TY - CHAP A1 - Müller, Stefan A1 - Danner, Christian A1 - Breidbach, Jörg A1 - Seidl, Markus A1 - Hußlein, Thomas A1 - Lauf, Wolfgang T1 - Stochastic Modeling of Throughput Times of Supplier Parts for an Automotive Plant T2 - Angewandte Forschung in der Wirtschaftsinformatik - Prozesse, Technologie, Anwendungen, Systeme und Management 2014: Tagungsband zur 27. AKWI-Jahrestagung vom 07. bis zum 10.09.2014 an der Oberbayerischen Technischen Hochschule Regensburg N2 - For modern production industries optimized supply of parts is a critical success factor. Especially in the automotive industry, the reliable supply of painted bodies for the fi nal assembly is essential for OEMs considering just in time or just in sequence processes. Additionally one central cost target is the avoidance of resorting for JIT/JIS processes. An essential workfl ow analysis is therefore the basis for making strategic or operational decisions such as the adjustment of the plant structure or increasing the number of products and variants manufactured simultaneously. In order to support strategic planning using simulation and planning systems, the modeling of the production process is required. This is particularly important for scheduling as no real manufacturing data is available and therefore prognosis based on the model are essential. Due to the short time and the limited human resources for a detailed modeling, it is essential to create a robust model with good accuracy and reduced complexity Y1 - 2014 SN - 978-3-936527-38-4 U6 - https://doi.org/10.15771/978-3-936527-38-4 SP - 35 EP - 43 S PB - News & Media ER - TY - CHAP A1 - Schmidbauer, Lukas A1 - Riofrío, Carlos A. A1 - Heinrich, Florian A1 - Junk, Vanessa A1 - Schwenk, Ulrich A1 - Husslein, Thomas A1 - Mauerer, Wolfgang T1 - Path Matters: Industrial Data Meet Quantum Optimization T2 - 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque N2 - Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible transformation paths-from industry-relevant formulations through binary constrained linear programs (BILPs), to quadratic unconstrained binary optimization (QUBO), and finally to a hardware-executable representation-is remarkably large. Each step introduces free parameters, such as Lagrange multipliers, encoding strategies, slack variables, rounding schemes or algorithmic choices-making brute-force exploration of all paths intractable. In this work, we benchmark a representative subset of these transformation paths using a realworld industrial production planning problem with industry data: the optimization of work allocation in a press shop producing vehicle parts. We focus on QUBO reformulations and algorithmic parameters for both quantum annealing (QA) and the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA). Our goal is to identify a reduced set of effective configurations applicable to similar industrial settings. Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance. Hence, the choice of hardware and solver strategy significantly impacts performance. The problem formulation and especially the penalization strategy determine the solution quality. Most importantly, mathematically-defined penalization strategies are equally successful as hand-picked penalty factors, paving the way for automated QUBO formulation. Moreover, we observe a strong correlation between simulated and quantum annealing performance metrics, offering a scalable proxy for predicting QA behavior on larger problem instances. Y1 - 2025 U6 - https://doi.org/10.1109/QCE65121.2025.00230 SP - 2101 EP - 2111 PB - IEEE ER -