Table Of ContentLecture Notes in Artificial Intelligence 2148
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Lecture Notes in Computer Science
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Alexander Nareyek (Ed.)
Local Search
for Planning
and Scheduling
ECAI 2000 Workshop
Berlin, Germany, August 21, 2000
Revised Papers
1 3
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JaimeG.Carbonell,CarnegieMellonUniversity,Pittsburgh,PA,USA
Jo¨rgSiekmann,UniversityofSaarland,Saarbru¨cken,Germany
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Preface
With the increasing deployment of planning and scheduling systems, developers
oftenhavetodealwithverylargesearchspaces,real-timeperformancedemands,
anddynamicenvironments.Completerefinementmethodsdonotscalewell,ma-
kinglocalsearchmethodstheonlypracticalalternative.Adynamicenvironment
also promotes the application of local search, the search heuristics not normally
being affected by modifications of the search space. Furthermore, local search is
well suited for anytime requirements because the optimization goal is improved
iteratively.Suchadvantagesareoffsetbytheincompletenessofmostlocalsearch
methods, which makes it impossible to prove the inconsistency or optimality of
thesolutionsgenerated.Popularlocalsearchapproachesincludeevolutionaryal-
gorithms, simulated annealing, tabu search, min-conflicts, GSAT, and Walksat.
The first article in this book – an invited contribution by Stefan Voß – gives an
overview of these methods.
ThebookisbasedonthecontributionstotheWorkshoponLocalSearchfor
Planning & Scheduling, held on August 21, 2000 at the 14th European Confe-
rence on Artificial Intelligence (ECAI2000) in Berlin, Germany. The workshop
brought together researchers from the planning and scheduling communities to
explorethesetopicswithrespecttolocalsearchprocedures.Aftertheworkshop,
a second review process resulted in the contributions to the present volume.
Voß’s overview is followed by two articles, by Hamiez and Hao and Gerevini
andSerina,onspecific“classical”combinatorialsearchproblems.Thearticleby
Hamiez and Hao addresses the problem of sports-league scheduling, presenting
results achieved by a tabu search method based on a neighborhood of value
swaps. Gerevini and Serina’s article addresses the topic that dominates the rest
of the book: action planning. It builds on their previous work on local search
on planning graphs, presenting a new search guidance heuristic with dynamic
parameter tuning.
The next set of articles deal with planning systems that are able to incor-
porate resource reasoning. The first article, of which I am the author, makes it
clear why conventional planning systems cannot properly handle planning with
resourcesandgivesanoverviewoftheconstraint-basedExcaliburagent’splan-
ning system, which does not have these restrictions. The next three articles are
aboutNASAJPL’sASPEN/CASPERsystem.Thefirstone–byChien,Knight,
and Rabideau – focuses on the replanning capabilities of local search methods,
presenting two empirical studies in which a continuous planning process clearly
outperformsarestartstrategy.Thenextarticle,byEngelhardtandChien,shows
howlearningcanbeusedtospeedupthesearchforaplan.Thegoalistofinda
set of search heuristics that guide the search as well as possible. The last article
in this block – by Knight, Rabideau, and Chien – proposes and demonstrates,
a technique for aggregating single search moves so that distant states can be
reached more easily.
VI Preface
Thelastthreearticlesinthisbookaddresstopicsthatarenotdirectlyrelated
to local search, but the described methods make very local decisions during the
search. Refanidis and Vlahavas describe extensions to the GRT planner, e.g., a
hill-climbing strategy for action selection. The extensions result in much better
performance than with the original GRT planner. The second article – by Ona-
india, Sebastia, and Marzal – presents a planning algorithm that successively
refines a start graph by different phases, e.g., a phase to guarantee comple-
teness. In the last article, Hiraishi and Mizoguchi present a search method for
constructing a route map. Constraints with respect to memory and time can be
incorporated into the search process.
I wish to express my gratitude to the members of the program committee,
who acted as reviewers for the workshop and this volume. I would also like to
thank all those who helped to make this workshop a success – including, of
course, the participants and the authors of papers in this volume.
June 2001 Alexander Nareyek
Workshop Chair
Program Committee
Emile H. L. Aarts Philips Research
Jos´e Luis Ambite Univ. of Southern California
Blai Bonet University of California
Ronen I. Brafman Ben-Gurion University
Steve Chien NASA JPL
Andrew Davenport IBM T. J. Watson
Alfonso Gerevini Universita` di Brescia
Holger H. Hoos Univ. of British Columbia
Alexander Nareyek GMD FIRST
Angelo Oddi IP-CNR
Mar´ıa C. Riff Univ. T´ec. Fed. Santa Mar´ıa
Bart Selman Cornell University
Edward Tsang University of Essex
Table of Contents
Invited Paper
Meta-heuristics: The State of the Art................................. 1
Stefan Voß
Combinatorial Optimization
Solving the Sports League Scheduling Problem with Tabu Search ........ 24
Jean-Philippe Hamiez, Jin-Kao Hao
Lagrange Multipliers for Local Search on Planning Graphs .............. 37
Alfonso Gerevini, Ivan Serina
Planning with Resources
Beyond the Plan-Length Criterion ................................... 55
Alexander Nareyek
An Empirical Evaluation of the Effectiveness
of Local Search for Replanning ...................................... 79
Steve Chien, Russell Knight, Gregg Rabideau
Board-Laying Techniques Improve Local Search
in Mixed Planning and Scheduling ................................... 95
Russell Knight, Gregg Rabideau, Steve Chien
Empirical Evaluation of Local Search Methods
for Adapting Planning Policies in a Stochastic Environment ............. 108
Barbara Engelhardt, Steve Chien
Related Approaches
The GRT Planner: New Results ..................................... 120
Ioannis Refanidis, Ioannis Vlahavas
Incremental Local Search for Planning Problems ....................... 139
Eva Onaindia, Laura Sebastia, Eliseo Marzal
Map Drawing Based on a Resource-Constrained Search
for a Navigation System ............................................ 158
Hironori Hiraishi, Fumio Mizoguchi
Author Index ................................................. 171
Meta-heuristics: The State of the Art
Stefan Voß
Technische Universit¨at Braunschweig
Institut fu¨r Wirtschaftswissenschaften
Abt-Jerusalem-Straße 7
D-38106 Braunschweig, Germany
[email protected]
Abstract. Meta-heuristics support managers in decision-making with
robust tools that provide high-quality solutions to important applica-
tions in business, engineering, economics and science in reasonable time
horizons. In this paper we give some insight into the state of the art of
meta-heuristics.Thisprimarilyfocusesonthesignificantprogresswhich
general frames within the meta-heuristics field have implied for solv-
ingcombinatorialoptimizationproblems,mainlythoseforplanningand
scheduling.
1 Introduction
Many decision problems in business and economics, notably including those in
manufacturing, location, routing, and scheduling may be formulated as opti-
mization problems. Typically these problems are too difficult to be solved ex-
actly within a reasonable amount of time and heuristics become the methods of
choice.Incaseswheresimplyobtainingafeasiblesolutionisnotsatisfactory,but
where the quality of solution is critical, it becomes important to investigate effi-
cient procedures to obtain the best possible solutions within time limits deemed
practical.
Due to the complexity of many of these optimization problems, particularly
thoseoflargesizesencounteredinmostpracticalsettings,exactalgorithmsoften
perform very poorly (in some cases taking days or more to find moderately
decent, let alone optimal, solutions even to fairly small instances). As a result,
heuristic algorithms are conspicuously preferable in practical applications.
Among the most studied heuristics are those based on applying some sort
ofgreedinessorapplyingprioritybasedproceduresincluding,e.g.,insertionand
dispatching rules. As an extension of these, a large number of local search ap-
proacheshasbeendevelopedtoimprovegivenfeasiblesolutions.Themaindraw-
back of these approaches, their inability to continue the search upon becoming
trapped in a local optimum, leads to consideration of techniques for guiding
known heuristics to overcome local optimality. Following this theme, one may
investigate the application of intelligent search methods like the tabu search
meta-heuristic for solving optimization problems.
Inthispaperwesketchthestateoftheartofmeta-heuristicsmainlyfroman
operations research perspective, knowing that probably any attempt to be fully
A.Nareyek(Ed.):LocalSearchforPlanningandScheduling,LNAI2148,pp.1–23,2001.
(cid:1)c Springer-VerlagBerlinHeidelberg2001
