Improvement of execution times of scheduling algorithms with advanced data structures

Nowadays, developing technology and increasing product diversity require businesses to improve their processes in order to survive in competitive environments. Therefore, an businesses focus on scheduling to effectively use their limited resources. Scheduling algorithms are one of the most popular solution methods today in order to save time in the production processes or to increase the efficiency of limited resources by ensuring that a process is completed in a shorter time. However, when scheduling algorithms are applied, the solution durations are extensively large and applicable results cannot be attained in real time. In order to make scheduling algorithms applicable in realtime, the aim of study is to improve the performance of very large scheduling algorithms that cannot be solved despite technological developments with advanced data structures. Two algorithms, one with listdaha straucture, and the second with linked list data structure, were proposed. The data structures have a very important effect on the algorithm solution speed, and therefore synthetic data were created to show their effect especially on very large sized problems. The performances of the algorithms with two different data structures were evaluated in terms of running times. Three scenarios were designed with different parameters, and algorithms were written according to these scenarios with list and linked list data structures. It has been observed that the algorithm with the advanced data structure Works statistically significantly faster for all scenarios than the algorithm with basic data structure. It is predicted that CPU processing time will decrease when applied to other scheduling algorithms, especially population-based algorithms.