Modeling, simulation, and optimization of a failure-prone collection–reconditioning–distribution system under resource constraints

The global transition toward a circular economy has driven industrial organizations to adopt sustainable closed-loop supply chains, heavily integrating manufacturing–remanufacturing systems to reduce raw material dependency and mitigate greenhouse gas emissions. However, the simultaneous collection, reconditioning, and distribution of products introduce profound operational complexities and systemic stochasticity. Prevailing optimization models often rely on deterministic averages, failing to capture the volatile realities of industrial execution. This paper bridges a substantial gap in the literature by coupling the breakdown of transport vehicles within a vehicle routing problem with simultaneous pickup and delivery alongside the random failures of a manufacturing–remanufacturing facility. A discrete flow mathematical model is developed, utilizing a Hedging Point Control Policy bound by strict upstream and downstream inventory dependencies. To navigate carbon policy constraints, the framework integrates dynamic regulatory models ensuring environmental compliance. This research introduces an integrated mixed-integer linear optimization model and a responsive cooperative recourse heuristic. Numerical simulations demonstrate the framework’s capability to dynamically adjust routing and production schedules, maximizing systemic profitability and operational continuity in the face of compounded supply chain disruptions.