  In today's global business market place, individual firms no longer compete
as independent entities with unique brand names but as integral part of supply
chain links. Key to success of any business is satisfying customer's demands on
time which may result in cost reductions and increase in service level. In
supply chain networks decisions are made with uncertainty about product's
demands, costs, prices, lead times, quality in a competitive and collaborative
environment. If poor decisions are made, they may lead to excess inventories
that are costly or to insufficient inventory that cannot meet customer's
demands. In this work we developed a bi-objective model that minimizes system
wide costs of the supply chain and delays on delivery of products to
distribution centers for a three echelon supply chain. Picking a set of Pareto
front for multi-objective optimization problems require robust and efficient
methods that can search an entire space. We used evolutionary algorithms to
find the set of Pareto fronts which have proved to be effective in finding the
entire set of Pareto fronts.
