Abstract
The onset of climate change is expected to bring about more severe weather patterns which may lead to floods, drought, and even the outbreak of new types of diseases. These can potentially disrupt the operations of industries and firms as infrastructure can be damaged and the availability of resources and workforce are compromised. It is thus important to develop models which will assist in the efficient management of resources during times of crisis. Process systems engineering techniques have previously been used for the design and optimization of complex systems during crisis conditions. This work presents the development of a P-graph model for the optimal allocation of human resources within a firm when the available workforce has been limited due to a climate change-induced crisis. The model identifies an optimal strategy for maximizing firm productivity by prioritizing highly critical areas.
Similar content being viewed by others
References
Aviso KB, Amalin D, Promentilla MAB, Santos JR, Yu KDS, Tan RR (2015) Risk assessment of the economic impacts of climate change on the implementation of mandatory biodiesel blending programs: a fuzzy inoperability input–output modeling (IIM) approach. Biomass Bioenergy 83:436–447
Baghersad M, Zobel CW (2015) Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors. Int J Prod Econ 168:71–80
Benjamin MFD, Tan RR, Razon LF (2015) Probabilistic multi-disruption risk analysis in bioenergy parks via physical input–output modeling and analytic hierarchy process. Sustainable Production and Consumption 1:22–33
Correa H, Correa V (1996) An application of input-output analysis to the administration of a library. Library & Information Science Research 18(4):343–356
Correa H, Craft J (1999) Input–output analysis for organizational human resources management. Omega 27(1):87–99
Correa H and Guarardo SA (2001) An input-output analysis to a city's municipal government. Socio Econ Plan Sci 35:83–108
Correa H and Parker BR (2005) An application of organizational input-output analysis to hospital management. Socio Econ Plan Sci 39:307–333
Friedler F, Tarjan K, Huang YW, Fan LT (1992a) Graph-theoretic approach to process synthesis: axioms and theorems. Chem Eng Sci 47(8):1973–1988
Friedler F, Tarjan K, Huang YW, Fan LT (1992b) Combinatorial algorithms for process synthesis. Comput Chem Eng 16:S313–S320
Friedler F, Tarjan K, Huang YW, Fan LT (1993) Graph-theoretic approach to process synthesis: polynomial algorithm for maximal structure generation. Comput Chem Eng 17(9):929–942
Friedler F, Varga JB, Fan LT (1995) Decision-mapping: a tool for consistent and complete decisions in process synthesis. Chem Eng Sci 50(11):1755–1768
Friedler F, Varga JB, Feher E, Fan LT (1996) Combinatorially accelerated branch-and-bound method for solving the MIP model of process network synthesis. State of the Art in Global Optimization 7:609–626
Fung MK (2008) To what extent are labor-saving technologies improving efficiency in the use of human resources? Evidence from the banking industry. Prod Oper Manag 17(1):75–92
Haimes YY and Jiang P (2001) Leontief-based model of risk in complex interconnected infrastructures. J Inf Sys 7:1–12
Halasz L, Povoden G, Narodoslawsky M (2005) Sustainable processes synthesis for renewable resources. Resour Conserv Recycl 44(3):293–307
Heckl I, Friedler F, Fan LT (2010) Solution of separation-network synthesis problems by the P-graph methodology. Comput Chem Eng 34(5):700–706
Heckl I, Halász L, Szlama A, Cabezas H, Friedler F (2015) Process synthesis involving multi-period operations by the P-graph framework. Comput Chem Eng 83:157–164
Igos E, Rugani B, Rege S, Benetto E, Drouet L, Zachary DS (2015) Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios. Appl Energy 145:234–245
Janius R, Abdan K, and Zulkaflli ZA (2016) Development of a disaster action plan for hospitals in Malaysia pertaining to critical engineering infrastructure risk analysis. International Journal of Disaster Risk Reduction
Kalkman JP, de Waard EJ (2016) Inter-organizational disaster management projects: finding the middle way between trust and control. Int J Proj Manag. doi:10.1016/j.ijproman.2016.09.013
Klemeš JJ, Varbanov PS (2015) Spreading the message: P-graph enhancements: implementations and applications. Chem Eng Trans 45:1333–1338
Lam HL (2013) Extended P-graph applications in supply chain and process network synthesis. Current Opinion in Chemical Engineering 2(4):475–486
Lenzen M, Benrimoj C, Kotic B (2010) Input-output analysis for business planning: a case study of the University of Sydney. Econ Syst Res 22(2):155–179
Leontief WW (1936) Quantitative input and output relations in the economic systems of the United States. The Review of Economic Statistics 18:105–125
Narodoslawsky M, Cabezas H, Maier S, Heckl I (2016) Using regional resources sustainably and efficiently. Chem Eng Prog 112(10):48–48
Nascimento KRDS, Alencar MH (2016) Management of risks in natural disasters: a systematic review of the literature on NATECH events. J Loss Prev Process Ind 44:347–359
Niknejad A, Petrovic D (2016) A fuzzy dynamic inoperability input–output model for strategic risk management in global production networks. Int J Prod Econ 179:44–58
P-graph Community (2015) Retrieved 28 December 2016 from www.p-graph.com.
Song J, Yang W, Higano Y, Wang XE (2015) Dynamic integrated assessment of bioenergy technologies for energy production utilizing agricultural residues: an input–output approach. Appl Energy 158:178–189
Tan RR, Aviso KB (2016) An extended P-graph approach to process network synthesis for multi-period operations. Comput Chem Eng 85:40–42
Tan RR, Lam HL, Kasivisvanathan H, Ng DKS, Foo DCY, Kamal M, Hallale N, Klemeš JJ (2012) An algebraic approach to identifying bottlenecks in linear process models of multifunctional energy systems. Theor Found Chem Eng 46(6):642–650
Tan RR, Aviso KB, Cayamanda CD, Chiu ASF, Promentilla MAB, Ubando AT, and Yu KDS (2015) A fuzzy linear programming enterprise input–output model for optimal crisis operations in industrial complexes. Int J Prod Econ
Vance L, Heckl I, Bertok B, Cabezas H, Friedler F (2015) Designing sustainable energy supply chains by the P-graph method for minimal cost, environmental burden, energy resources input. J Clean Prod 94:144–154
Voll P, Jennings M, Hennen M, Shah N, Bardow A (2015) The optimum is not enough: a near-optimal solution paradigm for energy systems synthesis. Energy 82:446–456
Wang Z, Huang K, Yang S, Yu Y (2013) An input–output approach to evaluate the water footprint and virtual water trade of Beijing, China. J Clean Prod 42:172–179
Acknowledgement
This paper is supported by the National Research Council of the Philippines (NRCP) under the program “Investing in the Future through Basic Researches Today: Institutional Grant for Invigorating Basic Research” Project no. P-014 entitled “Development of Input-Output Models for Optimal Human Resource Management During Crisis Conditions.”
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
Aviso, K.B., Cayamanda, C.D., Mayol, A.P. et al. Optimizing Human Resource Allocation in Organizations During Crisis Conditions: a P-graph Approach. Process Integr Optim Sustain 1, 59–68 (2017). https://doi.org/10.1007/s41660-017-0004-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41660-017-0004-3