Supply Chain Optimization for Natural Gas Installation Using Dynamic Programming and Markov Chains

Abstract

This paper proposes a hybrid decision-making model for optimizing the material supply chain innatural gas installation projects, with a focus on companies providing natural gas installation services for condominiums, residential complexes, and large-scale residential civil works in Peru. In a context characterized by constrained budgets and critical delivery times, the model integrates Deterministic Dynamic Programming (DDP) for optimal supplier selection and Markov Chains to assess operational resilience. The results indicate that, after adjusting demand to a feasible operational scenario, the DDP model achieved a cost reduction of 28.35%, optimizing the standard budget for a 100-unit residential condominium from USD 29,700 to USD 21,280, while ensuring a supply lead time of 20 days. Furthermore, the Markov chain analysis yielded a steady-state compliance probability of 70.9%, identifying a failure risk of 6.0%, which highlights the need for contingency planning. The proposed approach enables small and medium-sized enterprises (SMEs) in the natural gas installation services sector to transition from empirical decision-making to a data-driven and scientific management framework, enhancing both financial sustainability and operational reliability in large-scale residential projects.