2026-07-09T07:58:29Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/63152024-05-15T15:57:41Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Peralta Abadía, José Joaquín author CUESTA ZABALAJAUREGUI, MIKEL author Larrinaga, Felix author ARRAZOLA, PEDRO JOSE author 2023 In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations. 2212-8271 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174161 https://hdl.handle.net/20.500.11984/6315 manufacturing optimization Decision-making Artificial Intelligence Machining ODS 9 Industria, innovación e infraestructura Optimal manufacturing configuration selection: sequential decision making and optimization using reinforcement learning