Abstract
This paper addresses an n-job, single-machine scheduling problem with an objective to minimize flow-time variance, a non-regular performance measure. A spreadsheet-based genetic algorithm (GA) approach is presented to solve the problem. A domain-independent general-purpose GA is used, which is an add-in to the spreadsheet software. The paper demonstrates an adaptation of the proprietary GA software to the problem of variance minimization. To test the performance of the proposed approach, test problems are taken from already published literature. The proposed approach is found to perform better than the previous GA, simulated annealing (SA) and Tabu search (TS) approaches for variance minimization in that it produces an optimal solution for all the test problems. Empirical analysis was carried out to study the effect of GA parameters, namely, the crossover rate, mutation rate, and population size.
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