Transcript Dynamic Vehicle Routing Patrick Prosser and Kostas

Issues in Dynamic Fleet
Management
Talk at
ROUTE 2000 - INTERNATIONAL WORKSHOP ON
VEHICLE ROUTING
SKODSBORG, DENMARK - AUGUST 16-19, 2000
Geir Hasle
Research Director, Department of Optimization
SINTEF Applied Mathematics
Oslo, Norway
[email protected]
http://www.oslo.sintef.no/am/
My talk
SINTEF Applied Mathematics (SAM)
 Fleet Management
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industrial potential, status, requirements
technology
research, science
bridging the gap between science and industry
Challenges
 Routing etc. at SAM
 Research Agenda
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SINTEF
The Foundation for Scientific and Industrial Research
at the Norwegian Institute of Technology
The vision:
Technology for a better society
Business concept:
SINTEF´s goal, in co-operation with NTNU and UiO, is to meet needs of the
private and public sectors for research-based innovation and development
Locations:
The SINTEF Group have 1800 employees, 400 in Oslo and 1400 in Trondheim.
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SINTEFs
council
SINTEFs
board
President/
Vice-president
SINTEF Petroleum Research
MARINTEK
The Norwegian Marine
Technology Research Institute
SINTEF Energy Research
SINTEF Applied Mathematics
SINTEF Civil and Environmental
Engineering
SINTEF Electronics and Cybernetics
SINTEF Applied Chemistry
SINTEF Materials Technology
SINTEF Fisheries and
Aquaculture
SINTEF Industrial Management
SINTEF Telecom and Informatics
SINTEF Unimed
SINTEF-Group turnover in
1999
Basic grants from
The Research Council
of Norway 3,3%
Strategic programs from
The Research Council of Norway 4,3%
Contracts 92,4%
-
Industrial and commercial
enterprises 53,0%
Public sector 12,5%
International contracts 10,5%
The Research Council of
Norway (project grants) 9,9%
Other sources 6,5%
SINTEF Applied Mathematics
http://www.oslo.sintef.no/am
A contract research institute in the
SINTEF group
Geometry
Modeling
Simulation
Optimisation
SINTEF Applied Mathematics
Department of Optimisation
Focus:
Applied research
Planning
Decision Support
Main business areas:
Transportation
Area management
Oil business
Manufacturing
Approach:
Generic Tools
Reuse
Methodology
Application types:
Resource optimisation
Design/configuration
Discrete
Basis:
Knowledge Based Systems
Operations Research
Computing science
Transportation of goods in Norway
and EU
12.000 companies (EU 1/2 mill.)
 Annual turnover 30 billion. (EU 1.200 billion.)
 Many SMEs
 36 % empty driving
 Capacity utilization with load: 60 %
 Logistics costs 12% of product costs (EU 7%)
 EU: 13 million trucks, 800 billion ton-kilometers (1990)
 Germany: freight income some 60 billion DM (1990)
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Industrial use of VRP Tools
Excess travel, huge potential
 Swedish report* 1999 (commercial road transport)
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large end users, food & beverage
generation of static routes
vendors claim operational tools
very high potential for savings
* A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten”
Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999
Increasing need for VRP Tools
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focus on
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time
cost
utilization
customer service
lead time reduction
reactivity
regulations, environmental concerns
 e-commerce, home shopping
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Reasons for mismatch
lack of awareness in industry
 lack of data and infrastructure
 price (SMEs)
 organizational problems, resistance
 practical constraints
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– information availability
– physical movement
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tools not good enough
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functionality, modelling power
user friendliness
integration
logistical performance
Existing tools - keywords
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Large variety: simple TSP - sophisticated VRP solvers
focus: road transportation of goods, local distribution
built for operative planning, used for generation of static routes
packages
primitive integration, but good import facilities
inflexible and simple or heavy on consultancy
Windows-platform
good user interfaces, map visualization, manual changes
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VRP algorithms?
real-time planning?
multiple users?
continuous optimization?
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priced at USD 40.000 and above (high end)
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Some Vendors
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Descartes Systems
Caps Logistics -> Baan
MicroAnalytics
Roadnet Technologies (UPS)
i2
ESRI
Kositzky and Associates
Manugistics
Carrier Logistics Inc
Insight Inc.
Caliper Corporation
Trapeze Software Group
Giro
DPS International
Paragon Software Systems
Optrak (Andersen Consulting)
Ilog
Diagma
PTV
Alfaplan
PLS
Prologos
USA
USA
USA/GB
USA
USA
USA
USA
USA
USA
USA/The Netherlands/GB
USA
USA/Canada
Canada
UK
UK
UK
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Typically claim 10 - 30% cost reductions - static routes
Few VRP Tools in Operation
in Norway
Coca-Cola
 Taxi companies
 Falken
 NAS
 NKL
 Tollpost-Globe
 Linjegods
 Postal service
 Hydro Agri
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Challenges - VRP Tools
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Functionality
Modelling
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constraints
criteria
uncertainty
dynamics
supply-chain coordination
Adaptability
Power: speed vs. quality
Large-size problems
User Interface
Integration
Support etc.
Dynamic, real-time routing Success stories?
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Paragon - Tesco
– “… Home shoppers simply log onto the
dedicated area of Tesco's website, select their
purchases and identify a two hour time window
for delivery to an address of their choosing” ...
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Truckstops
– “… In some UK applications it is even used to
recalculate routes during the day, modifying its
original calculations to take account of new
requirements and reflecting data transmitted
back from vehicles by radio” …
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PriceWaterhouseCoopers
Goal - VRP Technology
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real benefits for industry - logistical
performance
– solve right problem
– plan quality vs. response time
– user interaction, user-friendliness
– configurability
– reactivity
– price
Future VRP technology
GIS vendors
 ERP vendors
 ASP solutions, thin clients, Internet, www
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better tools for strategic/tactical planning
 supply-chain coordination, integrated
solutions
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dynamic, real time fleet management
Dynamic Fleet Management Prerequisites
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ICT infrastructure
– order data
– fleet data
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access to high quality traffic data
– speed predictions
– “organic” electronic road network
Better understanding of routing policies
 Better VRP algorithms
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Issues in VRP research
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Large, ill-structured problems
rich models
– uncertainty
– dynamics
– multiple criteria
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reactivity
– disruption?
– slack
– policies
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plan quality vs. response time performance
decomposition
human issues
Stochastic and dynamic VRPs
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what does “dynamic” mean?
– problem changes dynamically
– Psaraftis (1995): “... information on the problem is made known to the
decision maker or is updated concurrently with the determination of the set
of routes.”
– Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions have to be
taken at different times within some time horizon, and earlier decisions
influence later decisions.”
– a.k.a. “real-time”, “on-line”
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“organic” routing plans
– challenges
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information flow
physical goods
– good idea? (talk of Carlos Daganzo)
Literature - dynamic VRPs
6 INFORMS sessions since 1995, some 20 papers
 some 50 journal papers
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Some papers
Psaraftis (1995): Dynamic vehicle routing: Status and prospects
Bertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertainty
Crainic, TG / Laporte, G (1997): Planning models for freight transportation
Baita, F / Ukovich, W / Pesenti, R / Favaretto, D (1998): Dynamic routing-and-inventory problems: A review
Swihart, MR / Papastavrou, JD (1999): A stochastic and dynamic model for the single-vehicle pick-up and delivery problem
Savelsbergh, M / Sol, M (1998): Drive: Dynamic routing of independent vehicles
Ioachim, I / Desrosiers, J / Soumis, F / Belanger, N (1999): Fleet assignment and routing with schedule synchronization constraints
Gans, N / VanRyzin, G (1999): Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insights
Reiman, MI (1999): Heavy traffic analysis of the dynamic stochastic inventory-routing problem
Gendreau, M / Guertin, F / Potvin, JY / Taillard, E (1999): Parallel tabu search for real-time vehicle routing and dispatching
Powell, WB / Towns, MT / Marar, A (2000): On the value of optimal myopic solutions for dynamic routing and scheduling problems
in the presence of user noncompliance
Cheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networks
Gendreau, M / Laporte, G / Seguin, R (1996): Stochastic vehicle routing
Gendreau, M / Laporte, G / Seguin, R (1996): A tabu search heuristic for the vehicle routing problem with stochastic demands and
customers
Haughton, MA (1998): The performance of route modification and demand stabilization strategies in stochastic vehicle routing
Yang, WH / Mathur, K / Ballou, RH (2000): Stochastic vehicle routing problem with restocking
Haughton, MA (2000): Quantifying the benefits of route reoptimisation under stochastic customer demands
Secomandi, N (2000): Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands
Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight
demands requested in real-time
Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)
Approaches - uncertainty, dynamics
ignore
 deterministic model - and repair
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– crisp, optimized plans are brittle
– is disruption costly?
– add slack, how?
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stochastic model
– investigation of policies
– still need dynamic decision-making
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lessons to be learnt from factory scheduling
Dynamic VRP DSS
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dependent on high quality updated information
– fleet status
– order status
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“organic” route planning
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concept of current plan
when do we commit?
when do we include changes?
locking parts of plan
do we need to worry about disruption?
dependence on type of operation / business rules
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delivery vs. pickup
– applicable algorithms
– (how much) do we save by taking a dynamic approach?
Approaches
insertion heuristics + iterative improvement
 constraint propagation
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MP formulations?
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Minimal disruption possibly an additional goal
criterion component
Routing at SAM
SPIDER
 GreenTrip
 HAMMER - vessel routing with inventory
constraints
 Bus scheduling
 eCSPlain, EU FP V
 Distributed problem solving
 Proposals
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SPIDER
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a VRP Solver C++ program library
– UNIX
– Windows
– COM component
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instantiates to a module for optimised transport
management
– plan-administrasjon
– VRP optimisation
– cheapest path calculations
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adaptable to wide variety of applications
distribution through sw vendors
GreenTrip
 Esprit 20603, January 1996-March 1999, > 40 person-years
 Consortium
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Tollpost-Globe (N)
Pirelli (I)
Ilog (F)
University of Strathclyde (GB)
SINTEF (N)
 RTD effort in methods, algorithms, and generic sw for optimised
fleet management
The goal of GreenTrip
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Produce a cost-effective tool to optimise
routing of vehicles that
– is generic
– takes into account multiple business constraints
– permits efficient (re)configuration
– integrates easily in existing IT infrastructure
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GreenTrip Technical Approach
OO Programming
 Constraint Programming
 Iterative Improvement Techniques
 Applications Modelling
 Automated Systems (Re)Configuration
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The GreenTrip Consortium
TollpostGlobe
Pirelli
SINTEF
ILOG
UoS
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CASE : TOLLPOST-GLOBE
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Pick up orders : 600
Regular and non-regular customers
Deliveries : 2.400
Time windows - Customer service
Two days are not the same
some 100 vehicles
Different vehicles (size, volume, equipment)
Depot with automatic sorting / registration
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CASE : TOLLPOST-GLOBE
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Electronic road and address data are
available via the GIS Transportation
Demonstrator
Mobile communication installed in 15
vehicles
GPS installed in 5 vehicles
some 100.000 customers in the Oslo
region
goal: dynamic fleet management system
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The Pirelli (Cables) Case
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Logistics network simulator
Assessment of logistical performance
Detailed analysis of alternative structural changes
scenarios 6 months operation, 10.000 orders
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GreenTrip - GGT Systems
Architecture
ApplicationModelling
Application Server
Application
Model
VRP Solver
GIS
Road data
Legacy
Systems
The VRP Solver - Objects
Plans
 Locations
 Visits
 Vehicles
 Routes
 Dimensions
 Constraints
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VRP Solver - Algorithms
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Construction
– Savings
– Sweep
– Nearest ...
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Improvement, move operators
– 2-opt, Or-opt
– Relocate
– Exchange
– Cross
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VRP Solver - Search Control
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Basic heuristic
– Greedy Search (First Improvement)
– Steepest Descent (Best Improvement)
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Meta-heuristics
– Tabu Search
– Guided Local Search
– Guided Tabu Search
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GreenTrip - Results
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VRP Solver -> ILOG Dispatcher
GGT -> GreenTrip AS “Dynamic planner”
“best-until-now” results on OR benchmarks
 Industrial Test Cases
 Publications
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– some 20 scientific papers
– reports - “VRP Solving and IIT Survey”
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GreenTrip Dissemination
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Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97
De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments with
Tabu Search on the VRP”, Proc. MIC 97
De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint
Programming: Applications to vehicle routing problems”, CP 97 Scheduling
Workshop
Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Routing”,
NOAS 97
De Backer, Furnon: “Solving vehicle routing problems with Side Constraints Using
Constraint Programming”, INFORMS 97
De Backer, Furnon: “Modelling pickup and delivery problems in constraint
programming”, INFORMS 98
Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP
Applications and its Modelling Capabilities”, Proc. PACLP 99
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GreenTrip Papers
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De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing
problems with constraint programming and metaheuristics”, Journal of
Heuristics, Special Issue on CP
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Kilby, Prosser, Shaw: “A comparison of traditional and constraint-based
heuristic methods on vehicle routing problems with side constraints”,
Constraints, April 98
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De Backer, Furnon: “Local Search in Constraint Programming”, in METAHEURISTICS: Advances and Trends in Local Search Paradigms for
Optimization (Voss, Martello, Osman, Roucairol, 1999)
Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing
problem with Time Windows”, in META-HEURISTICS: Advances and
Trends in Local Search Paradigms for Optimization (Voss, Martello,
Osman, Roucairol, 1999)
Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios”
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(forthcoming)
Vessel Routing - Ammonia
Norsk Hydro Agri
 Producer - Consumer Harbours (25)
 Fleet (10)
 Strong Inventory Constraints
 External Trading
 Feasible solution
 Earlier approach: MIP
 Approach taken: Heuristic Sequencing + LP 44
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HAMMER Problem
Producing
harbours
Quantity
Time-window
Consuming
harbours
Fleet of vessels
Harbours with stock
inventory
Find the routing plan with the lowest cost
so that inventory limits are not exceeded
and all external orders included.
External orders
(laycans)
Combinatorial solution
Vessel View:
Harbour View:
3
2
6
1
4
7
5
H1:
H2:
H3:
H4:
H5:
H6:
H7:
Site
Route for Vessel 1
Vessel 1
Route for Vessel 2
Vessel 2
Vessel View:
Which harbours, and in which
sequence, each vessel will visit.
Harbour View:
Which vessels, and in which sequence, will
call at each harbour.
HAMMER - Linear solution
Vessel view:
max
Load
min
Time
Harbour view:
Call
Stock
Time
HAMMER - System overview
Problem data
Initial
solver
Iterative
improver
Combinatorial solution
Feasibility
check
Greedy Propagator
Feasible solution
Update
LP solver
HAMMER - Working with the system
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Initialisation of the problem
– Harbours, ships, laycans and planning parameters
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Schedule generation
– Initial solver - from scratch or existing
– Iterative improvement
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Analysis and user interaction
– plan statistics - slack, unserviced
– manually change plan
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Lock ship, harbour or time period
Flatberg, Haavardtun, Kloster, Løkketangen. (2000): Combining exact and Heuristic methods for
solving a Vessel Routing Problem with inventory constraints and time windows. To appear in Ricerca
Operativa, special issue on combined constraint programming and OR techniques
Research Agenda SAM: VRP
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construction heuristics
– construct and improve
– restart
– greedy + limited backtracking
IIT by local search and meta-heuristics
 exact methods subproblems / limited problems
 hybrid methods
 dynamic VRP
 empirical investigation
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Important topics, SAM
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configuration of transportation networks
VRPs and TSPs with side constraints in road
based and maritime transportation
cheapest path problems in large, dynamic
network topologies
Proposal to Research Council of Norway
Research Agenda SAM:
Optimisation / CSP
over-constrained problems
 multi-criterion problems
 supply-chain coordination
 distributed problem solving
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Research Agenda: VRP
rich models, large problems
 dynamic VRPs
 exact methods for limited (sub)problems
 over-constrained problems
 multi-criteria problems
 methodology: problem type - algorithm
 cooperating VRP solvers, hybrid methods
 decomposition
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Issues in Dynamic Fleet
Management
Talk at
ROUTE 2000 - INTERNATIONAL WORKSHOP ON
VEHICLE ROUTING
SKODSBORG, DENMARK - AUGUST 16-19, 2000
Geir Hasle
Research Director, Department of Optimization
SINTEF Applied Mathematics
Oslo, Norway
[email protected]
http://www.oslo.sintef.no/am/