Scientia Iranica

Scientia Iranica

A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy Genetic-Particle Swarm Optimization algorithm

Document Type : Research Article

Authors
Ehsan Ali Askari 1
Mahdi Bashiri 1
Reza Tavakkoli-Moghaddam 2
1 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
10.24200/sci.2017.4391
Abstract
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.
Keywords
Bike sharing systems
stochastic programming
Hybrid evolutionary algorithm
Sample average approximation
Subjects
Scientia Iranica
Volume 24, Issue 5
Transactions on Industrial Engineering (E)
September and October 2017
Pages 2567-2580

  • Receive Date 14 October 2015
  • Revise Date 08 June 2016
  • Accept Date 24 September 2016