6 research outputs found
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New orders
occur and others are canceled. In this case new schedules need to be
generated on-the-fly. Online optimization algorithms for dynamical
vehicle routing address this problem but so far they do not consider
time windows. Moreover, to match the scenarios found in real-world
problems adaptations of benchmarks are required. In this paper, a
practical problem is modeled based on the procedure of daily routing of a
delivery company. New orders by customers are introduced dynamically
during the working day and need to be integrated into the schedule. A
multiple ant colony algorithm combined with powerful local search
procedures is proposed to solve the dynamic vehicle routing problem with
time windows. The performance is tested on a new benchmark based on
simulations of a working day. The problems are taken from Solomon’s
benchmarks but a certain percentage of the orders are only revealed to
the algorithm during operation time. Different versions of the MACS
algorithm are tested and a high performing variant is identified.
Finally, the algorithm is tested in situ: In a field study, the
algorithm schedules a fleet of cars for a surveillance company. We
compare the performance of the algorithm to that of the procedure used
by the company and we summarize insights gained from the implementation
of the real-world study. The results show that the multiple ant colony
algorithm can get a much better solution on the academic benchmark
problem and also can be integrated in a real-world environment
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New
orders occur and others are canceled. In this case new schedules
need to be generated on-the-fly. Online optimization algorithms for
dynamical vehicle routing address this problem but so far they do
not consider time windows. Moreover, to match the scenarios found
in real-world problems adaptations of benchmarks are required. In
this paper, a practical problem is modeled based on the procedure
of daily routing of a delivery company. New orders by customers are
introduced dynamically during the working day and need to be integrated
into the schedule. A multiple ant colony algorithm combined with
powerful local search procedures is proposed to solve the dynamic
vehicle routing problem with time windows. The performance is tested
on a new benchmark based on simulations of a working day. The problems
are taken from Solomon's benchmarks but a certain percentage of the
orders are only revealed to the algorithm during operation time.
Different versions of the MACS algorithm are tested and a high performing
variant is identified. Finally, the algorithm is tested in situ:
In a field study, the algorithm schedules a fleet of cars for a surveillance
company. We compare the performance of the algorithm to that of the
procedure used by the company and we summarize insights gained from
the implementation of the real-world study. The results show that
the multiple ant colony algorithm can get a much better solution
on the academic benchmark problem and also can be integrated in a
real-world environment.Algorithms and the Foundations of Software technolog
Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an
extension of the well-known Vehicle Routing Problem (VRP), which takes into
account the dynamic nature of the problem. This aspect requires the vehicle
routes to be updated in an ongoing manner as new customer requests arrive in
the system and must be incorporated into an evolving schedule during the
working day. Besides the vehicle capacity constraint involved in the classical
VRP, DVRPTW considers in addition time windows, which are able to better
capture real-world situations. Despite this, so far, few studies have focused
on tackling this problem of greater practical importance. To this end, this
study devises for the resolution of DVRPTW, an ant colony optimization based
algorithm, which resorts to a joint solution construction mechanism, able to
construct in parallel the vehicle routes. This method is coupled with a local
search procedure, aimed to further improve the solutions built by ants, and
with an insertion heuristics, which tries to reduce the number of vehicles used
to service the available customers. The experiments indicate that the proposed
algorithm is competitive and effective, and on DVRPTW instances with a higher
dynamicity level, it is able to yield better results compared to existing
ant-based approaches.Comment: 10 pages, 2 figure
Route Planning with Dynamic Information from the EPLOS System
The paper presents the problem of distribution route planning with dynamic information about sudden customers\u27 needs. Particular attention was paid to dynamic vehicle route planning and its influence on the distance covered by a distribution vehicle. In the article, authors assume that the quick information about customers’ sudden needs is transferred from the EPLOS tool data base. Authors analyze the available literature on transport route optimization and propose a solution to the problem of distribution among customers with sudden needs. In order to present the impact of quick information influence on the distribution route minimization, a simulation model of the vehicle routing problem was generated in the FlexSim environment
Dynamic planning of mobile service teams’ mission subject to orders uncertainty constraints
This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New
orders occur and others are canceled. In this case new schedules
need to be generated on-the-fly. Online optimization algorithms for
dynamical vehicle routing address this problem but so far they do
not consider time windows. Moreover, to match the scenarios found
in real-world problems adaptations of benchmarks are required. In
this paper, a practical problem is modeled based on the procedure
of daily routing of a delivery company. New orders by customers are
introduced dynamically during the working day and need to be integrated
into the schedule. A multiple ant colony algorithm combined with
powerful local search procedures is proposed to solve the dynamic
vehicle routing problem with time windows. The performance is tested
on a new benchmark based on simulations of a working day. The problems
are taken from Solomon's benchmarks but a certain percentage of the
orders are only revealed to the algorithm during operation time.
Different versions of the MACS algorithm are tested and a high performing
variant is identified. Finally, the algorithm is tested in situ:
In a field study, the algorithm schedules a fleet of cars for a surveillance
company. We compare the performance of the algorithm to that of the
procedure used by the company and we summarize insights gained from
the implementation of the real-world study. The results show that
the multiple ant colony algorithm can get a much better solution
on the academic benchmark problem and also can be integrated in a
real-world environment.Algorithms and the Foundations of Software technolog