An Optimal Computing Allocation Strategy for Swarm-Based Algorithm in Simulation-Based Optimization Problem with Stochastic Constraint

• Published in: Asia-Pacific journal of operational research
• Main Authors: Chiu, Chun-Chih, Lai, Chyh-Ming, Liu, Yu-Chi
• Format: Journal Article
• Language: English
• Published: 22.03.2024
• ISSN: 0217-5959; 1793-7019
• DOI: 10.1142/S0217595924400025

For realizing smart manufacturing, recent studies have been focusing on the capabilities of digital twins (DTs) that offer a virtual portrayal of a process or system and thereby facilitate real-time monitoring, analysis, and optimization. DTs construct a “digital model” of factories by using advanced technologies such as smart sensors, Internet of Things (IoT), artificial intelligence (AI) techniques, and optimization processes for executing critical decisions for enhancing productivity and predicting future events. Therefore, simulation-based optimization techniques play key supporting roles in the decision-making process of the DT shop. Thus, to enhance competitiveness in industrial companies, discourse on properly designing simulation-based optimization techniques begets significant study. This study aims to address a simulation-based optimization problem with stochastic constraint (SO[Formula: see text] using a hybrid particle swarm optimization (PSO). Given the large solution space, the PSO supports design space exploration. However, since all solutions must be verified for feasibility, the optimal computing allocation strategy (OCAS) strategy for SO[Formula: see text] is proposed to allocate the computing budget efficiently. OCAS is an improved version of optimal computing budget allocation for constrained optimization (OCBA-CO), specifically designed for complex systems with an expansive design space that possesses multiple local optimal solutions. During a typical PSO algorithm searching process, OCBA-CO is applied separately for each generation, resulting in a significant number of replications merely distinguishing similar solutions. To overcome this problem, our study proposes the PSO[Formula: see text], which combines PSO with OCAS to avoid wasting simulation runs on similar solutions. Two quantitative experiments are performed to assess the efficacy of PSO[Formula: see text] compared to other competitors. The simulation results indicate that the PSO[Formula: see text] algorithm presents a competitive and superior solution in terms of both quality and searching efficiency.