A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain

Supply-chain management and distribution networks design have attracted the attention of many researchers during recent years. Satisfying the customers' demands on time will lead to cost reductions, and will also increase the service level of the supply chain. The aim of this research is to develop and solve a model for just-in-time distribution in the context of supply-chain management. A bi-objective model is set up for the distribution network of a three-echelon supply chain, with two objective functions: minimizing costs, and minimizing the sum of back orders and surpluses of products in all periods. Delivery lead times and capacity constraints are also considered in a multi-period, multi-product and multi-channel network. A hybrid non-dominated sorting genetic algorithm is applied to solve real-size problems of this mixed-integer linear programming model.