Genetic Algorithm and Neural Network with Fuzzy Times for Solving Job Shop Scheduling problems: A real life case study at Al-Qadir company for printing and publishing

Abstract

In this research paper a methodology was adopted for solving Job Shop Scheduling and Planning (JSSP) problems which is one of the most important to the Decision makers in the field of production management. 
A Model was built by using Genetic Algorithm and Neural Network, namely adaptive search algorithms, it was taken into account the uncertainties times of processing and due date by applying the fuzzy theory for this situation. The model was tested and then applied to optimize the highly complex target for Job Shop Scheduling and planning problem at Al-Qadir for printing and publishing company to obtain the optimal possible job sequences.
It was concluded that the use of the genetic algorithm and Neural Network is the most appropriate method for solving the fuzzy job shop scheduling at printing industry in developing countries such as in Iraq, also the model showed the optimal results with clear image to the decision makers at the printing and publishing for the company under study and will contribute to the satisfaction of their customers and to the knowledge of application the model for the Industry in Iraq.