A hybrid neural network approach to minimize total completion time on a single batch processing machine

Abstract

AbstractIn this research, the problem of scheduling a single batch processing machine (BPM) with nonidentical job sizes is considered. The objective is to minimize the total completion time of the jobs. A BPM can process a group of jobs simultaneously as a batch as long as its capacity is not violated. The processing time of a batch is equal to the maximum processing time of all the jobs in the batch. The research problem is formulated as a mixed integer linear programming model. Since the problem under study is shown to be NP‐hard, a neural network based approach that combines a heuristic algorithm and the iterative learning approach is proposed. The computational experiments are designed to compare the effectiveness of the neural network approach with CPLEX and the other available heuristics in the literature. The results show that the proposed approach outperforms the other algorithms in terms of solution quality.