Abstract:
Currently, capacity planning is receiving more emphasis in management of operations in Industrial Engineering because insufficient capacity may lead to deteriorating delivery performance and high work-in-process inventories. On the other hand excess capacity may lead to wastage of resources. Even the most modern and sophisticated capacity planning systems may face a great deal of uncertainty, imprecision and vagueness due to uncertain market demand, set up resources, capacity constraints, pessimistic time standards, and subjective beliefs of managers etc., leading to inferior planning decisions. Under such circumstances fuzzy models which explicitly consider these uncertainties, generate more robust, flexible and efficient planning.
The traditional fuzzy logic-based models though are capable of dealing with some complex capacity-planning systems where various uncertain parameters and vagueness are involved, yet they use complex membership functions to calculate the degree of truth that involve complicated, time consuming and tedious mathematical operations. In this thesis, the solution techniques and methods developed are based on possibility theory. These techniques not only eliminate the need of calculation of complex membership functions but also yield crisp answers to fuzzy problems in capacity planning.
