Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366999294> ?p ?o ?g. }
Showing items 1 to 49 of
49
with 100 items per page.
- W4366999294 abstract "Scheduling problems requires to explicitly account for control considerations in their optimisation. The literature proposes two traditional ways to solve this integrated problem: hierarchical and monolithic. The monolithic approach ignores the control level's objective and incorporates it as a constraint into the upper level at the cost of suboptimality. The hierarchical approach requires solving a mathematically complex bilevel problem with the scheduling acting as the leader and control as the follower. The linking variables between both levels belong to a small subset of scheduling and control decision variables. For this subset of variables, data-driven surrogate models have been used to learn follower responses to different leader decisions. In this work, we propose to use ReLU neural networks for the control level. Consequently, the bilevel problem is collapsed into a single-level MILP that is still able to account for the control level's objective. This single-level MILP reformulation is compared with the monolithic approach and benchmarked against embedding a nonlinear expression of the neural networks into the optimisation. Moreover, a neural network is used to predict control level feasibility. The case studies involve batch reactor and sequential batch process scheduling problems. The proposed methodology finds optimal solutions while largely outperforming both approaches in terms of computational time. Additionally, due to well-developed MILP solvers, adding ReLU neural networks in a MILP form marginally impacts the computational time. The solution's error due to prediction accuracy is correlated with the neural network training error. Overall, we expose how - by using an existing big-M reformulation and being careful about integrating machine learning and optimisation pipelines - we can more efficiently solve the bilevel scheduling-control problem with high accuracy." @default.
- W4366999294 created "2023-04-27" @default.
- W4366999294 creator A5000180903 @default.
- W4366999294 date "2023-04-21" @default.
- W4366999294 modified "2023-09-30" @default.
- W4366999294 title "Bilevel optimisation with embedded neural networks: Application to scheduling and control integration" @default.
- W4366999294 doi "https://doi.org/10.48550/arxiv.2304.11244" @default.
- W4366999294 hasPublicationYear "2023" @default.
- W4366999294 type Work @default.
- W4366999294 citedByCount "0" @default.
- W4366999294 crossrefType "posted-content" @default.
- W4366999294 hasAuthorship W4366999294A5000180903 @default.
- W4366999294 hasBestOaLocation W43669992941 @default.
- W4366999294 hasConcept C11413529 @default.
- W4366999294 hasConcept C126255220 @default.
- W4366999294 hasConcept C137836250 @default.
- W4366999294 hasConcept C154945302 @default.
- W4366999294 hasConcept C206729178 @default.
- W4366999294 hasConcept C3309286 @default.
- W4366999294 hasConcept C33923547 @default.
- W4366999294 hasConcept C41008148 @default.
- W4366999294 hasConcept C41608201 @default.
- W4366999294 hasConcept C50644808 @default.
- W4366999294 hasConceptScore W4366999294C11413529 @default.
- W4366999294 hasConceptScore W4366999294C126255220 @default.
- W4366999294 hasConceptScore W4366999294C137836250 @default.
- W4366999294 hasConceptScore W4366999294C154945302 @default.
- W4366999294 hasConceptScore W4366999294C206729178 @default.
- W4366999294 hasConceptScore W4366999294C3309286 @default.
- W4366999294 hasConceptScore W4366999294C33923547 @default.
- W4366999294 hasConceptScore W4366999294C41008148 @default.
- W4366999294 hasConceptScore W4366999294C41608201 @default.
- W4366999294 hasConceptScore W4366999294C50644808 @default.
- W4366999294 hasLocation W43669992941 @default.
- W4366999294 hasOpenAccess W4366999294 @default.
- W4366999294 hasPrimaryLocation W43669992941 @default.
- W4366999294 hasRelatedWork W1495503294 @default.
- W4366999294 hasRelatedWork W1604175135 @default.
- W4366999294 hasRelatedWork W1994436307 @default.
- W4366999294 hasRelatedWork W2340378315 @default.
- W4366999294 hasRelatedWork W2745612259 @default.
- W4366999294 hasRelatedWork W2795565301 @default.
- W4366999294 hasRelatedWork W2896140431 @default.
- W4366999294 hasRelatedWork W3044651776 @default.
- W4366999294 hasRelatedWork W4285677055 @default.
- W4366999294 hasRelatedWork W4297925816 @default.
- W4366999294 isParatext "false" @default.
- W4366999294 isRetracted "false" @default.
- W4366999294 workType "article" @default.