TY - GEN
T1 - Predicting the Solution Time for Optimization Problems Using Machine Learning Case of Job Shop Scheduling Problem
AU - Pouya, Shaheen
AU - Toragay, Oguz
AU - Mohammadi, Mehrdad
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - In organizations that use optimization and other computerrelated problem-solving techniques, a better understanding of the required computational time is essential for efficient decision-making and resource allocation which also directly affects productivity and operational effectiveness. This study proposes the application of various Machine Learning (ML) methods to predict the computation time needed to solve job shop problems. Specifically, we implemented 11 ML models, including the Deep Neural Network (DNN), which delivered the most accurate results. The proposed approach involves utilizing a DNN algorithm to predict computation time for Integer Programming (IP) job shop problems, trained on synthetically generated data that indicate the gap-time correlation in a branch and bound tree. The developed model in this study estimates the total computation time with an accuracy of 92%. The model development process involves collecting data from a set of solved problems using the branch and bound method and training the ML models to estimate the computational time required to reach the optimal solution in unsolved similar problems.
AB - In organizations that use optimization and other computerrelated problem-solving techniques, a better understanding of the required computational time is essential for efficient decision-making and resource allocation which also directly affects productivity and operational effectiveness. This study proposes the application of various Machine Learning (ML) methods to predict the computation time needed to solve job shop problems. Specifically, we implemented 11 ML models, including the Deep Neural Network (DNN), which delivered the most accurate results. The proposed approach involves utilizing a DNN algorithm to predict computation time for Integer Programming (IP) job shop problems, trained on synthetically generated data that indicate the gap-time correlation in a branch and bound tree. The developed model in this study estimates the total computation time with an accuracy of 92%. The model development process involves collecting data from a set of solved problems using the branch and bound method and training the ML models to estimate the computational time required to reach the optimal solution in unsolved similar problems.
UR - https://www.scopus.com/pages/publications/85207840314
UR - https://www.scopus.com/pages/publications/85207840314#tab=citedBy
U2 - 10.1007/978-3-031-53025-8_31
DO - 10.1007/978-3-031-53025-8_31
M3 - Conference contribution
AN - SCOPUS:85207840314
SN - 9783031530241
T3 - Communications in Computer and Information Science
SP - 450
EP - 465
BT - Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers
A2 - Pereira, Ana I.
A2 - Fernandes, Florbela P.
A2 - Coelho, Joao P.
A2 - Mendes, Armando
A2 - Pacheco, Maria F.
A2 - Lima, Jose
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023
Y2 - 27 September 2023 through 29 September 2023
ER -