On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

Clustered tabu search optimization for reservation-based shared autonomous vehicles

This paper investigates the optimization of Reservation-based Autonomous Car Sharing (RACS) systems, aiming at minimizing the total vehicle travel time and customer waiting time. Thus, the RACS system and its routing are formulated with a consideration for system efficiency and passengers’ concerns. A meta-heuristic Tabu search method is investigated as a solution approach, in combination with K–Means (KMN–Tabu) or K–Medoids (KMD–Tabu) clustering algorithms. The proposed solution algorithms are tested in two different networks of varying complexity, and the performance of the algorithms is evaluated. The evaluation results show that the TS method is more suitable for small-scale problems, while KMD–Tabu is suitable for large-scale problems. However, KMN-Tabu has the least computation time, although the solution quality is lower

The concept and impact analysis of a flexible mobility on demand system

This paper introduces an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand responsive system in which a list of travel options is provided in real-time to each passen- ger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to different service types: taxi, shared-taxi and mini-bus. The allocation of the available fleet to these three services is carried out dynamically so that vehicles can change roles during the day. The FMOD system is built based on a choice model and consumer surplus is taken into account in order to improve passenger satisfac- tion. Furthermore, profits of the operators are expected to increase since the system adapts to changing demand patterns. In this paper, we introduce the concept of FMOD and present preliminary simulation results. It is shown that the dynamic allocation of the vehicles to different services provides significant benefits over static allocation. Furthermore, it is observed that the trade-off between consumer surplus and operator’s profit is critical. The optimization model is adapted in order to take into account this trade-off by control- ling the level of passenger satisfaction. It is shown that with such control mechanisms FMOD provides improved results in terms of both profit and consumer surplus

Dispatching Requests for Agent-Based Online Vehicle Routing Problems with Time Windows

Vehicle routing problems are highly complex problems. The proposals to solve them traditionally concern the optimization of conventional criteria, such as the number of mobilized vehicles and the total costs. However, in online vehicle routing problems, the optimization of the response time to the connected travelers is at least as important as the optimization of the classical criteria. Multi-agent systems on the one hand and greedy insertion heuristics on the other are among the most promising approaches to this end. In this paper, we propose a multi-agent system coupled with a regret insertion heuristic. We focus on the real-time dispatching of the travelers\u27 requests to the vehicles and its efficiency. A dispatching protocol determines which agents perform the computation to answer the travelers\u27 requests. We evaluate three dispatching protocols: centralized, decentralized and hybrid. We compare them experimentally based on their response time to online travelers. Two computational types are implemented: a sequential implementation and a distributed implementation. The results show the superiority of the centralized dispatching protocol in the sequential implementation (32.80% improvement in average compared to the distributed dispatching protocol) and the superiority of the hybrid dispatching protocol in the distributed implementation (59.66% improvement in average, compared with the centralized dispatching protocol)