A Tabu Search based metaheuristic for dynamic carpooling optimization

Abstract

The carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France.

Shared public transport within a physical internet framework: Reviews, conceptualization and expected challenges under COVID-19 pandemic

“…Increasing vehicle occupancy as an important challenge could be attained through collaborative transport including carpooling, carsharing, vanpooling, ridesharing, and PT patterns. The alternative of carpooling could involve corresponding a set of drivers' requests with a set of drivers' offers by matching their origins, destinations, and time frames [ 139 ]. In carpooling alternative, a driver and a rider group usually of different members share a part of their common itinerary, especially to work or school to minimize the travel expenses [ 169 ].…”

The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness

“…In order to solve the problem of ride hailing ride-sharing, single objective optimization, multi-objective optimization and other models are proposed. In order to find the optimal route for solving these models, various heuristic algorithms and exact algorithms are developed and used [16,42,43]. In this paper, both Genetic Algorithm and branch and bound algorithm are adopted, and the two algorithms are compared to verify the feasibility of the model.…”

“…Although Genetic Algorithms have been widely used, there are still some other algorithms in use. Cheikh-Graiet et al [16] presented the so-called dynamic ride-sharing optimization system, which took decisions using a novel tabu search based metaheuristic. At the same time, they developed a simulation environment based on realistic ride-sharing demand data.…”

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

“…To overcome the complexity and real-time aspect of the dynamic carpooling optimisation system (DyCOS), in [65] proposed a Tabu Search (TS) i proposed that employs an explicit memory system with several searching strategies to make optimal decisions automatically. To ensure high-quality solutions, DyCOS adopted detour and transfer concepts.…”