Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

Abstract

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

A Comparative study of gray relational analysis and VlseKriterijumska Optimizacija I Kompromisno Resenje approaches for enhancing mechanical properties and productivity in 3D-Printed copper-filled PLA parts

“…This lack in the literature is the principal motivation of our work. Hence, in this article, we optimize the printing parameters of composite printed samples copper-filled PLA using the Taguchi method for the design of experiments coupled to the Gray Relational Analysis (GRA), and the Multi-Criteria Decision VlseKriterijuska Optimizacija I Komoromisno Re-senje (VIKOR) methods for multi-objective optimization of flexural strength 18, 19, 20, 21, 22,23 The aim of this paper is to assess the accuracy of various models in predicting the optimal parameters for multi-objective optimization. Additionally, it explores the lack of consensus regarding the impact of parameters on both mechanical properties and printing time…”