Automated mobile robots routing and job assignment in automated factory

Abstract

In the era of Industry 4.0, smart manufacturing and automated factories require advanced software and hardware resources. The decisions on AMR routing and job assignment are two of the key issues in automated factories. In this paper, we investigate the key issues by establishing a mixed integer linear programming model to minimize the makespan for a batch of products. As the problem considers the joint optimization of AMR routing, job-to-AMR assignments, job-to-machine assignments, and job sequencing on machines, the proposed model is comprehensive but complex, with million integer variables and constraints. To tackle this complexity, we introduce a hybrid heuristic approach based on variable neighborhood search and adaptive large neighborhood search algorithms. Three accelerating tactics, two specific neighborhood structures, and six tailored operators are devised to handle large-scale instances efficiently. Remarkably, our metaheuristic can handle 280 jobs within 800 s, resulting in twice the efficiency achieved by traditional factories. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful to increase the operational efficiency in automated factory management.