A multi-stage stochastic programming approach for overhaul and supply chain planning of modular physical assets: case study

Mohammad Asghari () and Armin Jabbarzadeh ()
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Mohammad Asghari: University of Quebec, Department of Systems Engineering, École de Technologie Supérieure (ETS)
Armin Jabbarzadeh: University of Quebec, Department of Systems Engineering, École de Technologie Supérieure (ETS)

Operational Research, 2026, vol. 26, issue 1, No 8, 31 pages

Abstract: Abstract Effective overhaul planning is vital for optimizing performance and cost-efficiency in industrial organizations reliant on physical assets. Focusing on modular subsystems within a closed-loop supply chain, this paper addresses the challenge of determining the optimal timing for overhauling repairable modules to minimize costs, meet deadlines, and ensure asset availability. The closed-loop structure encompasses inventory holding, transportation, and overhaul operations, capturing the interdependencies between logistics, maintenance, and spare parts management. To manage uncertainties from major asset failures and variable demand, a multi-stage stochastic programming model is proposed. A sample average approximation method is employed to address the computational complexity arising from the large scenario space. Real data from a port operator’s asset management case is used to evaluate the model. Results show a potential 17% reduction in total ownership costs. Additionally, the model provides managerial insights, enabling decision-makers to determine the required number of spare units and the level of investment needed to reduce future uncertainty.

Keywords: Overhaul planning; Rotables; Supply chain planning; Multi-stage stochastic programming; Sample average approximation (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s12351-025-00988-0

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