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2418. A multi-stage stochastic programming model for lot-sizing with onsite generation of renewable energy
Invited abstract in session TB-49: Lot-sizing with energy aspects, stream Lot Sizing, Lot Scheduling and Production Planning.
Tuesday, 10:30-12:00Room: M1 (building: 101)
Authors (first author is the speaker)
| 1. | Ruiwen Liao
|
| LISN Paris-Saclay University | |
| 2. | Franco Quezada
|
| Industrial Engineering Department, University of Santiago of Chile | |
| 3. | Céline Gicquel
|
| Laboratoire Interdisciplinaire des Sciences du Numérique, Université Paris Saclay | |
| 4. | Safia Kedad-Sidhoum
|
| CNAM - CEDRIC |
Abstract
One way to achieve energy efficiency in manufacturing is to equip plants with on-site renewable energy generation system to partially power industrial processes.However, renewable energy sources are intermittent and the availability is difficult to predict accurately.Therefore,we study an integrated industrial production planning and energy supply problem under uncertain renewable energy availability.We propose a multi-stage stochastic programming model for this problem. The planning horizon is divided into multiple stages according to the time at which the PV availability information is updated.The intermittency of PV generation is represented by a scenario tree.The resulting production and energy supply planning can be seen as a multi-stage decision process where some decisions are made at the beginning of the planning horizon whereas the others are postponed to later decision stages when more information on the uncertain parameters are revealed.At the beginning of the planning horizon, we build a production plan for a proportional lot-sizing and scheduling problem in a single-machine multi-item setting. Then based on the available information on PV generation, an energy supply plan is constructed for the upcoming stage, which has to satisfy the energy demand of the system which has been previously determined by the production plan at the beginning of the planning horizon. Computational experiments will be presented to show the practical efficiency of the proposed approach.
Keywords
- Programming, Stochastic
- Manufacturing
- Scheduling
Status: accepted
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