Work

Integrated Scheduling of Multi-Objective Job Shops and Material Handling Robots with Reinforcement Learning Guided Meta-Heuristics

Year: 2024

Type: article

Abstract: This study investigates the integrated multi-objective scheduling problems of job shops and material handling robots (MHR) with minimising the maximum completion time (makespan), earliness or tardiness, and total energy consumption. The collaborative scheduling of MHR and machines can enhance efficiency and reduce costs. First, a mathematical model is constructed to articulate the concerned problems. Second, three meta-heuristics, i.e., genetic algorithm (GA), differential evolution, and harmony search, are employed, and their variants with seven local search operators are devised to enhance solution quality. Then, reinforcement learning algorithms, i.e., Q-learning and state–action–reward–state–action (SARSA), are utilised to select suitable local search operators during iterations. Three reward setting strategies are designed for reinforcement learning algorithms. Finally, the proposed algorithms are examined by solving 82 benchmark instances. Based on the solutions and their analysis, we conclude that the proposed GA integrating SARSA with the first reward setting strategy is the most competitive one among 27 compared algorithms.

Source: Mathematics

DOI: 10.3390/math13010102

Authors: Zhangying Xu, Qi Jia, Kaizhou Gao, Yaping Fu, Li Yin, Qiangqiang Sun

Institutions: Macau University of Science and Technology, Qingdao University, Shandong University of Aeronautics

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province