Ant colony optimization for one-to-many network inventory routing problem

The integration of inventory and routing is a very important aspect of supply chain management. In this paper, we present a one-to-many inventory routing problem (IRP) network consisting of a manufacturer that produces multi products to be transported to many geographically dispersed customers. We consider a finite horizon where a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, transports products from the warehouse to meet the demand specified by the customers in each period. The demand for each product is deterministic and time varying and each customer requests a distinct product. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount on inventory and to construct a delivery schedule that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX to get the lower bound and upper bound (the best integer solution) for each problem considered. We propose a modified ant colony optimization (ACO) by subdividing the ants into subpopulation where each subpopulation consists of different set of inventory level. The routes are improved by using local search. The algorithm performs better on large instances compared to the upper bound and performs equally well for small and medium instances.