Next Article in Journal
Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
Next Article in Special Issue
Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics
Previous Article in Journal
Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
Open AccessArticle

Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

1
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2
School of Transportation, Ludong University, Yantai 264025, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(2), 44; https://doi.org/10.3390/a13020044
Received: 1 February 2020 / Revised: 18 February 2020 / Accepted: 18 February 2020 / Published: 20 February 2020
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study. View Full-Text
Keywords: flexible job shop; energy-aware scheduling; energy consumption; modified migrating birds optimization flexible job shop; energy-aware scheduling; energy consumption; modified migrating birds optimization
Show Figures

Figure 1

MDPI and ACS Style

Li, H.; Zhu, H.; Jiang, T. Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem. Algorithms 2020, 13, 44. https://doi.org/10.3390/a13020044

AMA Style

Li H, Zhu H, Jiang T. Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem. Algorithms. 2020; 13(2):44. https://doi.org/10.3390/a13020044

Chicago/Turabian Style

Li, Hongchan; Zhu, Haodong; Jiang, Tianhua. 2020. "Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem" Algorithms 13, no. 2: 44. https://doi.org/10.3390/a13020044

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop