RESEARCH PAPER
Competence allocation planning robust to unexpected staff absenteeism
| 1 | Faculty of Electronics and Computer Science Koszalin University of Technology ul. Śniadeckich 2, 75-453 Koszalin, Poland |
| 2 | Institute of Management and Control Systems Kielce University of Technology, Al. 1000-lecia Państwa Polskiego, 25-314 Kielce, Poland |
Publication date: 2019-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(3):440–450
KEYWORDS
Competence assignmentrobust planningemployee competencesrobust employee competencestructureemployee absenteeism
ABSTRACT
In order to deal with unexpected events such as employee absenteeism and/or a demand for personnel that is higher or lower than
expected, organizations need to adopt proactive and reactive scheduling strategies to protect the personnel roster and to respond
to this operational variability, respectively. In this paper, we discuss a proactive approach that exploits the concept of employee
substitutability to improve the flexibility of a personnel shift roster to respond to schedule disruptions. With a view to developing a
DSS-driven method dedicated to competence allocation planning robust to unexpected staff absenteeism, we present the concept
of the so-called robust employee competence structure. A declarative model of the concept allows to find an employee competence
structure robust to a given set of disruptions while guaranteeing an admissible personnel allocation to the assumed set of tasks.
Since the problem of designing such a robust structure is NP-hard, another goal of the present study is to propose a sufficient
condition the fulfilment of which will guarantee the validity of calculations. Potential applications of the proposed solution are
discussed using examples
REFERENCES (50)
1.
Abernathy W, Baloff N, Hershey J. A three-stage man power planning and scheduling model - a service sector example. Operations Research 1973; 21: 693–711, https://doi.org/10.1287/opre.2....
2.
Antosz K. Maintenance – identification and analysis of the competency gap. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20(3): 484–494, https://doi.org/10.17531/ein.2....
3.
Banaszak Z, Bocewicz G. Declarative Modeling for Production Order Portfolio Scheduling. Foundations of Management 2014; 6(3): 7–24, https://doi.org/10.1515/fman-2....
4.
Bocewicz G, Banaszak Z. Decision Support Driven Models and Algorithms of Artificial Intelligence. Warsaw: Warsaw University of Technology, 2011.
5.
Bocewicz G, Bzdyra K, Banaszak Z. Robust Scheduling Subject to Multi-project Environment Constraints. In: Szewczyk R., Zieliński C., Kaliczyńska M. (eds.) Challenges in Automation, Robotics and Measurement Techniques. Advances in Intelligent Systems and Computing, vol. 440. Springer, 2016: 115–126, https://doi.org/10.1007/978-3-....
6.
Bombiak E. Human resources risk as an aspect of human resources management in turbulent environments. In: Pînzaru F., Zbuchea A., Brătianu C., Vătămănescu E.M., Mitan A. (eds.) Shift! Major challenges of today's economy. Bucharest: Tritonic Publishing House 2017: 121-132.
7.
Burke E, De Causmaecker P, Vanden Berghe G, Van Landeghem H. The state of the art of nurse rostering. Journal of Scheduling 2004; 7: 441–499, https://doi.org/10.1023/B:JOSH....
8.
Coolahan J. Key competencies. A developing concept in general compulsory education. Brussels: Eurydice, 2002.
9.
De Causmaecker P, Vanden Berghe G. Relaxation of coverage constraints in hospital personnel rostering. Lecture Notes in Computer Science 2003; 2740: 129-147, https://doi.org/10.1007/978-3-....
10.
Dillon J, Kontogiorgis S. US Airways optimizes the scheduling of reserve flight crews. Interfaces 1999; 29(5): 95–122, https://doi.org/10.1287/inte.2....
11.
Dowsland K, Thompson J. Solving a nurse scheduling problem with knapsacks, networks and tabu search. Journal of Operational Research Society 2000; 51(7): 825–833, https://doi.org/10.1057/palgra....
12.
Dück V, Ionescu L, Kliewer N, Suhl L. Increasing stability of crew and aircraft schedules. Transportation Research Part C: Emerging Technologies 2012; 20(1): 47–61, https://doi.org/10.1016/j.trc.....
13.
Ehrgott M, Ryan DM. Constructing robust crew schedules with bi-criteria optimization. Journal of Multi-Criteria Decision Analysis 2002; 11(3): 139–150, https://doi.org/10.1002/mcda.3....
14.
Feldstein E, Patalas-Maliszewska J, Kłos S, Kałasznikow A, Andrzejewski K. The use of Plackett-Burman plans and the analysis of expert opinions, in order to assess the significance of controllable parameters of the plasma cutting process. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20(3): 443–449, https://doi.org/10.17531/ein.2....
15.
Fonseka M.M., Wang P., Manzoor M.S. Impact of human resource slacks on firm performance: Evidence from a developing country. Proceedings of Rijeka Faculty of Economics 2013; 31(2): 279–306.
16.
Guidelines for Competency Management Systems for Downstream and Petroleum Sites. Petroleum Industry Association Ltd: Cogent 2017.
17.
Hazir O, Haouari M, Erel E. Robust scheduling and robustness measures for the discrete time/cost trade-off problem. European Journal of Operational Research 2010; 207(1): 633–643, https://doi.org/10.1016/j.ejor....
18.
Ingels J, Maenhout B. The impact of reserve duties on the robustness of a personnel shift roster: an empirical investigation. Computers & Operations Research 2015; 61: 153–169, https://doi.org/10.1016/j.cor.....
19.
Ingels J, Maenhout B. Optimised buffer allocation to construct stable personnel shift rosters. Omega 2019; 82: 102–117, https://doi.org/10.1016/j.omeg....
20.
Ionescu L, Kliewer N. Increasing flexibility of airline crew schedules. Procedia - Social and Behavioral Sciences 2011; 20: 1019–1028, https://doi.org/10.1016/j.sbsp....
21.
Korytkowski P. Competences-based performance model of multi-skilled workers with learning and forgetting. Expert Systems With Applications 2017; 77: 226–235, https://doi.org/10.1016/j.eswa....
22.
Kupczyk T, Stor M. Zarządzanie kompetencjami: teoria, badania i praktyka biznesowa. Wrocław, 2017 (in Polish).
23.
Lecuona J.R., Reitzig M. Knowledge worth having in 'excess': The value of tacit and firm‐specific human resource slack. Strategic Management Journal 2014; 35(7): 954–973, https://doi.org/10.1002/smj.21....
25.
Malen J, Vaaler PM. Organizational slack, national institutions and innovation effort around the world. Journal of World Business 2017;52(6): 782–797, https://doi.org/10.1016/j.jwb.....
26.
Małachowski B. Competence-based management of knowledge workers in project oriented organizations. In H. Xiong and W.B. Lee (Eds.), Knowledge science, engineering and management. Lecture Notes in Computer Science 2011; 7091: 281–292, https://doi.org/10.1007/978-3-....
27.
Małachowski B., Korytkowski P. Competences-based performance model of multi-skilled workers. Computers & Industrial Engineering 2016; 91: 165–177, https://doi.org/10.1016/j.cie.....
28.
Marlin D, Geiger SW. A reexamination of the organizational slack and innovation relationship. Journal of Business Research 2015; 68(12): 2683–2690, https://doi.org/10.1016/j.jbus....
29.
Moudani W, Mora-Camino F. Solving crew reserve in airlines using dynamic programming approach. International Journal of Optimization: Theory, Methods and Applications 2010; 2(4): 302–329.
30.
Nielsen I, Wójcik R, Bocewicz G, Banaszak Z. Towards constraint programming driven methodology for online multi-project planning and control. Information Systems Architecture and Technology, Wroclaw: Wroclaw University of Technology 2014: 65–74.
31.
Potthoff D, Huisman D, Desaulniers G. Column generation with dynamic duty selection for railway crew rescheduling. Transportation Science 2010; 44(4): 493–505, https://doi.org/10.1287/trsc.1....
32.
Rosenberger J, Schaefer A, Goldsman D, Johnson E, Kleywegt A, Nemhauser G. A stochastic model of airline operations. Transportation Science 2002; 36(4): 357–377, https://doi.org/10.1287/trsc.3....
33.
Schutle H, Smolka G, Wurtz J. Finite Domain Constraint Programming in Oz – A tutorial. Saarbrucken: German Research Center for Artificial Intelligence, 1998.
34.
Serpell A, Ferrada X. A competency-based model for constriction supervisor in developing countries. Personnel Review 2007; 36(4): 585–602, https://doi.org/10.1108/004834....
35.
Sitek P., Wikarek J., A Declarative Framework for Constrained Search Problems. New Frontiers in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence, Nguyen, NT., et al. (Eds.), Springer-Verlag, Berlin-Heidelberg, 2008; 5027: 728–737.
36.
Sitek P, Wikarek J. A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Scientific Programming 2016; Hindawi, https://doi.org/10.1155/2016/5....
37.
Sohoni M, Johnson E, Bailey T. Operational airline reserve crew planning. Journal of Scheduling 2006; 9(3): 203–221, https://doi.org/10.1007/s10951....
38.
Szwarc E, Bocewicz G, Bach-Dąbrowska I. Planning a teacher staff competence structure robust to unexpected personnel absence.Manufacturing Modelling, Management and Control (MIM); Berlin 2019 (in print).
39.
Szwarc E., Bach-Dąbrowska I., Bocewicz G. Competence Management in Teacher Assignment Planning, In: Damaševičius R., Vasiljevienė G (eds.) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science 2018; 920: 449–460, https://doi.org/10.1007/978-3-....
40.
Tam B, Ehrgott M, Ryan DM, Zakeri G. A comparison of stochastic programming and bi-objective optimisation approaches to robust airline crew scheduling. OR Spectrum 2011; 33(1): 49–75, https://doi.org/10.1007/s00291....
41.
Topaloglu S, Selim H. Nurse scheduling using fuzzy modelling approach. Fuzzy Sets and Systems 2010; 161(11): 1543–1563, https://doi.org/10.1016/j.fss.....
42.
Van den Bergh J, Beliën J, De Bruecker P, Demeulemeester E, De Boeck L. Personnel scheduling: a literature review. European Journal of Operational Research 2013; 226: 367–385, https://doi.org/10.1016/j.ejor....
43.
Woodruffe C. What is meant by competency? In: Boam R., Sparrow P. (Eds.), Designing and achieving competency. New York, 1992.
CITATIONS (9):
1.
The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises
Katarzyna Antosz, Lukasz Pasko, Arkadiusz Gola
Applied Sciences
Katarzyna Antosz, Lukasz Pasko, Arkadiusz Gola
Applied Sciences
2.
Competence-oriented project team planning – university case study
Eryk Szwarc, Izabela Nielsen, Czesław Smutnicki, Grzegorz Bocewicz, Zbigniew Banaszak, Jakub Bilski
Journal of Information and Telecommunication
Eryk Szwarc, Izabela Nielsen, Czesław Smutnicki, Grzegorz Bocewicz, Zbigniew Banaszak, Jakub Bilski
Journal of Information and Telecommunication
3.
A method for planning competency frameworks robust to disruptions - a case study of a manufacturing company
Eryk Szwarc, Peter Nielsen, Grzegorz Bocewicz, Henryk Budzisz, Zbigniew Banaszak
IFAC-PapersOnLine
Eryk Szwarc, Peter Nielsen, Grzegorz Bocewicz, Henryk Budzisz, Zbigniew Banaszak
IFAC-PapersOnLine
4.
The Use of Intelligent Systems to Support the Decision-Making Process in Lean Maintenance Management
Katarzyna Antosz, Lukasz Pasko, Arkadiusz Gola
IFAC-PapersOnLine
Katarzyna Antosz, Lukasz Pasko, Arkadiusz Gola
IFAC-PapersOnLine
7.
A competency-driven staff assignment approach to improving employee scheduling robustness
Grzegorz Bocewicz, Eryk Szwarc, Jaroslaw Wikarek, Peter Nielsen, Zbigniew Banaszak
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Grzegorz Bocewicz, Eryk Szwarc, Jaroslaw Wikarek, Peter Nielsen, Zbigniew Banaszak
Eksploatacja i Niezawodnosc - Maintenance and Reliability
8.
Proactive Planning of Project Team Members’ Competences
Eryk Szwarc, Jarosław Wikarek
Foundations of Management
Eryk Szwarc, Jarosław Wikarek
Foundations of Management
9.
Interactive Planning of Competency-Driven University Teaching Staff Allocation
Eryk Szwarc, Jaroslaw Wikarek, Arkadiusz Gola, Grzegorz Bocewicz, Zbigniew Banaszak
Applied Sciences
Eryk Szwarc, Jaroslaw Wikarek, Arkadiusz Gola, Grzegorz Bocewicz, Zbigniew Banaszak
Applied Sciences
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.