Demand and cost forecast error sensitivity analyses in aggregate production planning by possibilistic linear programming models

A production management system contains many imprecise natures. The convent ional deterministic and/or stochastic model in a computer integrated produc tion management system (CIPMS) may not capture the imprecise natures well. This study examines how the imprecise natures in the CIPMS affect the plann ing results. Possibilistic linear programming models are also proposed for the aggregate production planning problem with imprecise natures. The propo sed model can adequately describe the imprecise natures in a production sys tem and, in doing so, the CIPMS can adapt to a variety of non-crisp propert ies in an actual system. For comparison, the classic aggregate production p lanning problem given by Holt, Modigliani, and Simon (HMS) is solved using the proposed possibilistic model and the crisp model of Hanssmann and Hess (HH). Perturbing the cost coefficients and the demand allows one to simulat e the imprecise natures of a real world and evaluate the effect of the impr ecise natures to production plans by both the possibilistic and the crisp H H approaches. Experimental results indicate that the possibilistic model do es provide better plans that can tolerate a higher spectrum of imprecise pr operties than those obtained by the crisp HH model.