**5. Experiment**

In this section, the proposed SDABC algorithm and strategy will be evaluated through experiments. Firstly, the parameter settings of FHFGSP and the performance indicators of the evaluation algorithm are introduced. Then the proposed strategy is compared with other common strategies in experiments. Finally, SDABC is compared with other algorithms in experiments.

The algorithm proposed in this paper is coded in C++ and performed in Codeblocks 16.01. All experiments were run on a PC with an Intel(R) Core (TM) i3-8100U CPU, 3.60 GHz, and 8 GB RAM. Maximum CPU usage time *t* = 100 was used as a stopping criterion.

#### *5.1. Test Data*

In order to fully evaluate the performance of the algorithm from different levels, the performance of SDABC needs to be tested by selecting different problem instances. The parameters controlling the problem instances are *n*, *m,* and *st*. In this paper, to extensively test the ability of SDABC to solve HFSP of different sizes, five different levels of *n*, four different levels of *m* and four different levels of *st* were designed [34]. This results in 80 problem combinations of different levels. Once the *n, m*, and *st* of the problem instances have been determined, it is also necessary to set them separately for the job and the workshop environment. For job*<sup>i</sup>*, the processing speed *v* and the standard processing time *p* need to be set, and for the shop environment, the number of parallel machines per stage *k* needs to be set, by means of the previous problem description. It is also necessary to set the energy consumption per unit time of the machines in the processing phase, the setup phase and the idle phase. To avoid chance in the algorithm results, five instances were generated for each problem combination. In summary, the factors and their levels of FHFGSP in generating test data are summarized in Table 2.


