In a re-entrant flow shop scheduling problem we proposed some algorithms to get a better TAT (turn around time) with a genetic search method. One is an operation which searches for a solution that shifts the start timing in limited areas of each lot. Another is an operation which searches for a solution that shifts left and chooses the machine which starts fastest. Some algorithms are effective on the benchmark including those proposed by Taji et al. In the first step, it is easiest to choose the probabilistic problem by local search. The second step is to search for the solution that shifts the start timing in limited areas of each lot, makes the Gantt chart, chooses the machine and gets the results. The third step is to search for the solution that again shifts left, makes the Gantt chart, chooses the machine and gets the results. The proposed algorithms are more valid than local search methods by Taji et al, such as swap, move, swap-2 neighborhood and FIFO (first in first out). The first algorithm has produced the best result in an experimental test when interval time was short. The second algorithm produced the best result of all solutions. The results have shown that the proposed algorithms are effective for interval time cut and get better TAT than previous methods.