An Improved Genetic Algorithm for Solving the Multi-Objective Flexible Job Shop Scheduling Problem

Lei Chen; Joan P. Lazaro

Lei Chen Graduate School, University of the East Manila, Philippines
Joan P. Lazaro Graduate School, University of the East Manila, Philippines

Abstract

Purpose – Aiming at the flexible job shop scheduling problem (FJSP), a multi-objective scheduling model with the maximum completion time and the minimum total processing energy consumption was constructed as the optimization objectives.

Method – To optimize the performance of the genetic algorithm, a diversified population strategy was adopted, combining elite retention and a random roulette wheel selection mechanism to screen and retain offspring with excellent performance carefully At the operational level of the algorithm, an efficient single-point crossover strategy was adopted for the process coding part to promote the combination and transmission of excellent characteristics. For the machine allocation part, a flexible random crossover method was introduced to increase the diversity of solutions. To explore new solution spaces and avoid premature convergence, a single-point mutation mutation strategy based on minimum machine energy consumption was designed.

Results – Simulated the workshop manufacturing of six machines and six job positions in a company. Compared to the original scheme, the optimized one reduced the production interval by 19.67%, proving its effectiveness.

Conclusion – There are other important objectives in actual production, and dual objective optimization may not fully reflect the complexity and diversity of the production system. In addition to dual objective optimization, considering multi-objective optimization methods may better capture the authenticity of the production system comprehensively.

Recommendations – By optimizing production scheduling, overall production efficiency can be improved and profit margins can be increased.

Practical Implications – This study provides a solution to improve production efficiency and reduce energy consumption in manufacturing environments. The proposed method has been validated through enterprise cases, demonstrating its effectiveness in optimizing maximum completion time and total energy consumption. Implementing this algorithm can improve profit margins and promote more sustainable production practices, aligning with the goals of modern manufacturing.

Author Biographies

Lei Chen, Graduate School, University of the East Manila, Philippines

Lei Chen received the bachelor's degree from the Nanchang Hangkong University in 2010 and the master's degree from the Guangdong University of Technology in 2013. He is currently a lecturer with Jiangxi College of Applied Technology and is pursuing a the PhD in Information Technology at University of the East, Manila in the Philippines. His research interest includes mechatronics, electronics and information technology.

Joan P. Lazaro, Graduate School, University of the East Manila, Philippines

Dr. Joan P. Lazaro is a full-time professor under the College of Engineering, Computer Engineering Department and a special lecturer of IT programs at the Graduate School in University of the East. He is a graduated of Doctor of Information Technology and Master of Engineering Science in University of the East Manila Graduate School and a Bachelor of Science in Computer Engineering in University of the East Caloocan. Among the different certification he earned are the following: Professional Computer Engineer, Fortinet’s Network Security Expert Certification – NSE 1 and 2 Network Security Associate, Certified Microsoft Innovative Educator Program and National Certificate II in Mechatronics Servicing. His research interest includes software development, network security and engineering sciences.