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Predicting the solution time of branch-and-bound algorithms for mixed-integer programs

Özaltın, Osman Y.; Hunsaker, Brady; Schaefer, Andrew J. - In: INFORMS journal on computing : JOC 23 (2011) 3, pp. 392-403

Persistent link: https://www.econbiz.de/10009296301

Adaptive solution prediction for combinatorial optimization

Shen, Yunzhuang; Sun, Yuan; Li, Xiaodong; Eberhard, Andrew - In: European journal of operational research : EJOR 309 (2023) 3, pp. 1392-1408

Persistent link: https://www.econbiz.de/10014435020

Boosting ant colony optimization via solution prediction and machine learning

Sun, Yuan; Wang, Sheng; Shen, Yunzhuang; Li, Xiaodong; … - In: Computers & operations research : and their … 143 (2022), pp. 1-16

Persistent link: https://www.econbiz.de/10013343259

Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem

Yamashiro, Hirochika; Nonaka, Hirofumi - In: Operations research perspectives 8 (2021), pp. 1-9

Light gradient-boosted machine (LightGBM) algorithm and Ridge performed the best with MAPE and RMSE. The optimization of …

Persistent link: https://www.econbiz.de/10012596666

Application of predictive methods to financial data sets

Habibi, Reza - In: Financial internet quarterly 17 (2021) 1, pp. 50-61

Financial data sets are growing too fast and need to be analyzed. Data science has many different techniques to store and summarize, mining, running simulations and finally analyzing them. Among data science methods, predictive methods play a critical role in analyzing financial data sets. In...

Persistent link: https://www.econbiz.de/10012588934

Data-driven prediction of relevant scenarios for robust combinatorial optimization

Goerigk, Marc; Kurtz, Jannis - In: Computers & operations research : an international journal 174 (2025), pp. 1-14

Persistent link: https://www.econbiz.de/10015330169

A survey on AI-based scheduling models, optimization and prediction for hydropower generation : variants, challenges, and future directions

Villeneuve, Yoan; Séguin, Sara; Chehri, Abdellah - 2022

Persistent link: https://www.econbiz.de/10013502350

Explainable prediction of Qcodes for NOTAMs using column generation

Patel, Krunal Kishor; Desaulniers, Guy; Lodi, Andrea; … - In: Journal of the Operational Research Society 75 (2024) 1, pp. 173-183

Persistent link: https://www.econbiz.de/10014555622

Managing information and supplies inventory operations in a manufacturing environment : part 2 ; an order-timing and sizing algorithm

Baker, Tim; Jayaraman, Vaidyanathan - In: International journal of production research 50 (2012) 7, pp. 1767-1779

Persistent link: https://www.econbiz.de/10009563801

Towards a paradigm shift : how can machine learning extend the boundaries of quantitative management scholarship?

Valizade, Danat; Schulz, Felix; Nicoara, Cezara - In: British journal of management 35 (2024) 1, pp. 99-114

Persistent link: https://www.econbiz.de/10014471499