Mathirajan, M.Reddy, S.Vimala Rani, M.V.Dhaval, P.2026-02-042023International Journal of Industrial and Systems Engineering, 2023, 43, 1, pp. 20-5817485037https://doi.org/10.1504/IJISE.2021.10042607https://idr.nitk.ac.in/handle/123456789/22173This study applies artificial neural network (ANN) to achieve more accurate parameter estimations in calculating job-priority-data of jobs and the same is applied in a proposed dispatching rule-based greedy heuristic algorithm (DR-GHA) for efficiently scheduling a burn-in oven (BO) problem. The integration of ANN and DR-GHA is called as a hybrid neural network (HNN) algorithm. Accordingly, this study proposed eight variants of HNN algorithms by proposing eight variants of DR-GHA for scheduling a BO. The series of computational analyses (empirical and statistical) indicated that each of the variants of proposed HNN is significantly enhancing the performance of the respective proposed variants of DR-GHA for scheduling a BO. That is, more accurate parameter estimations in calculating job-priority-data for DR-GHA via back-propagation ANN leads to high-quality schedules w.r.t. total weighted tardiness. Further, proposed HNN variant: HNN-ODD is outperforming relatively with other HNN variants and provides very near optimal/estimated solution. © © 2023 Inderscience Enterprises Ltd.BackpropagationJob shop schedulingNeural networksOptimal systemsOvensParameter estimationScheduling algorithmsSemiconductor device manufactureDispatching rule-based greedy heuristic algorithmDispatching rulesEstimated optimal solutionGHAGreedy heuristic algorithmGreedy heuristicsHeuristics algorithmHybrid neural networksOptimal solutionsRule basedSemiconductor manufacturingHeuristic algorithms