Developing a Multi-Objective Meta-Heuristic Algorithm to Select Work Model and Staff Scheduling in Petrochemical Companies

In petrochemical companies, the existence of various, long job shifts can endanger the physical and mental health of employees, while having a proper timetable, in accordance with labor laws and policies, can help reduce the consequences of the disruption of their work shifts. The purpose of this research is to develop a model for timing the manpower of petrochemical companies in such a way that, by meeting the requirements of the company and the optimal number of employees in each shift, it can minimize the company's payment costs, and maximize the performance and preferences of the employees. The researchers used integer programming model. Because of the complexity of the issue and the multi-objective nature of the model, to solve the mathematical model, NSGA-II algorithm has been used. In order to obtain a better performance of the algorithm, its parameters were tuned using the Taguchi calibration method. Based on the findings from various scenarios, the 21-day working model is better than the 16-day working model. Since the model is multi-objective and is solved using the Pareto's approach, the decision maker can, according to the circumstances, choose one of the optimal Pareto solutions. Petrochemicals can apply scientific and optimum operation research approaches and its applications, in order to set up employee work schedules, create work-life balance, reduce work-related fatigue, decrease job burnout and improve their performance and productivity. Although scheduling and selecting the appropriate working model is complicated for petrochemical companies, NSGA-II algorithm can be used as an apt and powerful tool in decision making over choosing best working model.