TY - GEN
T1 - A Differential Evolution Algorithm for Military Workforce Planning Problems
T2 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
AU - Sallam, Karam M.
AU - Turan, Hasan Huseyin
AU - Chakrabortty, Ripon K.
AU - Elsawah, Sondoss
AU - Ryan, Michael J.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Appropriate workforce planning in an organization can ensure maximum profitability. Better way of planning and scheduling is also very important to avoid unexpected expenditures and high employee turnovers. This paper proposes a simulation-optimization approach to predispose the best strategic decisions for a long-term workforce planning problem by taking into account the complex interactions among many components (e.g., recruitment, attrition, promotion, training, and retention) of workforce planning. We use a differential evolution (DE) algorithm as the optimization method to couple with a system dynamics (SD) simulation model. The set of all feasible workforce planning policies is developed by the SD simulation and then searched by the DE algorithm while the fitness evaluation (i.e., total cost) of policies is evaluated by the simulation model. We demonstrate the approach through numerical experiments on a military workforce planning problem to provide insight into how the different strategies affect the overall system performance with regards to both total cost and fleet availability.
AB - Appropriate workforce planning in an organization can ensure maximum profitability. Better way of planning and scheduling is also very important to avoid unexpected expenditures and high employee turnovers. This paper proposes a simulation-optimization approach to predispose the best strategic decisions for a long-term workforce planning problem by taking into account the complex interactions among many components (e.g., recruitment, attrition, promotion, training, and retention) of workforce planning. We use a differential evolution (DE) algorithm as the optimization method to couple with a system dynamics (SD) simulation model. The set of all feasible workforce planning policies is developed by the SD simulation and then searched by the DE algorithm while the fitness evaluation (i.e., total cost) of policies is evaluated by the simulation model. We demonstrate the approach through numerical experiments on a military workforce planning problem to provide insight into how the different strategies affect the overall system performance with regards to both total cost and fleet availability.
KW - Differential evolution
KW - Modelling and simulation
KW - Simulation-Optimization
KW - Workforce planning problem
UR - http://www.scopus.com/inward/record.url?scp=85099715211&partnerID=8YFLogxK
UR - http://www.ieeessci2020.org/callofpapers.html
U2 - 10.1109/SSCI47803.2020.9308566
DO - 10.1109/SSCI47803.2020.9308566
M3 - Conference contribution
AN - SCOPUS:85099715211
SN - 9781728125480
T3 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
SP - 2504
EP - 2509
BT - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
A2 - Abbass, Hussein
A2 - Coello Coello, Carlos A.
A2 - Singh, Hemant Kumar
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
Y2 - 1 December 2020 through 4 December 2020
ER -