The sand-casting process: a meta-heuristic approach for an environment-friendly and profitable scheduling

Abstract

Sand-casting is a process with a high-environmental impact in terms of both energy consumption and pollution emission. This work presents a meta-heuristic approach for the typical scheduling problem of mid-size sand casting foundries, with the objective to minimise the costs associated with labour, energy and wastes of melted material. Mid-size foundries usually have multiple parallel melting lines, each composed by a rotary and an electric furnace, and a single casting line which is the bottleneck of the process. The proposed approach has been tested on data related to a real industrial case study. Results show that, with respect to the scheduling implemented by the company, the proposed algorithm achieves a 3.2% reduction in emissions and a 4.1% reduction in energy consumption. Furthermore, these environment-friendly results are achieved while also increasing the company's profitability, by reducing total costs of more than 1% on the total revenue.

KEYWORDS:

• Energy saving
• Genetic algorithm
• Sand-casting
• Pollution
• Environment

Data availability statement

The data that support the findings of this study are openly available in ResearchGate at: http://doi.org/10.13140/RG.2.2.22636.44162

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Lorenzo Tiacci

Lorenzo Tiacci is currently Associate Professor of Industrial Systems Engineering at the Department of Engineering at University of Perugia (Italy), where he also received his Ph.D. in Industrial Engineering. His work and research are mainly focused on industrial and manufacturing systems design and management using optimisation via simulation and other methods from Operations Research.

Andrea Rossi

Andrea Rossi is a Ph.D. student in Industrial Engineering at University of Perugia (Italy). He obtained his master’s degree in Mechanical Engineering from the same university. Andrea's research focuses on production planning, scheduling, and simulation of manufacturing systems.