Load Balancing Using a Co-Simulation/Optimization/Control Approach. Petros Ioannou

Load Balancing Using a Co-Simulation/Optimization/Control

Approach

Petros Ioannou

University of Southern California

Los Angeles, CA 90089

Email: [email protected]

Freight Transportation System

RAIL

OCEAN

ROAD

Motivation

•

Freight transport is fundamental to human survival especially in Metropolitan

areas

•

Globalization let to a rapid increase in import and export activities

•

Freight is transported via rail, trucks, ships and air (high value small size)

•

Freight rail and trucks share the same networks as passengers

•

Trucks are big, have slower dynamics, disturb traffic during merging and

changing lanes. They pollute more and waste more fuel.

•

Current road network treats trucks more or less like every other vehicle

(exception are some rules in some places)

Current Issues

•Both rail and road networks are in need of extra capacity

especially at Metropolitan areas

•Both networks exhibit low peak and high peak traffic

which has temporal but sometimes spacial characteristics

too

•This indicates load imbalances across the networks that

are due to several reasons. Some reasons are:

•Lack of information and lack of a system that can help

identify available free capacities and balance loads across

Trends

Intelligent Transportation Technologies

•

Information Systems and Internet

•

GPS, FRIDs, Bluetooth, communications

•

Connected vehicles

•

Traffic Management Systems

•

Vehicle Technologies: ADAS, Vehicle automation

.

New sensors and software tools are coming up and maturing in

accuracy and reliability

Problem Statement

The current Freight transportation network is

unbalanced w.r.t temporal and spacial

coordinates

Q: Can upcoming ITS technologies help?

ANS: Yes provided the appropriate

intelligence and tools are developed

Technical Challenges

• Both rail and road networks involve complex

operations

• They are complex dynamic systems with time

varying characteristics

• Lack of adequate sensors and data (changing with

ITS)

Traditional Approach to handle the problem

•

Simple models and adhoc techniques

Approach for control and optimization of complex

systems

• The use of simple mathematical models is a fundamental

practice in every engineering design for controlling and

optimizing systems

• The requirement is that these models are simple enough to

understand and lead to less complex designs that are easy to

implement yet they are complex enough to represent the

dominant characteristics of the system

• When it comes to complex networks such as the

transportation network simple models cannot always capture

the dynamics, interconnections and complexity involved

Approach for control and optimization of complex

systems

• The availability of fast computers and software tools point to

the next step in the evolution of models from very simple

during the Analog years to more Complex during the Digital

era to:

• Simulation models as part of the control and optimization

feedback system

• To handle bigger complexity and complex dynamics and

interconnections with the limit the computational speed

relative to the bandwidth of the control system.

Approach for control and optimization of complex

systems

Real Network

System

Network

states

Network

Data

Approach for control and optimization of complex

systems

Approach for control and optimization of complex

systems

Issues

• Validation of simulation models. Scalability and complexity

• Balancing control design

• Optimization

• Stability and robustness of closed loop system

• Convergence to equilibrium

Approach for control and optimization of complex

systems

Optimization part: Service network

N

N

N

N

N

Origin N

Destina-

_{tion N}

Approach for control and optimization of complex

systems

Optimization part:

Find minimum cost route for a given load over a time interval T in

the future.

The cost depends on the states of the networks i.e link flows

in a complex fashion that cannot be represented by a simple

model.

The simulation model estimates/predicts the states of the

network at each iteration

Approach for control and optimization of complex

systems

Load Balancing Control (LBC):

Currently adhoc

LBC: Starts with an initial load distribution.

SM: estimates the states of the network

Opt: computes the minimum cost route

LBC: Passes loads to the minimum cost route to

equalize cost with previous minimum cost route(s)

Approach for control and optimization of complex

systems

Stopping Criteria

• The balancing approach guarantees convergence as the cost

function is bounded from below and is non increasing due to

the procedure.

• In practice there may be limitations and the iterations may

have to stop after a certain time or after additional

improvements are small.

Example

PHYSICAL

EXAMPLE

Origin:

3 terminals A (1020) , B(1020), C(1020)

Destinations:

D1 (350), D2(450), D3(400), D4(600),D5(700),

D6(560)

Shippers:

Three, one for each terminal

Routes:

Road Network and Partial Rail (5 trains with capacity of

50 containers each)

Network Complexity: 12824 links and 4747 nodes

Simulation Model:

Macroscopic based on VISUM. Use historical

data and dynamic assignment to tune it.

EXAMPLE

EXAMPLE

Uncoordinated

.

Conclusions

• New approach for controlling and optimizing systems where

simple mathematical models are replaced with simulation models

in a closed loop configuration

• Application to Freight Load Balancing problem

• Todays available technologies and computational techniques

support the potential of the method for its widespread use

Already successfully applied

• traffic light control of the city of Chania, Crete, Greece (currently

operating)

Thank You