I have this case study and its answer : i want to know the steps and pics of simulation results (using any program for simulation)

case study :

Based on the two-stage optimization method which is combined with genetic algorithm and simulation technique, warehouse layout is designed under the background of warehouse construction in a battery manufacturing factory.

The total area of the warehouse was 4400 square meters, and there were 10 function regions. They were receiving space, inspection space, precious metal area, electrical components area, accessories area, standard parts area, imperfection space, sorting area, shipping space and office area respectively, and minimum areas of each function region were shown in table 1. According to correlation graph got by SLP method, initial scheme was formed. Logistics lines had three kinds, linear type, double-lined type and Utype. Genetic algorithm had 40 populations, 100 iterations, 0.4 crossover probabilities and 0.05 mutation probabilities. And the handling tool in the warehouse was track, the speed of which was 1m/s.

Table : The minimum area of each function region

the answer :

Based on the two-stage method referred to genetic algorithm and simulation technique, the location and the length and width of each function region was calculated using GA.

The first stage used 1.2s and the result was shown in table 2.

Table 4.2: The location and length and width of each function region obtained by genetic algorithm

According to table 4.2, simulation model was built in FlexSim platform on the basis of the type

of logistics lines. The logic of simulation units is shown in figure 4.1.

Every simulation unit operated one day and 5 times repeatedly. The second stage used 485s and

the total mileage of tracks was as follows in table 4.3.

It is clear that the mileage of double-lined logistics line is shortest, so it is the best scheme. If the

GA was only used to solve the problem, it is difficult to perform quantitative calculation for

determination of logistics line. But the computational efficiency optimization of GA is not the

research scope of this paper.