Production simulator system for flexible routing optimization in flexible manufacturing systems

Abstract

Optimizing flexible routing in flexible manufacturing systems ameliorates the efficiency of flexible manufacturing systems. In the present competitive market, accuracy in the planning stage plays an important role. Therefore, in this article, a production simulator system based on genetic algorithms is utilized to find the near-optimal flexible routing for flexible manufacturing systems. A combination of production simulator system and genetic algorithms is introduced to find the appropriate order of a set of operations for jobs that need to be operated on available machine tools in flexible manufacturing systems. In order to augment genetic algorithms, a matrix encoding method is incorporated. The proposed simulation is applied to numerical case studies of flexible routing for flexible manufacturing systems problem. The purpose of case studies is to demonstrate the successful applicability of the proposed method for flexible routing for flexible manufacturing systems problem.

The impact of organizational support on employee performance

“…On the one hand, the rapid development of information, automation and digital manufacturing technologies has led to the wide use of flexible manufacturing systems (FMS) in various industries, accelerating the application of intelligent processes to manufacturing enterprises. On the other hand, as China’s economy developed and consumers’ personalization requirements increased (Yizhong et al , 2019), wider product lines and the rapid introduction of new products have become key competitive levers where FMS play an important role (Abu Qudeiri, 2017). A FMS is defined as “an integrated group of processing CNC machines and material-handling equipment under computer control for the automatic processing of palletized parts” (Elmaraghy and Caggiano, 2016).…”

Mathematical Modeling of the Sequence of Machining Sections of Complex Surfaces When Milling on a Triaxial CNC Machine Tool

“…Based on a conducted literature review [25], [26], [27], [28], [29], [30] a method is proposed for determining the optimal route of cutters of one size when moving among one group of sections subject to machining. The method is implemented in two stages:  at the first stage, an option for minimizing the idle moves of the tool when changing the machined section is sought.…”

A tool path optimization approach based on blend feature simplification for multi-cavity machining of complex parts

“… 21 , 22 GAs do not directly use numerical values as operands, but express variables in the form of genetic code and then optimize them. 23 Through genetics, crossovers, and mutations, it is possible to search for the optimal sequence combination of the worldwide genetic code, which offers the advantage of high parallelism and strong robustness. 24 Therefore, it is more appropriate to use a GA to solve the optimization problem of a tool path.…”