Prof. Dr. Lars Mönch
Photo: Photo: Hardy Welsch
Contact
Email: lars.moench
Institutional Affiliation
Faculty of Mathematics and Computer Science
Chair of Enterprise-wide Software Systems
Additional information: Profile (faculty page, in German only)
Research Interests
(in the fields covered by the research center)
In the high-tech sector, and in the semiconductor industry in particular, integrated circuits are manufactured using extremely expensive machinery. Traditionally, the main focuses have been on ensuring on-time delivery and high utilization of these machines. In recent years, however, sustainability aspects have also gained importance. This research group is interested in how sustainability aspects can be modeled in the supply chains of the high-tech industry and incorporated into production planning and scheduling problem algorithms as well as simulation models. Embedding the developed methods in information systems is another area that is being researched. In terms of methodology, the group primarily uses linear and mixed-integer optimization, simulation-based optimization, and multi-objective metaheuristics.
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(in the fields covered by the research center)
- Rocholl, J./Mönch, L./Fowler, J. W. (2018): Electricity Power Cost-aware Scheduling of Jobs on Parallel Batch Processing Machines, Proceedings of the 2018 Winter Simulation Conference, Göteborg (accepted for publication).
- Ziarnetzky, T./Mönch, L./Kannaian, T./Jimenez, J. (2017): Incorporating Elements of a Sustainable and Distributed Generation System into a Production Planning Model for a Wafer Fab, Proceedings of the 2017 Winter Simulation Conference, Las Vegas, pp. 3519–3530.
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(in the fields covered by the research center)
Energy-Efficient Batch Optimization Algorithms (E2BOA)
The research project is funded as part of the progres.NRW program.
In the semiconductor industry, integrated circuits – otherwise known as “chips” – are fabricated on silicon wafers in production facilities known as wafer fabs. These typically comprise several hundred, often very expensive, machines that are set up in a clean room environment. Functionally similar machines are arranged to form machine groups. On-time delivery is very important due to the highly competitive nature of the market. Wafer fabs are among the most complex production systems in existence. Lots consisting of up to 50 wafers move through wafer fabs during processing. Depending on the configuration, the machines process single wafers, batches or entire batches. A batch is a group of lots that are processed simultaneously on a batch machine. Oxidation and diffusion stages take place on batch machines in the high-temperature zone of a wafer fab. The energy consumption of machines in the high-temperature zone is very high due to process temperatures of up to 1500°C. During the preliminary work [1], the team investigated an innovative scheduling model problem for groups of identical batch machines. This work is based on the assumption of time-dependent price tariffs for energy costs. The total weighted delay of the batches and the energy cost incurred in executing the flow schedule are considered as conflicting performance measures. Pareto-optimal flow charts are determined using natural-analog methods called genetic algorithms. On average, savings of about 25% related to the highest occurring energy costs are observable. Flow charts with low energy costs lead to reduced carbon dioxide emissions.
The model and the solutions being developed are being applied via the project “Energy-Efficient Batch Optimization Algorithms (E2BOA)” (funded by the NRW Ministry of Economic Affairs, Industry, Climate Action and Energy as part of their “Program for Rational Energy Use, Renewable Energies and Energy Saving – progres.nrw – Program Area Research”) in such a way that they are easier to use practically in a wafer fab. The algorithms from [1] are being modified to allow them to handle varying batch sizes per machine and batch family, as well as the very large instances found in practice. In addition to this, procedures are being developed to deal with uncertainties regarding the arrival of batches upstream of the machine group in the algorithms.
Literature:
Rocholl, J., Mönch, L., Fowler, J. (2020): Bi-criteria Parallel Batch Machine Scheduling to Minimize Total Weighted Tardiness and Electricity Cost. Journal of Business Economics, 90, 1345–1381. https://link.springer.com/article/10.1007/s11573-020-00970-6
