A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy genetic-particle swarm optimization algorithm

This paper considers a stochastic location-allocation problem for a capacitated bike sharing system (S-L A-CBSS), in which a bike demand is uncertain. To tackle this uncertainty, a sample average approximation (SAA) method is used. Because this problem is an NP-hard problem, a hybrid greedy evolutionary algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO), namely greedy GA-PSO is embedded in the SAA method in order to solve the given large-sized problems. The performance of the proposed hybrid algorithm is tested by a number of numerical examples and used for empirical test based on Tehran business zone. Furthermore, the associated results show its efficiency in comparison to an exact solution method in solving small-sized problems. Finally, the conclusion is provided.