The Bicycle Sharing System (BSS) is a transportation solution that allows users to rent a bicycle from a depot or “port” and move it back to the same port or another port. BSS is becoming more popular around the world because it is environmentally friendly, reduces traffic congestion and provides users with additional health benefits. However, in the end, BSS will fill or empty the port. This means that users can no longer rent (if empty) or return (if full) bicycles. To address this issue, the bike needs to be rebalanced between the BSS ports so that the user can use the bike at any time. This rebalancing should be done in a way that is beneficial to BSS companies. This will allow BSS companies to reduce labor costs as well as carbon emissions from rebalanced vehicles.
Although there are several existing approaches to BSS rebalancing, most solution algorithms are computationally expensive and take a lot of time to find an “correct” solution when there are many ports. Even finding an approximate solution is computationally expensive. Previously, a research team led by Professor Toru Ikeguchi of Tokyo University of Science introduced the “Bicycle Sharing System Routing Problem for Multiple Vehicles with Soft Restrictions” (mBSSRP-S), which can find the shortest travel time for multiple bicycle rebalancing vehicles. I made a suggestion. Keep in mind that the best solution may violate the actual limits of the problem.Now, in a recent study published at MDPI Applied science, The team has proposed two strategies for finding an approximate solution for mBSSRP-S that can reduce computational costs without impacting performance. Dr. Honami Tsushima of Tokyo University of Science and Professor Takafumi Matsuura of Nippon Institute of Technology also participated in the research team.
Professor Ikeguchi describes their research as follows. “I previously proposed mBSSRP-S, but it has improved performance compared to the original mBSSRP that did not allow constraint violations. I need to calculate both viable and infeasible solutions for mBSSRP. Because of the cost of the problem. Therefore, we proposed two consecutive search strategies to address this problem. “
The proposed search strategy looks for a solution that is much shorter and feasible than the one originally proposed for mBSSRP-S. The first strategy focuses on reducing the number of “adjacent” solutions (solutions that are numerically close to the solution of the optimization problem) before finding a viable solution. This strategy uses two well-known algorithms called “Or-opt” and “CROSS-exchange” to reduce the overall time it takes to calculate the solution. A viable solution here is a value that meets the constraints of mBSSRP.
The second strategy is to change the problem to be solved based on a viable solution to an mBSSRP or mBSSRP-S problem and use either Or-opt or CROSS-exchange to quickly find the right solution that is close to optimal. To do.
The research team then ran numerical experiments to evaluate the computational cost and performance of the algorithm. “By applying these two strategies, we succeeded in reducing the calculation time while maintaining performance,” reveals Professor Ikeguchi. “We also found that when we calculated a viable solution, we could quickly find a short travel time for a rebalanced vehicle by solving the hard constraint problem mBSSRP instead of mBSSRP-S.”
The popularity of BSS is expected to increase only in the future. The new solution search strategy proposed here will greatly help you achieve a convenient and comfortable BSS that benefits users, businesses, and the environment.
https://www.sciencedaily.com/releases/2022/05/220505114708.htm A newly proposed search strategy improves the computational cost of bicycle sharing problems-ScienceDaily