Scheduling comparison between multi-objective mathematical models and genetic algorithms approach in the textile industry

Can Celikbilek, Tugba Tunacan, Gürsel A. Süer and Omer Dulkadir

International Journal of Services and Operations Management, 2016, vol. 25, issue 2, 236-258

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.

Keywords: single objective models; multi-objective models; genetic algorithms; textile industry; textiles; scheduling; mixed integer programming; MIP; mathematical modelling; bottleneck machines; performance measures. (search for similar items in EconPapers)
Date: 2016
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