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

Document Type : Research Paper


1 Associate Prof., Department of Industrial Management, Persian Gulf University, Bushehr, Iran

2 MSc, Department of Industrial Management, Persian Gulf University, Bushehr, Iran.

3 MSc, Department of Industrial Management, Persian Gulf University, Bushehr, Iran

4 PhD Candidate, Department of Industrial Management, Persian Gulf University, Bushehr, Iran


Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.


Adham, A. (2009). Multi-Objective Energy Policy Modeling using Pareto Optimization Concept and After Search Decision-Making Methods. Electricity International Conference (pp. 1-15). Tehran: Tavanir Company. (in Persian)
Akbari, M. (2017). Part-Time Workforces Scheduling with Variable Productivity. Management Research in Iran, 21(3), 25-47. (in Persian)
Akbari, M., Dorri, B., & Zandieh, M. (2012). Multi Skill Staff Shift Scheduling with GA Approach. Industrial Management Outlook, 7, 87-102. (in Persian)
Altner, D. S., Rojas, A. C., & Servi, L. D. (2017). A Two-Stage Stochastic Program for Multi-Shift, Multi-analyst, Workforce Optimization with Multiple On Call Optins. Scheduling, 1-28.
Amin Tahmasebi, H., & Mohseni, M. (2012). Multi Objective Road Police Staff Scheduling, Case Study: Gilan Police. Operational Research and Its Applications (Applied Mathematics) - Lahijan Azad University, 9(4), 107-120. (in Persian)
Aslani, B., Zandieh, M., & Adeli, M. (2015). Bi-Objective Scheduling of No Wait Flexible Flow Lines With a Time Window and the Possibility of Work Rejecting. Industrial Management , 7(3), 445-468. (in Persian)
Baghbani, M., Ketabi, S., & Atigheh Chian, A. (2013). Surgical Room Scheduling Problem with Maximizing Doctors' Preferences and Hospital Management (Case Study: Saadi Hospital in Isfahan). 1st Accounting and Management National Conference (p. 11). Shiraz: Kharazmi Educational and Researches International Institute. (in Persian)
Baghipour Sarami, F., Bozorgiamiri, A., Mououdi, M., & Taghipour, M. (2016). Modeling of Nurses’ Shift Work Schedules According to Ergonomics: A case study in Imam Sajjad (AS) Hospital of Ramsar. Journal of Ergonomics, 4(1), 1-12. (in Persian)
Bard, J. F., Morton, D. P., & Wang, Y. M. (2007). Workforce Planning at USPS Mail Processing and Distribution Centers Using Stochastic Optimization. Annals of Operations Research, 155(1), 51-78.
Beckmann, F. R., & Klyve, K. K. (2016). Optimisation-Based Nurse Scheduling for Real-Life Instances. Trondheim: Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology.
Cai, X., & Li, K. N. (2000). A genetic algorithm for scheduling staff of mixed skills under multi-criteria. European Journal of Operational Research, 125(2), 359-369.
Choobineh, A., Soltanzadeh, A., Tabatabaee, H., Jahangiri, M., & Khavaji, S. (2012). Health Effects Associated With Shift Work in 12-Hour Shift Schedule Among Iranian Petrochemical Employees. International Journal of Occupational Safety and Ergonomics, 18(3), 419-427.
Choobineh, A., Soltanzadeh, A., Tabatabaie, S. H., & Jahangiri, M. (2012). Shift Work and its Related Health Problems in Petrochemical Industries. Journal of School of Public Health and Institute of Public Health Research, 9(4), 43-56. (in Persian)
Clark, A. R., & Walker, H. (2011). Nurse rescheduling with shift preferences and minimal disruption. Applied Operational Research, 3(3), 148-162.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41.
Eliasi, N. (2013). Multiple Routing Jobshop Scheduling Problem Using Metaheuristic Methods and Jons Ditribution Rules . Tafresh: Tafresh University. (in Persian)
Esmaelian, M., & Abdollahi, S. M. (2017). Proposing a Two-Phase Integer Linear Programming for University-Course Timetabling. Industrial Management Journal, 9(1), 19-42. (in Persian)
Haghighi, M., & Nayebpour, H. (2017). Prioritizing of Iranian Airlines by Fuzzy Synthetic Evaluation and Genetic Algorithm. Industrial Management Journal, 9(3), 409-434. (in Persian)
Hatami Manesh, M., & Zanjirchi, S. M. (2014). Taguchi Experiment Design, a Realistic Approach to Weight Indicators of Employee Performance Evaluation. Organizational Resources Management Researchs, 3(4), 46-67. (in Persian)
Health Sciences Research Center (2017). Evaluation of blood, liver and kidney complications due to exposure to organic chemical pollutants in South Pars petrochemical companies. Shiraz, Shiraz university of medical sciences.
Herawati,, A., Yuniartha, D., Purnama, I., & Dewi, L. (2017). Shift scheduling model considering workload and worker’s preference for security department. Materials Science and Engineering, 337, 1-7.
Hoboubi, N., Choobineh, A., Karimi Ghanavati, F., Keshavarzi, S., & Akbar Hosseini, A. (2017). The Impact of Job Stress and Job Satisfaction on Workforce Productivity in an Iranian Petrochemical Industry. Safety and Health at Work, 8(1), 67-71.
Hochdörffer, J., Hedler, M., & Lanza, G. (2018). Staff scheduling in job rotation environments considering ergonomicaspects and preservation of qualifications. Manufacturing Systems, 46, 103-114.
Hojati, M. (2018). A Greedy Heuristic for Shift Minimization Personnel Task Scheduling Problem. Computers and Operations Research, 100, 66-76.
Kazemi, M., Abadi, A., Zayeri, F., & Hassanzade, H. (2017). Assessing the effect of shift work among petrochemical Industries staff at Mahshahr, Iran. Paramedical Sciences, 8(4), 36-43.
Kolahan, F., & Yari Bakht, M. (2012). Surving Optimal Condition for 718 Inkunel Superalloy Maching with Taguchi DOE. 3national Conference on Manufacturing Engineering, (pp. 1-6). Najaf Abad. (in Persian)
Legrain, A., Bouarab, H., & Lahrichi, L. (2016). The nurse scheduling problem in real-life. Medical Systems, 39(160), 1-16.
Lim, G., Mobasher, A., & Cote, M. (2012). Multi-objective Nurse Scheduling Models with Patient Workload and Nurse Preferences. Management, 2(5), 149-160.
Lin, C.-C., Kang, J.-R., Chiang, D.-J., & Chen, C.-L. (2015). Nurse Scheduling with Joint Normalized Shift and Day-Off Preference Satisfaction Using a Genetic Algorithm with Immigrant Scheme. Distributed Sensor Networks, 11(7), 1-10.
Lin, C.-C., Kang, J.-R., Liu, W.-Y., & Deng, D.-J. (2014). Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off. Mathematical Problems in Engineering, 3, 1-11.
Moradi, E., Fatemi Ghomi, S. M., & Zandieh, M. (2011). Bi-Objective Optimization Research on Integrated Fixed Time Interval Preventive Maintenance and Production for Scheduling Flexible Jobshop Problem. Expert Systems with Applications, 38(6), 7169-7178.
Namazi, A. (2011). Jobshop Scheduling with Multiple Routing and Maintenance Requirements. Tafresh: Tafresh University. (in Persian)
Nikookar, G., Alidadi Nakhlestani, Y., Mahdavi, M., & Mousavi, S. J. (2014). Non-Dominated Sorting Genetic Algorithm to inegrated model for R&D members selection. Industrial Management, 6(2), 385-410. (in Persian)
Rajabi, M., & Khaloozadeh, H. (2015). Optimal Portfolio Prediction in Tehran Stock Market using Multi-Objective Evolutionary Algorithms, NSGA-II and MOPSO. Financial Research, 16(2), 253-270. (in Persian)
Shahmoradi, H., Ketabi, S., & Esmaelian, M. (2017). University Course Timetabling using Constraint Programming. Production and Operations Management, 13, 119-138. (in Persian)
Salimifard, K., Shahbandarzadeh, H., & Megatif, S. (2014). Readouting Surgical Scheduling Problem Solving Techniques. 2en Management Dynamics, Economic Development and Financial Management National Conference (p. 16). Shiraz: Pendar Andish Rahpoo Company. (in Persian)
Tahanian, A. R., & Khaleghi, M. (2013). Staff Scheduling by a Genetic Algorithm. Shiraz Journal of System Management, 1(4), 73-86.
Van Brummelen, E. (2018). Optimizing shift scheduling and task allocation in long-term care facilities to reduce waiting times. University Amsterdam.