Publications
Publications in Refereed Journals
R. Mohamed, M. Avgeris, A. Leivadeas; I. Lambadaris, J.W. Chinneck, T. Morris, P. Djukic (2024), “Service Function Chain Network Planning through Offline, Online and Infeasibility Restoration Techniques”, Computer Networks 242, April, 110241. https://doi.org/10.1016/j.comnet.2024.110241
Develops a network planning tool for service and infrastructure providers to allocate resources to handle expected network demand. Finds an offline optimal solution, then corrects for mispredictions via online methods, and incorporates a novel feasibility restoration approach to suggest where to place needed additional resources.
J. Liang, A.U. Chaudhry, J.W. Chinneck, H. Yanikomeroglu, G.K. Kurt, P. Hu, K. Ahmed, S. Martel (2023), “Latency versus Transmission Power Trade-off in Free-Space Optical (FSO) Satellite Networks with Multiple Inter-Continental Connections”, IEEE Open Journal on the Communications Society, vol. 4, pp. 3014-3029, 2023. https://ieeexplore.ieee.org/document/10287103
Latency is a critical factor in laser communication between optical satellites, as is power consumption because the satellites depend on solar panels for power supply. How do you minimize the total network latency subject to power constraints? We formulate a mathematical model to do this for the latest version of the Starlink satellite constellation.
B. Singh, R. Kaur, C.M. Woodside, J.W. Chinneck (2023), "Low-Power Multi-Cloud Deployment of Large Distributed Service Applications with Response-time Constraints", Journal of Cloud Computing, vol. 12, no. 1, pp. 1-17. https://doi.org/10.1186/s13677-022-00363-w.
Distributed applications may be distributed across a cloud, or multiple clouds, and must meet service quality goals like response time, while satisfying cloud resource constraints. At the same time, the deployment should minimize power consumption. The response time constraint is tricky since communication times between tasks depend on the clouds they are assigned to. This paper describes an algorithm for finding a deployment of the tasks in the application to hosts in the clouds that uses little power and that is fast enough for practical use.
F.F. Firouzeh, J.W. Chinneck, S. Rajan (2022), “Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices”, Computers and Operations Research, vol. 136, pp. 105633. https://doi.org/10.1016/j.cor.2021.105633
There are a number of applications that (i) have dense data matrices, and (ii) are well solved by conversion to an instance of the maximum feasible subset problem. In this case, much faster algorithms are possible, without loss of accuracy. Examples studied include binary classification and sparse recovery in compressive sensing.
M. Raithatha, A.U. Chaudhry, R.H.M. Hafez, J.W. Chinneck (2021), "A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks", IEEE Access, vol. 9, pp. 43653-43674, https://doi.org/10.1109/ACCESS.2021.3062655.
There are many small cells in an ultra-dense 5G network. What is the optimum way to locate backhaul gateways and associate each small cell with one? We develop a fast heuristic to solve this problem.
F.F. Firouzeh, J.W. Chinneck, and S. Rajan (2020), "Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech", IEEE Access, vol. 8, pp. 82539-82550, 2020, doi:10.1109/ACCESS.2020.2990155. Preprint: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9078122
Maximum Feasible Subsystem algorithms enable high quality signal recovery from more highly compressed speech signals than competing alternatives during the recovery phase of compressive sensing.
J.W. Chinneck (2019), “The Maximum Feasible Subset Problem (maxFS) and Applications”, INFOR: Information Systems and Operational Research, https://doi.org/10.1080/03155986.2019.1607715.
A review of the maxFS problem and its many applications, including some new ones.
J. Li, C.M. Woodside, J.W. Chinneck, M. Litoiu (2017), "Adaptive Cloud Deployment using Persistence Strategies and Application Awareness", IEEE Transactions on Cloud Computing, vol. 5, no. 2, pp. 277-290 (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7064811).
We modify our earlier CloudOpt method for task deployment in clouds to handle dynamic changes in workload using penalties and rewards to encourage stable deployments and discourage thrashing.
A.U. Chaudhry, R.H.M. Hafez, J.W. Chinneck (2016), “Realistic Interference-Free Channel Assignment for Dynamic Wireless Mesh Networks Using Beamforming”, Ad Hoc Networks, vol. 51, pp. 21-35. Available online at http://www.sciencedirect.com/science/article/pii/S1570870516301937
This paper develops the first channel assignment method for wireless mesh networks that incorporates beamforming in the conflict graph and matrix. This reduces the number of channels required significantly.
A.U. Chaudhry, J.W. Chinneck, R.H.M. Hafez (2016), “Fast Heuristics for the Frequency Channel Assignment Problem in Multi-Hop Wireless Networks”, European Journal of Operational Research, vol. 251, pp. 771-782, DOI: http://dx.doi.org/10.1016/j.ejor.2015.12.016.
Channel assignment in wireless networks can be cast as a minimum colouring problem, but this does not account for cumulative interference. We develop fast algorithms for this extended colouring problem that are orders of magnitude faster than an exact solution method while consistently returning near-optimum results.
A.U. Chaudhry, R.H.M. Hafez, and J.W. Chinneck (2015), “On the Impact of Interference Models on Channel Assignment in Multi-Radio Multi-Channel Wireless Mesh Networks,” Ad Hoc Networks, vol. 27, pp. 68-80.
The interference model assumed in channel assignment significantly affects the results. More frequency channels are needed when the interference model is more realistic than commonly assumed.
W. Ibrahim, M. Shousha, J.W. Chinneck (2015), “Accurate and Efficient Estimation of Logic Circuits Reliability Bounds”, IEEE Transactions on Computers, vol. 64, no. 5, pp. 1217-1229.
We develop a method to estimate the reliability bound for large and complex circuits. The method is fast and highly accurate in finding the set of input values that produces the lowest reliability.
A.U. Chaudhry, J.W. Chinneck and R.H.M. Hafez (2013), “On the Number of Channels Required for Interference-free Wireless Mesh Networks”, EURASIP Journal on Wireless Communications and Networking 2013:229 (14 September 2013). Online at http://jwcn.eurasipjournals.com/content/2013/1/229.
We develop a method for assigning channels in a wireless mesh network so that throughput is maximized, there is no interference, and the number of distinct channels is small.
H. Mahmoud and J.W. Chinneck (2013), "Achieving MILP Feasibility Quickly Using General Disjunctions", Computers and Operations Research, vol. 40, no. 8, pp. 2094-2102. Preprint version. Appendix.
We show that oblique disjunctions that branch on linear combinations of variables can reduce the time to find the first integer-feasible solution in hard mixed-integer linear programs. We develop new ways to construct the general disjunctions and methods for deciding when they should be used.
L. Smith, J.W. Chinneck, and V. Aitken (2013), “Constraint Consensus Concentration for Identifying Disjoint Feasible Regions in Nonlinear Programs”, Optimization Methods and Software, vol. 28, no. 2, pp. 339–363. Preprint version.
There may be multiple disjoint feasible regions in a constrained nonlinear program. We improve on multi-start methods for global optimization by using Constraint Consensus to quickly explore the variable space to find the feasible regions before launching an expensive local solver. Our solutions are more efficient because we usually launch the local solver only once near each feasible region.
L. Smith, J.W. Chinneck, V. Aitken (2013), “Improved Constraint Consensus Methods for Seeking Feasibility in Nonlinear Programs”, Computational Optimization and Applications, vol. 54, no. 3, pp. 555-578. DOI: http://dx.doi.org/10.1007/s10589-012-9473-z. Preprint version.
We develop several improvements to the Constraint Consensus method for quickly finding near-feasible points in nonlinear programs: (i) a simple new way of constructing the consensus vector, (ii) a predictor-corrector method, (iii) a better way of selecting the output point, and (iv) better ways of selecting the subset of violated constraints to operate on at each iteration, which is especially effective when used in conjunction with barrier method local solvers. We also investigate quadratic feasibility vectors.
M. St-Hilaire, J.W. Chinneck, S. Chamberland, and S. Pierre (2012), “Efficient Solution of the 3G Network Planning Problem”, Computers and Industrial Engineering, vol. 63, no. 4, pp. 819–830. DOI: http://dx.doi.org/10.1016/j.cie.2012.05.004
We compare MIP and heuristic approaches for solving a difficult mobile telecommunications planning problem.
J.W. Chinneck (2012), "Integrated Classifier Hyperplane Placement and Feature Selection", Expert Systems with Applications, vol. 39, no. 9, pp. 8193-8203. Preprint version.
Feature reduction and binary classifier hyperplane placement are normally treated as two distinct and separate processes. This paper combines the two using algorithms for solving the maximum feasible subset problem for a set of linear constraints. The method alternates between making progress on placing the separating hyperplane and adding or removing features. The empirical results are very good.
J. Pryor and J.W. Chinneck (2011), “Faster Integer-Feasibility in Mixed-Integer Linear Programs by Branching to Force Change”, Computers and Operations Research, vol. 38, pp. 1143–1152. Preprint version.
Surprisingly, it turns out that if you want to reach a feasible solution for a MIP quickly, you should select branches that have the lowest probability of returning a feasible solution! This is because branching this way forces many other candidate variables to change their values simultaneously.
D.T. Wojtaszek and J.W. Chinneck (2010), “Faster MIP Solutions via New Node Selection Rules”, Computers and Operations Research, vol. 37, no. 9, pp. 1544-1556. Preprint version. DOI: http://dx.doi.org/10.1016/j.cor.2009.11.011 available online here.
New heuristics for use in mixed-integer linear programming to determine when to backtrack, and which node to choose when backtracking, based on correlations and patterns commonly found in MIP solutions. Significantly improved solution speeds are demonstrated.
W. Ibrahim and J.W. Chinneck (2008), "Improving Solver Success in Reaching Feasibility for Sets of Nonlinear Constraints", Computers and Operations Research, Vol. 35, pp. 1394-1411 (available online October 2, 2006 at www.sciencedirect.com). Preprint version in pdf.
An improved initial point placement heuristic, coupled with the Constraint Consensus algorithm for point improvement provides starting points that greatly increase the success of various nonlinear solvers in reaching feasibility.
J. Patel and J.W. Chinneck (2007), "Active-Constraint Variable Ordering for Faster Feasibility of Mixed Integer Linear Programs", Mathematical Programming Series A, vol. 110, pp. 445-474. (pdf)
You can reach feasibility for MIPs much faster by selecting the branching variable based on its impact on the active constraints in the parent LP relaxation, instead of using the traditional approach of selecting the branching variable based on its impact on the objective function.
J.W. Chinneck (2004), "The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs", INFORMS Journal on Computing, vol. 16, no. 3, pp. 255-265. (pdf)
A fast algorithm for finding approximately feasible points from initial points that are at extreme distances from the feasible region.
S. Chao, J.W. Chinneck, and R.A. Goubran (2004), "Assigning Service Requests in Voice-over-Internet Gateway Multiprocessors", Computers and Operations Research, vol. 31, no. 14, pp. 2419-2437. Article available through ScienceDirect.
An online bin-packing approach improves the efficiency of call processing significantly.
Walid Ibrahim, John W. Chinneck and Shalini Periyalwar (2003), "A QoS-based Charging and Resource Allocation Framework for Next Generation Wireless Networks", Wireless Communications and Mobile Computing, vol. 3, no. 7, pp. 895-906.
J.W. Chinneck, V. Pureza, R.A. Goubran, G.M. Karam, M. Lavoie (2003), "A Fast Task-to-Processor Assignment Heuristic for Real-Time Multiprocessor DSP Applications", Computers & Operations Research, vol. 30, no. 5, pp. 643-670. (Publishers Web page, full text available to subscribers).
J.W. Chinneck (2002), "Discovering the Characteristics of Mathematical Programs via Sampling", Optimization Methods and Software, vol. 17, no.2, pp. 319-352. (pdf of draft version)
Related to the MProbe project.
J.W. Chinneck (2001), "Analyzing Mathematical Programs using MProbe", Annals of Operations Research vol. 104, pp. 33-48. (pdf of draft version)
Download the latest version of MProbe from here.
J.W. Chinneck (2001), "Fast Heuristics for the Maximum Feasible Subsystem Problem", INFORMS Journal on Computing, vol. 13, no. 3, pp. 210-223. (pdf)
Useful in analyzing infeasible linear programs, but also for hyperplane placement in data classification (examples are given), etc.
J.W.Chinneck and K. Ramadan (2000), "Linear Programming with Interval Coefficients", Journal of the Operational Research Society, vol. 51, pp. 209-220.
If the coefficients of your linear program are only approximately known, then how can you solve the LP? We present a method for finding the best optimum and the worst optimum when the coefficients are only known within specified intervals.
O. Guieu and J.W. Chinneck (1999), "Analyzing Infeasible Mixed-Integer and Integer Linear Programs", INFORMS Journal on Computing, vol. 11, no. 1, pp. 63-77. (pdf)
P.R. Larijani, J.W. Chinneck, R.H. Hafez (1998), "Nonlinear Power Assignment in Multimedia CDMA Wireless Networks", IEEE Communications Letters, vol. 2, no. 9, pp. 251-253.
R. Awad and J.W. Chinneck (1998), "Proctor Assignment at Carleton University", Interfaces, vol 28, no. 2, pp. 58-71.
Describes a hybrid genetic algorithm to solve a complex problem of assigning final examination proctors. (pdf)
E.W. Dravnieks and J.W. Chinneck, (1997), "Formulation Assistance for Global Optimization Problems", Computers and Operations Research, vol 24, no.2, pp. 1151-1168.
J.W. Chinneck, (1997), "Finding a Useful Subset of Constraints for Analysis in an Infeasible Linear Program", INFORMS Journal on Computing , vol. 9, no. 2.
There are now several algorithms for isolating Irreducible Infeasible Subsets (IISs) of constraints in an infeasible LP. However, some combinations of algorithms find IISs that are easier for humans to interpret... (PDF: 948 kbytes)
J.W. Chinneck, (1996) "Localizing and Diagnosing Infeasibilities in Networks", ORSA Journal on Computing, Vol. 8, No. 1, pp. 55-62. (pdf)
J.W. Chinneck, (1996), "Computer Codes for the Analysis of Infeasible Linear Programs ", Journal of the Operational Research Society, Vol. 47, pp. 61-72.
Gives an overview of algorithms for analyzing infeasible LPs, and compares the effectiveness of different computer codes in analyzing infeasibility. Includes several commercial codes.
J.W. Chinneck, (1996), "An Effective Polynomial-Time Heuristic for the Minimum-Cardinality IIS Set-Covering Problem", Annals of Mathematics and Artificial Intelligence, vol. 17, pp. 127-144.
Despite the title, this paper is about finding the smallest number of constraints to remove from an infeasible LP such that the remaining set of constraints is feasible. This is the same as finding the maximum cardinality feasible subset of constraints from among the original infeasible set. The problem is NP-complete, but the paper describes a remarkably effective polynomial-time heuristic. This has some surprising applications, including a way to train neural networks, and to linearly classify data.
J.W. Chinneck and R.H.H. Moll, (1995). "Processing Network Models for Forest Management", OMEGA, vol. 23, no. 5, pp. 499-510.
J.W. Chinneck, (1995), "Processing Network Models of Energy/Environment Systems", Computers and Industrial Engineering, vol. 28, no. 1, pp. 179-189.
J.W. Chinneck, (1995), "Analyzing Infeasible Nonlinear Programs", Computational Optimization and Applications, vol. 4, no.2, pp. 167-179.
J.W. Chinneck, (1994), "MINOS(IIS): Infeasibility Analysis Using MINOS", Computers and Operations Research, Vol. 21, No. 1, pp. 1-9.
You can download the latest version of MINOS(IIS) from here.
M.W. Carter, G. Laporte, J.W. Chinneck (1994), "A General Examination Scheduling System", Interfaces, Vol. 24, No. 3, pp. 109-120. (PDF)
R.H.H. Moll, and J.W. Chinneck, (1992), "Modeling Regeneration and Pest Control Alternatives for a Forest System in the Presence of Fire Risk", Natural Resource Modelling, Vol. 6, No. 1, pp. 23-49.
J.W. Chinneck, (1992), "Viability Analysis: A Formulation Aid for All Classes of Network Models", Naval Research Logistics, Vol. 39, pp. 531-543.
Nonviable network models have a peculiar property: some arc flows are forced to zero, not by added flow bounds, but by the structure of the network model itself. This paper and its earlier companion describe ways to find and diagnose such problems.
J.W. Chinneck and E.W. Dravnieks, (1991), "Locating Minimal Infeasible Constraint Sets in Linear Programs", ORSA Journal on Computing, Vol. 3, No. 2, pp. 157-168.
My earliest and widely referenced paper on finding Irreducible Infeasible Subsets (IISs) of constraints in infeasible LPs. For a review of the current state of the art in infeasibility analysis, see the chapter on "Feasibility and Viability" (listed under Book Chapters, below). (pdf: 978 kbytes)
J.W. Chinneck, (1990), "Formulating Processing Networks: Viability Theory", Naval Research Logistics, Vol. 37, pp. 245-261.
J.W. Chinneck, (1988), "Industrial Application of a Second-Law Modelling Procedure", Trans. of the CSME, Vol.12, No.3, pp.153-157.
J.W. Chinneck and M. Chandrashekar, (1984), "Models of Large-Scale Industrial Energy Systems. Part II: Optimization and Synthesis", Energy - The International Journal, Vol.9, No.8, pp.679-692.
J.W. Chinneck and M. Chandrashekar, (1984), "Models of Large-Scale Industrial Energy Systems. Part I: Simulation", Energy - The International Journal, Vol.9, No.1, pp.21-34.
K.G.T. Hollands, J.W. Chinneck and M. Chandrashekar, (1979), "Collector and Storage Efficiencies in Solar Heating Systems", Solar Energy, Vol.23, pp.471-478.
Books
J.W. Chinneck, B. Kristjansson, and M. Saltzman (eds.) (2009), "Operations Research and Cyber-Infrastructure", Operations Research / Computer Science Interfaces, ISBN 978-0-387-88842-2, Springer Science+Business Media, LLC (2009). Springer. Amazon.com. Amazon.ca.
J.W. Chinneck (2008), "Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods", Vol. 118, International Series in Operations Research and Management Sciences, ISBN 978-0387749310, Springer.
Table of Contents. Publishers web page. Amazon.com. Amazon.ca. Review in the ICS Newsletter Spring 2008. Review Excerpt from MathDL.
J.W. Chinneck (2000), "Practical Optimization: a Gentle Introduction", online textbook. See https://www.optimization101.org/ .
Book Chapters
J.W.Chinneck (2021). "Harvey Greenberg: Analyzing Infeasible Mathematical Programs". In: Holder A. (ed) "Harvey J. Greenberg". International Series in Operations Research & Management Science, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-56429-2_4 or https://www.springer.com/us/book/9783030564285.
John W. Chinneck, Michel Nakhla, and Q.J. Zhang (2004). "Computer-Aided Design for Electrical and Computer Engineering" in H.J. Greenberg (ed.) Tutorials On Emerging Methodologies And Applications In Operations Research, pp. 6-1 to 6-44, Springer, New York.
An introduction to the many applications of operations research in computer-aided design of microelectronic devices, and a survey of techniques developed by the CAD community that are of interest to operations researchers.
J.W. Chinneck, (1997), "Feasibility and Viability" in Advances in Sensitivity Analysis and Parametric Programming, T. Gal and H.J. Greenberg (eds.), Kluwer Academic Publishers, International Series in Operations Research and Management Science, Vol. 6.
This article provides an overview of the state-of-the art in analyzing infeasible (and nonviable) mathematical programs of all types. A good starting point if you are new to infeasibility analysis. Publisher info.
J.W. Chinneck and W. Michalowski, (1996), "MOLP Formulation Assistance Using LP Infeasibility Analysis", in Multi-Objective Programming and Goal Programming: Theories and Applications, M. Tamiz (ed.), Lecture Notes in Economics and Mathematical Systems, vol. 432, Springer-Verlag.
Refereed Special Issues Edited
J.W. Chinneck (2009), Guest Editor, INFORMS Journal on Computing, Special Cluster on High-Throughput Optimization, vol. 21, no. 3, Summer 2009. Foreword pp. 347–348.
J.W. Chinneck (2008), Guest Editor, Computers and Operations Research Special Issue on Algorithms and Computational Methods in Feasibility and Infeasibility, vol. 35. Foreword pp. 1377-1378 (available online 2006).
J.W. Chinneck (2006), Guest Editor, INFORMS Journal on Computing Special Cluster on Operations Research in Electrical and Computer Engineering, vol. 18, no. 2, Spring 2006.
J.W. Chinneck (2002), Guest Editor, INFORMS Journal on Computing Special Issue on the Merging of Mathematical Programming and Constraint Programming, vol. 14, no. 4, Fall 2002.
Refereed Technical Notes
J.W. Chinneck, and M.A. Saunders, (1995), "MINOS(IIS) Version 4.2: Analyzing Infeasibilities in Linear Programs", European Journal of Operational Research, vol. 81, pp. 217-218.
J.W. Chinneck, (1990), "VIABLE1-Code for Identifying Nonviabilities in Processing Network Models", European Journal of Operational Research, Vol. 44, pp. 119-120.
J.W. Chinneck, (1985), "On Systems Theory and Models of Heat Flow", IEEE Trans. on Systems, Man and Cybernetics, Vol.SMC-15, No.3, pp.423-426.
Publications in Refereed Conferences
R. Mohamed, I. Lambadaris, A. Leivadeas, J.W. Chinneck, T. Morris, and P. Djukic (2023), "Automatic Feasibility Restoration for 5G Cloud Gaming", IEEE International Conference on Communications (ICC): Communication Software and Multimedia Symposium, 28 May – 01 June, Rome, Italy.
F.F. Firouzeh, J.W. Chinneck and S. Rajan (2021), "Biological Data Classification via Faster MAXimum Feasible Subsystem Algorithm," 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2021, pp. 1-5, doi: 10.1109/MeMeA52024.2021.9478696.
F.F. Firouzeh, S. Rajan and J.W. Chinneck (2021), “Recovery of Noisy Compressively Sensed Speech via Regularized Maximum Feasible Subsystem Algorithm”, IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2021), online, May 17-20.
F. F. Firouzeh, S. Rajan and J. W. Chinneck (2020), "MAXimum Feasible Subsystem Recovery of Compressed ECG Signals," 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy, June 1-3, pp. 1-6, doi: 10.1109/MeMeA49120.2020.9137337.
A.U. Chaudhry, M. Raithatha, R.H.M. Hafez, and J.W. Chinneck (2020), "Using Machine Learning to Locate Gateways in the Wireless Backhaul of 5G Ultra-Dense Networks", The 2nd International Workshop on Machine Learning for Next Generation Systems and Networks (MLNGSN'2020), held in conjunction with IEEE ISNCC 2020, 16-18 June 2020, Montreal, Canada.
M. Raithatha, A.U. Chaudhry, R.H.M. Hafez, and J.W. Chinneck (2020), "Locating Gateways for Maximizing Backhaul Network Capacity of 5G Ultra-Dense Networks", WTS 2020 Conference, April 22-24, 2020 in Washington DC, USA (https://www.cpp.edu/~wtsi/).
A.U. Chaudhry, R.H.M. Hafez, and J.W. Chinneck (2015), “On the Performance of Beamforming-based Channel Assignment in Dense Wireless Mesh Networks”, the 28th Canadian Conference on Electrical and Computer Engineering, Halifax, Canada, May 3-6.
A.U. Chaudhry, R.H.M. Hafez, and J.W. Chinneck (2014), “Significantly Reducing the Number of Frequency Channels Required for Wireless Mesh Networks using Beamforming”, IEEE Wireless Communications and Networking Conference WCNC 2014, Istanbul, Turkey, April 6-9.
J.W. Chinneck, M. Litoiu, C.M. Woodside (2014), “Real-Time Multi-Cloud Management Needs Application Awareness”, 5th ACM/SPEC International Conference on Performance Engineering ICPE 2014, Dublin, Ireland, March 22-26.
A.U. Chaudhry, J.W. Chinneck, and R.H.M. Hafez (2013), “Channel Requirements for Interference-free Wireless Mesh Networks to Achieve Maximum Throughput”, ICCCN 2013, Nassau, Bahamas, July 30 - August 2.
Z. Li, C.M. Woodside, J.W. Chinneck, M. Litoiu (2011). "CloudOpt: Multi-Goal Optimization of Application Deployments across a Cloud", 7th International Conference on Network and Service Management (CNSM 2011), October 24-28, Paris, France.
Z. Li, J.W. Chinneck, C.M. Woodside, M. Litoiu (2009), “Deployment of Services in a Cloud Subject to Memory and License Constraints”, 2009 IEEE Second International Conference on Cloud Computing (CLOUD-II 2009), Bangalore, India, September 21-25.
Z. Li, J.W. Chinneck, C.M. Woodside, M. Litoiu, and G. Iszlai (2009), “Performance Model Driven QoS Guarantees and Optimization in Clouds”, CLOUD 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, Vancouver, May 23, in conjunction with the 2009 IEEE 31st International Conference on Software Engineering (ICSE 2009), May 16–24.
Z. Li, J.W. Chinneck, C.M. Woodside, M. Litoiu (2009), "Fast Scalable Optimization to Configure Service Systems having Cost and Quality of Service Constraints”, the 6th International Conference on Autonomic Computing and Communications (ICAC 2009), Barcelona, June 15-19.
J.W. Chinneck (2009), "Tailoring Classifier Hyperplanes to General Metrics", in J.W. Chinneck, B. Kristjansson, M. Saltzman (eds.) "Operations Research and Cyber-Infrastructure", Springer Science+Media LLC.
W. Ibrahim, J.W. Chinneck, H. El-Sayed (2005), "QoS Satisfaction Based Charging and Resource Management Policy for Next Generation Wireless Networks", IEEE WirelessCom 2005, Maui, Hawaii, June 13-16.
M.W. Carter, G. Laporte, and J.W. Chinneck, (1993), "An Introduction to EXAMINE: A Flexible Examination Scheduling System", Proceedings of the 79th AACRAO Annual Meeting, April 17-21, Orlando.
R.G. Brown, J.W. Chinneck and G.M. Karam, (1989) "Optimization with Constraint Programming Systems", Proceedings of "Impact of Recent Computer Advances on Operations Research", Williamsburg, Virginia, January 4-6. R. Sharda et al. (eds.), Publications in Operations Research Series, No.9, Elsevier Science Publishers, pp. 463-473.
J.W. Chinneck, S.C. Carpenter, and E.C. Shewen, (1982), "A Study of Methods of Improving the Performance of Solar Water Walls", Proceedings of the Energex Conference, Regina, August 23-29.
M. Chandrashekar, J.W. Chinneck and F.C. Wong, (1978), "System-Theoretic Models for Analysis of Production-Consumption Systems", Proceedings of the Second International Symposium on Large Engineering Systems, Waterloo, Ontario.
Refereed Poster Presentations
A. Scheer and J.W. Chinneck (2015), “Efficient Isolation of Irreducible Infeasible Subsets in Mixed-Integer Linear Programs”, Mixed Integer Programming Workshop, Chicago, June 1-4.
H.A. Mahmoud and J.W. Chinneck (2011), "Achieving Integer Feasibility Quickly by Alternating Axis-Parallel and General Disjunctions", 2011 Mixed Integer Programming Workshop, University of Waterloo, Waterloo, Canada, June 20-23.
H.A. Mahmoud and J.W. Chinneck (2010), “Achieving Integer Feasibility Quickly by Alternating Axis-Parallel and General Disjunctions”, 2010 Mixed Integer Programming Workshop, Georgia Institute of Technology, Atlanta, Georgia, July 26-29.
Encyclopedia Articles
J. Chinneck and R. Sharda (2011), "Computer Science and Operations Research Interfaces," Encyclopedia of Operations Research/Management Science, 2nd edition, forthcoming.
Technical Reports
F.F. Firouzeh, J.W. Chinneck, S. Rajan (2020), "Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech", https://arxiv.org/abs/2003.02927 .
New algorithms based on solutions for the maximum feasible subsystem problem (MAX FS) improve on the state of the art in recovery of compressed speech signals: more highly compressed signals can be successfully recovered with greater quality.
B. Singh, R. Kaur, C.M. Woodside, and J.W. Chinneck (2019), "Low-Power Deployment of Many-Process Applications on Multiple Clouds", Cahiers du GERAD G-2019-101. https://www.gerad.ca/en/papers/G-2019-101 .
Modern cloud-based applications often have processes that are distributed across multiple clouds. However there are often real-time constraints on response time which must be satisfied which means that inter-cloud latencies must be considered during deployment. At the same time, the cloud operator wishes to reduce costs by minimizing electricity consumption due to operating host machines. We present an algorithm for making deployment decisions under these difficult conditions.
J.W. Chinneck (2019), “Post-Separation Feature Reduction”, Cahiers du GERAD G-2019-31. https://www.gerad.ca/en/papers/G-2019-31.
Given any classifier hyperplane separation, find a new hyperplane that gives the same (or better) separation while using fewer features. There are two variants of the main algorithm: (i) use even fewer features while giving a substantially similar separation, and (ii) when the features have costs, find a substantially similar but low cost separation.
J.W. Chinneck (2018), “Sparse Solutions of Linear Systems via Maximum Feasible Subsets”, Cahiers du GERAD G-2018-104. https://www.gerad.ca/en/papers/G-2018-104.
Improved algorithms for sparse solutions to underdetermined linear systems of equations (useful in compressed sensing), and for general constrained linear systems that include inequalities and bounded variables.
M. Shafique and J.W. Chinneck (2017), “CCGO: Fast Heuristic Global Optimization”, Les Cahiers du GERAD G-2017-71. https://www.gerad.ca/en/papers/G-2017-71.
Describes the CCGO algorithm and provides comparisons to existing state-of-the-art complete and multistart solvers.
J.W. Chinneck (1998), "Improved Linear Classification via LP Infeasibility Analysis", Dept. of Systems and Computer Engineering Technical Report SCE-98-09.
Permits the placement of classification lines that tend to minimize the number of misclassified points (as opposed to a measure of the misclassification distance).
G.M. Karam, J.W. Chinneck, R.A. Goubran, V. Pureza, and M. Lavoie, (1994) “A Fast Task-to-Processor Assignment Heuristic for Real-Time Multiprocessor DSP Applications”.
Z. You, J.W. Chinneck and C.M. Woodside, (1993), "Localizing Problems for Structural Debugging of Petri Net Models", Department of Systems and Computer Engineering Technical Report, SCE-93-27.
Z. You, J.W. Chinneck and C.M. Woodside (1993), "Modular Structural Debugging of Large Petri Net Models", Department of Systems and Computer Engineering Technical Report SCE-93-28.
J.W. Chinneck (1993). "Finding Minimal Infeasible Sets of Constraints in Infeasible Mathematical Programs", Systems and Computer Engineering Technical Report SCE-93-01.
J.W. Chinneck (1993), "MINOS(IIS) 4.2 User's Manual", Systems and Computer Engineering Technical Report SCE-93-17.
P. Bishop, J.W. Chinneck, and J. Knight (1993), "Integrating Materials Planning and Production Scheduling: The Coherent Algorithm", Systems and Computer Engineering Technical Report SCE-93-19.
Z. You, J.W. Chinneck and C. M. Woodside, (1992). "Prescreening and Localization of Petri Nets Model Errors by Viability Analysis", Systems and Computer Engineering Technical Report.
R.G. Brown, G.M. Karam and J.W. Chinneck, (1991), "An Extensible Constraint Solver Architecture", Department of Systems and Computer Engineering Technical Report SCE-91-41.
J.W. Chinneck, R.A. Goubran, G.M. Karam and M. Lavoie, (1990), "A Design Approach for Real-Time Multiprocessor DSP Applications", Department of Systems and Computer Engineering Technical Report SCE-90-05.
J.W. Chinneck, R.A. Goubran, G.M. Karam, M. Lavoie and S. Kerr, (1990), "Graphical Interface for Multiprocessor Real-Time Digital Signal Processing Design", report to the Defence Research Establishment Pacific, March.
Includes software and software documentation manuals.
A. Sankaranarayanan, J.W. Chinneck, and M. Chandrashekar (1986), "Photovoltaic Powered Pumping Systems: Final Report", report to the Solar Energy Program, National Research Council, March.
J.W. Chinneck, (1984), "Assessment of a New Procedure for Modelling Industrial Energy Systems", report to the Office of Energy Research and Development, Energy Mines and Resources Canada, August.
M. Chandrashekar, J.W. Chinneck, and A. Sankaranarayanan, (1984), "Photovoltaic Powered Water Pumping Systems - Availability and Performance. Milestone Report I: Technical Literature and Computer Model Survey", report to the Solar Energy Program, National Research Council, June.
J.W. Chinneck and S.C. Carpenter, (1983), "Review of the ENERSAVE Industrial and Commercial Energy Audit Computer Program. Phase II: Proposed Improvements", report to the Energy Conservation and Oil Substitution Branch, Energy Mines and Resources Canada, March.
S.C. Carpenter and J.W. Chinneck, (1982), "Review of the ENERSAVE Industrial and Commercial Energy Audit Computer Program. Phase I: Preliminary Assessment", report to the Conservation and Renewable Energy Branch, Energy Mines and Resources Canada, March.
S.C. Carpenter, E.C. Shewen and J.W. Chinneck, (1982), "Feasibility Study for a High Performance Solar Water Wall", report to the Solar Energy Program, Energy Mines and Resources Canada, May.
J.W. Chinneck, (1982), "The Steel Plant Energy Model, Volume 2: Guide to the Input Data", report to the Energy Management Group, The Algoma Steel Corporation Limited, March.
J.W. Chinneck, (1981), "The Steel Plant Energy Model, Volume 1: Structure of the Model", report to the Energy Management Group, The Algoma Steel Corporation Limited, May.
J.W. Chinneck, E.C. Shewen and M. Chandrashekar, (1979), "WATSUN-III Solar Heating and Economic Evaluation Program Documentation", report to the Division of Building Research, National Research Council of Canada.
K.G.T. Hollands, J.W. Chinneck and M. Chandrashekar (1977), "A Method of Rating and Sizing Solar Collectors in Residential Heating Systems", University of Waterloo Research Institute Report No.77-03.
Presentations
C.H. Lam, Z. Zhou, J.W. Chinneck (2024), “Infeasible Model Analysis in the OptVerse Solver”, INFORMS, Seattle, October 20-23.
C.H. Lam, Z. Zhou, J.W. Chinneck (2024), “Faster Infeasibility Analysis for Linear Programs”, International Symposium on Mathematical Programming (ISMP 2024), Montreal, July 21-26.
J.W. Chinneck, P. Brooks (2023), “Fitting Hyperplanes to Minimize the Quantile Error in Noisy Data Sets”, INFORMS, Phoenix, Oct.15-18.
J.P. Brooks, J.W. Chinneck (2023), “New Methods for Regression in the Presence of Confounding Outliers”, INFORMS 18th Workshop on Data Mining & Decision Analytics, Phoenix, October 14.
J.W. Chinneck, P. Brooks (2023), "Fitting Hyperplanes to Minimize the Quantile Error in Noisy Data Sets", CORS / Optimization Days, Montreal, May 23-31.
J.W. Chinneck, P. Brooks (2022), “Fitting General and Regression Hyperplanes in the Presence of Confounding Outliers”, INFORMS, Indianapolis, Oct. 16-19.
J.W. Chinneck, P. Brooks (2022), "Fitting General and Regression Hyperplanes in the Presence of Confounding Outliers", CORS-INFORMS International, Vancouver, B.C., June 5-8.
J.W. Chinneck, P. Brooks (2021), "Better Fitting Hyperplanes", INFORMS, Anaheim (online), October 24-27.
J.W. Chinneck (2021), "40 Years of OR Software Development: Lessons Learned", OR 2021, Bern (online), August 31 - September 3.
J.W. Chinneck, P. Brooks (2021), "Better Fitting Hyperplanes", CORS (online), June 7-10.
X. Lu, J.W. Chinneck, P. Kalab (2020), “Combining Node, Variable, and Direction Selection Heuristics for Faster MIP Solutions”, INFORMS (online), Nov. 7-13.
J.W. Chinneck (2019), “40 Years of OR Software Development: Lessons Learned”, INFORMS, Seattle, October 20-23 (invited).
J.W. Chinneck (2019), “Post-Separation Classifier Feature Reduction”, INFORMS, Seattle, October 20-23.
J.W. Chinneck (2019), “Teaching Optimization Algorithms”, EURO, Dublin, June 23-26.
J.W. Chinneck (2019), “Post-Separation Classifier Feature Reduction”, EURO, Dublin, June 23-26.
J.W. Chinneck (2019), “Post-Separation Classifier Feature Reduction”, Optimization Days, Montreal, May 13-15.
J.W. Chinneck (2019), “Following the Big Footprints”, ICS 2019, Knoxville, January 6-8 (invited).
J.W. Chinneck (2019), “Teaching Optimization Algorithms”, ICS 2019, Knoxville, January 6-8 (invited).
J.W. Chinneck (2019), “Sparse Solutions for Linear Systems”, ICS 2019, Knoxville, January 6-8.
J.W. Chinneck (2018), “LP-based Sparse Solutions Revisited”, INFORMS, Phoenix, Arizona, November 3-7.
J.W. Chinneck (2018), “LP-based Sparse Solutions Revisited”, International Symposium on Mathematical Programming, Bordeaux, France, July 1-6.
J.W. Chinneck (2018), “LP-Based Sparse Solutions Revisited”, Optimization Days, Montreal, May 7-9.
J.W. Chinneck (2017), “Fast Approximate Solution of Very Large Linear Programs”, INFORMS, Houston, October 22-25.
J.W. Chinneck (2017), “LP-Based Misclassification Minimization”, INFORMS, Houston, October 22-25.
J.W. Chinneck (2017), “Fast Approximate Solution of Very Large Linear Programs”, Optimization Days, Montreal, May 10-11.
J.W. Chinneck, M. Shafique (2016), “A Fast Heuristic for Global Optimization”, INFORMS, Nashville, November 12-16.
J.W. Chinneck, E. Klotz, and A. Scherr (2016), “Improved Analysis of Infeasible Mixed-Integer Linear and Quadratic Programs”, INFORMS, Nashville, November 12-16.
J.W. Chinneck (2016), “MILP Insights and Algorithms”, COCANA Seminar Series, UBC Okanagan, Kelowna, October 27 (invited).
J.W. Chinneck (2016), “Projection Methods and Nonlinear Constraint Shape”, EURO 2016, Poznan, Poland, July 3-6.
J.W. Chinneck (2016), “Projection Methods and Nonlinear Constraint Shape”, Optimization Days, Montreal, May 2-4.
J.W. Chinneck, M. Shafique (2015), “A Fast Heuristic Global Optimizer”, INFORMS, Philadelphia, November 1-4.
J.W. Chinneck, A. Scherr (2015), “Faster Infeasibility Analysis for Mixed Integer Linear Programming”, INFORMS, Philadelphia, November 1-4.
J.W. Chinneck (2015), "MILP Insights and Algorithms", Lehigh University Department of Industrial and Systems Engineering, department seminar, September 29 (invited).
J.W. Chinneck, M. Shafique (2015), "CCGO: A Fast Heuristic Global Optimizer”, ISMP, Pittsburgh, July 12-17.
M. Shafique, J.W. Chinneck (2015), "CCGO: Fast Heuristic Global Optimization", CORS/INFORMS, Montreal, June 14-17.
J.W. Chinneck, S. Ernst (2014), “New Parallel Programming Languages for Optimization Research”, INFORMS, San Francisco, November 9-12.
We compare Google’s Go language with Julia for use in optimization research.
J.W. Chinneck and M. Shafique (2014). "A Fast Heuristic for Global Optimization and MINLP", INFORMS, San Francisco, November 9-12.
J.W. Chinneck, M. Shafique (2014), "A Heuristic Method for MINLP", MINLP 2014, Carnegie-Mellon University, Pittsburgh, June 2-5.
M. Shafique, J.W. Chinneck (2014), "A Fast Heuristic for Global Optimization and MINLP", CORS, Ottawa, May 26-28.
R. Kaur, J.W. Chinneck, C.M. Woodside (2014), " Task Assignment Across Clouds by Graph Partitioning", CORS, Ottawa, May 26-28.
D.W. Hawker, C.M. Woodside, J.W. Chinneck (2014), "Simulating Task Assignment Policies in an Edge-Core Cloud Architecture", CORS, Ottawa, May 26-28.
A. Chaudhry, J.W. Chinneck, R. Hafez (2014), "Frequency Channel Requirements for Interference-Free Wireless Mesh Networks", CORS, Ottawa, May 26-28.
J.W. Chinneck (2014), “Experiments in Using Google's Go Language for Optimization Research”, Optimization Days, Montreal, May 5-7.
J.W. Chinneck, M. Shafique (2013), “A Fast Heuristic Method for Global Optimization and MINLP”, INFORMS, Minneapolis, October 5-9.
J.W. Chinneck (2013), “Branching to Force Variable Value Propagation in MILP”, Workshop on Seeking Feasibility in Combinatorial Problems at CPAIOR 2013, Yorktown Heights, May 18-22 (pdf).
J.W. Chinneck and M. Shafique (2013), “Towards a Fast Heuristic for Global Optimization and MINLP”, CPAIOR 2013, Yorktown Heights, May 18-22.
M. Shafique and J.W. Chinneck (2013), “Heuristic Global Optimization via Quick Exploration of the Variable Space”, Optimization Days, Montreal, May 6-8.
J.W. Chinneck (2013), “Integrated Hyperplane Placement and Feature Selection”, ICS 2013, Santa Fe, January 6-8.
J.W. Chinneck and H. Mahmoud (2012), “Fast MILP Feasibility Using General Disjunctions” INFORMS, Phoenix, October 13-17.
J.W. Chinneck, A. Holder, K. Aardal, R. Fourer, S. Raghavan, E. Wasil, D. Woodruff (2012), “How to Publish your Paper in The INFORMS Journal on Computing”, INFORMS, Phoenix, October 13-17.
J.W. Chinneck (2012), "Integrated Hyperplane Placement and Feature Selection", CORS, Niagara Falls, Ontario, June 11-13.
J.W. Chinneck, L. Smith, V. Aitken (2012), "Better Placement of Local Solver Launch Points for Global Optimization", CORS, Niagara Falls, Ontario, June 11-13.
J.W. Chinneck (2012), "Recent Advances in Mixed-Integer Linear Programming", Optimization Days, Montreal, May 6-9. Invited one hour plenary. Slides in pdf.
J.W. Chinneck, J. Pryor (2011), "Faster Integer Feasibility in MIPs by Branching to Force Change", INFORMS, Charlotte, November 13-16.
J.W. Chinneck, L. Smith, V. Aitken (2011), "Better Placement of Local Solver Launch Points for Global Optimization", INFORMS, Charlotte, November 13-16.
J.W. Chinneck (2011), “Branching in Mixed-Integer Linear Programming”, CPAIOR 2011, Berlin, May 23-27 (invited 75 minute tutorial). Slides in pdf on conference website. Youtube video of the tutorial.
J.W. Chinneck and J. Pryor (2011), "Faster Integer Feasibility in MIPs by Branching to Force Change", GERAD seminar, Ecole Polytechnique, Montreal, February 16.
J.W. Chinneck and J. Pryor (2011), "Faster Integer Feasibility in MIPs by Branching to Force Change", INFORMS Computing Society Conference, Monterey, January 9-11.
J.W. Chinneck and J. Pryor (2011), "Faster Integer Feasibility in MIPs by Branching to Force Change", seminar series, Department of Econometrics and Operations Research, Tilburg University, Tilburg Netherlands, January 5.
H. Mahmoud and J.W. Chinneck (2010), “Achieving Integer Feasibility Quickly by Alternating Axis-Parallel and General Disjunctions”, INFORMS, Austin, Texas, November 7-10.
J.W. Chinneck and J. Pryor (2010), “Faster Integer Feasibility in MIPs by Branching to Force Change, Austin, Texas, November 7-10.
T. Harrison, J. Camm, J.W. Chinneck, M. Gendreau, S. Graves (2010), “Panel Discussion with INFORMS Editors, Austin, Texas, November 7-10.
J.W. Chinneck (2010), "Heuristics for Feasibility and Optimality in Mixed-Integer Programming", full day of presentations in conjunction with Andrea Lodi (University of Bologna, Italy) at the CIRRELT Spring School on Combinatorial Optimization in Logistics, University of Montreal, May 17 (invited). Slides in pdf.
J.W. Chinneck and J. Pryor (2010), "Faster Integer Feasibility in MIPs by Branching to Force Change", Operations Research Department seminar series, Naval Postgraduate School, Monterey, California, April 22 (invited).
J.W. Chinneck and L. Smith (2009), “Variable Space Exploration in Large-Scale NLP”, INFORMS, San Diego, October 11-14.
J.W. Chinneck and J. Pryor (2009), “A Study of MIP Branching Direction Heuristics”, International Symposium on Mathematical Programming ISMP 2009, Chicago, August 23-28.
J.W. Chinneck (2009), “Tailoring Classifier Hyperplanes to General Metrics”, ICS 2009, Charleston, South Carolina, January 11-13.
D.T. Wojtaszek and J.W. Chinneck (2008), “New MIP Node Selection Heuristics”, INFORMS Washington D.C., October 12-15.
D.T. Wojtaszek and J.W. Chinneck (2008), “Faster MIP Solutions via New Node Selection Rules” CORS/Optimization Days, Quebec City, May 12-14.
J.W. Chinneck (2008). A series of talks for Microsoft, Redmond, Washington, April 23-25 (invited):
Solving MIPs: a Personal View
Special Ordered Sets
Reaching Feasibility Quickly in Mixed-Integer Programs Part I: Tutorial on the State of the Art
Feasibility and Infeasibility in Optimization
Faster MIP Solutions via New Node Selection Rules
Active-Constraint Variable Ordering for Faster Feasibility of Mixed Integer Linear Programs
Integrating Constraint Programming and MIP
J.W. Chinneck (2008), “The Maximum Feasible Subsystem Problem and Applications”, Industrial Optimization Seminar, the Fields Institute, Toronto, February 5 (invited). Slides and audio.
J.W. Chinneck (2007), "Connecting with the Academic World", DRDC, Department of National Defence, December 6.
D. Wojtaszek and J.W. Chinneck (2007), "Faster MIP Solutions via New Node Selection Rules", INFORMS Seattle, November 4-7.
J.W. Chinneck, W. Ibrahim, M. MacLeod (2007), "Fast NLP Feasibility via Constraint Consensus Methods", ICCOPT-II/MOPTA'07, Hamilton, Canada, August 13-16.
J. W. Chinneck (2007), "Feasibility and Infeasibility in Optimization", CP-AI-OR-07 conference, Brussels May 23-26. Invited tutorial (1.5 hours). Pdf.
J.W. Chinneck and M. MacLeod (2007), "Multistart Constraint Consensus for Seeking Feasibility in Nonlinear Programs", CORS, London, Ontario, May 14-16.
J.W. Chinneck (2007), "Reaching Feasibility Quickly in Mixed-Integer Programs. Part I: Tutorial on the State of the Art", Optimization Days 2007, May 7-9, Montreal.
J.W. Chinneck (2007), "Reaching Feasibility Quickly in Mixed-Integer Programs. Part II: New Branching Heuristics", Optimization Days 2007, May 7-9, Montreal.
D.T. Wojtaszek and J.W. Chinneck (2007), "Faster MIP Solutions via New Node Selection Rules" , Optimization Days 2007, May 7-9, Montreal.
J.W. Chinneck (2007), "Algorithms for the Maximum Feasible Subset Problem", Operations Research Department seminar series, Naval Postgraduate School, Monterey, California, April 19. Invited.
J.W.Chinneck and M. MacLeod (2006), "Effective Multistart for Reaching Feasibility in Difficult Nonlinear Programs", INFORMS, Pittsburgh, November 5-8.
J.W. Chinneck (2006), "Open Source Texts and Teaching Materials", INFORMS, Pittsburgh, November 5-8.
J.W. Chinneck (2006), "Active-Constraint Variable Ordering for Faster Feasibility of Mixed Integer Linear Programs ", Workshop on Hybrid Methods and Branching Rules in Combinatorial Optimization, Centre de Recherches Mathématiques, Université de Montréal, Montréal, September 18-22.
M. MacLeod and J.W. Chinneck (2006), "Efficient Multistart for Reaching Feasibility in Difficult Nonlinear Programs", CORS/Optimization Days, May 8-10, Montreal, Canada.
J.W. Chinneck (2006), "Analyzing Infeasible Optimization Models", Department of Mathematics, University of Haifa, Jan. 10, Haifa, Israel. Invited.
J.W. Chinneck (2006), "Analyzing Infeasible Optimization Models", School of Computational Engineering and Science, McMaster University, Feb. 13, Hamilton, Canada. Invited.
J.W. Chinneck and W. Ibrahim (2005), "Improving Solver Success in Reaching Feasibility for Sets of Nonlinear Constraints", INFORMS, San Francisco, November 12-16, and CORS, Halifax, May 16-18.
D. Wojtaszek and J.W. Chinneck (2005), "Faster MIP Solutions by Early Estimates of the Optimal Objective Function Value", Optimization Days, Montreal, May 9-11.
J.W. Chinneck and W. Ibrahim (2005), "Improving Solver Success in Reaching Feasibility for Sets of Nonlinear Constraints", ICS 2005, Annapolis, USA, January 5-7.
J.W. Chinneck, M. Nakhla, and Q.J. Zhang (2004), "Computer-Aided Design for Electrical and Computer Engineering", INFORMS 2004, Denver, Colorado, October 24-27. Invited tutorial.
J.W. Chinneck(2004), "Infeasibility and Optimization", CIST04 Conference, Denver, Colorado, October 23-26.
J.W. Chinneck (2004), "MProbe Analysis of Nonlinear Function Shape in NEOS", INFORMS 2004, Denver, Colorado, October 24-27.
J.W. Chinneck and A. Moghrabi (2004), "Constructing Better Decision Trees for Classification", INFORMS 2004, Denver, Colorado, October 24-27.
J.W. Chinneck (2004), "Analyzing Infeasible Optimization Models", CORS/INFORMS Joint International Meeting, Banff, Canada, May 16-19. Invited tutorial (pdf).
J.W. Chinneck and Jagat Patel (2003), "Faster MIP Solutions Through Better Variable Ordering", ISMP2003, Copenhagen, August 17-22.
John W. Chinneck, Suryani Chao, and Rafik Goubran (2003), "Assigning Tasks inVoice-over-IP Gateway Multiprocessors", CORS Conference, Vancouver, June1-4.
J.W. Chinneck and J. Patel (2002), "Faster MIP Solutions through Better Variable Ordering", INFORMS, San Jose, November 17-20.
J.W. Chinneck (2002), "The Constraint Consensus Method for Tightening Variable Bounds in Nonlinear Programs", MOPTA ’02, McMaster University, Hamilton, Canada, August 1-3.
J.W. Chinneck (2002), "Developments in MProbe: Approximating the Feasible Region", APMOD 2002, Varenna, Italy, June 16-19.
J.W. Chinneck and J. Patel (2002), "Faster MIP Solutions Through Better Variable Ordering", CORS, Toronto, Canada, May 2-5.
A. Moghrabi, and J.W. Chinneck (2001), "Adaptive Classification Decision Trees", INFORMS, Miami, November 4-7.
J.W. Chinneck (2001), "Fast Algorithms for the Maximum Feasibility Problem", INFORMS, Miami, November 4-7.
J.W. Chinneck (2001), "Discovering the Characteristics of Mathematical Programs via Sampling", MOPTA01, Hamilton, August 2-4.
J.W. Chinneck (2000), "MProbe: Software for Analyzing Mathematical Programs",INFORMS, San Antonio, November 5-8.
J.W. Chinneck (2000), "Analyzing Mathematical Programs via Sampling", ISMP2000, Atlanta, August 7-11.
J.W. Chinneck (2000), "Pre-Solution Analysis of Complex Mathematical Programs via Sampling", Applied Mathematical Programming and Modeling (APMOD2000), London, England, April 17-19.
J.W. Chinneck (2000), "MProbe: What's in your Mathematical Program?", INFORMS, Salt Lake City, May 7-10.
J.W. Chinneck (2000), "Discovering the Characteristics of Mathematical Programs via Sampling", CORS, Edmonton, May 29-31.
J.W. Chinneck (1999), "Modeling Support for Constraint Programming", INFORMS, Philadelphia, November 7-10.
J.W. Chinneck and H. Li (1999), "A Generic Graphical Interface for Network Modeling", INFORMS, Philadelphia, November 7-10.
J.W. Chinneck (1999), "MProbe: Software for Analyzing Mathematical Programs", CORS Conference, Windsor, June 7-9.
J.W. Chinneck (1999), "MProbe: Software for Analyzing Mathematicl Programs", Optimization Days, Montreal, May 10-12.
J.W. Chinneck (1999), "Classification Using Linear Programming Infeasibility Analysis", INFORMS, Cincinnati, May 2-5.
J.W. Chinneck (1999), "MProbe: Analyzing a Mathematical Program", INFORMS, Cincinnati, May 2-5.
J.W. Chinneck (1998), "Nonlinear Programming for the Rest of Us", INFORMS, Seattle, October 25-28.
J.W. Chinneck (1998), "Multicategory Classification via LP Infeasibility Analysis", INFORMS, Seattle, October 25-28.
J.W. Chinneck (1998), "Estimating the Shape of Nonlinear Functions and Regions", CORS/INFORMS, Montreal April 26-29.
J.W. Chinneck (1998), "Analyzing Infeasible Mathematical Programs", INFORMS CSTS, Monterey, January 7-9.
J.W. Chinneck (1997), "Computer Tools for Analyzing Infeasible Mathematical Programs", ISMP'97, Lausanne, August 24-29.
J.W. Chinneck (1997), "New IMPS Software: A Requirements Analysis", ISMP'97, Lausanne, August 24-29.
J.W. Chinneck (1997), "MProbe: Software for Exploring Nonlinear Models", CORS'97, Ottawa, May 26-28. Also presented at Optimization Days 1997, Montreal, May 12-14.
M. Lavoie and J.W. Chinneck (1997), "SLA Optimizer: Optimization by Sequential Large-Scale Approximation", CORS'97, Ottawa May 26-28. Also presented at Optimization Days 1997, Montreal, May 12-14.
R. Awad and J.W. Chinneck (1997), "Proctor Scheduling at Carleton University", CORS'97, Ottawa, May 26-28 (invited).
J.W. Chinneck (1997), "MProbe: Estimating the 'Shape' of Nonlinear Functions", INFORMS, San Diego, May 4-7.
J.W. Chinneck (1996) "Help! Computer Tools Can Assist When Your Mathematical Program is Infeasible or Otherwise Broken", Optimization Days, 1996, Montreal, May 13-15.
J.W. Chinneck (1995), "Tools for the Pre-Solution Analysis of Nonlinear Programs", CORS Conference, Calgary, May 23-25.
J.W. Chinneck (1995), "An Effective Heuristic for the IIS Set-Covering Approach to Analyzing Infeasible LPs", Optimization Days 1995, Montreal, May 10-12.
J.W. Chinneck (1995), "A Toolkit for Pre-solution Analysis of Nonlinear Programs", INFORMS Conference, Los Angeles, April 23-26.
J.W. Chinneck (1995), "A Toolkit for Pre-solution Analysis of Nonlinear Programs", INFORMS Conference, Los Angeles, April 23-26.
J.W. Chinneck (1994), "An Introduction to Processing Networks", CORS/Optimization Days, Montreal, May, 30 - June 1.
M. Lavoie and J.W. Chinneck (1994), "PROFLOW: Formulating, Analyzing, and Solving Processing Networks", CORS/Optimization Days, Montreal, May 30 - June 1.
R.H.H. Moll, and J.W. Chinneck (1994), "Processing Network Models for Forest Management", CORS/Optimization Days, Montreal, May 30 - June 1.
J.W. Chinneck, (1994), "Computer Codes for the Analysis of Infeasible LPs",TIMS/ORSA Conference, Boston, April 24 - 26.
V. Pureza, J.W. Chinneck and G.M. Karam (1993), "CLPF: Uma Heuristica para Aloca?ao de Tarefas em Ambiente de Multiprocessadores de Sinal Digital", Conference of the Operations Research Society of Brazil.
J.W. Chinneck, (1993), Analyzing Infeasible Mathematical Programs", Optimization Days 1993, Montreal May 12-14.
M.W. Carter, G. Laporte and J.W. Chinneck, (1993), "An Introduction to EXAMINE: A Flexible Examination Scheduling System", AACRAO Conference, Orlando, April 17-21.
J.W. Chinneck, (1993), "Finding Minimal Infeasible Sets of Constraints in Infeasible Mathematical Programs", APMOD93 Symposium on Applied Mathematical Programming and Modelling, Budapest, January 6-8 (invited).
M.W. Carter, G. Laporte and J.W. Chinneck, (1992), "A General Examination Scheduling System", Optimization Days, Montreal, May 4-6.
J.W. Chinneck, (1992), "Debugging Infeasible Instances of Linear and Nonlinear Programs", TIMS/ORSA Conference, San Francisco, November, (invited).
J.W. Chinneck, (1992), "Analyzing Infeasible Nonlinear Programs", TIMS/ORSA Conference, Orlando, April.
J.W. Chinneck, (1991), "Focusing the Diagnosis of Infeasible LPs", TIMS/ORSA Conference, Nashville, May 12-15.
J.W. Chinneck, (1990), "Localizing and Analyzing Formulation Errors in Networks", CORS Conference, Ottawa, May 22-24.
J.W. Chinneck (1989), "A Program for Identifying Formulation Errors in Processing Network Models", CORS/TIMS/ORSA Conference, Vancouver, May 8- 10.
E.W. Dravnieks and J.W. Chinneck (1989), "Identification of Constraint Sets Causing Infeasibility in LPs", CORS/TIMS/ORSA Conference, Vancouver, May 8-10.
J.W. Chinneck (1988), "Detecting Pathological Structures in Processing Networks", TIMS-ORSA Conference, Washington, D.C., April 25-27.
J.W. Chinneck (1987), "Building Processing Networks", ORSA-TIMS Conference, St. Louis, October 25-28.
S.C. Boyd and J.W. Chinneck, (1987), "The Feasibility Problem for Processing Networks", 18th Southeastern International Conference on Combinatorics, Graph Theory, Computing, Florida Atlantic University, Boca Raton, February 23-27.
J.W. Chinneck and M. Chandrashekar, (1978), "Assessment of Alternatives in Community Energy Planning", First International Conference on Energy and Community Development, Athens, July 10-15.
Theses
J.W. Chinneck, (1983) "System-Theoretic Overview Models of Industrial Plant Energy Systems Incorporating Exergy", Ph.D. Thesis, Systems Design, University of Waterloo.
J.W. Chinneck, (1978), "Models for Analysis of Energy Systems", M.A.Sc. Thesis, Systems Design, University of Waterloo.
Other
J.W. Chinneck and H.J. Greenberg (1999), "Intelligent Mathematical Programming Software: Past Present, and Future", Newsletter of the INFORMS Computing Society, Vol. 20, no. 1. Pdf source.
J.W. Chinneck, (1992), "Advent of the Operations Analyst", article, OR/MS Today, October.
J.W. Chinneck and G.M. Karam, (1991), Book Review of "Constraint Satisfaction in Logic Programming" by Pascal van Hentenryck, ORSA Journal on Computing, Vol. 3, No. 1, pp. 82-83.