Evaluation of scheduling techniques for solving flowshop problems with no intermediate storage

The flowshop scheduling problem with no intermediate storage (NIS problem) was studied in this research. This problem, a modification of the classical flowshop scheduling problem, arises when a set of jobs, once started, must be processed with no wait between consecutive machines. By eliminating the need for intermediate storage, reduction of capital investment in work-in-process inventory can be achieved. This approach can be practically applied to a steel mill, in which the metal should be continuously processed in order to maintain high temperature, as well as many other similar processes. To provide insight into selecting an appropriate scheduling technique for solving the NIS problem, six methods were compared in terms of the quality and efficiency of the scheduling solutions they produced. The quality of solution was measured by makespan and the efficiency of solution was measured by the computational time requirements. The six methods examined in this study included: the Gupta algorithm, the Szwarc algorithm, an integer linear programming method, the Campbell et al. algorithm, the Dannenbring rapid access with extensive search algorithm, and a mixed integer linear programming procedure. The problem factors considered in this study were number of jobs, number of machines, and range of processing times. Relatively small-sized problems were tested with up to ten jobs, five machines, and 1–100 processing time units. Six solution techniques were selected and compared, with respect to makespan and computational time requirements, for multiple combinations of the three problem variables. The resulting test data were investigated using graphical procedures and formal statistical analyses. Initially, plots of mean values were used to graphically compare the six solution methods for the two performance criteria. Next, a multivariate analysis of variance study was conducted to investigate the quality and efficiency of the algorithms with respect to the problem factors. Then, a multiple comparison procedure was employed to analyze treatment mean differences among the six solution techniques. Results from the statistical analyses are summarized in this article. It was concluded that the two mathematical programming methods, the integer linear programming procedure and the mixed integer linear programming methods, produced the best performance in terms of makespan. These two methods, however, used a far greater amount of computational time than the other four sol