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A Quest for Projects with Scarce Resources : Seeking Schedule Intelligence Through Project Data Discovery

Vanhoucke, Mario - 2024

Do We Have Enough Project Data? (Part 1) -- Do the Projects Exist in Reality? -- Do We Have Good Schedules for the Projects? -- Can We Solve Every Project Instance? -- Why Is the RCPSP So Difficult? (Part 1) -- Can We Make the Projects Easier? (Part 1) -- Why Is the RCPSP So Difficult? (Part 2)...

Persistent link: https://www.econbiz.de/10015100713

Efficient optimization of robust project scheduling for industry 4.0 : a hybrid approach based on machine learning and meta-heuristic algorithms

Goli, Alireza - In: International journal of production economics 278 (2024), pp. 1-13

Persistent link: https://www.econbiz.de/10015133757

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

Evaluating early predictive performance of machine learning approaches for engineering change schedule : a case study using predictive process monitoring techniques

Radišić-Aberger, Ognjen; Burggräf, Peter; Steinberg, … - In: Supply chain analytics 8 (2024), pp. 1-35

a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random … reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm …

Persistent link: https://www.econbiz.de/10015325099

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

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

Lambelho, Miguel; Mitici, Mihaela; Pickup, Simon; … - In: Journal of air transport management 82 (2020), pp. 1-11

Persistent link: https://www.econbiz.de/10012253052

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

A novel virtual machine scheduling policy based on performance prediction model

Liu, Dongbo; Li, Yongjian - In: International journal of networking and virtual … 18 (2018) 4, pp. 279-293

Persistent link: https://www.econbiz.de/10011961415

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

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