Mathematical modeling and problem solving Integrated production planning and preventive maintenance with limited human resources

Subject Areas : Statistics

، mohammd  sharifzadegan ¹ (Department of Industrial Engineering, Masjed Soleiman Branch, Islamic Azad University, Masjed Soleiman, Iran)
Adel  Pourghader Chobar ² (Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran,)

Received: 2021-03-04 Accepted : 2022-01-10 Published : 2022-12-22

Keywords: واژه‌های کلیدی: برنامه‌ریزی تولید ادغامی, تعمیرات و نگهداری پیشگیرانه, برنامه‌ریزی منابع انسانی,

Abstract :

The need for integration has long been the focus of researchers and industry managers. Therefore, some researchers sought to coordinate and integrate production, maintenance and repair. The issues of integrating production planning, maintenance and repair and quality control with other parts of production and industrial systems reduce costs and increase the profitability of production organizations. Accordingly, in this research, integration and continuity in simultaneous planning of production areas and support in production organizations have been discussed. These areas include the schedule of production, maintenance and preventive maintenance, and human resources. Therefore, the mathematical optimization model is presented with the aim of manpower planning and increasing the volume of the company's production by considering the limitations of human resources. In this model, personnel skills, equipment usage rate and equipment failure rate in case of uncertainty with fuzzy method are used in the model parameters. The purpose of the proposed model is to minimize the labor force deficit over production efficiency. The results obtained from the implementation in a production organization by comparing the indefinite and meta-innovative solution show the improvement in the company's production with the least reasonable time, provide answers with the least possible error. Sensitivity analysis shows that the rate of equipment failure in the intervals before and after the preventive maintenance process has a great impact on the value of the objective function of the mathematical model.

References:

 

• M.J. Mirzapour Al-e-Hashem, M.B. Aryanezhad, S.J. Sadjadi. An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment, International Journal of Advance Manufacturing Technology, 58. 765–782 (2012)

 

• R. Moghadam, O. Yousefi, M. Karbasian, B. Khayambashi. Integrated production-distribution planning in a reverse supply chain via multi-objective mathematical modeling; case study in a high-tech industry, Journal of Production and Operations Management, 9(2), 1-22 (2019)

 

• Berrichi, L. Amodeo, F. Yalaoui, E. Châtelet, M. Mezghiche. Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. Journal of Intelligent Manufacturing, 20(4), 389 (2009)

 

• Xiang, C.R. Cassady, T. Jin, C.W. Zhang. Joint production and maintenance planning with machine deterioration and random yield, International Journal of Production Research, 52 (6), pp. 1644-1657 (2014)

 

• Gholamian, I. Mahdavi, R. Tavakkoli-Moghaddam, N. Mahdavi-Amiri. A comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty, Applied Soft Computing, 37. 585-607 (2015)

 

• Nourelfath, N. Nahas, M. Ben-Daya. Integrated preventive maintenance and production decisions for imperfect processes. Reliability engineering & system safety, 148, 21-31 (2016)


• Ettaye, A. El Barkany, A. El Khalfi. Modeling and optimization a production/maintenance integrated planning. In International Journal of Engineering Research in Africa (Vol. 28, pp. 169-181) (2017)

 

• Ekin, Integrated maintenance and production planning with endogenous uncertain yield. Reliability Engineering & System Safety, 179, 52-61 (2018)

 

• Hamrol. A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9 (2018)

 

• Glawar, M. Karner, T. Nemeth, K. Matyas, W. Sihn. An approach for the integration of anticipative maintenance strategies within a production planning and control model. Procedia CIRP, 67, 46-51 (2018)

 

• Schreiber, J. Klöber-Koch, C. Richter, G. Reinhart. Integrated Production and Maintenance Planning for Cyber-physical Production Systems. Procedia CIRP, 72, 934-939 (2018)

 

• Yang, W. Li, B. Wang. Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning. Reliability Engineering & System Safety, 214, 107713. (2021)

 

• Bensmain, M. Dahane, M. Bennekrouf, Z. Sari. Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: A hybrid genetic algorithm based approach. Reliability Engineering & System Safety, 185, 546-566 (2019)

 

• Lu, J. Pei, X. Liu, P. M. Pardalos. A hybrid DBH-VNS for high-end equipment production scheduling with machine failures and preventive maintenance activities. Journal of Computational and Applied Mathematics, 384, 113195. (2021)

 

• Shariatmadari Serkani, F. Hosseinzadeh Lotfi, E. Najafi, M. Ahadzadeh Namin. Efficiency Measurement for Hierarchical Network Systems Using Network DEA and Intuitionistic Fuzzy ANP. Scientia Iranica, -. doi: 10.24200/ 54619.3836 (2020)

 

• Bahria, I. Harbaoui Dridi, A. Chelbi, H, Bouchriha. "Joint design of control chart, production and maintenance policy for unreliable manufacturing systems", Journal of Quality in Maintenance Engineering, 27 (4), 586-610 (2021)

 

• Saha, A. Azeem, K. W. Hasan, S. M. Ali, S. K. Paul. Integrated economic design of quality control and maintenance management: Implications for managing manufacturing process. International Journal of System Assurance Engineering and Management, 12(2), 263-280 (2021)

 

• Y. Jaber, Z. S. Givi, W. P. Neumann. Incorporating human fatigue and recovery into the learning–forgetting process. Applied Mathematical Modelling, 37(12-13), 7287-7299 (2013)

 

• Goli, E. B. Tirkolaee, N. S. Aydin. Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors. IEEE Transactions on Fuzzy Systems (2021)