A new hybrid GA-PSO method for solving multi-period inventory routing problem with considering financial decisions

Purpose: Integration of various logistical components in supply chain, such as transportation, inventory control and facility location are becoming common practice to avoid suboptimization in nowadays' competitive environment. The integration of transportation and inventory decisions is known as inventory routing problem (IRP) in the literature. The problem aims to determine the delivery quantity for each customer and the network routes to be used in each period, so that the total inventory and transportation costs are to be minimized. On the contrary of conventional IRP that each retailer can only provide its demand from the supplier, in this paper, a new multi-period, multi-item IRP model with considering lateral trans-shipment and financial decisions is proposed as a business model in a distinct organization. The main concern of this paper is to propose a new decision making approach usable for an organization to decide economically whether establish a new agent. Design/methodology/approach: The problem is formulated as a Mixed Integer Linear Programming (MILP) model. A new hybrid genetic algorithm (GA)-particle swarm optimization (PSO) metaheuristic algorithm is proposed which showed to be applicable and reliable comparing its numerical results with GAMS. Finally, a decision procedure with three phases is proposed to help an organization to find whether establishing a new agent has economic justification or not. Findings: Numerical results of new proposed algorithm comparing with GAMS are showed that the proposed algorithm produce good answers and the unique chromosome represented for the proposed solving methodology is adaptive with the essence of IRP. Originality/value: Motivated by real world and analyzing gap in literature, a new MILP model for multi-period, multi-item IRP with considering lateral trans-shipment and financial decisions is proposed and then the model is solved with a new hybrid GA-PSO meta-heuristic algorithm. The chromosome represented for the proposed solving methodology is unique and is another contribution of this paper which showed to be adaptive with the essence of IRP problem.