This paper presents a real-value version of particle swarm optimization (PSO) for solving the open vehicle
routing problem (OVRP) that is a well-known combinatorial optimization problem. In OVRP a vehicle
does not return to the depot after servicing the last customer on a route. A particular decoding method
is proposed for implementing PSO for OVRP. In the decoding method, a vector of the customer’s position
is constructed in descending order. Then each customer is assigned to a route with taking into account
feasibility conditions. Finally one-point move has been applied on constructed routes that seem promising
to result in a better solution. Experimental evaluations on benchmark data sets demonstrate the competitiveness
of the proposed algorithm.