An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times

Abstract

The distributed manufacturing mode which is widely used in the modern manufacturing system often contains the different status of different workshops, called as the heterogeneous workshops. However, the existing work of distributed shop scheduling assume that there are identical workshops, which lacks the consideration of practical constraints about heterogeneous workshops. Therefore, this paper firstly focuses on the distributed heterogeneous hybrid flowshop scheduling problem (DHHFSP) with unrelated parallel machines (UPM) and the sequence-dependent setup time (SDST). This is a typical NP-hard problem which is quite hard to be solved. This paper designs a machine position-based mathematical model and proposes an improved artificial bee colony (IABC) algorithm for this problem. The proposed IABC employs a two-level encoding and a decoding method of the machine selection to ensure feasible schedules. The IABC adopts the factory assignment rule and greedy iterative strategy to generate high quality initial solutions. And the IABC adopts solutions update techniques: the local exploitation around critical factories, a hybrid search strategy combines the advantages of simulated annealing (SA) and a retention mechanism. These techniques can keep the diversity of solution space and enhance the computational efficiency. There are 320 instances randomly generated and used to verify the performance of the IABC. Through the comparison with the reported state-of-the-art algorithms, the effectiveness of proposed IABC is shown clearly.