Advanced Production Planning: Models and Algorithms for Advanced Planning and Scheduling

Our department »Operations Research« develops powerful mathematical models and optimization algorithms for Advanced Planning and Scheduling (APS) systems. This enables us to support companies in efficiently planning, flexibly controlling and continuously improving their complex production processes - even under dynamic conditions. Our solutions make even extensive production networks manageable and help to make optimal use of resources, meet delivery deadlines and minimize throughput times.

Efficient Production Planning in Complex Environments

Planners in manufacturing companies are faced with the challenge of optimally utilizing resources such as machines, tools and personnel while at the same time ensuring agreed delivery dates. The main objective is to avoid delays, but short throughput times, high resource utilization and the prevention of unnecessary pre-production are just as relevant.

Production resources are often heterogeneous: capacities, availabilities and stock levels must be taken into account, as must the exact sequence of the individual work steps. These are often not linear, but form complex networks that contain multiple dependencies.

Digital Planning for Thousands of Orders

With several thousand production orders, dozens of machines and extensive material flows, manual planning approaches quickly reach their limits. Digital APS systems have therefore become indispensable.

At Fraunhofer ITWM, we develop flexible mathematical models that can capture all relevant operational restrictions and thus ensure the feasibility of production plans even under complex conditions. Powerful optimization algorithms use these models to determine solutions that take several objectives into account simultaneously. Flexible parameterization allows the models to be tailored precisely to different production scenarios.

Rolling Planning: Flexible and Stable

Production plans usually extend months or even years into the future in order to secure capacities, order materials in good time and communicate binding delivery dates. However, as order situations, availability and other framework conditions are constantly changing, APS systems must support rolling plan updates.

The challenge here is to optimize plans (delays, lead times, premature deliveries, resource utilization) on the one hand, while on the other hand taking into account preparations that have already been made and avoiding unnecessary changes in the near future. The models and algorithms we have developed explicitly take this challenge into account. They make it possible to evaluate various trade-offs between optimization and stability and to identify the effects of planning decisions at an early stage.