6,303 research outputs found
Agent-based transportation planning compared with scheduling heuristics
Here we consider the problem of dynamically assigning vehicles to transportation orders that have di€erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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
Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems
We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level
Constrained time-critical routing for multiple mobile agents
In
the
Information
Era,
integrating
technology
with
the
real-Ââworld
environment
is
a
trending
paradigm
that
attracts
researchers
in
many
fields.
For
example,
Smart
Citiesâ
applications
integrate
information
technology
with
existing
infrastructures
to
optimize
many
aspects,
such
as
time,
energy,
and
cost.
However,
many
difficulties
show
up,
including
a
time
constraint
in
some
of
the
applications
when
it
is
implemented
in
the
real
world.
One
of
these
applications
is
smart
transportation.
This
thesis
explores
Vehicle
Routing
Problem
(VRP)
and
introduces
a
variant
of
VRP
that
relates
to
time
constraints
called
VRP
with
Time
Window
(VRPTW).
Firstly,
the
problem
is
formulated
into
a
linear
mathematic
program
with
the
objective
of
minimizing
the
number
of
agents
used
in
routing
and
minimizing
the
time
spent
in
agentsâ
routing.
A
heuristic
approach
solves
this
problem
by
using
a
combined
of
A*
Search
and
Ruin
and
Recreate
algorithms
to
find
the
shortest
path
for
agents.
Additionally,
the
Local
Search
Algorithm
is
used
to
minimize
the
number
of
agents
used
in
routing.
Two
case
studies
test
this
heuristic
approach:
a
case
study
in
changing
number
of
nodes,
and
a
case
study
in
changing
nodesâ
duration.
The
results
are
represented
in
numbers
to
show
the
reduced
number
of
agents
and
time
cost,
while
graph
plots
show
the
agentsâ
routings.Department of Computer ScienceBackground -- Methodologies and design -- Hueristic approach -- Simulation results.Thesis (M.S.
Opportunity costs calculation in agent-based vehicle routing and scheduling
In this paper we consider a real-time, dynamic pickup and delivery problem with timewindows where orders should be assigned to one of a set of competing transportation companies. Our approach decomposes the problem into a multi-agent structure where vehicle agents are responsible for the routing and scheduling decisions and the assignment of orders to vehicles is done by using a second-price auction. Therefore the system performance will be heavily dependent on the pricing strategy of the vehicle agents. We propose a pricing strategy for vehicle agents based on dynamic programming where not only the direct cost of a job insertion is taken into account, but also its impact on future opportunities. We also propose a waiting strategy based on the same opportunity valuation. Simulation is used to evaluate the benefit of pricing opportunities compared to simple pricing strategies in different market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization and delivery reliability
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