A long-term fleet renewal problem under uncertainty: A simulation-based optimization approach. (1st May 2020)

• Record Type: Journal Article
• Authors: Turan, Hasan Hüseyin; Elsawah, Sondoss; Ryan, Michael J.
• Is Part Of: Expert systems with applications. Volume 145(2020)
• Publication Date: 2020-05-01
• Subjects: Fleet renewal -- Simulation-optimization -- System dynamics -- Genetic algorithm -- Scenario discovery; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113158
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A stochastic naval fleet renewal problem is modeled and solved. The life-cycle of a fleet is simulated by a hybrid simulation model. The simulation model is coupled with an enhanced genetic algorithm. Robust renewal strategies are identified by the proposed approach. The scenario discovery analysis helps examine extreme scenarios. Abstract: In this paper, we model and solve a strategic problem of fleet renewal to meet future operational needs under uncertain conditions. The fleet renewal problem focuses on mainly strategic decisions involving from fleet size, fleet mix and timing of replacement, yet it is essential to consider a significant amount of detail regarding short-term decisions to prevent inferior or infeasible strategies. In this direction, we develop a hybrid simulation model by combining system dynamics (SD) and discrete event simulation (DES) approaches. The standalone use of this model enables the decision maker to analyze the effects of both short- and long-term decisions on availability by simulating the processes that the fleet undertakes through its life-cycle from asset acquisition to retirement. Nevertheless, the simulation neither suggests nor seeks the best renewal strategy(ies). To alleviate this difficulty, we propose a simulation-based optimization that uses a genetic algorithm (GA) to effectively search a very large set of feasible fleet renewal strategies and uses the developed hybrid simulation model to evaluate candidate strategies found by GA. To provide a decision context where the approach has been developed and applied, we use a naval fleet renewal application. The extensive numerical experiments show that the proposed approach not only finds good and robust renewal strategies but also identify critical resources that influence the fleet's availability. Finally, the robustness of optimized strategies under uncertainty is tested by sensitivity analysis, and mappings between implemented strategies and the fleet performance are constructed by scenario discovery analysis to provide insights for decision makers.