Hybrid Algorithms for Service, Computing and Manufacturing Systems: Routing and Scheduling Solutions

354 Pages·2011·5.574 MB·English

by  Jairo R. Montoya-Torres, Jairo R. Montoya-Torres, Angel A. Juan, Luisa Huaccho Huatuco, Javier Faulin, Gloria L. Rodriguez-Verjan

Hybrid Algorithms for
Service, Computing and
Manufacturing Systems:
Routing and Scheduling
Solutions
Jairo R. Montoya-Torres
Universidad de La Sabana, Colombia
Angel A. Juan
Open University of Catalonia, Spain
Luisa Huaccho Huatuco
University of Leeds, UK
Javier Faulin
Public University of Navarre, Spain
Gloria L. Rodriguez-Verjan
Ecole Nationale Supérieure des Mines de Saint-Étienne, France
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Library of Congress Cataloging-in-Publication Data
Hybrid algorithms for service, computing and manufacturing systems: routing and scheduling solutions / Jairo R. Montoya-
Torres ... [et al.]
p. cm.
Includes bibliographical references and index.
Summary: “This book explores research developments and applications from an interdisciplinary perspective that combines
approaches from operations research, computer science, artificial intelligence, and applied computational mathematics”--
Provided by publisher.
ISBN 978-1-61350-086-6 (hardcover) -- ISBN 978-1-61350-087-3 (ebook) -- ISBN 978-1-61350-088-0 (print & perpetual
access) 1. Operations research. 2. Artificial intelligence. 3. Computer science--Mathematics. I. Montoya-Torres, Jairo R.,
1977-
T57.6.H93 2012
658.5’3--dc23
2011023571
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
Editorial Advisory Board
Ajith Abraham, Norwegian University of Science and Technology, Norway
Sana Belmokhtar, Research Center for Automatic Control of Nancy (CRAN), France
Gaston Cedillo, COMIMSA, Mexico
Stéphane Dauzère-Pérès, École nationale supérieure des mines de Saint-Étienne, France
Scott Grasman, Missouri University of Science and Technology, USA
M. Grazia Speranza, University of Brescia, Italy
Patrick Hirsch, University of Natural Resources and Applied Life Sciences, Austria
Carlos A. Méndez, INTEC, Argentina
Helena Ramalhinho Lourenço, Universidad Pompeu Fabra, Spain
Janet Smart, University of Oxford, UK
Andres Weintraub, Universidad de Chile, Chile
List of Reviewers
Edgar H. Alfonso, Ecole Nationale Supérieure des Mines de Saint-Étienne, France
Burcin Bozkaya, Sabanci University, Turkey
Tom Burgess, University of Leeds, UK
Juan Pablo Caballero, Pontificia Universidad Javeriana, Colombia
Ani Calinescu, University of Oxford, UK
Linda Castro, École Polytechnique de Montréal, Canada
Gaston Cedillo, COMIMSA, Mexico
Julio Mario Daza, Universidad Autónoma del Caribe, Colombia
Javier Faulin, Public University of Navarre, Spain
Andreas Fink, Helmut-Schmidt-University & UniBw Hamburg, Germany
Jorge Freire de Sousa, Universidade do Porto, Portugal
Alvaro Garcia, Universidad Politécnica de Madrid, Spain
Marco Gavanelli, Università di Ferrara, Italy
Alex Grasas, Universitat Pompeu Fabra, Spain
Scott Grasman, Missouri University of Science and Technology, USA
Edgar Gutierrez, Universidad de La Sabana, Colombia
Patrick Hirsch, University of Natural Resources and Applied Life Sciences, Austria
Luisa Huaccho Huatuco, University of Leeds, UK
Antonia Huertas, Open University of Catalonia, Spain
Angel Juan, Open University of Catalonia, Spain
Fabian López, Universidad Autónoma de Nuevo León (CEDEEM), México
Laura Lotero, Universidad Nacional de Colombia, Colombia
Helena R. Lourenço, Universitat Pompeu Fabra, Spain
Toni Mancini, Università di Roma La Sapienza, Italy
Vanessa Manotas, Universidad de La Sabana, Colombia
Alberto Márquez, University of Seville, Spain
Carlos A. Méndez, INTEC, Argentina
Jairo R. Montoya-Torres, Universidad de La Sabana, Colombia
Miguel Ortega Mier, Universidad Politécnica de Madrid, Spain
Pedro Palominos, University of Santiago of Chile, Chile
Daniel Riera, Open University of Catalonia, Spain
Gloria L. Rodríguez-Verjan, Ecole Nationale Supérieure des Mines de Saint-Étienne, France
Janet Smart, University of Oxford, UK
Elyn L. Solano-Charris, Universidad de La Sabana, Colombia
M. Grazia Speranza, University of Brescia, Italy
Gülfem Tuzkaya, Yildiz Technical University, Turkey
Eva Vallada, Universidad Politénica de Valencia, Spain
Mario Vélez, EAFIT University, Colombia
Claude Yugma, Ecole Nationale Supérieure des Mines de Saint-Étienne, France
Table of Contents
Foreword .............................................................................................................................................viii
Preface ...................................................................................................................................................xi
Acknowledgment ................................................................................................................................xvi
Section 1
Hybrid Algorithms for Routing Problems
Chapter 1
Matheuristics for Inventory Routing Problems .......................................................................................1
Luca Bertazzi, University of Brescia, Italy
M. Grazia Speranza, University of Brescia, Italy
Chapter 2
Vehicle Routing Models and Algorithms for Winter Road Spreading Operations ...............................15
Nathalie Perrier, École Polytechnique de Montréal, Canada
James F. Campbell, University of Missouri-St. Louis, USA
Michel Gendreau, École Polytechnique de Montréal, Canada
André Langevin, École Polytechnique de Montréal, Canada
Chapter 3
Routing Solutions for the Service Industry ...........................................................................................46
Burcin Bozkaya, Sabanci University, Turkey
Buyang Cao, Tongji University, China
Kaan Aktolug, Aktolug Consultancy Ltd., Turkey
Chapter 4
A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective
Vehicle Routing Problem with Time Windows .....................................................................................79
Gülfem Tuzkaya, Yildiz Technical University, Turkey
Bahadır Gülsün, Yildiz Technical University, Turkey
Ender Bildik, Yildiz Technical University, Turkey
E. Gözde Çağlar, Yildiz Technical University, Turkey
Chapter 5
Strategies for an Integrated Distribution Problem ................................................................................98
Helena R. Lourenço, Universitat Pompeu Fabra, Spain
Rita Ribeiro, Catholic University of Portugal (Porto), Portugal
Chapter 6
A Hybrid Algorithm Based on Monte-Carlo Simulation for the Vehicle Routing Problem with
Route Length Restrictions ...................................................................................................................122
Angel A. Juan, Open University of Catalonia, Spain
Javier Faulin, Public University of Navarre, Spain
Tolga Bektaş, University of Southampton, UK
Scott E. Grasman, Missouri University of Science and Technology, USA
Section 2
Hybrid Algorithms for Scheduling Problems
Chapter 7
A Hybrid Particle Swarm Algorithm for Resource-Constrained Project Scheduling .........................137
Jens Czogalla, Helmut-Schmidt-University Hamburg, Germany
Andreas Fink, Helmut-Schmidt-University Hamburg, Germany
Chapter 8
Marriage in Honeybee Optimization to Scheduling Problems ...........................................................158
Pedro Palominos, University of Santiago of Chile, Chile
Victor Parada, University of Santiago of Chile, Chile
Gustavo Gatica, University of Santiago of Chile, Chile
Andrés Véjar, Université de Nancy, France
Chapter 9
Global Bacteria Optimization Meta-Heuristic: Performance Analysis and Application
to Shop Scheduling Problems .............................................................................................................178
Elyn L. Solano-Charris, Universidad de La Sabana, Colombia
Libardo S. Gómez-Vizcaíno, Universidad Autónoma del Caribe, Colombia
Jairo R. Montoya-Torres, Universidad de La Sabana, Colombia
Carlos D. Paternina-Arboleda, Universidad del Norte, Colombia
Chapter 10
Hybrid Algorithms for Manufacturing Rescheduling: Customised vs. Commodity Production ........195
Luisa Huaccho Huatuco, University of Leeds, UK
Ani Calinescu, University of Oxford, UK
Section 3
Other Applications of Hybrid Algorithms
Chapter 11
HMIP Model for a Territory Design Problem with Capacity and Contiguity Constraints .................226
Fabian Lopez, Universidad Autónoma de Nuevo León (CEDEEM), México
Chapter 12
Hybrid Heuristics for the Territory Alignment Problem .....................................................................258
Jorge Freire de Sousa, Universidade do Porto, Portugal
José Barros-Basto, Universidade do Porto, Portugal
Paulo Lima Júnior, Universidade do Vale do São Francisco, Brazil
Chapter 13
A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice
Network Design Problems ..................................................................................................................294
Chi Xie, The University of Texas at Austin, USA
Mark A. Turnquist, Cornell University, USA
S. Travis Waller, The University of Texas at Austin, USA
About the Contributors ....................................................................................................................325
Index ...................................................................................................................................................333
viii
Foreword
Most fundamental problems in combinatorial optimization field have been proven to be computation-
ally hard to solve to optimality and are known as NP-hard problems in the literature. Knowing that a
problem of interest is NP-hard implies, on the one hand, that the problem is unlikely to be solved
within a reasonable amount of computation time and, on the other, that one has to be satisfied with solv-
ing the problem approximately or near-optimally.
An important class of algorithms that have shown their usefulness in solving many computationally
hard optimization problems is that of meta-heuristics. This is by no chance –meta-heuristics methods
possess many good features, among which we could distinguish: they are able to find high quality solu-
tions in a reasonable amount of computation time, are robust, generic, flexible and easy to implement on
sequential, parallel and networked computer systems. This, together with the fact that for most practical
applications in industry and businesses high quality solution would suffice, have converted meta-heuristics
into de facto approaches to cope in practice with the computationally hard optimization problems. In
fact, even when a polynomial time algorithm is known for a certain problem, solving large-size/real-life
instances (e.g. instances at enterprise scale) calls again for the application of meta-heuristics methods.
Not less importantly, meta-heuristic approaches can tackle with efficacy both single and multi-objective
optimization problems.
Meta-heuristics methods have been applied for decades now. Besides using them as stand alone ap-
proaches, during the last years, the attention of researchers has shifted to consider another type of high
level algorithms, namely hybrid algorithms. These algorithms do not follow any concrete meta-heuristic,
but rather combine meta-heuristics with meta-heuristics and/or other methods (e.g. divide-and-conquer,
linear programming, dynamic programming, constraint programming or other AI techniques) yielding
thus hybrid meta-heuristics. One fundamental question here is how can be achieved for hybrid approaches
to outperform stand alone approaches? The hybridization aims at exploring the synergies among stand
alone methods in order to achieve better results for the optimization problem under study. For instance,
using hybrid approaches one can explore the synergies between exploration of solution space (through
population based meta-heuristics, such as Genetic Algorithms—GAs) with the exploitation of the solu-
tion space (through local search methods, such as Tabu Search –TS); the GA could them be used as a
main search method while TS can improve locally the individuals of the population.
The rationale behind the hybridization resides in the ``no free lunch theorem” stating that ``... all
algorithms that search for an extremum of a cost function perform exactly the same, when averaged over
all possible cost functions. In particular, if algorithm A outperforms algorithm B on some cost functions,
then loosely speaking there must exist exactly as many other functions where B outperforms A.” Based
ix
on this theorem, existing algorithms can be used as components for designing new efficient search
algorithms and expect improved performance of the newly obtained algorithm for some cost functions.
Naturally, there are major issues in designing hybrid meta-heuristics for a given optimization problem,
such as: (a) how to choose heuristic and/or meta-heuristic methods to be combined (within the same
family or from different families of existing algorithms), and, (b) how to combine the chosen methods
into new hybrid approaches. Unfortunately, there are no theoretical foundations for these issues, yet there
are interesting evidences, experiences and reports on the literature. For the former, different classes of
search algorithms can be considered for the purposes of hybridization, such as exact methods, simple
deterministic or random heuristic methods and meta-heuristics. Moreover, meta-heuristics themselves
are classified into local search based methods, population based methods and other classes of nature
inspired meta-heuristics. Therefore, in principle, one could combine any methods from the same class
or methods from different classes. Regarding the later, there are some attempts for taxonomies of hybrid
meta-heuristics; in fact, the common approach is to try out in smart ways, based on domain knowledge
of problem at hand and characteristics of heuristics methods, different hybrid approaches and shed light
on the performance of the resulting hybrid approach. The level of hybridization here plays an important
role, namely the degree of coupling between the meta-heuristics (e.g. coercive vs. cooperative). It should
as well be noted that frameworks that facilitate fast prototyping have been also provided in the hybrid
meta-heuristics literature.
This book brings excellent contributions to the field of hybrid algorithms, their design, implemen-
tation and experimental evaluation. The proposed hybrid approaches tackle fundamental problems in
the domain of logistics, industry services, commercial distribution and manufacturing systems. The
studied problems include routing, different forms of scheduling, such as permutation scheduling and
shop scheduling problems, service allocation problems, etc. The proposed approaches include hybrid-
ization of meta-heuristic methods with other meta-heuristic methods such as Genetic Algorithms (GA)
and Simulated Annealing (SA) or the hybridization of meta-heuristics with the exact solution of one or
several mathematical programming models.
Besides advancing in the design of more sophisticated hybrid solution strategies for routing and
scheduling problems, the contributions of the book have a practical focus for solving real life problems.
The aim is to support decision processes in companies and thus to enable achieving better business ob-
jectives by solving the problems at company scale. Also, the use of benchmarks and software packages
are good examples of best practices in the field.
The editors of this volume bring together experts and researchers from the field whose contributions
explore new research findings, developments and future directions in the hybrid approaches for routing
and scheduling problems arising in service, computing and manufacturing systems. Finally, although
focused on the concrete field of the routing and scheduling for service, computing and manufacturing
systems, most of the conclusions provided in the volume could be extended to routing and scheduling
in other research fields.
Fatos Xhafa
Technical University of Catalonia, Spain