Optimizing Flow Shop Scheduling for Energy Efficiency and Sustainability in Semiconductor Manufacturing: A Comparative Study of NEH and PIG_NEH Algorithms
| Authors | Nameet Kumar Sethy1, Dhiren Kumar Behera2 |
| Affiliations |
1Department of MECH, Indira Gandhi Institute of Technology, Sarang, BPUT, Rourkela, Odisha, India 2Department of PROD, Indira Gandhi Institute of Technology, Sarang, BPUT, Rourkela, Odisha, India |
| Е-mail | nameet158@gmail.com |
| Issue | Volume 17, Year 2025, Number 5 |
| Dates | Received 12 August 2025; revised manuscript received 16 October 2025; published online 30 October 2025 |
| Citation | Nameet Kumar Sethy, Dhiren Kumar Behera, J. Nano- Electron. Phys. 17 No 5, 05032 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(5).05032 |
| PACS Number(s) | 07.05.Tp, 73.61.Jc |
| Keywords | NEH, PIG_NEH, span, Flow-shop, Scheduling, Back-end semiconductors manufacturing. |
| Annotation |
This work contributes to an important challenge faced by the semiconductor backend manufacturing industry with high operational complexity and energy consumption. Traditional NEH scheduling algorithms minimize makespan but do not consider the energy and sustainability objective. To address this, we introduced and evaluated a new hybrid algorithm, PIG_NEH (Population-based Iterated Greedy NEH), which incorporates iterative refinement and population based search methodologies. The method is based on computer simulations implemented in Python (v3. 11) on a Windows 11 machine powered by a 2.41GHz AMD Ryzen 3 CPU paired with 4GB of RAM. The experiments, through the use of static data, saw evaluations of scheduling performance for five distinct parameter configurations, including four (4) datasets with sizes of 8, 20, 30 and 50, using 5 and 20 machines. We evaluated metrics like makespan, energy efficiency and computational time. Statistical comparisons were made between NEH and PIG_NEH and shown as t-tests to highlight trade-offs. PIG_NEH reduces makespan by a further 1.85% over NEH as well as increases energy efficiency although a 37,622.54 % computational overhead is attained. These results contribute to sustainable scheduling practices in two ways, with the first being improved utilization of resources and the second being the integration of energy-saving goals into the manufacturing systems. |
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