Journal of Quality Engineering and Management

Mathematical modeling of resource allocation in critical conditions with the aim of increasing the level of resilience of operational processes: the case study of the textile industry

Document Type : Original Article

Authors

Mahnaz Ebrahimi-Sadrabadi
Ali Husseinzadeh Kashan
Mohammad Mehdi Sepehri

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.48313/jqem.2022.159884
Abstract
As time goes on and crises increase in societies, organizations are increasingly exposed to disruption. These crises can be of natural (such as earthquakes, floods, and fires) or human (such as terrorist attacks, infectious diseases, and intentional or inadvertent employee errors). Therefore, organizations need to be resilient to protect themselves from harmful consequences. The basic aspect of resilience involves the ability of an element to return to normal after disruption and resource allocation. Obviously, in any organization, the primary goal is to allocate the least resources to recover operations and to bring activities back to the tolerance threshold so that destructive events do not stop vital activities. In this paper, a quantitative model for resource allocation is presented, which minimizes the lack of resilience. The problem has a basic assumption, that there is a shortage of resources in at least one of the available resources due to excessive demand. After solving the model by numerical experiment, the results of the model were described and it was found that destructive events were retrieved before the tolerance threshold.

Keywords

Process optimization
Process resilience
Resource allocation
Lack of resources
  • Taleb-Berrouane, M., & Khan, F. (2019). Dynamic Resilience Modelling of Process Systems. CHEMICAL ENGINEERING, 77.
  • Zio, E. (2018). The future of risk assessment. Reliability Engineering & System Safety, Volume 177, September, Pages 176-190.
  • Peteraf, M. A.(2005). Research complementarities: A resource-based view of the resource allocation process model (and vice versa). From resource allocation to strategy, 409-426.‏
  • Amirteimoori, A., & Tabar, M. M. (2010). Resource allocation and target setting in data envelopment analysis. Expert Systems with Applications, 37(4), 3036-3039.
  • Lozano, S., Villa, G., & Adenso-Dıaz, B. (2004). Centralised target setting for regional recycling operations using DEA. Omega, 32(2), 101-110.
  • Joglekar, N. R., & Ford, D. N. (2005). Product development resource allocation with foresight. European Journal of Operational Research, 160(1), 72-87.‏
  • Withanachchi, N., Uchida, Y., Nanayakkara, S., Samaranayake, D., & Okitsu, A. (2007). Resource allocation in public hospitals: Is it effective?. Health policy, 80(2), 308-313.
  • Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47-61.
  • Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Towards organizational resilience. European Journal of Operational Research, 242(1), 261-273.
  • Torabi, S. A., Giahi, R., & Sahebjamnia, N. (2016). An enhanced risk assessment framework for business continuity management systems. Safety science, 89, 201-218.‏
  • Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2018). Building organizational resilience in the face of multiple disruptions. International Journal of Production Economics, 197, 63-83.‏
  • Ostadi, B., Ghorbani, A., & Mokhtarian, R. (2019). Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry). Advances in Industrial Engineering, 53(1), 509-521.
  • Ostadi B, Mokhtarian Daloie,, Sepehri, M M. (2019). A Developed Method for Handling Uncertainty in Activity - Based Costing for Hospital Services Using Fuzzy Approach. jhosp, 17 (4) :19-34.
  • Rezaei Soufi, H., Torabi, S. A., & Sahebjamnia, N. (2019). Developing a novel quantitative framework for business continuity planning. International Journal of Production Research, 57(3), 779-800.
  • Mokhtarian Daloie. R, Ostadi. B. (2019). Developing a discrete-event simulation model for improving the quality of services: A case study in urology unit at a kidney center. Journal of Engineering and Quality Management, 9(3), 244-260, Persian.
  • Shokri, M. and Saghaee, A. (2020). Developing Emergency organization resources Management Productivity to Increase the Welfare of Stroke and heart attack patients Using Statistical and Spatial Statistics Analysis in Tehran. Journal of Quality Engineering and Management, 10(2), 145-158. ( in persian)
  • Yu, W., Jia, M., Fang, X., Lu, Y., & Xu, J. (2020). Modeling and analysis of medical resource allocation based on Timed Colored Petri net. Future Generation Computer Systems, 111, 368-374.
  • Alkaabneh, F., Diabat, A., & Gao, H. O. (2021). A unified framework for efficient, effective, and fair resource allocation by food banks using an Approximate Dynamic Programming approach. Omega, 100, 102300.
  • Deng, Y. J., Li, Y. Q., Qin, Y. H., Dong, M. R., & Liu, B. (2020). Optimal defense resource allocation for attacks in wireless sensor networks based on risk assessment model. Chaos, Solitons & Fractals, 137, 109780.
  • Ketabi, S., Ghandehari, M., & Bolandi, D. (2020). Efficiency Analysis and Hospital Resource Allocation Using Centralized Data Envelopment Analysis. Journal of Production and Operations Management. 11.3: 1-16.‏
  • Najarian, M., & Lim, G. J. (2020). Optimizing infrastructure resilience under budgetary constraint. Reliability Engineering & System Safety, 198, 106801.‏
  • Ostadi B, Seifi M.M., Husseinzadeh Kashan A. (2021). A multi-objective model for resource allocation in disaster situations to enhance the organizational resilience and maximize the value of business continuity with considering events interactions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, doi:10.1177/1748006X21991027.
  • Ostadi, B., Ebrahimi Sadrabadi, M., Hosseinzadeh Kashan, A. and Sepehri, M.M. (2021). Presenting a model for the optimal allocation of human resources to operational processes using the Markowitz model: A case study in urology unit at a kidney center. Journal of Quality Engineering and Management, 11(1).
  • Hao, L. (2022). Optimal Allocation of Human Resource Structure Based on Capability Maturity Model Integration. Security and Communication Networks, 2022.
  • Liao, P. H., Chu, W., & Ho, C. S. (2022, April). An Analysis of Waiting Time for Emergency Treatment and Optimal Allocation of Nursing Manpower. In Healthcare (Vol. 10, No. 5, p. 820). MDPI.
  • Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375-5393.
  • Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. M. & Von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 19(4), 733-752.
  • Chang, S. E., & Shinozuka, M. (2004). Measuring improvements in the disaster resilience of communities. Earthquake spectra, 20(3), 739-755.
  • Cox, A., Prager, F., & Rose, A. (2011). Transportation security and the role of resilience: A foundation for operational metrics. Transport policy, 18(2), 307-317.
  • Madni, A. M., & Jackson, S. (2009). Towards a conceptual framework for resilience engineering. IEEE Systems Journal, 3(2), 181-191.
  • Enjalbert, S., Vanderhaegen, F., Pichon, M., Ouedraogo, K. A., & Millot, P. (2011). Assessment of transportation system resilience. In Human modelling in assisted transportation, (pp. 335-341). Springer, Milano.
  • Ouyang, M., Dueñas-Osorio, L., & Min, X. (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural safety, 36, 23-31.
  • Azadeh, A., Salehi, V., Ashjari, B., & Saberi, M. (2014). Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant. Process Safety and Environmental Protection, 92(3), 231-241.
  • Azadeh, A., Salehi, V., Arvan, M., & Dolatkhah, M. (2014). Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: A petrochemical plant. Safety Science, 68, 99-107.‏
  • Bhavathrathan, B. K., & Patil, G. R. (2015). Capacity uncertainty on urban road networks: A critical state and its applicability in resilience quantification. Computers, Environment and Urban Systems, 54, 108-118.
  • Qomi, M., Jalalinaeeni, Gh. R., Tavakoli Moghadam, R. and Jabarzadeh, A. (2021). Closed loop supply chain network design under disturbance and uncertainty conditions considering quality and resilience strategy. Journal of Quality Engineering and Management, 6(2), 133-145. ( in persian)
  • Azadeh, A., Salehi, V., Mirzayi, M., & Roudi, E. (2017). Combinatorial optimization of resilience engineering and organizational factors in a gas refinery by a unique mathematical programming approach. Human Factors and Ergonomics in Manufacturing & Service Industries, 27(1), 53-65.‏
  • Gong, J., & You, F. (2018). Resilient design and operations of process systems: Nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement. Computers & Chemical Engineering, 116, 231-252.‏
  • Jain, P., Pistikopoulos, E. N., & Mannan, M. S. (2019). Process resilience analysis based data-driven maintenance optimization: Application to cooling tower operations. Computers & Chemical Engineering, 121, 27-45.
  • Yarveisy, R., Gao, C., & Khan, F. (2020). A simple yet robust resilience assessment metrics. Reliability Engineering & System Safety, 197, 106810.‏
  • Pishnamazzadeh, M., Sepehri, M. M., & Ostadi, B. (2020). An Assessment Model for Hospital Resilience according to the Simultaneous Consideration of Key Performance Indicators: A System Dynamics Approach. Perioperative Care and Operating Room Management, 20, 100118.

 

Journal of Quality Engineering and Management
Volume 11, Issue 4
Winter 2022
Pages 393-412

Home

Editorial Board

Indexing and Abstracting

Guide for Authors

Submit Manuscript

Reviewers

Contact Us