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An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

Ramadan Saleem Zeyad, Almasarwah Najat, Abdelall Esraa S., Suer Gursel A., Albashabsheh Nibal T.

Management and Production Engineering Review

2023

Vol. 14, No. 4

28--40

This paper uses a Genetic Algorithm (GA) to reduce total tardiness in an identical parallel machine scheduling problem. The proposed GA is a crossover-free (vegetative reproduction) GA but used for four types of mutations (Two Genes Exchange mutation, Number of Jobs mutation, Flip Ends mutation, and Flip Middle mutation) to make the required balance between the exploration and exploitation functions of the crossover and mutation operators. The results showed that use of these strategies positively affects the accuracy and robustness of the proposed GA in minimizing the total tardiness. The results of the proposed GA are compared to the mathematical model in terms of the time required to tackle the proposed problem. The findings illustrate the ability of the propounded GA to acquire the results in a short time compared to the mathematical model. On the other hand, increasing the number of machines degraded the performance of the proposed GA.

Heuristic algorithm for lot sizing and scheduling on identical parallel machines

Książek Roger

2022

Vol. 16

47-–65

In this paper a newly developed heuristic algorithm for lotsizing and scheduling on identical parallel machines is presented. A basic concept and an aim of lotsizing and scheduling are described and clarified. Furthermore, models for considered problems for fixed and variable demand as well as for one and many identical parallel machines are presented. A concept and a formal description of newly developed heuristic algorithm is described. The paper includes in details the obtained results and solutions of conducted experiments and the description of used data sets.