international journal of industrial Engineering & Production Research

en Stochastic programming and robust optimization approaches for the product mix problem under uncertainty (case study: lubricant refinery) Production Planning & Control Production Planning & Control پژوهشي Research <div style="text-align: justify;">Determining optimal product mix under uncertain demand and capacity is a critical challenge in the lubricant industry. This study proposes four MILP models: deterministic, robust scenario-based, downside-risk two-stage stochastic, and CVaR two-stage stochastic. All models incorporate real-world constraints including multi-period, multi-product settings, dual-warehouse inventory, backlog/lost-sale shortages, and mandatory production of unprofitable products. Using real data from a major Iranian lubricant refinery, the models improve profit from the company's actual 300 million monetary units (MU) to 535 (deterministic) and 504 million MU (CVaR). Among stochastic models, the robust approach provides the highest worst-case profit and most stable performance. This is the first systematic comparison of these three stochastic approaches in the lubricant industry under identical constraints, demonstrating the value of uncertainty-aware production planning.</div> Product mix,inventory and shortage system,uncertainty,robust model,two-stage stochastic model,lubricant industry 0 0 http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-841-7&slc_lang=en&sid=1 Mohammadreza Nemati mohammadreza.nemati@modares.ac.ir 1800319475328460014927 1800319475328460014927 No PhD candidate in Industrial Engineering Mehrdad Kargari m_kargari@modares.ac.ir 1800319475328460014928 1800319475328460014928 Yes Associate Professor of Industrial and Systems Engineering, Tarbiat Modares University Ehsan Nikbakhsh nikbakhsh@modares.ac.ir 1800319475328460014929 1800319475328460014929 No Assistant Professor of Industrial and Systems Engineering, Tarbiat Modares University