Abstract
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.
Highlights
Heat treatment processes represent one of the most energy-intensive manufacturing processes of cast steel production and usually accounts for ~70% of the total process energy consumption [1]
The manual optimization currently implemented in the company is equivalent to the results of the batching heuristic, which is the starting point for the optimization trends for the hybrid heuristic and genetic algorithm (GA); the optimization potential at the end of the simulation runs is roughly equivalent to the potential gains through the planning method over the current manual planning process for the heat treatment
Our contribution shows that the basic concept of a multi-stage hybrid optimization featuring hybrid simulation for the planning task of batching and scheduling, plus assigning orders—batches of orders in this case—to parallel machines is practically feasible and provides considerably better performance than the limited manual planning currently implemented in the company
Summary
Heat treatment processes represent one of the most energy-intensive manufacturing processes of cast steel production and usually accounts for ~70% of the total process energy consumption [1]. The manufacturing industry accounts for 36% of CO2 emissions and is responsible for 31% of the corresponding primary energy consumption [2] and digital planning methods are a major potential contributor toward increasing energy efficiency for production companies [3]. The optimized planning considers a batch process and order scheduling/sequencing on parallel machines, with several technological restrictions—e.g., not all products are eligible for processing on all machines—and a complex system behavior, i.e., limited crane handling capacity for loading/unloading of heat treatment furnaces and a complex energy system with heat exchange between considerable material masses, the furnaces and the production hall. Cheng developed an ant colony optimization for batching in a considerably simpler system of a single machine process [14]
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