American Institute of Mathematical Sciences

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July  2020, 16(4): 1835-1859. doi: 10.3934/jimo.2019031

Determining personnel promotion policies in HEI

Rocio de la Torre 1,, , Amaia Lusa 2,3,4, , Manuel Mateo 2,3,4, and  El-Houssaine Aghezzaf 2,3,4,

1. 

Department of Management / ETSEIB. Universitat Politcnica de Catalunya, Av. Diagonal 647, 7th floor, Barcelona, 08028, Spain
2. 

Department of Management / IOC / ETSEIB. Universitat Politcnica de Catalunya, Av. Diagonal 647, 11th floor, Barcelona, 08028, Spain
3. 

Department of Management / IOC / ETSEIB. Universitat Politcnica de Catalunya, Av. Diagonal 647, 7th floor, Barcelona, 08028, Spain
4. 

Department of Industrial Management. Ghent University, Technologiepark 902, 9052 Zwijnaarde, Belgium

* Corresponding author: R. de la Torre

Received  March 2018 Revised  October 2018 Published  May 2019

This paper addresses the determination of personnel promotion policies in public Higher Education Institutions (HEI) considering aspects such as worker's promotion rules, hiring and laying off, workforce diversity and budget constraints. The problem is formulated as a Mixed Integer Linear Program. The objective of the proposed optimization model is not only expressed in economic terms but also addressing the achievement of a preferable staff composition and service level. The model is formulated generally, hence it can be useful for different types of universities taken into account their specificities and characteristics. Specifically, this paper addresses the problem of finding the relationship between economic resources for workers'promotion and the pursued preferable staff composition. The model is applied to a real case, in which several analyses are performed under different scenarios characterized by possible trends in the available budget and the demand. The analyses are for different workforce structures, which reflect different academic and personnel policies. The results address the performance of the proposed model in achieving the preferable structure and also on how promotions --and associated expenditures--, are for young researchers and experienced personnel according to each considered scenario.

Keywords: MILP, personnel planning, higher education institutions, promotions planning, personnel policies.
Mathematics Subject Classification: Primary: 90C11, 90C05; Secondary: 65K99.
Citation: Rocio de la Torre, Amaia Lusa, Manuel Mateo, El-Houssaine Aghezzaf. Determining personnel promotion policies in HEI. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1835-1859. doi: 10.3934/jimo.2019031
References:
[1]

T. Agasisti, M. Arnaboldi and G. Azzone, Strategic management accounting in universities: The Italian experience, Higher Education, 55 (2008), 1-15.  doi: 10.1007/s10734-006-9032-6.  Google Scholar

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B. R. Clark, Sustaining change in universities: Continuities in case studies and concepts, Tertiary Education and Management, 9 (2003), 99-116.  doi: 10.1080/13583883.2003.9967096.  Google Scholar

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A. Corominas, R. Pastor and E. Rodríguez, Rotational allocation of tasks to multifunctional workers in a service industry, International Journal of Production Economics, 103 (2005), 3-9.  doi: 10.1016/j.ijpe.2005.05.015.  Google Scholar

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A. Corominas, A. Lusa and J. Olivella, A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management, European Journal of Operational Research, 216 (2012), 445-458.  doi: 10.1016/j.ejor.2011.06.027.  Google Scholar

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J. J. da Cunha Jr and M. C. de Souza, A linearized model for academic staff assignment in a Brazilian university focusing on performance gain in quality indicators, International Journal of Production Economics, 197 (2018), 43-51. Google Scholar

[9]

R. de la Torre, A. Lusa and M. Mateo, A MILP Model for the Long Term Academic Staff Size and Composition Planning in Public Universities, Omega, 63 (2016), 1-11.   Google Scholar

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R. de la Torre, A. Lusa and M. Mateo, Evaluation of the impact of strategic staff planning in a university using a MILP model, European Journal of Industrial Engineering, 11 (2017), 328-352.  doi: 10.1504/EJIE.2017.084879.  Google Scholar

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L. Elwood and A. Rainnie, Strategic planning in Ireland's Institutes of Technology, Higher Education Policy, 23 (2012), 107-129.   Google Scholar

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A. T. Ernst, H. Jiang, M. Krishnamoorthy and D. Sier, Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, 153 (2004), 3-27.  doi: 10.1016/S0377-2217(03)00095-X.  Google Scholar

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C. Heimerl and R. Kolisch, Scheduling and staffing multiple projects with a multi-skilled workforce, OR Spectrum, 32 (2010), 343-368.  doi: 10.1007/s00291-009-0169-4.  Google Scholar

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W. H. Hu, M. S. Lavieri, A. Toriello and X. Liu, Strategic health workforce planning, IIE Transaction, 48 (2016), 1127-1138.  doi: 10.1080/0740817X.2016.1204488.  Google Scholar

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[17]

P. M. Koeleman, S. Bhulai and M. van Meersbergen, Optimal patient and personnel scheduling policies for care-at-home service facilities, European Journal of Operational Research, 219 (2012), 557-563.  doi: 10.1016/j.ejor.2011.10.046.  Google Scholar

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D. Lillis, Steering by Engagement: Towards an Integrated Planning and Evaluation Framework in Higher Education Institutes, European forum for quality assurance: Embedding quality culture in higher education, Dublin, 2006. Google Scholar

[19]

X. Llinàs-Audet, M. Girotto and F. Solé, University strategic management and the efficacy of the managerial tools: The case of the Spanish universities, Revista de Educación, 355 (2011), 33-54.   Google Scholar

[20]

M. Lounsbury, Institutional sources of practice variation: Staffing College and university recycling programs, Administrative Science Quarterly, 46 (2001), 29-56.   Google Scholar

[21]

J. A. D. Machuca, M. M. González-Zamora and V. G. Aguilar-Escobar, Service operations management research, Journal of Operations Management, 25 (2007), 585-603.  doi: 10.1016/j.jom.2006.04.005.  Google Scholar

[22]

B. Maenhout and M. Vanhoucke, An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems, Omega, 41 (2013), 485-499.  doi: 10.1016/j.omega.2012.01.002.  Google Scholar

[23]

C. Martínez-Costa, M. Mas-Machuca, E. Benedito and A. Corominas, A review of mathematical programming models for strategic capacity planning in manufacturing, International Journal of Production Economics, 153 (2014), 65-85.   Google Scholar

[24]

M. Mckelvet and M. Holmen, Learning to Compete in European Universities: From Social Institution to Knowledge Business, Edward Elgar Publishing Limited, Cheltenham, 2009. Google Scholar

[25]

A. V. Roth and L. J. Menor, Insights into service operations management: A research agenda, Production and Operations Management, 12 (2003), 145-164.  doi: 10.1111/j.1937-5956.2003.tb00498.x.  Google Scholar

[26]

G. Sharrock, Four management agendas for Australian universities, Journal of Higher Education Policy and Management, 34 (2012), 323-337.  doi: 10.1080/1360080X.2012.678728.  Google Scholar

[27] M. Shattock, Managing Successful Universities, Society for Research in Higher Education & Open University Press, London, 2003.   Google Scholar
[28]

L. Simeone, G. Secundo and G. Schiuma, Adopting a design approach to translate needs and interests of stakeholders in academic entrepreneurship: The MIT Senseable City Lab case, Technovation, 64/65 (2017), 58-67.  doi: 10.1016/j.technovation.2016.12.001.  Google Scholar

[29]

H. Song and H. C. Huang, A successive convex approximation method for multistage workforce capacity planning problem with turnover, European Journal of Operational Research, 188 (2008), 29-48.  doi: 10.1016/j.ejor.2007.04.018.  Google Scholar

[30]

R. Subbaye and R. Vithal, Teaching criteria that matter in university academic promotions, Assessment & Evaluation in Higher Education, 42 (2016), 37-60.  doi: 10.1080/02602938.2015.1082533.  Google Scholar

[31]

I. Ugboro, K. Obeng and O. Spam, Strategic planning as an effective tool of strategic management in public sector organizations: evidence from public transit organizations, Administration & Society, 43 (2011), 87-123.  doi: 10.1177/0095399710386315.  Google Scholar

[32]

G. Willis, C. Siôn and M. Kunc, Strategic workforce planning in healthcare: A multi-methodology approach, European Journal of Production Research, 267 (2018), 250-263.  doi: 10.1016/j.ejor.2017.11.008.  Google Scholar

[33]

M. Würmseher, To each his own: Matching different entrepreneurial models to the academic scientist's individual needs, Technovation, 59 (2017), 1-17.   Google Scholar

show all references

References:
[1]

T. Agasisti, M. Arnaboldi and G. Azzone, Strategic management accounting in universities: The Italian experience, Higher Education, 55 (2008), 1-15.  doi: 10.1007/s10734-006-9032-6.  Google Scholar

[2]

G. Agiomirgianakis, D. Serenis and N. Tsounis, A distance learning university and its economic impact in a country's peripheries: The case of Hellenic Open University, Operational Research, 17 (2017), 165-186.  doi: 10.1007/s12351-015-0220-y.  Google Scholar

[3]

H. S. Ahn, R. Righter and J. G. Shanthikumar, Staffing decisions for heterogeneous workers with turnover, Mathematical Methods of Operations Research, 62 (2005), 499-514.  doi: 10.1007/s00186-005-0033-5.  Google Scholar

[4]

R. Birnbaum, The life cycle of academic management fads, Journal of Higher Education, 71 (2000), 1-16.  doi: 10.2307/2649279.  Google Scholar

[5]

B. R. Clark, Sustaining change in universities: Continuities in case studies and concepts, Tertiary Education and Management, 9 (2003), 99-116.  doi: 10.1080/13583883.2003.9967096.  Google Scholar

[6]

A. Corominas, R. Pastor and E. Rodríguez, Rotational allocation of tasks to multifunctional workers in a service industry, International Journal of Production Economics, 103 (2005), 3-9.  doi: 10.1016/j.ijpe.2005.05.015.  Google Scholar

[7]

A. Corominas, A. Lusa and J. Olivella, A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management, European Journal of Operational Research, 216 (2012), 445-458.  doi: 10.1016/j.ejor.2011.06.027.  Google Scholar

[8]

J. J. da Cunha Jr and M. C. de Souza, A linearized model for academic staff assignment in a Brazilian university focusing on performance gain in quality indicators, International Journal of Production Economics, 197 (2018), 43-51. Google Scholar

[9]

R. de la Torre, A. Lusa and M. Mateo, A MILP Model for the Long Term Academic Staff Size and Composition Planning in Public Universities, Omega, 63 (2016), 1-11.   Google Scholar

[10]

R. de la Torre, A. Lusa and M. Mateo, Evaluation of the impact of strategic staff planning in a university using a MILP model, European Journal of Industrial Engineering, 11 (2017), 328-352.  doi: 10.1504/EJIE.2017.084879.  Google Scholar

[11]

L. Elwood and A. Rainnie, Strategic planning in Ireland's Institutes of Technology, Higher Education Policy, 23 (2012), 107-129.   Google Scholar

[12]

A. T. Ernst, H. Jiang, M. Krishnamoorthy and D. Sier, Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, 153 (2004), 3-27.  doi: 10.1016/S0377-2217(03)00095-X.  Google Scholar

[13]

C. Heimerl and R. Kolisch, Scheduling and staffing multiple projects with a multi-skilled workforce, OR Spectrum, 32 (2010), 343-368.  doi: 10.1007/s00291-009-0169-4.  Google Scholar

[14]

W. H. Hu, M. S. Lavieri, A. Toriello and X. Liu, Strategic health workforce planning, IIE Transaction, 48 (2016), 1127-1138.  doi: 10.1080/0740817X.2016.1204488.  Google Scholar

[15]

M. L. Jorge, J. H. Madueno and F. J. A Pena, Factors influencing the presence of sustainability initiatives in the strategic planning of Spanish universities, Environmental Education Research, 21 (2015), 1155-1187.   Google Scholar

[16]

S. Kim and D. Nembhard, Cross-trained staffing level with heterogeneous learning/forgetting, IEEE Transactions of Engineering Management, 57 (2010), 560-574.  doi: 10.1109/TEM.2010.2048905.  Google Scholar

[17]

P. M. Koeleman, S. Bhulai and M. van Meersbergen, Optimal patient and personnel scheduling policies for care-at-home service facilities, European Journal of Operational Research, 219 (2012), 557-563.  doi: 10.1016/j.ejor.2011.10.046.  Google Scholar

[18]

D. Lillis, Steering by Engagement: Towards an Integrated Planning and Evaluation Framework in Higher Education Institutes, European forum for quality assurance: Embedding quality culture in higher education, Dublin, 2006. Google Scholar

[19]

X. Llinàs-Audet, M. Girotto and F. Solé, University strategic management and the efficacy of the managerial tools: The case of the Spanish universities, Revista de Educación, 355 (2011), 33-54.   Google Scholar

[20]

M. Lounsbury, Institutional sources of practice variation: Staffing College and university recycling programs, Administrative Science Quarterly, 46 (2001), 29-56.   Google Scholar

[21]

J. A. D. Machuca, M. M. González-Zamora and V. G. Aguilar-Escobar, Service operations management research, Journal of Operations Management, 25 (2007), 585-603.  doi: 10.1016/j.jom.2006.04.005.  Google Scholar

[22]

B. Maenhout and M. Vanhoucke, An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems, Omega, 41 (2013), 485-499.  doi: 10.1016/j.omega.2012.01.002.  Google Scholar

[23]

C. Martínez-Costa, M. Mas-Machuca, E. Benedito and A. Corominas, A review of mathematical programming models for strategic capacity planning in manufacturing, International Journal of Production Economics, 153 (2014), 65-85.   Google Scholar

[24]

M. Mckelvet and M. Holmen, Learning to Compete in European Universities: From Social Institution to Knowledge Business, Edward Elgar Publishing Limited, Cheltenham, 2009. Google Scholar

[25]

A. V. Roth and L. J. Menor, Insights into service operations management: A research agenda, Production and Operations Management, 12 (2003), 145-164.  doi: 10.1111/j.1937-5956.2003.tb00498.x.  Google Scholar

[26]

G. Sharrock, Four management agendas for Australian universities, Journal of Higher Education Policy and Management, 34 (2012), 323-337.  doi: 10.1080/1360080X.2012.678728.  Google Scholar

[27] M. Shattock, Managing Successful Universities, Society for Research in Higher Education & Open University Press, London, 2003.   Google Scholar
[28]

L. Simeone, G. Secundo and G. Schiuma, Adopting a design approach to translate needs and interests of stakeholders in academic entrepreneurship: The MIT Senseable City Lab case, Technovation, 64/65 (2017), 58-67.  doi: 10.1016/j.technovation.2016.12.001.  Google Scholar

[29]

H. Song and H. C. Huang, A successive convex approximation method for multistage workforce capacity planning problem with turnover, European Journal of Operational Research, 188 (2008), 29-48.  doi: 10.1016/j.ejor.2007.04.018.  Google Scholar

[30]

R. Subbaye and R. Vithal, Teaching criteria that matter in university academic promotions, Assessment & Evaluation in Higher Education, 42 (2016), 37-60.  doi: 10.1080/02602938.2015.1082533.  Google Scholar

[31]

I. Ugboro, K. Obeng and O. Spam, Strategic planning as an effective tool of strategic management in public sector organizations: evidence from public transit organizations, Administration & Society, 43 (2011), 87-123.  doi: 10.1177/0095399710386315.  Google Scholar

[32]

G. Willis, C. Siôn and M. Kunc, Strategic workforce planning in healthcare: A multi-methodology approach, European Journal of Production Research, 267 (2018), 250-263.  doi: 10.1016/j.ejor.2017.11.008.  Google Scholar

[33]

M. Würmseher, To each his own: Matching different entrepreneurial models to the academic scientist's individual needs, Technovation, 59 (2017), 1-17.   Google Scholar

Figure 1.  Academic pathway
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Figure 2.  Scope of the paper
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Figure 3.  Relationship between assumed additional resources for encouraging worker's promotions $ c_{kt}{\theta}_k $ and category $ k $. Values are expressed as relative to $ {c_{1, 1}}{\theta_1} $ (category 1)
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Figure 4.  Relationship between assumed additional resources for encouraging worker's promotions and category, expressed as relative to workers' capacity
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Figure 5.  Global discrepancy for scenarios 1 to 21 (so considering the institution model A as the preferable composition), considering different initial compositions as well as different temporal trends in budget and demand
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Figure 6.  Global discrepancy for scenarios 22 to 42 (so considering the institution model B as the preferable composition), considering different initial compositions as well as different temporal trends in budget and demand
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Figure 7.  Global discrepancy for scenarios 43 to 63 (so considering the institution model C as the preferable composition), considering different initial compositions as well as different temporal trends in budget and demand
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Figure 8.  Comparison between the achieved workforce composition per group of scenarios and the initial workforce structures per categories, while pursuing institution models A, B and C. The initial number of workers adopting institution models A, B and C are included in parenthesis
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Figure 9.  Average promotional ratio for personnel within $ KT $ and $ KC $, under the conditions of all the scenarios
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Figure 10.  Total number of promotions for all groups of scenarios and for the considered time horizon
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Figure 11.  Total cost incurred for personnel promotions (during the time horizon and for all the scenarios), expressed as relative to the average cost in scenarios within Groups X, R and U
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Table 1.  List of category groups and their associated tasks
Category groupDescription of workersTasks
Full Professor (highest categories within KC and KP groups of categories) This category is composed by the most skilled and experienced workers. Workers within these categories can perform some managerial tasks in the HEI governor (i.e. dean of the faculty). ⅰ) Lead projects / processes, ⅱ) Conduct research, ⅲ) Provide strategic vision for projects in research and technology transfer, as well as for the strategic objectⅳes for the department, ⅳ) Publish scientific results from research, ⅴ) Teach professionals in lower categories, ⅶ) Develop management tasks
Tenured Assistant Professor (within KC group of categories) and Tenured Professor (within KP group of categories). This group of categories is composed by high experienced workers in all aspects of teaching and research. Workers have also skills in scientific project leading. ⅰ) Lead projects / processes, ⅱ) Conduct research, ⅲ) Publish scientific results from research, ⅳ) Teach professionals in lower categories.
Tenure-track Lecturer (within KT group of categories). This group of categories is composed by professionals with high capacity for carrying out teaching and research actⅳities, with more expertise and knowledge than those in the categories under KT ⅰ) Collaborate in the management of projects and processes, ⅱ) Execute projects required high degree of specialization, ⅲ) Supervise research, ⅳ) Publish scientific results from research, ⅴ) Teach young researchers and pupils.
Assistant Lecturer (within KT group of categories) This group of categories is composed by workers starting their career; thus, they are still in training processes for teaching and research purposes. ⅰ) Participate in research and technology transfer projects, ⅱ) Execute projects under the advice of colleagues in upper categories, ⅲ) Support teaching actⅳities.
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Category groupDescription of workersTasks
Full Professor (highest categories within KC and KP groups of categories) This category is composed by the most skilled and experienced workers. Workers within these categories can perform some managerial tasks in the HEI governor (i.e. dean of the faculty). ⅰ) Lead projects / processes, ⅱ) Conduct research, ⅲ) Provide strategic vision for projects in research and technology transfer, as well as for the strategic objectⅳes for the department, ⅳ) Publish scientific results from research, ⅴ) Teach professionals in lower categories, ⅶ) Develop management tasks
Tenured Assistant Professor (within KC group of categories) and Tenured Professor (within KP group of categories). This group of categories is composed by high experienced workers in all aspects of teaching and research. Workers have also skills in scientific project leading. ⅰ) Lead projects / processes, ⅱ) Conduct research, ⅲ) Publish scientific results from research, ⅳ) Teach professionals in lower categories.
Tenure-track Lecturer (within KT group of categories). This group of categories is composed by professionals with high capacity for carrying out teaching and research actⅳities, with more expertise and knowledge than those in the categories under KT ⅰ) Collaborate in the management of projects and processes, ⅱ) Execute projects required high degree of specialization, ⅲ) Supervise research, ⅳ) Publish scientific results from research, ⅴ) Teach young researchers and pupils.
Assistant Lecturer (within KT group of categories) This group of categories is composed by workers starting their career; thus, they are still in training processes for teaching and research purposes. ⅰ) Participate in research and technology transfer projects, ⅱ) Execute projects under the advice of colleagues in upper categories, ⅲ) Support teaching actⅳities.
Table 2.  Preferable workforce compositions for institution models A, B and C
Model A Model B Model C
Proportion of workers in $KT$42%34%27%
Proportion of workers in $KC$17%18%16%
Proportion of workers in $KP$41%48%57%
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Model A Model B Model C
Proportion of workers in $KT$42%34%27%
Proportion of workers in $KC$17%18%16%
Proportion of workers in $KP$41%48%57%
Table 3.  List of scenarios for analysis
Sc.Initial comp.Pref. comp.Dem.-BudgetSc.Initial comp.Pref. comp.Dem.-BudgetSc.Initial comp.Pref. comp.Dem.-Budget
1AACC22ABCC43ACCC
2AACD23ABCD44ACCD
3AADC24ABDC45ACDC
4AADD25ABDD46ACDD
5AAIC26ABIC47ACIC
6AAII27ABII48ACII
7AAID28ABID49ACID
8BACC29BBCC50BCCC
9BACD30BBCD51BCCD
10BADC31BBDC52BCDC
11BADD32BBDD53BCDD
12BAIC33BBIC54BCIC
13BAII34BBII55BCII
14BAID35BBID56BCID
15CACC36CBCC57CCCC
16CACD37CBCD58CCCD
17CADC38CBDC59CCDC
18CADD39CBDD60CCDD
19CAIC40CBIC61CCIC
20CAII41CBII62CCII
21CAID42CBID63CCID
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Sc.Initial comp.Pref. comp.Dem.-BudgetSc.Initial comp.Pref. comp.Dem.-BudgetSc.Initial comp.Pref. comp.Dem.-Budget
1AACC22ABCC43ACCC
2AACD23ABCD44ACCD
3AADC24ABDC45ACDC
4AADD25ABDD46ACDD
5AAIC26ABIC47ACIC
6AAII27ABII48ACII
7AAID28ABID49ACID
8BACC29BBCC50BCCC
9BACD30BBCD51BCCD
10BADC31BBDC52BCDC
11BADD32BBDD53BCDD
12BAIC33BBIC54BCIC
13BAII34BBII55BCII
14BAID35BBID56BCID
15CACC36CBCC57CCCC
16CACD37CBCD58CCCD
17CADC38CBDC59CCDC
18CADD39CBDD60CCDD
19CAIC40CBIC61CCIC
20CAII41CBII62CCII
21CAID42CBID63CCID
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