A joint problem of strategic workforce planning and fleet renewal: With an application in defense

Hasan Hüseyin Turan, Fatemeh Jalalvand, Sondoss Elsawah and Michael J. Ryan

European Journal of Operational Research, 2022, vol. 296, issue 2, 615-634

Abstract: Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addressing a trade-off among several costs (e.g., workforce, maintenance, operating, etc.) and operational readiness (availability) of the fleet. To make such trade-offs, the decisions associated with workforce planning (i.e., the recruitment and the career progression of the workforce) and fleet renewal (i.e., the timing of asset replacements) strategies have to be simultaneously considered and optimized. We develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to solve the joint problem. In the developed approach, the GA generates candidate workforce planning and fleet renewal strategies to find the best joint strategy. Then, the candidate workforce planning and renewal strategies are passed to the SD model which simulates both the career progression of the workforce and the life-cycle of assets to evaluate the total cost. We illustrate the applicability and effectiveness of the joint model on a realistic case study motivated by the recent modernization efforts of the Royal Australian Navy. The results obtained indicate that this approach leads to a considerable cost reduction and identifies the causes of inferior performance. We also test the robustness of the optimized strategies under uncertainty by sensitivity and scenario discovery analyses to infer further insights.

Keywords: OR in defense; Military workforce planning; Fleet renewal; Simulation-optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221721003222
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:296:y:2022:i:2:p:615-634

DOI: 10.1016/j.ejor.2021.04.010

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().