Discipline: Technology and Engineering
Subcategory: Mathematics and Statistics
Session: 1
Room: Marriott Balcony B
Awnalisa Walker - Binghamton University
Co-Author(s): Soongeol Kwon, Binghamton University, Binghamton, New York
Bike-sharing systems are becoming more popular in many major cities because of their convenience, environmental impact, and health benefits. Due to this, there exists an urgent need to design a better decision-making model designed to improve the operations of bike-sharing systems. This study is motivated to address the inventory rebalancing of bike-sharing systems, one of the most important operational decision-making problems. Due to the uncertainty and fluctuation in demand, the usability of bike-sharing systems can be limited, e.g., a user cannot check-in/out bikes. To improve system functionality, inventory rebalancing through the relocation of bikes across stations should be done properly to maximize the availability of service. The main objectives of this study are to develop a decision-making model designed to derive an optimal daily inventory rebalancing plan based on real-world practice. In particular, this study proposes a risk-averse two-stage stochastic program that can be formulated to determine optimal inventory levels at each station while minimizing operational cost for relocating bikes and expected penalty cost from unmet demand caused by uncertainty. Specifically, this study adopts the conditional value at risk (CVaR) to properly address the risk associated with unmet demand. The sampling based approach is used to solve the problem and numerical experiments are conducted based on scenario data generated using real operational data from the Houston BCycle to validate and evaluate the proposed model. The results showed that risk-averse model, based on the CVaR risk measure, is recommended over therisk-neutral model based on the favorable results. The service levels achieved using the risk-averse model were similar to the results of the risk-neutral model. The risk-averse model reduced the cost standard deviation which will help provide more consistent customer satisfaction. Future research would include developing and analyzing a decision-making model to improve the location of the bike system stations. There is a relationship between station demand and the location characteristics of the station. By understanding this relationship, more efficient bike-systems can be installed, thus improving upon the sustainability of bike-sharing systems. References: D. Alem and R. Morabito, “Risk-averse two-stage stochastic programs in furniture plants,”OR spectrum, vol. 35, no. 4, pp. 773–806, 2013. Houston BCycle, “How to bcycle.” [Online], Available:https://houston.bcycle.com/, Ac-cessed on 09.01.2018. J. Schuijbroek, R. C. Hampshire, and W.-J. Van Hoeve, “Inventory rebalancing and vehiclerouting in bike sharing systems,”European Journal of Operational Research, vol. 257, no. 3,pp. 992–1004, 2017. R. Schultz and S. Tiedemann, “Conditional value-at-risk in stochastic programs with mixed-integer recourse,”Mathematical programming, vol. 105, no. 2-3, pp. 365–386, 2006.
Funder Acknowledgement(s): N/A
Faculty Advisor: Soongeol Kwon, skwon@binghamton.edu
Role: I conducted all of the research with guidance from my advisor