Optimizing circular supply chains for recycling drilling waste in the circular economy of the oil industry

The project builds and tests a stochastic math model that optimizes reverse‑logistics and circular supply chains for drilling waste in the oil‑and‑gas industry. Drilling by‑products pose serious environmental risks and create complex logistics, so moving to a circular‑economy approach is essential. The model tries to do two things at once: * Cut total network costs – covering the building of recycling centers, their operation, transportation, and waste processing. * Boost the importance of each supplier in the network. To reflect real‑world uncertainty, the model includes random variations in the quality of returned waste and in market demand. Two advanced metaheuristic algorithms are used to solve the multi‑objective problem: * Deer

Hunting Optimization Algorithm (DHOA) * Grey Wolf Optimizer (GWO) The results show a clear trade‑off. DHOA achieves lower overall costs, while GWO produces higher scores for supplier importance. This work gives environmental engineers a practical decision‑support tool for designing waste‑recycling networks that are sustainable, cost‑effective, and resilient.

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