This time-consuming step is now solved by hyperparameter optimization (HPO)<\/strong> – an “autopilot”<\/strong> from the world of machine learning. Find out how HPO can automatically increase<\/strong> the performance of heuristic planning algorithms and how HPO can be used to achieve better plant utilization and thus greater adherence to schedules using real industry examples. Read this technical article<\/strong> to find out how your company can revolutionize planning efficiency. <\/p>\n\n The optimization of hyperparameters is not only used in machine learning projects with sophisticated models and high performance or production maturity requirements – it is also rapidly becoming a practical enabler in modern manufacturing control systems by enabling better decisions. For optimization problems in this area, it is often difficult to determine optimal solutions due to their high combinatorial complexity. Therefore, heuristic methods are often used, which can generate good solutions within a reasonable runtime. One challenge of these solution approaches is that it is often difficult to determine a good parameterization and the solution quality achieved can therefore vary considerably for the same runtime. By automatically optimizing the parameters of planning algorithms, as well as predictive models and real-time decision rules, companies can achieve significant performance improvements. The result? Better production schedules, shorter lead times, smarter energy use. <\/p>\n<\/div>\n<\/div> Detailed planning is one of the most demanding tasks in production planning. Numerous work steps, which are linked to each other by predecessor and successor relationships, must be scheduled under a variety of restrictions. The restrictions include, for example, the appropriate resources, resource-dependent processing, setup and teardown times, overlaps, waiting or transportation times. Orders that consist of several work steps should not violate their earliest start times and should be completed on time. In addition, various optimization goals can be taken into account, such as minimizing order delays, minimizing order lead times or setup times. <\/p>\n\n\n\n Detailed planning in the smart factory is a challenge due to the enormous size of the problem and the large number of restrictions. In [1], a detailed planning algorithm based on tabu search was proposed that can solve real planning problems from industry. This optimization framework has been extended to further increase the performance on large-scale scheduling problems, see [2] and [3]. The optimization framework was used by RISC Software GmbH in cooperation with one of our industrial partners to solve production planning problems. In order to be able to flexibly adapt the detailed planning algorithm to the respective customer, many different parameters were introduced. However, this results in the difficulty of finding parameter sets that are very well suited to a customer’s planning data. This is usually very time-consuming and requires a comprehensive understanding of the data. Another challenge is to find parameter sets for different customers if they are looking at the same optimization target. <\/p>\n\n\n\n These questions regarding parameter sets were addressed and solved using hyperparameter optimization.<\/p>\n<\/div>\n<\/div> Hyperparameter optimization (HPO) is the process of determining the optimal hyperparameters for a machine learning model in order to maximize its performance for a given task. While early approaches were mainly based on manual adaptation and trial-and-error strategies, the increase in computing capacity and the rapid growth of machine learning in the 2000s enabled the development of systematic methods. Initially, automated methods such as grid search and random search were used, in which hyperparameters were tested either in a fixed search grid or by random sampling. From the 2010s onwards, Bayesian optimization found its way into HPO. More sophisticated techniques such as Gaussian processes or the Tree-Structured Parzen Estimator (TPE) became established, which made it possible to model the objective function better and to control the choice of hyperparameters more specifically. At the same time, powerful frameworks and open source tools such as Hyperopt, Optuna or Ray Tune were developed, making the use of hyperparameter optimization much easier and more widely accessible. <\/p>\n\n\n\n The main components of hyperparameter optimization include:<\/p>\n\n\n\n Figure 1: Overview of the HPO search algorithms that have become established in practice.<\/em><\/p>\n<\/div>\n<\/div>Contents<\/h2>\n\n\n\n
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<\/figure><\/div>\nWhat measurable added value does hyperparameter optimization deliver when applied to real production planning challenges?<\/h3>\n\n\n\n
Detailed planning<\/strong>– an essential component of the smart factory<\/h3>\n\n\n\n
Hyperparameter optimization: is the model as good as it could be?<\/h3>\n\n\n\n
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