2017
DOI: 10.1504/ejie.2017.084879
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Evaluation of the impact of strategic staff planning in a university using a MILP model

Abstract: A mathematical model for optimising the strategic staff planning in universities is used to analyse the impact of different personnel and academic policies on the strategic staff plan, considering a preferable staff composition. The personnel policies are evaluated allowing or not the dismissals of permanent workers; the ratio of internal promotion for workers and the personnel budget. The academic policies are tested through the impact of different demand trends. Addressing the specificities of the university… Show more

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Cited by 3 publications
(3 citation statements)
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References 14 publications
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“…Note that analogously to the classification in the Groups X, Y and Z of scenarios with institution model A, scenarios pursuing institution model B ( Figure 6) are classified in Groups R, S and T, and scenarios pursuing institution model C are divided into Groups U, V and W ( Figure 7). The benefits from controlling the promotion ratios can be seen comparing the results for the Global Discrepancy in [10] and this paper. In the former, the proportion of workers in the unit uthat can promote is a maximum established value.…”
Section: Table 2 Preferable Workforce Compositions For Institution Momentioning
confidence: 82%
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“…Note that analogously to the classification in the Groups X, Y and Z of scenarios with institution model A, scenarios pursuing institution model B ( Figure 6) are classified in Groups R, S and T, and scenarios pursuing institution model C are divided into Groups U, V and W ( Figure 7). The benefits from controlling the promotion ratios can be seen comparing the results for the Global Discrepancy in [10] and this paper. In the former, the proportion of workers in the unit uthat can promote is a maximum established value.…”
Section: Table 2 Preferable Workforce Compositions For Institution Momentioning
confidence: 82%
“…Equation (9) includes the relation between the number of workers and the corresponding binary variables and possible values (this variable has been discretized to linearise the product of the number of workers by the promotional ratio). Equations (10) and (11) limit, for each category, the change in the promotional ratio between two consecutive periods.…”
mentioning
confidence: 99%
“…The existing literature on KIOs is rich (Lönnqvist and Laihonen 2017), with a primary focus on its personnel-how knowledge workers are managed (e.g., de la Torre et al 2016;Lafuente and Berbegal-Mirabent 2019;Millar et al 2018); information flows-how these organizations contribute to economic growth and business innovation (e.g., Horváth and Berbegal-Mirabent 2020); and knowledge management-the decisions linked to strategic and networking, capacity planning, knowledge retention, and dynamic capabilities (e.g., de la Torre et al 2017;Dietrich et al 2010). However, the current economic and social crisis invites us to go a step further and dive deeper into the innovative capacity of these organizations.…”
mentioning
confidence: 99%