Probabilistic multi-objective optimization approach to solve production planning and raw material supplier selection problem under probabilistic demand value

Abstract

This article is addressed to study the development of a probabilistic multi-objective optimization model that can be used to optimize the production planning and raw material procurement in a manufacturing industry where the demand value is unknown. First, the unknown demand value is assumed to be a random variable with some known probability distribution. Then, we formulate the multi-objective optimization model with two objective functions which are the total procurement cost that is minimized and the total production number that is maximized. Some related constraints that should be satisfied are also be formulated. We solve this multi-objective optimization problem by finding the Pareto solution. The calculation is performed in LINGO 18.0. To simulate and observe how the optimal decision is made, a computational simulation using generated data was performed. From the results, the optimal decision is obtained (the number of the raw material that should be purchased from each supplier and the number of the product that should be produced).