Authors:
Andrej Škraba
1
;
Eugene Semenkin
2
;
Davorin Kofjac
1
;
Maria Semenkina
2
;
Anja Znidaršic
1
;
Matjaž Maletic
1
;
Shakhnaz Akhmedova
2
;
Crtomir Rozman
3
and
Vladimir Stanovov
2
Affiliations:
1
University of Maribor, Slovenia
;
2
Siberian State Aerospace University, Russian Federation
;
3
Faculty of Agriculture and Life Sciences and University of Maribor, Slovenia
Keyword(s):
Manpower, Supply Chain, Optimization, System Dynamics, Genetic Algorithms, Optimal Control.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Evolutionary Computation and Control
;
Facilities Planning and Management
;
Health Engineering and Technology Applications
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems
;
Resources and Knowledge Management in Industry
;
Supply Chain and Logistics Engineering
;
Symbolic Systems
Abstract:
This paper addresses the problem of the hierarchical manpower system control in the restructuring process.
The restructuring case study is described where eight topmost ranks are considered. The desired and actual
structure of the system is given by the actual numbers of men in a particular rank. The system was modelled
in the dicrete state space with state elements and flows representing the recruitment, wastages and retirements.
The key issues were identified in the process as the stating of the criteria function, which are time variant
boundaries on the parameter values, the chain stucture of the system and the tendency for the system to oscilate
at given initial conditions. The oscillatory case is presented and the dynamic programming approach
was considered in the optimization as unsuitable, examining the oscillations. The boundary space and optimal
solution space were considered by indicating the small area where the solution could be optimal. The augmented
finite automaton was
defined which was used in the optimization with the adaptive genetic algorithm.
The developed optimization method enabled us to successfully determine proper restructuring strategy for the
defined manpower system.
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