مقاله انگلیسی یک الگوریتم کلونی زنبور مصنوعی بهبود یافته برای مسئله مکان یابی سبز
Highlights
Abstract
Keywords
1. Introduction
2. Literature review
3. Problem formulation
4. Methodology
5. Computational experiments
6. Conclusions
CRediT authorship contribution statement
Acknowledgments
References
Abstract
The Bike Repositioning Problem (BRP) has raised many researchers’ attention in recent years to improve the service quality of Bike Sharing Systems (BSSs). It is mainly about designing the routes and loading instructions for the vehicles to transfer bikes among stations in order to achieve a desirable state. This study tackles a static green BRP that aims to minimize the CO2 emissions of the repositioning vehicle besides achieving the target inventory level at stations as much as possible within the time budget. Two types of bikes are considered, including usable and broken bikes. The Enhanced Artificial Bee Colony (EABC) algorithm is adopted to generate the vehicle route. Two methods, namely heuristic and exact methods, are proposed and incorporated into the EABC algorithm to compute the loading/unloading quantities at each stop. Computational experiments were conducted on the real-world instances having 10–300 stations. The results indicate that the proposed solution methodology that relies on the heuristic loading method can provide optimal solutions for small instances. For large-scale instances, it can produce better feasible solutions than two benchmark methodologies in the literature.
1. Introduction
In Bike Sharing Systems (BSSs), people can rent shared bikes to satisfy the needs for short-distance travel, such as the connection between home and a subway station, or just a leisure trip. However, due to the existence of asymmetric bike flows, some stations may be filled-in with returned bikes while some others are empty. For example, during the morning peak, the bike stations in residential areas might be empty, whereas those in the business area might be full. In a hilly area, the stations at the bottom can be full whereas those at the top can be empty because downhill trips are usually more than uphill trips. Therefore, the bikes need to be redistributed among all the stations after a certain period, so that a desirable inventory level can be maintained at each station to satisfy upcoming users’ demand. This operational problem is referred to as the Bike Repositioning Problem (BRP).
The repositioning operation is mainly about using a dedicated fleet of trucks or trailers to transport bikes among stations in order to reach the expected inventory level at each station (Raviv et al., 2013). There are generally two types of repositioning: static and dynamic. For the static repositioning that most of the current studies focus on (e.g., Cruz et al., 2017, Ho and Szeto, 2017, Liu et al., 2018, Szeto and Shui, 2018), the bike inventory and demand levels at each station are assumed to be relatively stable over time. The dynamic repositioning considers the variations of bike inventory and demand levels over time within a day (see Ghosh et al., 2017, Zhang et al., 2017, Shui and Szeto, 2018, Brinkmann et al., 2019). These two types of repositioning consider different application scenarios.