3,044 research outputs found
Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows
In the vehicle routing problem with multiple time windows (VRPMTW), a single time window must be selected for each customer from the multiple time windows provided. Compared with classical vehicle routing problems with only a single time window per customer, multiple time windows increase the complexity of the routing problem. To minimize the duration of any given route, we present an exact polynomial time algorithm to efficiently determine the optimal start time for servicing each customer. The proposed algorithm has a reduced worst-case and average complexity than existing exact algorithms. Furthermore, the proposed exact algorithm can be used to efficiently evaluate neighborhood operations during a local search resulting in significant acceleration. To examine the benefits of exact neighborhood evaluations and to solve the VRPMTW, the proposed algorithm is embedded in a simple metaheuristic framework generating numerous new best known solutions at competitive computation times
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances
Recent studies in maritime logistics have introduced a general ship routing
problem and a benchmark suite based on real shipping segments, considering
pickups and deliveries, cargo selection, ship-dependent starting locations,
travel times and costs, time windows, and incompatibility constraints, among
other features. Together, these characteristics pose considerable challenges
for exact and heuristic methods, and some cases with as few as 18 cargoes
remain unsolved. To face this challenge, we propose an exact branch-and-price
(B&P) algorithm and a hybrid metaheuristic. Our exact method generates
elementary routes, but exploits decremental state-space relaxation to speed up
column generation, heuristic strong branching, as well as advanced
preprocessing and route enumeration techniques. Our metaheuristic is a
sophisticated extension of the unified hybrid genetic search. It exploits a
set-partitioning phase and uses problem-tailored variation operators to
efficiently handle all the problem characteristics. As shown in our
experimental analyses, the B&P optimally solves 239/240 existing instances
within one hour. Scalability experiments on even larger problems demonstrate
that it can optimally solve problems with around 60 ships and 200 cargoes
(i.e., 400 pickup and delivery services) and find optimality gaps below 1.04%
on the largest cases with up to 260 cargoes. The hybrid metaheuristic
outperforms all previous heuristics and produces near-optimal solutions within
minutes. These results are noteworthy, since these instances are comparable in
size with the largest problems routinely solved by shipping companies
Hybrid Metaheuristics for the Clustered Vehicle Routing Problem
The Clustered Vehicle Routing Problem (CluVRP) is a variant of the
Capacitated Vehicle Routing Problem in which customers are grouped into
clusters. Each cluster has to be visited once, and a vehicle entering a cluster
cannot leave it until all customers have been visited. This article presents
two alternative hybrid metaheuristic algorithms for the CluVRP. The first
algorithm is based on an Iterated Local Search algorithm, in which only
feasible solutions are explored and problem-specific local search moves are
utilized. The second algorithm is a Hybrid Genetic Search, for which the
shortest Hamiltonian path between each pair of vertices within each cluster
should be precomputed. Using this information, a sequence of clusters can be
used as a solution representation and large neighborhoods can be efficiently
explored by means of bi-directional dynamic programming, sequence
concatenations, by using appropriate data structures. Extensive computational
experiments are performed on benchmark instances from the literature, as well
as new large scale ones. Recommendations on promising algorithm choices are
provided relatively to average cluster size.Comment: Working Paper, MIT -- 22 page
A Hybrid Heuristic for a Broad Class of Vehicle Routing Problems with Heterogeneous Fleet
We consider a family of Rich Vehicle Routing Problems (RVRP) which have the
particularity to combine a heterogeneous fleet with other attributes, such as
backhauls, multiple depots, split deliveries, site dependency, open routes,
duration limits, and time windows. To efficiently solve these problems, we
propose a hybrid metaheuristic which combines an iterated local search with
variable neighborhood descent, for solution improvement, and a set partitioning
formulation, to exploit the memory of the past search. Moreover, we investigate
a class of combined neighborhoods which jointly modify the sequences of visits
and perform either heuristic or optimal reassignments of vehicles to routes. To
the best of our knowledge, this is the first unified approach for a large class
of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants.
The efficiency of the algorithm is evaluated on 643 well-known benchmark
instances, and 71.70\% of the best known solutions are either retrieved or
improved. Moreover, the proposed metaheuristic, which can be considered as a
matheuristic, produces high quality solutions with low standard deviation in
comparison with previous methods. Finally, we observe that the use of combined
neighborhoods does not lead to significant quality gains. Contrary to
intuition, the computational effort seems better spent on more intensive route
optimization rather than on more intelligent and frequent fleet re-assignments
Real-Time Optimization for Dynamic Ride-Sharing
Throughout the last decade, the advent of novel mobility services such as ride-hailing,
car-sharing, and ride-sharing has shaped urban mobility. While these types of services
offer flexible on-demand transportation for customers, they may also increase the load
on the, already strained, road infrastructure and exacerbate traffic congestion problems.
One potential way to remedy this problem is the increased usage of dynamic ride-sharing
services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously.
This leads to more efficient vehicle utilization, reduced prices for customers,
and less traffic congestion at the cost of slight delays compared to direct transportation in
ride-hailing services.
In this thesis, we consider the planning and operation of such dynamic ride-sharing
services. We present a wider look at the planning context of dynamic ride-sharing and
discuss planning problems on the strategical, tactical, and operational level. Subsequently,
our focus is on two operational planning problems: dynamic vehicle routing, and idle
vehicle repositioning.
Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing
and present a solution procedure. Our algorithmic approach consists of two
phases: a fast insertion heuristic, and a local search improvement phase. The former
handles incoming trip requests and quickly assigns them to suitable vehicles while the
latter is responsible for continuously improving the current routing plan. This way, we
enable fast response times for customers while simultaneously effectively utilizing available
computational resources.
Concerning the idle vehicle repositioning problem, we propose a mathematical model that
takes repositioning decisions and adequately reflects available vehicle resources as well as
a forecast of the upcoming trip request demand. This model is embedded into a real-time
planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an
adaptive parameter calculation process, our algorithm dynamically adapts to changes in
the current system state.
To evaluate our algorithms, we present a modular simulation-based evaluation framework.
We envision that this framework may also be used by other researchers and developers.
In this thesis, we perform computational evaluations on a variety of scenarios based on
real-world data from Chengdu, New York City, and Hamburg. The computational results
show that we are able to produce high-quality solutions in real-time, enabling the usage in
high-demand settings. In addition, our algorithms perform robustly in a variety of settings
and are quickly adapted to new application settings, such as the deployment in a new city
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