeburg | |
| 2. | Maximilian Hubmann
|
| TU Dresden | |
| 3. | Markus Höfling
|
| TU Dresden | |
| 4. | Daniel Zähringer
|
| TU Dresden |
Abstract
Lot streaming is a well-known technique to speed up production processes by forwarding some job units to the next production stage before the entire job has been processed. Especially for small-sized instances, significant reductions in makespan can be achieved by this strategy. However, a quantification of the actual potential in hybrid flow shop settings is still missing in the literature, especially for medium and large problems. For this purpose, we formulate a MILP model and develop a problem-specific iterated greedy algorithm to efficiently solve the hybrid flow shop scheduling problem with lot streaming. In a comprehensive computational study, we analyze the impact of relevant parameters such as setup times, processing time distributions, the maximum number of sublots, and the impact of policies with equal or consistent sublot sizes. The results highlight the relevance, effect, and limitations of lot streaming in hybrid flow shop settings and enable decision-makers to assess under which circumstances the benefits outweigh the additional organizational complexity.
Keywords
- Scheduling
- Metaheuristics
- Programming, Mixed-Integer
Status: accepted
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