A Knowledge-Based Estimation of Distribution Algorithm for Product Assembly Line Balancing

Assembling is a critical process within the manufacturing industries, where the efficiency of assembly lines directly affects the manufacturing productivity. To improve the overall efficiency, this paper proposes a knowledge-based Estimation of Distribution Algorithm (kEDA) specifically designed for the Robotic Assembly Line Balancing Problem. The algorithm utilizes a sampling technique based on a tailored probability model within kEDA to determine the task-workstation assignment. Additionally, it incorporates knowledge-based solution repair and optimization operators to minimize cycle time. The simulation results of various problem scales demonstrate that our algorithm outperforms classic algorithms in terms of the cycle time.

I. Introduction

In recent years, automation and mechanization have been widely applied in the manufacturing industry. This trend is apparent in the use of electronic product assembly lines, which are designed to streamline the assembly process for electronic products. These assembly lines contain a series of workstations, each accountable for a specific task in the assembly process. Task assignment within the assembly line is complex and time-consuming. To address this challenge and determine the robot-workstation and the task-workstation assignment within the electronic production assembly line, the Robotic Assembly Line Balancing Problem (RALBP) is considered in this paper to ensure efficient and effective production.