Post on 12-Jan-2016
DECISION SUPPORT SYSTEM FOR
REAL-TIME URBAN FREIGHT MANAGEMENT
LOGISTICS BASE
LOGISTICS BASE
CEDM City Distribution Terminal
CEDM City Distribution Terminal
Hanna Grzybowska hanna.grzybowska@upc.edu
andJaume Barceló
jaume.barcelo@upc.edu
Dept. of Statistics and Operations Research
CENIT (Center for Innovation in Transport) www.cenit.es
Universitat Politècnica de Catalunya
New customer Calls at time t
Position of vehicle 1 at
time t
New route for vehicle 2
Position of vehicle 2 at
time t
New route for vehicle 1
CONCEPTUAL APPROACHES TO REAL TIME FLEET MANAGEMENT AND THE
ROLE IF ICT TECHNOLOGIES
2The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
Supplier
Supplier’s Supplier
Manufacturer
Wholesaler/Distributor
Retailer
CITY AREA- client
CITY LOGISTICS SCENARIO
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Fleet management in urban areas has to explicitly account for the dynamics of traffic conditions leading to congestions and variability in travel times affecting the distribution of goods and the provision of services
City Logistics activities are impacted by traffic congestion must consider time-varying traffic congestion and operational constraints in routing and logistics optimization models
Last-mile logistics
Decisions must take into account all factors conditioning the problem Decision Support System (DSS)
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
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GPS Satellites
Vehicle’s Position
Vehicle
Vehicle’s Data
Updated Route Updated Route
Cellular Antenna
Vehicle’s Data
• Global Positioning System (GPS)• GPS device pickups signal from satellites• GPS device calculates position• Establish communication with network• Vehicle Data is sent to Fleet Management Center.• Fleet Manager updates routes and returns them to vehicle.
Fleet management Centre
ICT TECHNOLOGIES AND REAL-TIME FLEET MANAGEMENT
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
BARCELONA’S TRAFFIC INFORMATION SYSTEM
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Current and Short-Term Forecasted Travel Time
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
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Initial Demand and Fleet Specifications
Initial Demand and Fleet Specifications
ROUTING AND SCHEDULING
MODULE
ROUTING AND SCHEDULING
MODULE
Initial Operational Plan
DYNAMIC ROUTER AND SCHEDULER
DYNAMIC ROUTER AND SCHEDULER
Dynamic Operational Plan
Dynamic Operational Plan
CONCEPTUAL SCHEME FOR REAL-TIME FLEET
MANAGEMENT SYSTEMS
(Adapted from Regan, Jaillet, Mahmassani)
Known/predicted demand for
service
Known/predicted demand for
service
Known/predicted driver/vehicle
availability
Known/predicted driver/vehicle
availability
Load Acceptance Policies
Load Acceptance Policies
Pool of accepted demands
REAL-TIME INFORMATION
• New demandsUnsatisfied demandsTraffic conditions Fleet availability
ASSUMING:
• A given Initial Operational Plan• Fleets equipped with AVL tecnologies (i.e. GPS+GPRS)• A Real-Time Information System
OBJECTIVE:
• Design a DSS for Dynamic Routing and Scheduling
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
New Customer Request
Cancelation of Service
Arrival of Vehicle at Location
Changes in Travel Times
Delays in Delivery Start Times
Breakdown of Vehicle
Changes in Time Windows Bounds
DYNAMIC ROUTER AND SCHEDULER
Insertion Heuristics
Local Search Operators
TABU SEARCH
Reactive Strategies• One-by-one• Pooling
Preventive Strategies• Vehicle Relocation
Waiting Strategies• Drive-First• Wait-First• Combined
DYNAMIC EVENT AND VEHICLE TRACKING
SIMULATOR
NEW ROUTING PLAN
EX
TE
RN
AL
EV
EN
TS
INT
ER
NA
L E
VE
NT
S
INTERNAL EVENTS DYNAMIC MONITORING
SOLVING STRATEGIES
PROPOSED DECISION SUPPORT SYSTEM FOR REAL-TIME
FLEET MANAGEMENT
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
DYNAMIC ROUTER AND SCHEDULER • Decide where to insert the new customer.• New customer arrives at time t > 0. • Fleet vehicles can be in one of the three status:
– In service at some customer i (SER).– Moving to the next planned customer on the route or waiting at the
customer location to start service within the time window. (MOV).– Idle at the Depot, without a previously assigned route (IDL).– Waiting at the client’s i (WAIT)
• This status determines when a vehicle should be diverted from its current route, be assigned to a new one if is idle or keep the planned trip.
• Whenever a new customer arrives, the status of a vehicle must be known to compute travel times for this new customer.
• If the vehicle has a MOV status, the travel time is computed from the current position of the vehicle to the location of the new customer.
• If the vehicle has IDL status, the travel time is just the travel time from the depot to the new customer.
• If the vehicle is has SER status, the amount of time needed to arrive to the customer is the remaining service time at the current customer plus the travel time between the current customer and the new customer.
• If the vehicle has WAIT status it can be send to other client.The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
1. Initial fleet scheduling
2. Start services while tracking vehicles
3. New client call is received at time t
4. Check vehicle positions and current travel times
5. Reject unfeasible routes (insertion / diversion)
6. Recalculate routes.7. Execute new plan.
In Transit
In Transit
In Service
In Service
New Client
DYNAMIC ROUTING AND SCHEDULING: Example of Dynamic Insertion Heuristic
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
section of the original route
section removed from the original route
new section of modified original route
new client request registered at time t
initially known client already visited by the assigned vehicle
P2
P1
D2
D1
P4
P3
D3
D4
D5
P5
P6
D6
DEPOT
1
2
3
DYNAMIC REROUTING WITH REAL-TIME INFORMATION (ADJUSTMENT SOF ROUTE 2 - GREEN AND 3 - BLUE )
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section removed from the original route
new section of modified original route
new client request registered at time t
initially known client already visited by the assigned vehicle (before time instant t)
initially known client still not visited by the assigned vehicle (before time instant t)
on of the fleet vehicle serving route 2 at time t
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
AVERAGE LINK TRAVEL TIMES AND VARIANCES (Barcelona “Eixample”-CBD)
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Working exclusively with average travel times may lead to significant deviations in city logistics problems where temporality is an
important factor such as the VRPTW.
A report on urban distribution in Barcelona (Robusté, 2005) found that:There were more than 62,000 commercial outlets more than 60,000 daily unloading operationsService time between 13 and 16 minutes50% of deliveries were made by 11:00 hrs
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
DEALING WITH TIME-DEPENDENT TRAVEL TIMES
• di = departure time from client i
• si = sevice time for client i
• Tij(di) = travel time from i to j when departing at time di from client i
• Tij(di) Tij(di’)
si
Sj
Sk
i
j
k
di
dj
dk )
i(d
ijT
)j
S)i
(dij
Ti
(djk
T)j
(djk
T
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Ad Hoc version of the algorithm of Ziliaskopoulos and Mahmassani
Calculates the time-dependent shortest paths from all nodes of to one, specified as destination point.
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
REAL-TIME TRAFFIC INFORMATION SYSTEM
REAL-TIME FLEET MONITORING SYSTEM
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
FRAMEWORK
14The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
TIME-DEPENDENT TRAVEL TIMES DATA FORECASTING MODULE
MULTIPLE SIMULATION ITERATIONS
“future”travel times
actualtravel times
Ajtcttctc Hj
Pjj ),())(1()()((t)F
historical travel times data base
SINGLE SIMULATION ITERATION
t0
TRAFFIC SIMULATOR
cjH(t0) cj
H(t0+1) cjH(t0+T)…cj
P(t0) cjP(t0+1) cj
P(t0+T)…
t0
Historical Data BaseActual Travel Times
+Expected Travel Times
ATIS
1
2)(
Tt
15The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
Time-Dependent Routing Plan:
sequences of clients to visit
Time-Dependent Shortest Paths: sequences of nodes between the clients for the
vehicle to go through
Present Travel Times Data Base
Vehicle Fleet Performance Simulator
Vehicles’ current:• status• position• load
List of clients:• served• omitted
INPUT
OUTPUT
e.g.: instant of appearance of
new event
Depending on the current vehicle’s status, the value of time left to complete:• service• waiting• trip on the current arc
EXTERNAL TRIGGER
INTERNAL EVENT e.g.: • arrival to a client whose TW is closed• end of providing a client with service
VEHICLE FLEET PERFORMANCE SIMULATOR
16The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
MATHEMATICAL FORMULATION OF THE VEHICLE ROUTING PROBLEM WITH PICKUP AND DELIVERIES AND TIME WINDOWS
Kk Aji
ijkijk
k
xc),(
Min
kijk
kijk
kko
kikkin
kkiki
kjkjikijk
kkinkiniik
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koDjkkdi
kdNiijk
koNiijk
kdPjkjko
Nik
Njkinjijk
Kk kdNjijk
AjiKkx
AjiKkx
KkL
DinKkqCL
PiKkCLq
AjiKkLqLx
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),( , 0
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,,,
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Subject to:
Each customer is served by the same vehicle
Time windows feasibility
Precedence requirement
Route for vehicle k, from o(k) to d(k)
Route and vehicle loads requirements
17The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
PICK UP & DELIVERY PROBLEMS WITH TIME WINDOWS
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• The route of each vehicle starts and ends at the depot., • Each customer must be served within a time interval
[ai,bi] during Si units of time. • Each customer must be visited exactly once by
exactly one vehicle. Pairs of associated clients are serviced by the same
vehicle (pairing constraint), Cargo’s sender is always visited before its recipient
(precedence constraint), • For each customer, the pickup location i+ must be served
in the same route and before delivery location i- • The sum of demands of customers on a route cannot
exceed vehicle capacity. The entire routing cost is minimized.
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
COMPOSITE HEURISTICS TO CONTRUCT THE INITIAL AND THE DYNAMIC ROUTING AND SCHEDULING
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ALGORITHM PROVIDING THE INITIAL SOLUTION
Based on the Simple Pairing Approach
Based on the Sweep Algorithm
Based on Customers’ Aggregation Areas
PARALLEL TABU SEARCH PROCEDUREUSING SIMULTANEOUSLY TWO LOCAL SEARCH HEURISTICS
Pickup and Delivery Pair Shift Operator
Pickup and Delivery Pair Exchange Operator
POST-OPTIMIZATION
Pickup and Delivery Pair Rearrange Operator
2-opt procedure
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
P2
D1
P6
D6
DEPOT
D7P7
P3 D3
P5
D5
D4
D2
P4
P1
NEIGHBOURHOOD 1 NEIGHBOURHOOD 2
NEIGHBOURHOOD 3
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Algorithm Initial Solution
1. Order known clients (Client’s Sorting Pre-Processing Algorithm using the Definitions of Customers’ Aggregation Areas
2. Create initial solution • Select the farthest client in the listing • Find its PD partner and delete them both from the listing • IF it is the first iteration OR it is not possible to insert the pair into existing route THEN create route: depot-pickup customer-delivery customer-depot • ELSE insert the pair in location causing minimal increment of the cost of the existing route
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
PARALLEL TABU SEARCH
Works with two concurrent different search processes:
•The Pickup and Delivery Pair Shift Operator (NPDPSO)
•The Pickup and Delivery Pair Exchange Operator (NPDPEO).
21The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
POST-OPTIMIZATION PROCESS
Post-optimization is realised by: Pickup and Delivery Pair Shift Operator
22The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
COMPUTATIONAL EXPERIMENTS AND RESULTS
23The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
THE SELECTED SCENARIO: Barcelona’s CBD
24The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
KTS seminar at Linkoping University, Norrkoping 16.03.2011
Clients
Depot
• Downtown area of Barcelona.• Commercial activities and tourism. • It covers 747 hectares• 1,570 links y 721 nodes.
THE SIMULATION MODEL
25The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
• 100 Customers:• Constant demand and service time.• Time windows
• 1 Depot; Opening hours 08:00 – 16:00• Fleet: 8 homogeneous vehicles with large capacity. • Vehicles are equipped with GPS and real-time
communication system with fleet manager.• Real-time traffic information system.• Simulation time: 10 hours (07:00 – 17:00)
MODELING ASSUMPTIONS
26The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
Table 1. Collection of the DSS performance testing scenarios
TESTING SCENARIOS
Scenario No Initially Known Clients TDSP Calculator Triggered Traffic Information Used Module
1 100 once at the start average static IRSM
2 100 at the start and after multiple times time-dependant IRSM+DRSM with CRRM
3 80 at the start and after multiple times time-dependant IRSM+DRSM with CRRM
Initial solution Final solution
ScenarioIniNor
Total TravelTime [s]
TotalWaiting Time [s]
SolutionCost [s]
FinNor
Total Travel Time [s]
Total WaitingTime [s]
SolutionCost [s]
ServiceLevel
1 5 206022 16695 222717.0 5 213207 17189 230396 100%
2 3 149144 10372 159516.0 3 155825 10449 166274 100%
3 3 136028 10393 146420.0 4 188248 10254 198501 100%
Table 2. Results on fleet performance depending on the traffic information input
27The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
TESTING SCENARIOS, USING SPI
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No
scenario TW No of
static clients Method Travel
time data TDSP Calculator
used Forecaster
used 1 narrow 100 SPI perfect once - 2 wide 100 SPI perfect once - 3 narrow 100 SPI AS once - 4 wide 100 SPI AS once - 5 narrow 100 SPI TD multiple times multiple times 6 wide 100 SPI TD multiple times multiple times 7 narrow 80 SPI TD multiple times multiple times 8 wide 80 SPI TD multiple times multiple times 9 narrow 60 SPI TD multiple times multiple times
10 wide 60 SPI TD multiple times multiple times 11 narrow 50 SPI TD multiple times multiple times 12 wide 50 SPI TD multiple times multiple times 13 narrow 40 SPI TD multiple times multiple times 14 wide 40 SPI TD multiple times multiple times 15 narrow 20 SPI TD multiple times multiple times 16 wide 20 SPI TD multiple times multiple times
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
TESTING SCENARIOS USING CSR
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No
scenario TW No of
static clients Method Travel
time data TDSP Calculator
used Forecaster
used 17 narrow 100 CSR perfect once - 18 wide 100 CSR perfect once - 19 narrow 100 CSR AS once - 20 wide 100 CSR AS once - 21 narrow 100 CSR TD multiple times multiple times 22 wide 100 CSR TD multiple times multiple times 23 narrow 80 CSR TD multiple times multiple times 24 wide 80 CSR TD multiple times multiple times 25 narrow 60 CSR TD multiple times multiple times 26 wide 60 CSR TD multiple times multiple times 27 narrow 50 CSR TD multiple times multiple times 28 wide 50 CSR TD multiple times multiple times 29 narrow 40 CSR TD multiple times multiple times 30 wide 40 CSR TD multiple times multiple times 31 narrow 20 CSR TD multiple times multiple times 32 wide 20 CSR TD multiple times multiple times
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
SERVICE LEVELS IN SCENARIOS WITH DYNAMIC CLIENTS
30
80
85
90
95
100
105
SPI narrow TW
SPI w ide TW
CSR narrow TW
CSR w ide TW
No stat. 100 80 60 50 40 20cust. (%)
service level
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
TOTAL TRAVEL TIMES IN SCENARIOS WITH DYNAMIC CLIENTS
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0
20000
40000
60000
80000
100000
120000
SPI narrow TW
SPI w ide TW
CSR narrow TW
CSR w ide TW
total travel time [s]
No stat. 100 80 60 50 40 20cust. (%)
The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
CONCLUSIONS• The obtained results prove that the performance of the fleet
strongly depends on the traffic information used to create and update the routing and scheduling plan.
• The usage of the time-dependent shortest paths, computed whenever a new even occurs, brings better results than when the average travel times’ estimates are employed, even in the case when not all the information on the clients to be served is initially available.
• The comparison of the initially planned and performed routing and scheduling plan indicates that the total travel time, total waiting time and the solution cost are always higher when executed. It is due to the fact that the plan does not take into consideration the most recent and forecasted changes in the traffic flow.
• No customer is left unserved.
32The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011
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THANK YOU VERY MUCH FOR YOUR ATTENTION