TY - GEN A1 - Herrmann, Frank A1 - Schoen, Maximilian A1 - Claus, Thorsten A1 - Dai, Chenghao T1 - Aggregate Production Planning under Risk of Disruption T2 - OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich N2 - In recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network. The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs. Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation. This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified. Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated. KW - Supply Chain Resiliance KW - Aggregate Production Planning KW - Supply Chain Disruption Y1 - 2024 ER - TY - CHAP A1 - Dai, Chenghao A1 - Schön, Maximilian A1 - Claus, Thorsten A1 - Herrmann, Frank T1 - Multi-Site Aggregate Production Planning With Resilience Consideration T2 - Proceedings of the 38th ECMS International Conference on Modeling and Simulation (ECMS 2024): June 4th - June 7th, 2024, Cracow, Poland N2 - The recent years have shown a high frequency of disruptions like natural disasters or manmade disruptions, leading, for example, to transport routes or production facilities being unavailable for extended periods. Many manufacturing companies, which nowadays operate in a global production network, are heavily exposed to these disruptions. This results in both significant costs and substantial exceeding of promised deadlines. If it concerns the first company in a supply chain, it causes correspondingly significant delays in promised deadlines for subsequent companies in the supply chain, including a correspondingly significant increase in costs. Both are exemplified in this paper through a case study. This case study demonstrates that through resilience, both implications can be significantly reduced. KW - Production Planning and Control KW - Aggregate Production Planning KW - Linear Programming KW - Multi-Site Production Planning and Control KW - Supply Chain Resilience Y1 - 2024 SN - 978-3-937436-84-5 U6 - https://doi.org/10.7148/2024-0345 SN - 2522-2422 SP - 345 EP - 351 PB - ECMS ER -