Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management

Aggregate production–distribution planning is one of the most important activities in supply chain management(SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production–distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, this final project proposed an approach that combine fuzzy and genetic algorithm. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). Testing results show that the optimization result using genetic algorithm could close on the real world result. The profit