A STOCHASTIC APPROACH FOR AGGREGATE PRODUCTION PLANNING CONSIDERING RANDOM DEMAND AND RANDOM QUALITY

Abstract

ABSTRACT: This paper presents a multi-stage stochastic model to address an aggregate production planning problem. The proposed stochastic model includes two random variables, namely, random demand and random quality, where the production system generates a random amount of defectives. The main contribution of this paper is to extend the traditional deterministic production planning model to take into account the impact of uncertainty, which yields to the development of an integrated stochastic model that jointly optimizes production management decisions considering quality concerns under a certain service level. The performance of the proposed stochastic model is analyzed to draw relevant managerial insights regarding the effects of such a service level target. Also, a sensitivity analysis is conducted to study the effect of varying several costs and system uncertainty parameters to validate the cost-effectiveness of integrated stochastic modelling. The obtained results demonstrate that uncertainty has a significant influence on the decision variables and the total incurred cost, and it is not advisable to ignore the presence of the stochastic variables since they significantly modify the optimal decisions. Keywords: aggregate production planning, quality, random demand, stochastic programming, optimization