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VOL. 2, ISSUE 1 (2026)
Dynamic production scheduling with financial constraints: A stochastic control approach
Authors
Chukwuyem Precious Mormah
Abstract
Dynamic production scheduling has gained critical importance in modern
manufacturing due to increasing market volatility, demand uncertainty, and
resource limitations. Traditional static scheduling models are inadequate for
environments characterized by stochastic demand, machine reliability, and
operational costs. This study investigates a stochastic control approach to
dynamic production scheduling that explicitly incorporates financial
constraints, such as limited working capital and budget restrictions,
reflecting real-world manufacturing challenges. By modeling inventory,
production rates, and costs as stochastic processes, the approach enables the
derivation of adaptive control policies that respond in real time to
operational disruptions and financial limitations. Integrating financial
constraints ensures that production decisions remain feasible within budgetary
limits, balancing output expansion with liquidity preservation. This research
bridges operations management and financial sustainability, providing a robust
framework applicable to capital-intensive and volatile industries. The study
underscores the importance of smart manufacturing technologies and data-driven
production management systems in supporting continuous optimization via stochastic
control algorithms. The conceptual framework developed here offers theoretical
insights and practical implications for manufacturing firms seeking to enhance
operational efficiency and financial stability under uncertainty. This
integrated approach advances the understanding of how dynamic scheduling and
financial resource management jointly impact manufacturing performance,
contributing to both academic literature and managerial practice in uncertain,
resource-constrained production environments.
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Pages:257-260
How to cite this article:
Chukwuyem Precious Mormah "Dynamic production scheduling with financial constraints: A stochastic control approach". International Journal of Applied Review
, Vol 2, Issue 1, 2026, Pages 257-260
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