Designing and Explaining a Redundancy Resource and Buffer allocation Model for Project Reliability Improvement with Time and Cost Uncertainty (The Case of Oil and Gas Industry Projects)

Document Type : Research Paper


1 Ph.D. Candidate in Production and Operations Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

2 Prof., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

3 Assistant Prof., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.


Objective: Achieving the time and cost objectives of projects in the real world is difficult because of unforeseen and uncertain issues. Time and cost estimates at the beginning of the project are usually optimistic and do not take into account the uncertainties that lead to deviations from the objectives. The purpose of this study is to provide a combined method of allocating redundancy resource and buffer to improve the achievement of time and cost objectives (project reliability).
Methods: In this method, by allocating a precautionary time reserve at the end of each activity and the end of the project and allocating redundancy resource, the reliability of the project is improved so that time and cost are not sacrificed and the most desirable cost and time for the project will be achieved. For this purpose, the mathematical model of optimal allocation of buffer and redundancy resource to improve time and cost stability has been presented and its application in one of the real projects of the country's oil and gas industry has been studied and validated.
Results: The research findings indicate a 37 percent improvement in time reliability and a 28 percent improvement in project cost reliability using the proposed method.
Conclusion: The results of the study indicate an improvement in the attention span in achieving the time and cost objectives of the project using the proposed method and considering the real conditions of projects, including uncertainties and special conditions such as sanctions.


Amiri, M., Taghavifard, M.T., Azimi, P., & Aghaei, M. (2019). Multi-Objective Model for determining Optimal Buffer Size and Redundancy-Availability Allocation Simultaneously in Manufacturing Systems. Industrial Management Journal, 11(3), 427-460. (in Persian) 
Biuseh, Reza, & Momeni, Mansour, & Hamidizadeh, Mohamad Reza. (2012). Identifying weaknesses and problems of domestic companies in EPC projects og the oil and gas industry using AHP method. 3rd national conference on Improving Domestic Power with an Approach to Remove Production Barriers in condition of Sanctions. Tehran, Center for Technology Studies, 2012. (In Persian)
Burdett, R., L., & Kozen, E. (2015). Techniques to effectively buffer schedules in the face of uncertainties. Computers & Industrial Engineering, 87, 16–29.
Chang, Kuo-Hao, & Kuo, Po-Yi (2018). An Efficient Simulation Optimization Method for the Generalized Redundancy Allocation Problem. European Journal of Operational Research, 265 (3), 1094-1101.
Chief of the Bureau of Naval Weapons (2016). HANDBOOK RELIABILITY ENGINEERING.
Farughi, H., Payandeh, S., & Abdi, F. (2019). Multi-objective Project Scheduling Considering Discrete Resource Constraints Problem with Multiple Crashable Modes and Modeidentity Capabilities. Industrial Management Journal, 11(2), 351- 379. (in Persian)
Fu, Na, & Lau, Hoong Chuin, & Varakantham, Pradip (2015). Robust execution strategies for project scheduling with unreliable resources and stochastic durations. J Sched, DOI 10.1007/s10951-015-0425-1.
Herroelen, Willy, & Leus, Roel (2004). The construction of stable project baseline schedules. European journal of operational research, 156(2004), 550-565.
Herroelen, Willy, & Leus, Roel (2005). Project scheduling under uncertainty: Survey and research potentials. European journal of operational research, 165(2) , 289-306.
Huang, Ding-Hsiang, & Huang, Cheng-Fu, & Lin, Yi-Kuei (2020). Exact project reliability for a multi-state project network subject to time and budget constraints. Reliability Engineering and System Safety, 195(2020) 106744.    
Izmailov, Azar, & Korneva, Diana, & Kozhemiakin, Artem (2016). Project management using the buffers of time and resources. Procedia - Social and Behavioral Sciences, 235(2016), 189 – 197.
Kim, Heungseob, & Kim, Pansoo (2017). Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm. Reliability Engineering & System Safety, 159(2017), 153-160.
Kuchta, Dorota (2014). A new concept of project robust schedule – use of buffers. Procedia Computer Science, 31(2014) , 957 – 965.
Lambrechts, Olivier, & Demeulemeester, Erik, & Herroelen, Willy (2008). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. J Sched, 11(2008), 121–136.
Lambrechts, Olivier, & Demeulemeester, Erik, & Herroelen, Willy (2011). Time slack-based techniques for robust project scheduling subject to resource uncertainty. Annals of Operations Research, 186(2011), 443–464.
Lambrechts, Olivier, & Demeulemeester, Erik, & Herroelen, Willy (2007). Exact and suboptimal reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Available at SSRN: or
Ma, Zhigiang, & Demeulemeester, Erik, & He, Zhengwen, Wang, Nengmin (2019). A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments. Computers & Industrial Engineering, 131(2019), 382-390.
O’Donovan, Ronan, & Uzsoy, Reha, & McKay, Kenneth (1999). Predictable scheduling of a single machine with breakdowns and sensitive jobs. International Journal of Production Research, 37(18), 4217–4233. 
Poshdar, Mani, & gonzález, Vicente A., & Raftery, Gary M., & Orozco, Francisco, & Guillermo, G. Cabrera-Guerrero (2018). A multi-objective probabilistic-based method to determine optimum allocation of time buffer in construction schedules. Automation in Construction, 92(2018), 46-58.
Project Management Institute (2017). Project management body of knowledge, PMBOK Guide. 6th edition. Pennsylvania: Project Management Institute.
Rohaninejad, M., & Tavakkoli-Moghaddam, R., & Vahedi-Nouri, B. (2015). Redundancy resource allocation for reliable project scheduling: A game-theoretical approach. Procedia Computer Science, 64(2015), 265 – 273.
Reyes, Francisco, & Cerpa, Narciso, & Candia-Véjar, Alfredo, Bardeen, Matthew (2011). The optimization of success probability for software projects using genetic algorithms. The Journal of Systems and Software, 84(2011),  775–785.
Saputra, Yudha Andrian, & Latiffianti, Effi (2015). Project Reliability Model Considering Time–Cost–Resource Relationship under Uncertainty. Procedia Computer Science, 72(2015), 561 – 568.
Shahrokhi, M. (2018). Developing an Approach to Calculate Fuzzy Reliability Based on Fuzzy Failure Rate. Industrial Management Journal, 10(2), 183-200. (in Persian)
She, Bingling, & Chen, Bo, & G. Hall, Nicholas (2020). Buffer Sizing in Critical Chain Project Management by Netwrok Decomposition. Omega (2020), doi:
Tao, Ran, & Tam, Chi-Ming (2012). System reliability optimization model for construction projects via system reliability theory. Automation in Construction, 22(2012), 340–347.
Wang, Pidong, & Zhang, Jianguo, & Zhai, Hao, & Qiu, Jiwei (2017). A new structural reliability index based on uncertainty theory. Chinese Journal of Aeronautics, 30(4), 1451–1458.
Zhao, Ping, & Hao, Fengtian (2011). Risk Study on Subway Construction based on Reliability Theory. Applied Mechanics and Materials, 44-47, 1872-1877