Possibilistic Linear Programming for Aggregate Production Planning with Labor Replacement in a Parallel Machine Environment

Abstract

This work presents a Possibilistic Linear Programming (PLP) approach for solving a multi-product. Aggregate Production Planning (APP) problem with labor replacement in a parallel machine environment where forecasted demand, material cost, and equipment cost is imprecise. The proposed approach attempts to maximize the total profit. It uses the strategy of simultaneously maximizing the most possible, pessimistic, and optimistic values of the imprecise total profit. Labor replacement by equipment is considered in the model for capacity expansion and increasing system’s efficiency. The proposed model yields an efficient compromised solution, which is more preferable and contains more information than conventional approaches. It can also be easily manipulated to obtain the preferred plan according to a decision maker’s preference. A real industrial case is also demonstrated.