Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management

278 Pages·2008·4.35 MB·english

by  Theodore Tsekeris

AndreasFinkandFranzRothlauf(Eds.)
AdvancesinComputationalIntelligenceinTransport,Logistics,andSupplyChain
Management
StudiesinComputationalIntelligence,Volume144
Editor-in-Chief
Prof.JanuszKacprzyk
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PolishAcademyofSciences
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Vol.132.DanilProkhorov(Ed.) AdvancesinComputationalIntelligenceinTransport,Logistics,
ComputationalIntelligenceinAutomotiveApplications,2008 andSupplyChainManagement,2008
ISBN978-3-540-79256-7 ISBN978-3-540-69024-5
Andreas Fink
Franz Rothlauf
(Eds.)
Advances in Computational
Intelligence in Transport,
Logistics, and
Supply Chain Management
123
Prof.Dr.AndreasFink
FacultyofEconomicsandSocialSciences
Helmut-Schmidt-UniversityHamburg
Holstenhofweg85
22043Hamburg
Germany
Email:andreas.fi[email protected]
Prof.Dr.FranzRothlauf
Lehrstuhlfu¨rWirtschaftsinformatik
Johannes-Gutenberg-Universita¨tMainz
JakobWelder-Weg9
D-55099Mainz
Germany
Email:[email protected]
ISBN978-3-540-69024-5 e-ISBN978-3-540-69390-1
DOI10.1007/978-3-540-69390-1
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Preface
Logistics and supply chain management deal with managing the flow of goods
or services within a company, from suppliers to customers, and along a supply
chain where companies act as suppliers as well as customers. As transportation
is at the heart of logistics, the design of traffic and transportation networks
combined with the routing of vehicles and goods onthe networksare important
and demanding planning tasks. The influence of transport, logistics, and sup-
ply chain management on the modern economy and society has been growing
steadily over the last few decades. The worldwide division of labor, the connec-
tion of distributed productioncenters, and the increasedmobility of individuals
lead to an increased demand for efficient solutions to logistics and supply chain
management problems. On the company level, efficient and effective logistics
and supply chain management are of critical importance for a company’s suc-
cessanditscompetitiveadvantage.Properperformanceofthelogisticsfunctions
can contribute both to lower costs and to enhanced customer service.
Computational Intelligence (CI) describes a set of methods and tools that
often mimic biological or physical principles to solve problems that have been
difficult to solve by classical mathematics. CI embodies neural networks, fuzzy
logic,evolutionarycomputation,localsearch,andmachine learningapproaches.
Researchersthatworkinthisareaoftencomefromcomputerscience,operations
research,ormathematics,aswellasfrommanydifferentengineeringdisciplines.
Popular and successful CI methods for optimization and planning problems are
heuristic optimization approaches such as evolutionary algorithms, local search
methods, and other types of guided search methods. Such methods do not enu-
merate all possible solutions but move “heuristically” through the search space
searchingforsuperiorsolutions.Heuristicoptimizationapproachesmustbalance
intensification,whichfocusesthesearchonhigh-qualitysolutions(exploitation),
and diversification, which ensures that the search can escape from local optima
and does not focus on small parts of the search space (exploration).
The book at hand presents a careful selection of relevant applications of CI
methods for transport, logistics, and supply chain management problems. The
chapters illustrate the current state-of-the-art in the application of CI methods
VI Preface
inthesefieldsandshouldhelpandinspireresearchersandpractitionerstoapply
anddevelopefficientmethods.Afewcontributionsinthisbookareextendedver-
sions of papers presented at EvoTransLog2007: The First European Workshop
on Evolutionary Computation in Transportation and Logistics which was held
in Valencia, Spain, in 2007. The majority of contributions are from additional,
specially selected researchers,who have done relevant work in different areas of
transport,logistics,andsupply chainmanagement.The goalis to broadly cover
representativeapplicationsinthesefieldsaswellasdifferenttypesofsolutionap-
proaches.Ontheapplicationside,thecontributionsfocusondesignoftrafficand
transportationnetworks,vehicle routing,andother importantaspects ofsupply
chainmanagementsuchasinventorymanagement,lotsizing,andlotscheduling.
On the method side, the contributions deal with evolutionary algorithms, local
search approaches, and scatter search combined with other CI techniques such
as neural networks or fuzzy approaches.
The book is structured according to the application domains. Thus, it has
three parts dealing with traffic and transportation networks, vehicle routing,
and supply chain management.
The majority of the contributions of Part I focus on road traffic in urban
settings. The first contribution on “Combined Genetic Computation of Micro-
scopic Trip Demand in Urban Networks” by T. Tsekeris, L. Dimitriou, and
A. Stathopoulos presents an approach to predicting the expected trip demand
in urban networks. A proper estimation of dynamic origin-destination demands
is fundamental for all network design and routing decisions. The authors for-
mulate the multi-objective problem and present an evolutionary computation
approach combined with a microscopic simulation model for estimating the ex-
pected traffic flow.
The next two contributions focus on design of road networks. The chapter
“Genetically Optimized Infrastructure Design Strategies in Degradable Trans-
port Networks” by the same authors builds on their first contribution. The au-
thors take the origin-destinationdemands as givenandformulate the stochastic
equilibrium network design problem as a game-theoretic, combinatorial bi-level
program. The design process of a transport network is considered as a game
between the network designer and the network user. The system designer is the
leader in a two-stage leader-follower Stackelberg game. He modifies the struc-
ture of the road network to optimize the system performance, while the users
are the followersreacting to alternative design plans. The options of the system
designer are either to build new roads or to add additional lanes to existing
roads.Tosolvethe problem,the authorsagaincombine evolutionaryalgorithms
with simulation.
The contribution “Genetic Algorithm for Constraint Optimal Toll Ring De-
sign” by A. Sumalee focuses on designof toll rings.A toll ring is a ring of roads
which encloses a specific area (e.g. city center) such that all vehicles travelling
totheareamustusearoadthatispartofthe tollringatleastonce.Theauthor
presents an evolutionary algorithm combined with a traffic simulator to find a
Preface VII
tollringsuchthatthebenefitsgainedlessthecostsofimplementingthetollring
are maximized.
The final contribution in the context of road networks “Real Time Iden-
tification of Road Traffic Control Measures” by K. Almejalli, K. Dahal, and
M.A.Hoasaindealswithcontrolofroadtraffic andpresentsasystemfordeter-
miningappropriateroadtrafficcontroloptions.Theoptionsarechosenaccording
to different traffic states. The performance of the control actions is determined
bytraveltime,fuelconsumption,andaveragelengthoftrafficjams.Tosolvethe
problem,the authorscombine severalCI approaches,namely fuzzy logic,neural
networks, and evolutionary algorithms.
The contribution “Simultaneous Airline Scheduling” by T. Grosche and
F. Rothlauf forms the bridge between Part I and Part II of the book. It ad-
dressesbothdesignoftransportationnetworksandroutingofvehicles(airplanes)
onthosenetworks.The authorspresentanovelandintegratedapproachforair-
line scheduling which allows airlines simultaneously to determine an optimal
structure of the flight network, routing of the airplanes on the flight network,
and scheduling of flights. The optimization goal is to maximize the revenue of
an airline. Since the problem is too complex to be efficiently solved by classical
optimizationmethods, differenttypes of heuristic optimizationmethods suchas
evolutionary algorithms and threshold accepting are studied. Important for a
highperformanceofthe heuristic optimizationmethods arethe properchoiceof
the representation/operatorcombination,repairoperators,fitness function, and
initial solution.
PartIIofthe bookstartswiththreecontributionsonvehicleandarcrouting.
Thecontribution“GRASPwithPathRelinkingfortheCapacitatedArcRouting
ProblemwithTimeWindows”byN.Labadi,C.Prins,andM.Reghiouicombines
several heuristic search approaches such as greedy randomized search, a tour-
splitting algorithm which diversifies the search process, a local search, and an
optional path relinking process to solve the undirected capacitated arc routing
problemwithtimewindows.Theproposedapproachfindsnewoptimalsolutions
for the problem and is as effective as state-of-the-art algorithms, while being
significantly faster.
The second contribution “A Scatter Search Algorithm for the Split Delivery
Vehicle Routing Problem”by V. Campos,A.Corbera´n,andE.Mota dealswith
a similar problem and presents a scatter search approach for vehicle routing
problemswherethedemandsofclientscanbesplit,thismeansanyclientcanbe
servedbymorethanonevehicle.Again,thecomputationalexperimentsindicate
that the proposed heuristic results in similar performance to state-of-the-art
methods.
The third contribution on vehicle routing, “Stochastic Local Search Proce-
duresfortheProbabilisticTwo-DayVehicleRoutingProblem”byK.F.Doerner,
W. J. Gutjahr, R. F. Hartl, and G. Lulli, describes a vehicle routing problem
where two types of service are provided: an urgent service that delivers within
onedayandaregularservicethatneedstwodaysbutcomesatalowerprice.To
exploit synergies in building the delivery tours regular orders may be delivered
VIII Preface
immediately(likeurgentservices).Assumingadynamic(online)problemsetting
witharollingplanninghorizon,theproblemisformalizedasastochasticproblem
andsolvedusinganapproachbasedonantcolonyoptimization.
The contribution “The Oil Drilling Model and Iterative Deepening Genetic
Annealing Algorithm for the Traveling Salesman Problem” by H. C. Lau and
F.Xiaopresentsahybridapproach,whichimitatestheoildrillingprocess:Search
takesplaceinmanyplaces(apopulationofcandidatesolutions)andeachplaceis
evaluatedbydrilling(performingalocalsearch).Searchdiversificationismainly
provided by genetic algorithm concepts, while simulated annealing is used for
intensification by means of local search. In the course of the search process the
population shrinks and the local search is intensified (drilling deeper). That is,
the balance shifts from diversification to intensification. Results show that a
propercombinationofintensificationanddiversificationelements outperformsa
straightforward hybrid algorithm as well as using local or recombination-based
search alone.
The contribution “Online Transportation and Logistics Using Computation-
ally Intelligent Anticipation” by P. A. N. Bosman and H. L. Poutr´e forms the
bridge between Part II and Part III of the book. It discusses the importance of
anticipation in online decision making and describes how CI can be used to de-
sign approaches that perform anticipation. The proposed methods are designed
suchthatthey learnfromthe consequencesofpreviousdecisions,whichleadsto
anauto-adaptivedesign. The authors presenttwo applications:dynamic vehicle
routing,whichassumesthattheloadstobetransportedareannouncedwhilethe
vehiclesarealreadyenroute,andinventorymanagementwherehighercustomer
satisfaction leads to an increased number of transactions.
The final PartIII on supply chain managementstarts with a contribution on
“Supply Chain Inventory Optimisation with Multiple Objectives: An Industrial
CaseStudy”byL.Amodeo,H.Chen,andA.E.Hadji.Theauthorsoptimizesup-
ply chain inventory policies using a multi-objective optimization approach that
combines genetic algorithms with a Petri net-based simulation tool for perfor-
manceevaluation.Inareal-worldstudy,theauthorsuseinventorycost,customer
service level, and computation time as optimization goals.
The contribution “Decomposition of Dynamic Single-Product and Multi-
Product Lotsizing Problems and Scalability of EDAs” by J. Grahl, S. Minner,
and F. Rothlauf shows that certain lotsizing problems are decomposable. Thus,
such problems can be solved efficiently by evolutionary algorithms such as es-
timation of distribution algorithms (EDA). A scalability analysis for EDAs on
the dynamic single-product and multi-product lotsizing problem confirms ex-
isting scalability theory and shows that solution effort grows with a low-order
polynomial depending on the problem size.
Thefinalcontribution“HybridGeneticAlgorithmsfortheLotProductionand
Delivery Scheduling Problemin a Two-EchelonSupply Chain” by S. A. Torabi,
M.Jenabi,andS.A.Mansouriconsidersasinglesupplierwhoproducesitemson
a flexible flow line under a cyclic policy and delivers them directly to an assem-
bly facility. The authors formulate the problem as a mixed zero-one nonlinear
Preface IX
programand minimize the averagesetup, inventory-holding,and delivery costs.
For solving the problem, a hybrid approachis proposed which combines genetic
algorithms and neighborhood search.
Inclosingwewishtothankallauthorswhocontributedtothisbook.Without
their workandpassion,this book would nothave been possible. In addition,we
thank all reviewers for their help in improving the quality of the contributions
in the book. We also want to thank Inka Lo¨lfer for proofreading parts of the
book, as well as Jens Czogalla for helping with the technical preparation of
some chapters. Last, but not least, we want to thank the editor of this book
series, Janusz Kacprzyk, for making this book possible and Thomas Ditzinger
who fully supported the project.
Hamburg and Mainz (Germany) Andreas Fink
March 2008 Franz Rothlauf
Contents
Part I: Traffic and Transport Networks
Combined Genetic Computation of Microscopic Trip Demand
in Urban Networks
Theodore Tsekeris, Loukas Dimitriou, Antony Stathopoulos............. 3
Genetically Optimized Infrastructure Design Strategies in
Degradable Transport Networks
Loukas Dimitriou, Theodore Tsekeris, Antony Stathopoulos............. 23
Genetic Algorithm for Constraint Optimal Toll Ring Design
Agachai Sumalee.................................................. 45
Real Time Identification of Road Traffic Control Measures
Khaled Almejalli, Keshav Dahal, M. Alamgir Hossain.................. 63
Simultaneous Airline Scheduling
Tobias Grosche, Franz Rothlauf ..................................... 81
Part II: Vehicle Routing
GRASP with Path Relinking for the Capacitated Arc Routing
Problem with Time Windows
Nacima Labadi, Christian Prins, Mohamed Reghioui................... 111
A Scatter Search Algorithm for the Split Delivery Vehicle
Routing Problem
Vicente Campos, Angel Corbera´n, Enrique Mota ...................... 137