Search results for "Dial a ride"
showing 3 items of 3 documents
Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem
2019
In the dial-a-ride problem, user-specified transport requests from origin to destination points have to be served by a fleet of homogeneous vehicles. The problem variant we consider aims at finding a set of minimum-cost routes satisfying constraints on vehicle capacity, time windows, maximum route duration, and maximum user ride times. We propose an adaptive large neighborhood search (ALNS) for its solution. The key novelty of the approach is an exact amortized constant-time algorithm for evaluating the feasibility of request insertions in the repair steps of the ALNS. In addition, we use two optional improvement techniques: a local-search-based intraroute improvement of routes of promisin…
Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks
2020
App-based services and ridesharing in the field of mobility-on-demand (MoD) create a new mode of transport between motorized individual transport and public transportation whose long-term role in the urban mobility landscape and within public transport systems is not fully understood as of today. For the public transport industry, these new services offer new chances but also risks, making planning models and tools for integrated intermodal network planning indispensable. We develop a strategic network planning optimization model for bus lines that allows for intermodal trips with MoD as a first or last leg. Starting from an existing public transport network, we decide simultaneously on th…
A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem
2006
Abstract This work deals with a dynamic dial-a-ride problem with time window constraints. In particular, new unplanned requests for service may arise at a vehicle stop and the driver must decide in real-time whether to accept or reject them. For this problem, we have developed a two-phase insertion algorithm based on route perturbations: the first phase, which is run off-line when the vehicle moves between two successive stops, aims at creating a feasible neighborhood of the current route; while the second phase, which is run in real-time every time a new request occurs, inserts, when possible, the delivery stop of the new customer in the current route.