This research demonstrates a robust optimization model to improve sustainability in oil and gas supply chains, indicating a shift towards more efficient operations.
Oil and gas products are routinely transported from producers to distributors, and then from distributors to retailers. In the process, operating costs, transportation expenses, and environmental impacts often increase due to time constraints, high transportation frequency, limited inventory storage, and other factors. To reduce costs, meet end-customer demands, and create competitive advantages, sustainable development (SD) has become a key consideration for supply chain (SC) managers, prompting a shift towards more sustainable supply networks. This study aims to design a sustainable multi-period, multi-product oil and gas SC consisting of producers, distributors, and retailers under uncertain conditions. To address uncertainty in demand, production, transportation, holding costs, and facility capacity, and to maximize net present value (NPV) while considering social impacts, the study develops a Robust Counterpart Optimization model based on the robust optimization method. The model is implemented in the programming environment of SAS Studio 3.5. Numerical results provide valuable insights into strategic and planning decisions for SC design. Finally, sensitivity analyses are conducted on key parameters, with the changes in objective functions being thoroughly examined.
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Irani et al. (2025) studied this question.
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