Singgih, Ivan Kristianto and Soegiharto, Stefanus (2023) Predicting Job Waiting Times in A Stochastic Scheduling Environment Using Simulation and Regression Machine Learning Models. In: Proceedings of the 2023 Winter Simulation Conference, 10-13 Dec. 2023, San Antonio, Texas. (Submitted)
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Abstract
Scheduling real systems is complicated because of the consideration of various working conditions. Although various combinatorial optimization methods, ranging from mathematical models, heuristics, metaheuristics, etc., have been developed, these methods could require a long computational time due to the complexity of the problems. This study proposes a framework to understand the system’s behavior using regression machine learning techniques. The considered system could be any type, e.g., the flow shop, job shop, and their variants, with a certain scheduling method. The framework consists of (1) the development of the simulation for generating the data and (2) how the data could be used for training the regression machine learning models. An example of the stochastic single-machine problem with the First-In-First-Out rule is considered. The framework could be used to simplify the process of understanding the system’s behavior without solving the optimization problem, which could be time-consuming.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Industrial Engineering |
| Depositing User: | IVAN KRISTIANTO SINGGIH |
| Date Deposited: | 09 Feb 2024 08:40 |
| Last Modified: | 09 Feb 2024 08:40 |
| URI: | http://repository.ubaya.ac.id/id/eprint/45915 |
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