TASK SCHEDULING IN FLOW SHOP DEALS WITH DETERMINATION OF OPTIMAL SEQUENCE OF TASKS (JOBS) TO BE PROCESSED ON SOME PROCESSORS (MACHINES) IN A PREDETERMINED ORDER SO AS TO SATISFY CERTAIN OBJECTIVES HEURISTICS WERE REVEALED TO YIELD NEAR TO OPTIMAL SOLUTIONS IN A REASONABLE TIME AS THE PROBLEM BELONGS TO NP HARD. THE HEURISTIC ALGORITHM WITH ALGORITHMIC PARAMETER (K) OF 6 AND 24 ACHIEVED THE BEST-KNOWN RESULTS, WHILE MAINTAINING THE SAME ALGORITHMIC COMPLEXITY FOR MAKESPAN MINIMIZATION. HOWEVER, THE SOLUTION QUALITY MAY ALSO DEPEND ON TUNING OF PARAMETER K. IN THE PRESENT WORK, AN ATTEMPT WAS MADE TO ANALYZE THE IMPROVED HEURISTIC ALGORITHM WITH DIFFERENT ALGORITHMIC PARAMETER K. ALL THE COMPUTATIONAL EXPERIMENTS WERE DONE ON TAILARD BENCHMARKS INSTANCES UP TO 500 JOBS AND 20 MACHINE PROBLEMS IN THE FLOW SHOP ENVIRONMENT. ANOVA WAS APPLIED FOR STATTISTICAL ANALYSIS WITH VARIATION OF MACHINE SIZE ALONG WITH PARAMETER K. IT WAS FOUND THAT ALGORITHMIC PARAMETER K DOES NOT CONTRIBUTE SIGNIFICANT RESULTS AT 5% LEVEL OF SIGNIFICANCE FOR THE PROBLEM CONSIDERED, RATHER INCREASE IN VALUE OF K INCREASES THE COMPUTATIONAL TIME OF THE IMPROVED HEURISTICS. IT IS THE SIZE OR MACHINES WHICH WAS A SIGNIFICANT EFFECT ON THE RESULTS. HOWEVER, OPTIMAL ALGORITHMIC PARAMETER, K=24 FOR 5 MACHINES, K = 12 FOR 10 MACHINES AND K = 6 FOR 20 MACHINES FOR DIFFERENT JOB SIZE PROBLEMS PROVIDES THE MINIMUM MARGINAL MEANS OF RESULTS.