42 research outputs found
A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version
We consider a dynamic vehicle routing problem with time windows and
stochastic customers (DS-VRPTW), such that customers may request for services
as vehicles have already started their tours. To solve this problem, the goal
is to provide a decision rule for choosing, at each time step, the next action
to perform in light of known requests and probabilistic knowledge on requests
likelihood. We introduce a new decision rule, called Global Stochastic
Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing
decision rules, such as MSA. In particular, we show that GSA fully integrates
nonanticipativity constraints so that it leads to better decisions in our
stochastic context. We describe a new heuristic approach for efficiently
approximating our GSA rule. We introduce a new waiting strategy. Experiments on
dynamic and stochastic benchmarks, which include instances of different degrees
of dynamism, show that not only our approach is competitive with
state-of-the-art methods, but also enables to compute meaningful offline
solutions to fully dynamic problems where absolutely no a priori customer
request is provided.Comment: Extended version of the same-name study submitted for publication in
conference CPAIOR201
A Constraint Programming Approach for Non-Preemptive Evacuation Scheduling
Large-scale controlled evacuations require emergency services to select
evacuation routes, decide departure times, and mobilize resources to issue
orders, all under strict time constraints. Existing algorithms almost always
allow for preemptive evacuation schedules, which are less desirable in
practice. This paper proposes, for the first time, a constraint-based
scheduling model that optimizes the evacuation flow rate (number of vehicles
sent at regular time intervals) and evacuation phasing of widely populated
areas, while ensuring a nonpreemptive evacuation for each residential zone. Two
optimization objectives are considered: (1) to maximize the number of evacuees
reaching safety and (2) to minimize the overall duration of the evacuation.
Preliminary results on a set of real-world instances show that the approach can
produce, within a few seconds, a non-preemptive evacuation schedule which is
either optimal or at most 6% away of the optimal preemptive solution.Comment: Submitted to the 21st International Conference on Principles and
Practice of Constraint Programming (CP 2015). 15 pages + 1 reference pag
Transfer-Expanded Graphs for On-Demand Multimodal Transit Systems
This paper considers a generalization of the network design problem for
On-Demand Multimodal Transit Systems (ODMTS). An ODMTS consists of a selection
of hubs served by high frequency buses, and passengers are connected to the
hubs by on-demand shuttles which serve the first and last miles. This paper
generalizes prior work by including three additional elements that are critical
in practice. First, different frequencies are allowed throughout the network.
Second, additional modes of transit (e.g., rail) are included. Third, a limit
on the number of transfers per passenger is introduced. Adding a constraint to
limit the number of transfers has a significant negative impact on existing
Benders decomposition approaches as it introduces non-convexity in the
subproblem. Instead, this paper enforces the limit through transfer-expanded
graphs, i.e., layered graphs in which each layer corresponds to a certain
number of transfers. A real-world case study is presented for which the
generalized ODMTS design problem is solved for the city of Atlanta. The results
demonstrate that exploiting the problem structure through transfer-expanded
graphs results in significant computational improvements.Comment: 9 pages, 4 figure
A fast Reoptimization approach for the dynamic technician routing and scheduling problem
The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper we tackle the Dynamic TRSP (D-TRSP) in which new requests appear over time. We propose a fast reoptimization approach based on a parallel Adaptive Large Neighborhood Search (RpALNS) able to achieve state-of-the-art results on the Dynamic Vehicle Routing Problem with Time Windows. In addition, we solve a set of randomly generated D-TRSP instances and discuss the potential gains with respect to a heuristic modeling a human dispatcher solution
An Approximate Dynamic Programming Approach to Urban Freight Distribution with Batch Arrivals
We study an extension of the delivery dispatching problem (DDP) with time windows, applied on LTL orders arriving at an urban consolidation center. Order properties (e.g., destination, size, dispatch window) may be highly varying, and directly distributing an incoming order batch may yield high costs. Instead, the hub operator may wait to consolidate with future arrivals. A consolidation policy is required to decide which orders to ship and which orders to hold. We model the dispatching problem as a Markov decision problem. Dynamic Programming (DP) is applied to solve toy-sized instances to optimality. For larger instances, we propose an Approximate Dynamic Programming (ADP) approach. Through numerical experiments, we show that ADP closely approximates the optimal values for small instances, and outperforms two myopic benchmark policies for larger instances. We contribute to literature by (i) formulating a DDP with dispatch windows and (ii) proposing an approach to solve this DDP
The school bus routing problem: An analysis and algorithm
In this paper we analyse a flexible real world-based model
for designing school bus transit systems and note a number of parallels
between this and other well-known combinatorial optimisation problems
including the vehicle routing problem, the set covering problem, and
one-dimensional bin packing. We then describe an iterated local search
algorithm for this problem and demonstrate the sort of solutions that we
can expect with different types of problem instance
Enabling the freight traffic controller for collaborative multi-drop urban logistics: practical and theoretical challenges
There is increasing interest in how horizontal collaboration between parcel carriers might help alleviate problems associated with last-mile logistics in congested urban centers. Through a detailed review of the literature on parcel logistics pertaining to collaboration, along with practical insights from carriers operating in the United Kingdom, this paper examines the challenges that will be faced in optimizing multicarrier, multidrop collection, and delivery schedules. A âfreight traffic controllerâ (FTC) concept is proposed. The FTC would be a trusted third party, assigned to equitably manage the work allocation between collaborating carriers and the passage of vehicles over the last mile when joint benefits to the parties could be achieved. Creating this FTC concept required a combinatorial optimization approach for evaluation of the many combinations of hub locations, network configuration, and routing options for vehicle or walking to find the true value of each potential collaboration. At the same time, the traffic, social, and environmental impacts of these activities had to be considered. Cooperative game theory is a way to investigate the formation of collaborations (or coalitions), and the analysis used in this study identified a significant shortfall in current applications of this theory to last-mile parcel logistics. Application of theory to urban freight logistics has, thus far, failed to account for critical concerns including (a) the mismatch of vehicle parking locations relative to actual delivery addresses; (b) the combination of deliveries with collections, requests for the latter often being received in real time during the round; and (c) the variability in travel times and route options attributable to traffic and road network conditions
Research trends in combinatorial optimization
Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD