Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling
Mixed-integer programming (MIP) can be used to formulate and solve complex production scheduling problems in the field of process systems engineering. However, the solution of MIP models may require a long computing time due to the combinatorial complexity of the problems. In this work, we propose s...
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2025
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| _version_ | 1852022711690199040 |
|---|---|
| author | Teemu J. Ikonen (20724395) |
| author2 | Boeun Kim (1234278) Christos T. Maravelias (206037) Iiro Harjunkoski (2331031) |
| author2_role | author author author |
| author_facet | Teemu J. Ikonen (20724395) Boeun Kim (1234278) Christos T. Maravelias (206037) Iiro Harjunkoski (2331031) |
| author_role | author |
| dc.creator.none.fl_str_mv | Teemu J. Ikonen (20724395) Boeun Kim (1234278) Christos T. Maravelias (206037) Iiro Harjunkoski (2331031) |
| dc.date.none.fl_str_mv | 2025-02-14T04:45:57Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.iecr.4c03045.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Supervised_Learning_of_the_Optimal_Objective_Function_Value_in_Chemical_Production_Scheduling/28415843 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified studied objective functions process systems engineering predictions allow us different problem features 2 </ sup best prediction models prediction models r </ >< sup supervised learning makespan minimization integer programming instance parameters high four classes combinatorial complexity |
| dc.title.none.fl_str_mv | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Mixed-integer programming (MIP) can be used to formulate and solve complex production scheduling problems in the field of process systems engineering. However, the solution of MIP models may require a long computing time due to the combinatorial complexity of the problems. In this work, we propose supervised learning models to predict the optimal objective function value on four classes of scheduling problems, which can be useful in a number of settings. To improve the accuracy of the prediction models, we device a number of machine learning features based on the instance parameters. The studied objective functions are cost and makespan minimization. Based on the results, the prediction accuracy is highthe coefficients of determination with the best prediction models are <i>r</i><sup>2</sup> > 0.97 on the four classes of problems. These predictions allow us to predict how different problem features (e.g., new orders or disturbances) affect the optimal objective function value. |
| eu_rights_str_mv | openAccess |
| id | Manara_1ebcbdb21bbd5767ea8e8cd323bcdaad |
| identifier_str_mv | 10.1021/acs.iecr.4c03045.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28415843 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Supervised Learning of the Optimal Objective Function Value in Chemical Production SchedulingTeemu J. Ikonen (20724395)Boeun Kim (1234278)Christos T. Maravelias (206037)Iiro Harjunkoski (2331031)BiochemistryBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstudied objective functionsprocess systems engineeringpredictions allow usdifferent problem features2 </ supbest prediction modelsprediction modelsr </>< supsupervised learningmakespan minimizationinteger programminginstance parametershigh four classescombinatorial complexityMixed-integer programming (MIP) can be used to formulate and solve complex production scheduling problems in the field of process systems engineering. However, the solution of MIP models may require a long computing time due to the combinatorial complexity of the problems. In this work, we propose supervised learning models to predict the optimal objective function value on four classes of scheduling problems, which can be useful in a number of settings. To improve the accuracy of the prediction models, we device a number of machine learning features based on the instance parameters. The studied objective functions are cost and makespan minimization. Based on the results, the prediction accuracy is highthe coefficients of determination with the best prediction models are <i>r</i><sup>2</sup> > 0.97 on the four classes of problems. These predictions allow us to predict how different problem features (e.g., new orders or disturbances) affect the optimal objective function value.2025-02-14T04:45:57ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.iecr.4c03045.s002https://figshare.com/articles/dataset/Supervised_Learning_of_the_Optimal_Objective_Function_Value_in_Chemical_Production_Scheduling/28415843CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284158432025-02-14T04:45:57Z |
| spellingShingle | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling Teemu J. Ikonen (20724395) Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified studied objective functions process systems engineering predictions allow us different problem features 2 </ sup best prediction models prediction models r </ >< sup supervised learning makespan minimization integer programming instance parameters high four classes combinatorial complexity |
| status_str | publishedVersion |
| title | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| title_full | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| title_fullStr | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| title_full_unstemmed | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| title_short | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| title_sort | Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling |
| topic | Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified studied objective functions process systems engineering predictions allow us different problem features 2 </ sup best prediction models prediction models r </ >< sup supervised learning makespan minimization integer programming instance parameters high four classes combinatorial complexity |