Hyper parameter optimization in genetic algorithm with reinforcement learning for production scheduling with lot size

Name: Vannak, Seng

keyword: Genetic algorithm

; Reinforcement learning

; Tabu list

; Scheduling

; Lot-sizing

; Simulation

Abstract: Production scheduling with lot-size involves the process of arranging sequence of products and optimizing number of products to be produced in each batch. The variation in sequence and batch size of production could affect the machine utilization, overall equipment efficiency (OEE), work in process (WIP), and on-time delivery (OTD). The Genetic Algorithm has been used for solving production scheduling with lot size, but it could trap in the local optimal. We propose the method of Reinforcement Learning (RL) and Tabu List to improve GA searching performance and called GARL. RL is used for dynamically adjusting the hyper-parameters. Moreover, the perturbation process is also included to assists GARL to jump out of local optimum. There are three test data sets and three sizes: small, medium, large size. Our results and findings demonstrate that GARL finds better solutions in both of the constraints which included soft and hard than GA even though it takes up a bit more time than GA. The performance of this proposed technique has strengthened the efficiency and minimized the cost of the production process

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Chawalit Jeenanunta

Role: advisor

Created: 2021

Modified: 2024-08-23

Issued: 2024-08-23

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2021.1288

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

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