Minimization of total completion time on a batch processing machine with arbitrary release dates: an effectual teaching–learning based optimization approach

In this research study, a single machine batch-processing problem with release dates to minimize the total completion times of jobs is considered. The machine is able to process at most a certain number of jobs at the same time and the total size of the jobs allocated to a batch cannot exceed the machine capacity. Since the research problem has been shown to be NP-hard, an effective Teaching–Learning Based Optimization (TLBO) is proposed. A constructive heuristic approach is developed to generate initial feasible solutions for the TLBO. In order to enhance the efficiency of the proposed TLBO, a Tabu Search (TS) with three different neighborhood generation mechanisms is incorporated into the teaching phase and learner phase separately. To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature. The computational results show that the proposed TLBO with the incorporation of TS in its learning phase is able to come up with very good quality solutions.