Performance Analysis of Bernoulli Serial Lines With Small Batch Production and Machine Switch On/Off Control

Abstract

Manufacturing industries play a pivotal role in global economies. It is a powerful driver of economic growth, creating a significant number of job opportunities and providing support for innovation and technological development. In the present world, where environmental concerns and resource conservation are paramount, reducing energy consumption while ensuring high processing quality is a significant challenge. During production, controlling the on/off of the machine reasonably can reduce energy consumption. This paper explores a method to analyze the performance of production lines containing buffers of finite capacity and machines that can be switched on and off when the specific conditions are met. We focus on finite production processes where the number of products is limited. In this paper, we establish a mathematical model for a three-machine production line and propose analytical methods for calculating production performance indicators. The analysis of the three-machine line employs the Markov method. Additionally, an aggregation method is introduced to extend the analysis to multi-machine systems, yielding promising results through numerical experiments. This research contributes to the advancement of production systems, improving a sustainable and competitive future for the manufacturing industry. Note to Practitioners—Small-batch production refers to the production of a small quantity of products, which can achieve customized order processing and improve resource utilization. Due to the dynamic nature of such processes, the traditional steady-state analysis method may not be applicable. This paper proposes an analytical method to predict the dynamic behavior of small batch production systems with Bernoulli machines that can be switch-on/off controlled and with finite capacity buffers. The algorithm developed in this paper can be used and support the production managers and engineers to predict the dynamic performance of the system with high accuracy. It can assist in decision-making in production control activities.