Scheduling comparison between multi-objective mathematical models and genetic algorithms approach in the textile industry
by Can Celikbilek; Tugba Tunacan; Gürsel A. Süer; Omer Dulkadir
International Journal of Services and Operations Management (IJSOM), Vol. 25, No. 2, 2016

Abstract: This paper discusses mixed integer mathematical models and genetic algorithms (GAs) approach for finding an optimal schedule of the bottleneck machine for a company in the textile industry. Single-objective and multi-objective mathematical models and GA are used to obtain an optimal solution to minimise the maximum tardiness (T_max). The comparison is made between mathematical models and GA according to the primary and secondary performance measures. Primary performance measure is T_max, while secondary performance measure is number of tardy jobs (n_t) and total tardiness (TT) values. The experimentation is performed for small and large size problems. All jobs have five different instances except for 150-job and 200-job problems. Due to memory and time limitations, only one sample could be solved for 150-job and 200-job problem. The experimental results indicated that, most of the time GA finds an optimal solution and proposes alternative schedules for both single-objective and multi-objective mathematical models.

Online publication date: Sun, 04-Sep-2016

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