Sobottka, T., Kamhuber, F., & Heinzl, B. (2020). Simulation-Based Multi-Criteria Optimization of Parallel Heat Treatment Furnaces at a Casting Manufacturer. Journal of Manufacturing and Materials Processing, 4(3), 94. https://doi.org/10.3390/jmmp4030094
E191-03 - Forschungsbereich Automation Systems E330-02 - Forschungsbereich Betriebstechnik, Systemplanung und Facility Management
Journal of Manufacturing and Materials Processing
Number of Pages:
Mechanical Engineering; Mechanics of Materials; optimization; simulation; scheduling; Industrial and Manufacturing Engineering; case study; energy efficiency; heuristics; genetic algorithm; production planning and control; heat treatment; batching
This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a major requirement for production enterprises, especially for energy intensive production sectors such as casting. Despite the significant energy-efficiency potential through optimized planning and the acknowledged application potential for sophisticated simulation-based methods, digital tools for practical planning applications are still lacking. The authors develop a planning method featuring a hybrid (discrete-continuous) simulation-based multi-criteria optimization (a multi-stage hybrid heuristic and metaheuristic method) for a metal casting manufacturer and apply it to a heat treatment process, that requires order batching and sequencing/scheduling on parallel machines, considering complex restrictions. The results show a ~10% global goal optimization potential, including traditional business goals and energy efficiency, with a ~6% energy optimization. A basic feasibility demonstration of applying the method to synchronize energy demand with fluctuating supply by considering flexible energy prices is conducted. The method is designed to be included in the planning loop of metal casting companies: receiving orders, machine availability, temperature data and (optional) current energy market price-data as input and returning an optimized plan to the production-IT systems for implementation.
Adaptive Smoothed Production (FFG - Österr. Forschungsförderungs- gesellschaft mbH)