Dhaval Pujara
Brij Disa Centre for Data Science and Artificial Intelligence, Indian Institute of Management, Ahmedabad, 380015, Gujarat, India.

M. Mathirajan
Department of Management Studies, Indian Institute of Science, Bangalore, 560012, Karnataka, India.

DOI https://doi.org/10.33889/IJMEMS.2024.10.3.038

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Abstract

This study addresses a new problem, close to real life on scheduling of non-identical parallel Burn-in ovens (BO) in Semiconductor Manufacturing (SM) industry to minimize Total Weighted Tardiness (TWT) of the jobs. Due to computational intractability in using mathematical programming approach, many researchers considered Dispatching Rules (DR) based heuristic algorithm in SM industry for scheduling of various batch processing machines, including BO. However, there is no study that compares the various DR considered for scheduling of BO in general, particularly in (a) Dynamic Scheduling (DS) of Non-identical Parallel Burn-in Ovens (NPBO) with Machine Eligibility Restriction (MER) and (b) Real-Time DS (RTDS) of NPBO with MER. To address this research gap, this study proposed 25 variants of DR based Heuristic Algorithm (DR-HA). Empirical and statistical performance analyses, carried out with 240 test data, revealed that variants of DR-HA that outperform during DS-NPBO with MER also outperform in RTDS-NPBO with MER. Furthermore, this study gives an important inference that whenever any RTE occurs in RTDS-NPBO, there is no need to modify existing efficient algorithm or no need to develop new algorithm for RTDS-NPBO and updating input data related to type of occurred RTE is sufficient before running existing efficient DR-HA for DS-NPBO.

Keywords- Scheduling, Burn-in ovens, Dispatching rules, Total weighted tardiness, Real-time events.

Citation

Pujara, D., & Mathirajan, M. (2025). Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction. International Journal of Mathematical, Engineering and Management Sciences, 10(3), 754-776. https://doi.org/10.33889/IJMEMS.2024.10.3.038.