Modeling and Solving the Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry)

<table class="table-group" id="tab11"><tr><td><table class="table"><tr><td class="thead-hr" colspan="1"><hr/></td></tr><tr><td class="align_left">Iteration 1: Best fit = 275.83</td></tr><tr><td class="align_left">Iteration 2: Best fit = 271.27</td></tr><tr><td class="align_left">Iteration 3: Best fit = 266.83</td></tr><tr><td class="align_left">Iteration 4: Best fit = 261.79</td></tr><tr><td class="align_left">Iteration 5: Best fit = 259.83</td></tr><tr><td class="align_left">Iteration 6: Best fit = 259.83</td></tr><tr><td class="align_left">Iteration 7: Best fit = 259.83</td></tr><tr><td class="align_left">Iteration 8: Best fit = 254.83</td></tr><tr><td class="align_left">Iteration 9: Best fit = 254.83</td></tr><tr><td class="align_left">Iteration 10: Best fit = 254.83</td></tr><tr class="table-tr"><td colspan="1"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Results of objective functions in the ten iterations.</div>

Discrete Dynamics in Nature and Society

tab11

Table 11

Table 11: Modeling and Solving the Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry) 

Table 11 | Modeling and Solving the Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry) 