Multi-objective Energy-Aware Batch Scheduling Using Ant Colony Optimization Algorithm.

A Abstracted from industrial manufacturing process, scheduling on batch processing machines (BPMs) is known to be an NP-hard discrete optimization problem. Therefore, researchers have resorted to meta-heuristics to tackle such challenging tasks. This paper investigates the scheduling problem on a set of BPMs, arranged in parallel, which have different processing powers. The jobs have different sizes, processing times and release times. A bi-objective ant colony optimization algorithm is proposed to minimize the makespan and the total energy consumption. Due to the complex constraints in the problem, how to find a feasible solution is a challenging issue in discrete optimization. Thus, an effective method to construct the feasible solutions is presented so that the ant colony only needs to focus on the promising area in the search space. Additionally, the user's preferences are incorporated to build the solutions. Furthermore, a neighborhood-based local optimization is used to improve the solutions so that the exploration and exploitation capabilities of the ant colony are able to be exerted adequately. The proposed algorithm is verified by elaborately designed simulations. The results show that the proposed algorithm provides the better solutions than the state-of-the-art algorithms, especially on large-scale problems.