Aggregate production planning using chance-constrained goal programming

Abstract

Multiple objective models have frequently been proposed to assist in solving aggregate production planning problems. Although such models are an improvement over those with single objectives, demand is usually considered deterministic. For this reason, previous attempts at solving production problems have often lacked realism and could not be successfully applied in many real decision environments. This paper suggests a chance-constrained goal programming (CCGP) approach to production planning which allows the decision maker to specify both probabilistic product demands and production line operating characteristics more in keeping with actual situations. The CCGP approach is based on the sequential solution of a linear programming formulation, allowing efficient solution of large-scale real-world problems using commercially available LP codes. The procedure is demonstrated with a hypothetical example, and proper interpretation of goal achievement is discussed. The findings in the paper are applicable whether preemptive goal programming or a weighted objective function approach is used.