This study aims to convert a multi-choice linear programming problem into a conventional mathematical programming problem, focusing on constraints with a "multi-choice" nature on their right-hand side.Any limitation may have many objectives, each necessitating careful selection. To choose objectives wisely, ensure that combining the choices for each constraint yields the best approach to an objective function. For the best results, try a few different combinations. Nonetheless, conventional linear programming techniques are inadequate for resolving the problem. This study introduces a novel transformation technique to address the current multi-choice linear programming problem. A non-linear mixed-integer programming model is generated using binary variables in the transformation approach. The best answer for the suggested model can be found using conventional non-linear programming methods.The multi-choice model was employed to manage the variable demand for different gasoline varieties in oil refineries, ensuring the requisite amounts were met while addressing uncertainty; the model demonstrated its efficacy in identifying the ideal option.
A. Sh (Sun,) studied this question.