The Probabilistic Programming Approach for Procurement and Production Planning in Recovery Time after a Pandemic

Manufacturers face extraordinary post-pandemic recovery situations for their procurement planning. This study aimed to propose a new mathematical optimization model useful as decision-making support for procurement planning problems in recovery time after a pandemic. It included several extraordinary situations, such as excess demand and uncertain parameters. The data available regarding the uncertain parameters were used to model the problem as probabilistic linear programming. The optimal decision regarding the raw parts and the product brands to be produced to maximize the expected profit was calculated by solving the proposed optimization model. Furthermore, numerical experiments were carried out with randomly generated data. The results showed that the optimal solution was derived, and all the extraordinary situations were handled. Therefore, practitioners could use the proposed model to solve their production planning problems.