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Operations Research and Cyber Infrastructure John W. Chinneck

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Operations Research and Cyber Infrastructure John W. Chinneck Digital Instant Download Author(s): John W. Chinneck, Bjarni Kristjansson, MatthewJ. Saltzman ISBN(s): 9780387888422, 038788842X Edition: Kindle File Details: PDF, 9.70 MB Year: 2009 Language: english
Operations Research and Cyber-Infrastructure
OPERATIONS RESEARCH/COMPUTER SCIENCE INTERFACES Professor Ramesh Sharda Prof. Dr. Stefan Voß Oklahoma State University Universität Hamburg Bierwirth / Adaptive Search and the Management of Logistics Systems Laguna & González-Velarde / Computing Tools for Modeling, Optimization and Simulation Stilman / Linguistic Geometry: From Search to Construction Sakawa / Genetic Algorithms and Fuzzy Multiobjective Optimization Ribeiro & Hansen / Essays and Surveys in Metaheuristics Holsapple, Jacob & Rao / Business Modelling: Multidisciplinary Approaches — Economics, Operational and Information Systems Perspectives Sleezer, Wentling & Cude / Human Resource Development and Information Technology: Making Global Connections Voß & Woodruff / Optimization Software Class Libraries Upadhyaya et al / Mobile Computing: Implementing Pervasive Information and Communications Technologies Reeves & Rowe / Genetic Algorithms — Principles and Perspectives: A Guide to GA Theory Bhargava & Ye / Computational Modeling and Problem Solving In the Networked World: Interfaces in Computer Science & Operations Research Woodruff / Network Interdiction and Stochastic Integer Programming Anandalingam & Raghavan / Telecommunications Network Design and Management Laguna & Martí / Scatter Search: Methodology and Implementations in C Gosavi / Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning Koutsoukis & Mitra / Decision Modelling and Information Systems: The Information Value Chain Milano / Constraint and Integer Programming: Toward a Unified Methodology Wilson & Nuzzolo / Schedule-Based Dynamic Transit Modeling: Theory and Applications Golden, Raghavan & Wasil / The Next Wave in Computing, Optimization, and Decision Technologies Rego & Alidaee / Metaheuristics Optimization via Memory and Evolution: Tabu Search and Scatter Search Kitamura & Kuwahara / Simulation Approaches in Transportation Analysis: Recent Advances and Challenges Ibaraki, Nonobe & Yagiura / Metaheuristics: Progress as Real Problem Solvers Golumbic & Hartman / Graph Theory, Combinatorics, and Algorithms: Interdisciplinary Applications Raghavan & Anandalingam / Telecommunications Planning: Innovations in Pricing, Network Design and Management Mattfeld / The Management of Transshipment Terminals: Decision Support for Terminal Operations in Finished Vehicle Supply Chains Alba & Martí / Metaheuristic Procedures for Training Neural Networks Alt, Fu & Golden / Perspectives in Operations Research: Papers in Honor of Saul Gass’ 80thBirthday Baker et al / Extending the Horizons: Adv. In Computing, Optimization, and Dec. Technologies Zeimpekis et al / Dynamic Fleet Management: Concepts, Systems, Algorithms & Case Studies Doerner et al / Metaheuristics: Progress in Complex Systems Optimization Goel / Fleet Telematics: Real-time Management & Planning of Commercial Vehicle Operations Gondran & Minoux / Graphs, Dioïds and Semirings: New Models and Algorithms Alba & Dorronsoro / Cellular Genetic Algorithms Golden, Raghavan & Wasil / The Vehicle Routing Problem: Latest Advances and New Challenges Raghavan, Golden & Wasil / Telecommunications Modeling, Policy and Technology Battiti, Brunato & Mascia / Reactive Search and Intelligent Optimization Wilson & Nuzzolo / Schedule-Based Modeling of Transportation Networks
John W. Chinneck • Bjarni Kristjansson Matthew J. Saltzman Editors Operations Research and Cyber-Infrastructure ABC
John W. Chinneck Carleton University Dept. Systems & Computer Engineering Ottawa ON K1S 5B6 Canada chinneck@sce.carleton.ca Bjarni Kristjansson Maximal Software, Inc. 2111 Wilson Blvd. Arlington VA 22201 USA bjarni@maximalsoftware.com Dr. Matthew J. Saltzman Clemson University Dept. of Mathematical Sciences College of Engineering and Science Clemson SC 29634 Box 340975 USA mjs@clemson.edu ISSN: 1387-666X ISBN: 978-0-387-88842-2 e-ISBN: 978-0-387-88843-9 DOI: 10.1007/978-0-387-88843-9 Library of Congress Control Number: 2008937988 c ° Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com Editors
Dedicated to the memory of Chester MacRae “Mac” Chinneck, 1926–2008. He was the kind of father everyone hopes for, but few are lucky enough to have.
Preface This book is the companion volume to the Eleventh INFORMS Computing Society Conference (ICS 2009), held in Charleston, South Carolina, from January 11 to 13, 2009. It includes 24 high-quality refereed research papers. The focus of interest for ICS is the interface between Operations Research and Computer Science, and the papers in this volume reflect that interest. This is nat- urally an evolving area as computational power increases rapidly while decreasing in cost even more quickly. The papers included here illustrate the wide range of topics at this interface. For convenience, they are grouped in broad categories and subcategories. There are three papers on modeling, reflecting the impact of recent development in computing on that area. Eight papers are on optimization (three on integer programming, two on heuristics, and three on general topics, of which two involve stochastic/probabilistic processes). Finally, there are thirteen papers on ap- plications (three on the conference theme of cyber-infrastructure, four on routing, and six on other interesting topics). Several of the papers could be classified in more than one way, reflecting the interactions between these topic areas. We thank the members of the program committee (listed below), local arrange- ments co-coordinator Chris Starr (College of Charleston) and the many authors, referees, and stream organizers who contributed their time and effort. JOHN CHINNECK, BJARNI KRISTJANSSON, AND MATTHEW SALTZMAN Program Committee: Edward Baker, University of Miami John Chinneck, Carleton University Robert Fourer, Northwestern University Bruce Golden, University of Maryland Lou Hafer, Simon Fraser University William Hart, Sandia National Laboratories Allen Holder, Rose-Hulman Institute of Technology Bjarni Kristjansson, Maximal Software Manuel Laguna, University of Colorado at Boulder Laurent Michel, University of Connecticut vii
viii Preface Warren Powell, Princeton University S. Raghu Raghavan, University of Maryland Ted Ralphs, Lehigh University Matthew Saltzman, Clemson University Douglas Shier, Clemson University Cole Smith, University of Florida Michael Trick, Carnegie Mellon University Pascal Van Hentenryck, Brown University Edward Wasil, American University David Woodruff, University of California, Davis
Contents Part 1 Modeling Python Optimization Modeling Objects (Pyomo) . . . . . . . . . . . . . . . . . . . . . 3 W.E. Hart Object Oriented Modeling of Multistage Stochastic Linear Programs . . . . 21 L. Lopes and R. Fourer AEON: Synthesizing Scheduling Algorithms from High-Level Models . . . . 43 J.-N. Monette, Y. Deville, and P.V. Hentenryck Part 2 Optimization Part 2.1 Integer Programming A Branch-and-cut Algorithm for Integer Bilevel Linear Programs . . . . . . 65 S.T. DeNegre and T.K. Ralphs A Principled Approach to Mixed Integer/Linear Problem Formulation . . 79 J.N. Hooker Experiments with Branching using General Disjunctions . . . . . . . . . . . . . . 101 A. Mahajan and T.K. Ralphs Part 2.2 Heuristics and Metaheuristics A Weight Annealing Algorithm for Solving Two-dimensional Bin Packing Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 K.-H. Loh, B. Golden, and E. Wasil Solving the Maximum Cardinality Bin Packing Problem with a Weight Annealing-Based Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 K.-H. Loh, B. Golden, and E. Wasil ix
x Contents Part 2.3 Miscellaneous Combinatorial Design of a Stochastic Markov Decision Process . . . . . . . . . 167 N.B. Dimitrov and D.P. Morton A Primal-Dual Slack Approach to Warmstarting Interior-Point Methods for Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 A. Engau, M.F. Anjos, and A. Vannelli Linear Dynamic Programming and the Training of Sequence Estimators . 219 C. Raphael and E. Nichols Part 3 Applications Part 3.1 Cyberinfrastructure Approximate Algorithms for Maximizing the Capacity of the Reverse Link in Multiple–Class CDMA Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 A. Abadpour and A.S. Alfa Mathematical Formulations and Metaheuristics Comparison for the Push-Tree Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 M. Caserta, A. Fink, A. Raiconi, S. Schwarze, and S. Voß Two-Edge Disjoint Survivable Network Design Problem with Relays . . . . 279 A. Konak, S. Kulturel-Konak, and A.E. Smith Part 3.2 Routing Generating Random Test Networks for Shortest Path Algorithms . . . . . . . 295 D.J. Adams-Smith and D.R. Shier A Branch-and-Price Algorithm for Combined Location and Routing Problems Under Capacity Restrictions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Z. Akca, R.T. Berger, and T.K. Ralphs Using Oriented Random Search to Provide a Set of Alternative Solutions to the Capacitated Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . 331 A.A. Juan, J. Faulin, R. Ruiz, B. Barrios, M. Gilibert, and X. Vilajosana Optimizing Paths in the Presence of Spherical Impediments . . . . . . . . . . . . 347 M. Verhoeven and D.L. Woodruff Part 3.3 Miscellaneous Tailoring Classifier Hyperplanes to General Metrics . . . . . . . . . . . . . . . . . . 365 J.W. Chinneck
Contents xi The Multi-Sensor Nuclear Threat Detection Problem . . . . . . . . . . . . . . . . . 389 D.S. Hochbaum Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 R. Acosta, W. Brick, A. Hanna, A. Holder, D. Lara, G. McQuilen, D. Nevin, P. Uhlig, and B. Salter Integrated Forecasting and Inventory Control for Seasonal Demand . . . . . 427 G. Metan and A. Thiele A Provably Good Global Routing Algorithm in Multilayer IC and MCM Layout Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 M. Saad, T. Terlaky, A. Vannelli, and H. Zhang Eliminating Poisson’s Spot with Linear Programming . . . . . . . . . . . . . . . . . 455 R.J. Vanderbei Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
Python Optimization Modeling Objects (Pyomo) William E. Hart Abstract We describe Pyomo, an open-source tool for modeling optimization appli- cations in Python. Pyomo can be used to define abstract problems, create concrete problem instances, and solve these instances with standard solvers. Pyomo provides a capability that is commonly associated with algebraic modeling languages like AMPL and GAMS. Pyomo leverages the capabilities of the Coopr software, which integrates Python packages for defining optimizers, modeling optimization applica- tions, and managing computational experiments. Key words: Python, Modeling language, Optimization, Open Source Software 1 Introduction Although high quality optimization solvers are commonly available, the effective integration of these tools with an application model is often a challenge for many users. Optimization solvers are typically written in low-level languages like Fortran or C/C++ because these languages offer the performance needed to solve large nu- merical problems. However, direct development of applications in these languages is quite challenging. Low-level languages like these can be difficult to program; they have complex syntax, enforce static typing, and require a compiler for development. There are several ways that optimization technologies can be more effectively integrated with application models. For restricted problem domains, optimizers can be directly interfaced with application modeling tools. For example, modern spread- sheets like Excel integrate optimizers that can be applied to linear programming and simple nonlinear programming problems in a natural way. Similarly, engineering design frameworks like the Dakota toolkit (Eldred et al, 2006) can apply optimizers William E. Hart Sandia National Laboratories, Discrete Math and Complex Systems Department, PO Box 5800, Albuquerque, NM 87185 e-mail: wehart@sandia.gov J.W. Chinneck et al. (eds.), Operations Research and Cyber-Infrastructure, Operations 3 Research/Computer Science Interfaces Series 47, DOI: 10.1007/978-0-387-88843-9 1, c Springer Science+Business Media, LLC 2009
4 W.E. Hart to nonlinear programming problems by executing separate application codes via a system call interface that use standardized file I/O. Algebraic Modeling Languages (AMLs) are alternative approach that allows applications to be interfaced with optimizers that can exploit problem structure. AMLs are high-level programming languages for describing and solving mathemat- ical problems, particularly optimization-related problems (Kallrath, 2004). AMLs like AIMMS (AIMMS, 2008), AMPL (AMPL, 2008; Fourer et al, 2003) and GAMS (GAMS, 2008) have programming languages with an intuitive mathemati- cal syntax that supports concepts like sparse sets, indices, and algebraic expressions. AMLs provide a mechanism for defining variables and generating constraints with a concise mathematical representation, which is essential for large-scale, real-world problems that involve thousands of constraints and variables. A related strategy is to use a standard programming language in conjunction with a software library that uses object-oriented design to support similar math- ematical concepts. Although these modeling libraries sacrifice some of the intu- itive mathematical syntax of an AML, they allow the user to leverage the greater flexibility of standard programming languages. For example, modeling tools like FlopC++ (FLOPC++, 2008), OPL (OPL, 2008) and OptimJ (OptimJ, 2008) enable the solution of large, complex problems with application models defined within a standard programming language. The Python Optimization Modeling Objects (Pyomo) package described in this paper represents a fourth strategy, where a high level programming language is used to formulate a problem that can be solved by optimizers written in low-level languages. This two-language approach leverages the flexibility of the high-level language for formulating optimization problems and the efficiency of the low- level language for numerical computations. This approach is increasingly common in scientific computing tools, and the Matlab TOMLAB Optimization Environ- ment (TOMLAB, 2008) is probably the most mature optimization software using this approach. Pyomo supports the definition and solution of optimization applications using the Python scripting language. Python is a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo was strongly influenced by the design of AMPL. It includes Python classes that can concisely represent mixed-integer linear programming (MILP) models. Pyomo is interated into Coopr, a COmmon Optimization Python Repository. The Coopr Opt package supports the execution of models developed with Pyomo using standard MILP solvers. Section 2 describes the motivation and design philosophy behind Pyomo, includ- ing why Python was chosen for the design of Pyomo. Section 3 describes Pyomo and contrasts Pyomo with AMPL. Section 4 reviews other Python optimization pack- ages that have been developed, and discusses the high-level design decisions that distinguish Coopr. Section 5 describes the Coopr Opt package and contrasts its ca- pabilities with other Python optimization tools. Finally, Section 6 describes future Coopr developments that are planned.
Python Optimization Modeling Objects (Pyomo) 5 2 Pyomo Motivation and Design Philosophy The design of Pyomo is motivated by a variety of factors that have impacted ap- plications at Sandia National Laboratories. Sandia’s discrete mathematics group has successfully used AMPL to model and solve large-scale integer programs for many years. This application experience has highlighted the value of AMLs for real- world applications, which are now an integral part of operations research solutions at Sandia. Pyomo was developed to provide an alternative platform for developing math programming models that facilitates the application and deployment of optimiza- tion capabilities. Consequently, Pyomo is not intended to perform modeling better than existing tools. Instead, it supports a different modeling approach for which the software is designed for flexibility, extensibility, portability, and maintainability. 2.1 Design Goals and Requirements 2.1.1 Open Source A key goal of Pyomo is to provide an open-source math programming modeling capability. Although open-source optimization solvers are widely available in pack- ages like COIN-OR, surprisingly few open-source tools have been developed to model optimization applications. An open-source capability for Pyomo is motivated by several factors: • Transparency and Reliability: When managed well, open-source projects facil- itate transparency in the software design and implementation. Since any devel- oper can study and modify the software, bugs and performance limitations can be identified and resolved by a wide range of developers with diverse software experience. Consequently, there is growing evidence that managing software as open-source can improve its reliability. • Customizable Capability: A key limitation of commercial modeling tools is the ability to customize the modeling or optimization process. An open-source project allows a diverse range of developers to prototype new capabilities. These extensions can customize the software for specific applications, and they can motivate capabilites that are integrated into future software releases. • Flexible Licensing: A variety of significant operations research applications at Sandia National Laboratories have required the use of a modeling tool with a non-commercial license. Open-source license facilitate the free distribution of Pyomo within other open-source projects. Of course, the use of an open-source model is not a panacea. Ensuring high reliabil- ity of the software requires careful software management and a commited developer community. However, flexible licensing appears to be a distinct feature of open- source software. The Coopr software, which contains Pyomo, is licensed under the BSD.
6 W.E. Hart 2.1.2 Flexible Modeling Language Another goal of Pyomo is to directly use a modern programming language to sup- port the definition of math programming models. In this manner, Pyomo is similar to tools like FlopC++ and OptimJ, which support modeling in C++ and Java respec- tively. The use of an existing programming language has several advantages: • Extensibility and Robustness: A well-used modern programming language provides a robust foundation for developing and applying models, because the language has been well-tested in a wide variety of contexts. Further, extensions typically do not require changes to the language but instead involve additional classes and modeling routines that can be used in the modeling process. Thus, support of the modeling language is not a long-term factor when managing the software. • Documentation: Modern programming languages are typically well- documented, and there is often a large on-line community to provide feedback to new users. • Standard Libraries: Languages like Java and Python have a rich set of libraries for tackling just about every programming task. For example, standard libraries can support capabilities like data integration (e.g. working with spreadsheets), thereby avoiding the need to directly support this in a modeling tool. An additional aspect of general-purpose programming languages is that they can support modern language features, like classes and first-class functions, that can be critical when defining complex models. Pyomo is implemented in Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. When com- pared with AMLs like AMPL, Pyomo has a more verbose and complex syntax. Thus, a key issue with this approach concerns the target user community and their level of comfort with standard programming concepts. Our examples in this paper compare and contrast AMPL and Pyomo models, which illustrate this trade-off. 2.1.3 Portability A requirement of Pyomo’s design is that it work on a diverse range of compute platforms. In particular, working well on both MS Windows and Linux platforms is a key requirement for many Sandia applications. The main impact of this re- quirement has been to limit the choice of programming languages. For example, the .Net languages were not considered for the design of Pyomo due to portability considerations. 2.1.4 Solver Integration Modeling tools can be roughly categorized into two classes based on how they in- tegrate with optimization solvers: tightly coupled modeling tools directly link in
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22. Lord Morpeth speaks here of the saving of life in large towns only. 23. In the House of Lords. 24. Epidemics considered with relation to their Common Nature and to Climate and Civilisation. Published by Edmonston Douglas, Edinburgh, 1856. 25. See Appendix II., p. 164. Transcriber's Notes: Missing or obscured punctuation was corrected. Typographical errors were silently corrected.
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Operations Research and Cyber Infrastructure John W. Chinneck

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    Download the fullversion and explore a variety of ebooks or textbooks at https://ebookultra.com Operations Research and Cyber Infrastructure John W. Chinneck _____ Follow the link below to get your download now _____ https://ebookultra.com/download/operations-research-and- cyber-infrastructure-john-w-chinneck/ Access ebookultra.com now to download high-quality ebooks or textbooks
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    Operations research 2ed.Edition P. Rama Murthy https://ebookultra.com/download/operations-research-2ed-edition-p- rama-murthy/ Operations Research 1st Edition Vasant Lakshman Mote And T. Madhavan https://ebookultra.com/download/operations-research-1st-edition- vasant-lakshman-mote-and-t-madhavan/ Operations Research Center at MIT Ingrid Yoerg Larson https://ebookultra.com/download/operations-research-center-at-mit- ingrid-yoerg-larson/ Operations Research Applications 1st Edition A. Ravi Ravindran https://ebookultra.com/download/operations-research-applications-1st- edition-a-ravi-ravindran/ Excel Models for Business and Operations Management 2nd Edition John Barlow https://ebookultra.com/download/excel-models-for-business-and- operations-management-2nd-edition-john-barlow/
  • 5.
    Operations Research andCyber Infrastructure John W. Chinneck Digital Instant Download Author(s): John W. Chinneck, Bjarni Kristjansson, MatthewJ. Saltzman ISBN(s): 9780387888422, 038788842X Edition: Kindle File Details: PDF, 9.70 MB Year: 2009 Language: english
  • 7.
    Operations Research andCyber-Infrastructure
  • 8.
    OPERATIONS RESEARCH/COMPUTER SCIENCEINTERFACES Professor Ramesh Sharda Prof. Dr. Stefan Voß Oklahoma State University Universität Hamburg Bierwirth / Adaptive Search and the Management of Logistics Systems Laguna & González-Velarde / Computing Tools for Modeling, Optimization and Simulation Stilman / Linguistic Geometry: From Search to Construction Sakawa / Genetic Algorithms and Fuzzy Multiobjective Optimization Ribeiro & Hansen / Essays and Surveys in Metaheuristics Holsapple, Jacob & Rao / Business Modelling: Multidisciplinary Approaches — Economics, Operational and Information Systems Perspectives Sleezer, Wentling & Cude / Human Resource Development and Information Technology: Making Global Connections Voß & Woodruff / Optimization Software Class Libraries Upadhyaya et al / Mobile Computing: Implementing Pervasive Information and Communications Technologies Reeves & Rowe / Genetic Algorithms — Principles and Perspectives: A Guide to GA Theory Bhargava & Ye / Computational Modeling and Problem Solving In the Networked World: Interfaces in Computer Science & Operations Research Woodruff / Network Interdiction and Stochastic Integer Programming Anandalingam & Raghavan / Telecommunications Network Design and Management Laguna & Martí / Scatter Search: Methodology and Implementations in C Gosavi / Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning Koutsoukis & Mitra / Decision Modelling and Information Systems: The Information Value Chain Milano / Constraint and Integer Programming: Toward a Unified Methodology Wilson & Nuzzolo / Schedule-Based Dynamic Transit Modeling: Theory and Applications Golden, Raghavan & Wasil / The Next Wave in Computing, Optimization, and Decision Technologies Rego & Alidaee / Metaheuristics Optimization via Memory and Evolution: Tabu Search and Scatter Search Kitamura & Kuwahara / Simulation Approaches in Transportation Analysis: Recent Advances and Challenges Ibaraki, Nonobe & Yagiura / Metaheuristics: Progress as Real Problem Solvers Golumbic & Hartman / Graph Theory, Combinatorics, and Algorithms: Interdisciplinary Applications Raghavan & Anandalingam / Telecommunications Planning: Innovations in Pricing, Network Design and Management Mattfeld / The Management of Transshipment Terminals: Decision Support for Terminal Operations in Finished Vehicle Supply Chains Alba & Martí / Metaheuristic Procedures for Training Neural Networks Alt, Fu & Golden / Perspectives in Operations Research: Papers in Honor of Saul Gass’ 80thBirthday Baker et al / Extending the Horizons: Adv. In Computing, Optimization, and Dec. Technologies Zeimpekis et al / Dynamic Fleet Management: Concepts, Systems, Algorithms & Case Studies Doerner et al / Metaheuristics: Progress in Complex Systems Optimization Goel / Fleet Telematics: Real-time Management & Planning of Commercial Vehicle Operations Gondran & Minoux / Graphs, Dioïds and Semirings: New Models and Algorithms Alba & Dorronsoro / Cellular Genetic Algorithms Golden, Raghavan & Wasil / The Vehicle Routing Problem: Latest Advances and New Challenges Raghavan, Golden & Wasil / Telecommunications Modeling, Policy and Technology Battiti, Brunato & Mascia / Reactive Search and Intelligent Optimization Wilson & Nuzzolo / Schedule-Based Modeling of Transportation Networks
  • 9.
    John W. Chinneck• Bjarni Kristjansson Matthew J. Saltzman Editors Operations Research and Cyber-Infrastructure ABC
  • 10.
    John W. Chinneck CarletonUniversity Dept. Systems & Computer Engineering Ottawa ON K1S 5B6 Canada chinneck@sce.carleton.ca Bjarni Kristjansson Maximal Software, Inc. 2111 Wilson Blvd. Arlington VA 22201 USA bjarni@maximalsoftware.com Dr. Matthew J. Saltzman Clemson University Dept. of Mathematical Sciences College of Engineering and Science Clemson SC 29634 Box 340975 USA mjs@clemson.edu ISSN: 1387-666X ISBN: 978-0-387-88842-2 e-ISBN: 978-0-387-88843-9 DOI: 10.1007/978-0-387-88843-9 Library of Congress Control Number: 2008937988 c ° Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com Editors
  • 11.
    Dedicated to thememory of Chester MacRae “Mac” Chinneck, 1926–2008. He was the kind of father everyone hopes for, but few are lucky enough to have.
  • 12.
    Preface This book isthe companion volume to the Eleventh INFORMS Computing Society Conference (ICS 2009), held in Charleston, South Carolina, from January 11 to 13, 2009. It includes 24 high-quality refereed research papers. The focus of interest for ICS is the interface between Operations Research and Computer Science, and the papers in this volume reflect that interest. This is nat- urally an evolving area as computational power increases rapidly while decreasing in cost even more quickly. The papers included here illustrate the wide range of topics at this interface. For convenience, they are grouped in broad categories and subcategories. There are three papers on modeling, reflecting the impact of recent development in computing on that area. Eight papers are on optimization (three on integer programming, two on heuristics, and three on general topics, of which two involve stochastic/probabilistic processes). Finally, there are thirteen papers on ap- plications (three on the conference theme of cyber-infrastructure, four on routing, and six on other interesting topics). Several of the papers could be classified in more than one way, reflecting the interactions between these topic areas. We thank the members of the program committee (listed below), local arrange- ments co-coordinator Chris Starr (College of Charleston) and the many authors, referees, and stream organizers who contributed their time and effort. JOHN CHINNECK, BJARNI KRISTJANSSON, AND MATTHEW SALTZMAN Program Committee: Edward Baker, University of Miami John Chinneck, Carleton University Robert Fourer, Northwestern University Bruce Golden, University of Maryland Lou Hafer, Simon Fraser University William Hart, Sandia National Laboratories Allen Holder, Rose-Hulman Institute of Technology Bjarni Kristjansson, Maximal Software Manuel Laguna, University of Colorado at Boulder Laurent Michel, University of Connecticut vii
  • 13.
    viii Preface Warren Powell,Princeton University S. Raghu Raghavan, University of Maryland Ted Ralphs, Lehigh University Matthew Saltzman, Clemson University Douglas Shier, Clemson University Cole Smith, University of Florida Michael Trick, Carnegie Mellon University Pascal Van Hentenryck, Brown University Edward Wasil, American University David Woodruff, University of California, Davis
  • 14.
    Contents Part 1 Modeling PythonOptimization Modeling Objects (Pyomo) . . . . . . . . . . . . . . . . . . . . . 3 W.E. Hart Object Oriented Modeling of Multistage Stochastic Linear Programs . . . . 21 L. Lopes and R. Fourer AEON: Synthesizing Scheduling Algorithms from High-Level Models . . . . 43 J.-N. Monette, Y. Deville, and P.V. Hentenryck Part 2 Optimization Part 2.1 Integer Programming A Branch-and-cut Algorithm for Integer Bilevel Linear Programs . . . . . . 65 S.T. DeNegre and T.K. Ralphs A Principled Approach to Mixed Integer/Linear Problem Formulation . . 79 J.N. Hooker Experiments with Branching using General Disjunctions . . . . . . . . . . . . . . 101 A. Mahajan and T.K. Ralphs Part 2.2 Heuristics and Metaheuristics A Weight Annealing Algorithm for Solving Two-dimensional Bin Packing Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 K.-H. Loh, B. Golden, and E. Wasil Solving the Maximum Cardinality Bin Packing Problem with a Weight Annealing-Based Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 K.-H. Loh, B. Golden, and E. Wasil ix
  • 15.
    x Contents Part 2.3Miscellaneous Combinatorial Design of a Stochastic Markov Decision Process . . . . . . . . . 167 N.B. Dimitrov and D.P. Morton A Primal-Dual Slack Approach to Warmstarting Interior-Point Methods for Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 A. Engau, M.F. Anjos, and A. Vannelli Linear Dynamic Programming and the Training of Sequence Estimators . 219 C. Raphael and E. Nichols Part 3 Applications Part 3.1 Cyberinfrastructure Approximate Algorithms for Maximizing the Capacity of the Reverse Link in Multiple–Class CDMA Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 A. Abadpour and A.S. Alfa Mathematical Formulations and Metaheuristics Comparison for the Push-Tree Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 M. Caserta, A. Fink, A. Raiconi, S. Schwarze, and S. Voß Two-Edge Disjoint Survivable Network Design Problem with Relays . . . . 279 A. Konak, S. Kulturel-Konak, and A.E. Smith Part 3.2 Routing Generating Random Test Networks for Shortest Path Algorithms . . . . . . . 295 D.J. Adams-Smith and D.R. Shier A Branch-and-Price Algorithm for Combined Location and Routing Problems Under Capacity Restrictions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Z. Akca, R.T. Berger, and T.K. Ralphs Using Oriented Random Search to Provide a Set of Alternative Solutions to the Capacitated Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . 331 A.A. Juan, J. Faulin, R. Ruiz, B. Barrios, M. Gilibert, and X. Vilajosana Optimizing Paths in the Presence of Spherical Impediments . . . . . . . . . . . . 347 M. Verhoeven and D.L. Woodruff Part 3.3 Miscellaneous Tailoring Classifier Hyperplanes to General Metrics . . . . . . . . . . . . . . . . . . 365 J.W. Chinneck
  • 16.
    Contents xi The Multi-SensorNuclear Threat Detection Problem . . . . . . . . . . . . . . . . . 389 D.S. Hochbaum Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 R. Acosta, W. Brick, A. Hanna, A. Holder, D. Lara, G. McQuilen, D. Nevin, P. Uhlig, and B. Salter Integrated Forecasting and Inventory Control for Seasonal Demand . . . . . 427 G. Metan and A. Thiele A Provably Good Global Routing Algorithm in Multilayer IC and MCM Layout Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 M. Saad, T. Terlaky, A. Vannelli, and H. Zhang Eliminating Poisson’s Spot with Linear Programming . . . . . . . . . . . . . . . . . 455 R.J. Vanderbei Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
  • 17.
    Python Optimization ModelingObjects (Pyomo) William E. Hart Abstract We describe Pyomo, an open-source tool for modeling optimization appli- cations in Python. Pyomo can be used to define abstract problems, create concrete problem instances, and solve these instances with standard solvers. Pyomo provides a capability that is commonly associated with algebraic modeling languages like AMPL and GAMS. Pyomo leverages the capabilities of the Coopr software, which integrates Python packages for defining optimizers, modeling optimization applica- tions, and managing computational experiments. Key words: Python, Modeling language, Optimization, Open Source Software 1 Introduction Although high quality optimization solvers are commonly available, the effective integration of these tools with an application model is often a challenge for many users. Optimization solvers are typically written in low-level languages like Fortran or C/C++ because these languages offer the performance needed to solve large nu- merical problems. However, direct development of applications in these languages is quite challenging. Low-level languages like these can be difficult to program; they have complex syntax, enforce static typing, and require a compiler for development. There are several ways that optimization technologies can be more effectively integrated with application models. For restricted problem domains, optimizers can be directly interfaced with application modeling tools. For example, modern spread- sheets like Excel integrate optimizers that can be applied to linear programming and simple nonlinear programming problems in a natural way. Similarly, engineering design frameworks like the Dakota toolkit (Eldred et al, 2006) can apply optimizers William E. Hart Sandia National Laboratories, Discrete Math and Complex Systems Department, PO Box 5800, Albuquerque, NM 87185 e-mail: wehart@sandia.gov J.W. Chinneck et al. (eds.), Operations Research and Cyber-Infrastructure, Operations 3 Research/Computer Science Interfaces Series 47, DOI: 10.1007/978-0-387-88843-9 1, c Springer Science+Business Media, LLC 2009
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    4 W.E. Hart tononlinear programming problems by executing separate application codes via a system call interface that use standardized file I/O. Algebraic Modeling Languages (AMLs) are alternative approach that allows applications to be interfaced with optimizers that can exploit problem structure. AMLs are high-level programming languages for describing and solving mathemat- ical problems, particularly optimization-related problems (Kallrath, 2004). AMLs like AIMMS (AIMMS, 2008), AMPL (AMPL, 2008; Fourer et al, 2003) and GAMS (GAMS, 2008) have programming languages with an intuitive mathemati- cal syntax that supports concepts like sparse sets, indices, and algebraic expressions. AMLs provide a mechanism for defining variables and generating constraints with a concise mathematical representation, which is essential for large-scale, real-world problems that involve thousands of constraints and variables. A related strategy is to use a standard programming language in conjunction with a software library that uses object-oriented design to support similar math- ematical concepts. Although these modeling libraries sacrifice some of the intu- itive mathematical syntax of an AML, they allow the user to leverage the greater flexibility of standard programming languages. For example, modeling tools like FlopC++ (FLOPC++, 2008), OPL (OPL, 2008) and OptimJ (OptimJ, 2008) enable the solution of large, complex problems with application models defined within a standard programming language. The Python Optimization Modeling Objects (Pyomo) package described in this paper represents a fourth strategy, where a high level programming language is used to formulate a problem that can be solved by optimizers written in low-level languages. This two-language approach leverages the flexibility of the high-level language for formulating optimization problems and the efficiency of the low- level language for numerical computations. This approach is increasingly common in scientific computing tools, and the Matlab TOMLAB Optimization Environ- ment (TOMLAB, 2008) is probably the most mature optimization software using this approach. Pyomo supports the definition and solution of optimization applications using the Python scripting language. Python is a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo was strongly influenced by the design of AMPL. It includes Python classes that can concisely represent mixed-integer linear programming (MILP) models. Pyomo is interated into Coopr, a COmmon Optimization Python Repository. The Coopr Opt package supports the execution of models developed with Pyomo using standard MILP solvers. Section 2 describes the motivation and design philosophy behind Pyomo, includ- ing why Python was chosen for the design of Pyomo. Section 3 describes Pyomo and contrasts Pyomo with AMPL. Section 4 reviews other Python optimization pack- ages that have been developed, and discusses the high-level design decisions that distinguish Coopr. Section 5 describes the Coopr Opt package and contrasts its ca- pabilities with other Python optimization tools. Finally, Section 6 describes future Coopr developments that are planned.
  • 19.
    Python Optimization ModelingObjects (Pyomo) 5 2 Pyomo Motivation and Design Philosophy The design of Pyomo is motivated by a variety of factors that have impacted ap- plications at Sandia National Laboratories. Sandia’s discrete mathematics group has successfully used AMPL to model and solve large-scale integer programs for many years. This application experience has highlighted the value of AMLs for real- world applications, which are now an integral part of operations research solutions at Sandia. Pyomo was developed to provide an alternative platform for developing math programming models that facilitates the application and deployment of optimiza- tion capabilities. Consequently, Pyomo is not intended to perform modeling better than existing tools. Instead, it supports a different modeling approach for which the software is designed for flexibility, extensibility, portability, and maintainability. 2.1 Design Goals and Requirements 2.1.1 Open Source A key goal of Pyomo is to provide an open-source math programming modeling capability. Although open-source optimization solvers are widely available in pack- ages like COIN-OR, surprisingly few open-source tools have been developed to model optimization applications. An open-source capability for Pyomo is motivated by several factors: • Transparency and Reliability: When managed well, open-source projects facil- itate transparency in the software design and implementation. Since any devel- oper can study and modify the software, bugs and performance limitations can be identified and resolved by a wide range of developers with diverse software experience. Consequently, there is growing evidence that managing software as open-source can improve its reliability. • Customizable Capability: A key limitation of commercial modeling tools is the ability to customize the modeling or optimization process. An open-source project allows a diverse range of developers to prototype new capabilities. These extensions can customize the software for specific applications, and they can motivate capabilites that are integrated into future software releases. • Flexible Licensing: A variety of significant operations research applications at Sandia National Laboratories have required the use of a modeling tool with a non-commercial license. Open-source license facilitate the free distribution of Pyomo within other open-source projects. Of course, the use of an open-source model is not a panacea. Ensuring high reliabil- ity of the software requires careful software management and a commited developer community. However, flexible licensing appears to be a distinct feature of open- source software. The Coopr software, which contains Pyomo, is licensed under the BSD.
  • 20.
    6 W.E. Hart 2.1.2Flexible Modeling Language Another goal of Pyomo is to directly use a modern programming language to sup- port the definition of math programming models. In this manner, Pyomo is similar to tools like FlopC++ and OptimJ, which support modeling in C++ and Java respec- tively. The use of an existing programming language has several advantages: • Extensibility and Robustness: A well-used modern programming language provides a robust foundation for developing and applying models, because the language has been well-tested in a wide variety of contexts. Further, extensions typically do not require changes to the language but instead involve additional classes and modeling routines that can be used in the modeling process. Thus, support of the modeling language is not a long-term factor when managing the software. • Documentation: Modern programming languages are typically well- documented, and there is often a large on-line community to provide feedback to new users. • Standard Libraries: Languages like Java and Python have a rich set of libraries for tackling just about every programming task. For example, standard libraries can support capabilities like data integration (e.g. working with spreadsheets), thereby avoiding the need to directly support this in a modeling tool. An additional aspect of general-purpose programming languages is that they can support modern language features, like classes and first-class functions, that can be critical when defining complex models. Pyomo is implemented in Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. When com- pared with AMLs like AMPL, Pyomo has a more verbose and complex syntax. Thus, a key issue with this approach concerns the target user community and their level of comfort with standard programming concepts. Our examples in this paper compare and contrast AMPL and Pyomo models, which illustrate this trade-off. 2.1.3 Portability A requirement of Pyomo’s design is that it work on a diverse range of compute platforms. In particular, working well on both MS Windows and Linux platforms is a key requirement for many Sandia applications. The main impact of this re- quirement has been to limit the choice of programming languages. For example, the .Net languages were not considered for the design of Pyomo due to portability considerations. 2.1.4 Solver Integration Modeling tools can be roughly categorized into two classes based on how they in- tegrate with optimization solvers: tightly coupled modeling tools directly link in
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    Exploring the Varietyof Random Documents with Different Content
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    22. Lord Morpethspeaks here of the saving of life in large towns only. 23. In the House of Lords. 24. Epidemics considered with relation to their Common Nature and to Climate and Civilisation. Published by Edmonston Douglas, Edinburgh, 1856. 25. See Appendix II., p. 164. Transcriber's Notes: Missing or obscured punctuation was corrected. Typographical errors were silently corrected.
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