Abstract
Production planning and control pursues high delivery reliability and short delivery time of the production system at the lowest possible costs. Especially in energy-intensive industries, energy cost account for a significant amount of manufacturing costs. The consideration of variable electricity market prices using energy-flexibility measures facilitates reduced costs by adapting the load profile of production to an electricity price forecast. However, it also increases the production planning and control system’s complexity by additional input variables and possible risks due to the influence of flexibility measures on the production system. In the case of unexpected events, such as failure of machines or faulty materials, it is difficult to adapt the complex production system to the new situation quickly. There is a risk of high additional costs by various causes, such as delays in deadlines or load peaks. Therefore, this paper presents an approach for developing and evaluating risk treatment paths, which include possible combinations of risks and measures for the mitigation of risk effects. The advantage of these paths compared to a situational reaction is that all effects and possible further interactions can be considered and thus overall cost-efficient solutions can be found. The approach is based on the determination of interactions through interpretive structural modelling and the calculation of conditional probabilities using Bayesian Networks. The approach was implemented in MATLAB® and applied using real order and energy data from a foundry. The results show that the presented approach enables structured and data-based comparison of risk treatment paths.
Highlights
Operational production planning and control (PPC) is an area of in-house production planning and includes the tasks of lot size planning, scheduling and sequence planning [1]
The PPC has ambitious goals for the performance of the production system: high delivery reliability and short delivery time, which should be achieved with the lowest possible production costs [2]
energy-flexibility measures (EFM) are risk treatment measures (RTMs) summarised in the term “measures.” The similar treatment of risks and measures, which are the opposite, enables their consideration in a uniform and common system that is essential for the approach presented in the following
Summary
Operational production planning and control (PPC) is an area of in-house production planning and includes the tasks of lot size planning, scheduling and sequence planning [1]. A fault occurs on machine one, which leads to repair work and a delay in the delivery date of the affected order 3 The effects of these faults on the electrical load profile are shown in the lower part of Fig. 1. In addition to a deviation, the unplanned follow-up order on machine two causes a load peak, leading to the abovementioned increased grid charges. The influencing factors and interactions of energy-flexible production systems are too complex to find a cost-efficient measure when an unexpected event occurs. This may represent a major obstacle to EFM implementation, since the production system’s performance should not be endangered for price advantages on the electricity markets [7, 8].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
