Browsing by Author "Schweitzer, Felicia Cathrin"

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    Algorithm selector for dynamic AGV scheduling in a smart manufacturing environment using machine learning
    (Stellenbosch : Stellenbosch University, 2022-11) Schweitzer, Felicia Cathrin; Louw, Louis; Bitsch, Günter; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Artificial intelligence is considered as significant technology for driving the future evolution of smart manufacturing environments forward. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems because they have such great potential when it comes to improving internal logistics by increasing production flexibility. Consequently, the productivity of the entire system relies on the quality of the schedule, which is capable of achieving massive cost savings by minimizing delay and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. That is why the analysis of scheduling problem classes can help to identify the most suitable algorithm depending on a given problem. Currently, the focus in the literature lies on individual algorithm approaches for specific AGV scheduling scenarios, but the influence of framework conditions to the algorithm performance lacks attention. More research is necessary in terms of the dynamic and independent reaction for optimizing the AGV scheduling procedure without human surveillance in case of failures. To develop an algorithm selection approach for AGV scheduling scenarios, this research answered the question of how machine learning approaches must be implemented so that the allocation of tasks in the context of dynamic AGV scheduling can be improved to increase performance. This study followed Design Science Research, particularly the cognition process based on Osterle et al. (2011) that builds on an analysis, design, evaluation, and diffusion phase. During the design phase, laboratory experiments unveiled the successful implementation of two constraint programming solvers for solving scheduling problems based on the Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP). OR-Tools developed by Google and CP Optimizer of IBM solved large instances in reasonable time, and the performance of the solver strongly depended on the given scheduling problem class and problem instance. Consequently, it is beneficial to make use of an algorithm selection, as the overall production performance increased by selecting the most suitable algorithm for a given instance. The field experiment within the learning factory of Reutlingen University enabled the implementation of the approach within a dynamic environment, that can react to disruptions like machine break-downs or AGV failures. As a limitation, the research considered a simplification of the AGV scheduling problem based on the JSSP and FJSSP. As such, the parameters are limited to transport orders, transport durations, sequences and AGVs. Furthermore, the training of the selector was with a limited amount of 544 benchmark instances. Nevertheless, this research showed an exemplary extension of existing scheduling approaches to develop an algorithm selection model which can be built upon in the future. This research places the focus on constraint programming solutions for scheduling problems and emphasizes the benefits of applying machine learning for algorithm selection on a per-instance base. In this way, scheduling systems can be computationally faster and more efficient in the future and help to achieve the desired overall performance of smart manufacturing systems.