An improved memetic algorithm to minimize earliness–tardiness on a single batch processing machine

In this research, a single batch processing machine scheduling problem with minimization of total earliness and tardiness as the objective function is investigated.We first formulate the problem as a mixed integer linear programming model. Since the research problem is shown to be NP-hard, an improved memetic algorithmis proposed to efficiently solve the problem. To further enhance the memetic algorithm and avoid premature convergence, we hybridize it with a variable neighborhood search procedureas its local search engine. A dynamic programming approach is also proposed to find optimal schedule for a given set of batches. Wedesign a Taguchi experiment to evaluate the effects of different parameters on the performance of the proposed algorithm. The results of an extensive computational study demonstrate the efficacy of the proposed algorithm.