Work

Multi-Objective Optimization of Energy-Efficient Multi-Stage, Multi-Level Assembly Job Shop Scheduling

Year: 2024

Type: article

Abstract: The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters, thus the convergence and diversity of the algorithmic search is enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving EEMsMlAJSP is verified by comparing with other algorithms.

Source: Applied Sciences

DOI: 10.3390/app14198712

Authors: Yingqian Dong, Weizhi Liao, Bizhen Bao, Weigang Xu, Guodong Xu

Institutions: Kunming University of Science and Technology, University of Electronic Science and Technology of China

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728