Journal of Engineering, Project, and Production Management, 2026, 16(7), 2025-340
Green Flexible Job Shop Scheduling Using Genetic Algorithms with Adaptive Neighborhood Search
1 Associate
Professor, School of Intelligent Manufacturing, Tianjin College,
University of Science and Technology Beijing, Tianjin 301800, China
2 Lecturer, School of Intelligent
Manufacturing, Tianjin College, University of Science and Technology
Beijing, Tianjin 301800, China, Email: Linawang.w@outlook.com
(corresponding author).
3 Bachelor of Engineering, Tianjin
College, University of Science and Technology Beijing, Tianjin 301800,
China
4 Bachelor of Materials Science and
Engineering, Tianjin College, University of Science and Technology
Beijing, Tianjin 301800, China
5 Bachelor of Engineering, Computer
Science and Technology, Tianjin College, University of Science and
Technology Beijing, Tianjin 301800, China
Production Management
Received December 17, 2025; revised May 26, 2026; accepted July 12, 2026
Available online July 25, 2026
Abstract: With the rise of the concept of green manufacturing, incorporating energy consumption-related objectives into scheduling problems has become an important research field. Combined with actual production scenarios, this study constructs a mathematical model for the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions. To address the limitation of the traditional Genetic Algorithm (GA) in terms of insufficient local search capability, an Adaptive Genetic Algorithm (AGA) is designed to solve the model. A population initialization method that integrates global and local load minimization is proposed to accelerate the elimination of inferior individuals; the elite retention and roulette wheel selection strategies are combined to prevent the algorithm from falling into local optima. Simulation tests based on standard benchmark instances show that the improved GA can effectively solve the MO-FJGSP, significantly improving both the solution speed and quality. This study provides a novel methodological approach to optimizing production scheduling in green manufacturing environments.
Keywords: Multi-objective; flexible job shop; green scheduling; genetic algorithm.
Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Requests for reprints and permissions at eppm.journal@gmail.com.
Citation: Li, M., Wang, L., Wang, J., Ma, S., and Liu, Z. (2026). Green Flexible Job Shop Scheduling Using Genetic Algorithms with Adaptive Neighborhood Search. Journal of Engineering, Project, and Production Management, 16(7), 2025-340.
