A hybrid imperialist competitive algorithm for energy-efficient flexible job shop scheduling problem with variable-size sublots

Variable-size batching method has a higher possibility to obtain the most flexible scheduling schemes, which can be better applied to the manufacturing process. However, changing the sizes and number of sublots will not only increase the energy consumption of the manufacturing process, but also increase the search space of the scheduling schemes. A multi-objective optimization problem is formulated considering the makespan and total energy consumption simultaneously. Then, a two-stage multi-objective hybrid algorithm (HICSA) combining imperialist competitive algorithm (ICA) and simulated annealing algorithm (SAA) is proposed to solve this problem and improve the searching efficiency. The ICA and SAA are used to search for the suitable job sequences with optimal machine assignment and the optimal lot splitting schemes in the two stages, respectively. In order to improve the search efficiency, a novel mapping strategy that turns the discrete space into continuous space is applied. Extensive experiments are conducted and the computational results show that HICSA provides prom-ising results for the problem.

Automated summary

This paper proposes a variable-size batching method and discusses its application in the manufacturing process. By formulating a multi-objective optimization problem and introducing an improved hybrid algorithm, the issue of increased energy consumption and search space of scheduling schemes due to changing batch sizes is addressed. Experimental results demonstrate the effectiveness of this approach.