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

The aim of this research is to develop and solve a model for just-in-time (JIT) 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 backorders 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.