Informs 2015

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INFORMS Philadelphia – 2015
434
WC24
24-Room 401, Marriott
Constraint and Mixed Integer Programming
Sponsor: Artificial Intelligence
Sponsored Session
Chair: Louis-Martin Rousseau, CIRRELT Ecole Polytechnique de
Montréal, CP 6079 Succ Centre-Ville, Montréal, H3C3A7, Canada,
1 - A Constraint Programming Approach for Solving Convex
Quadratically Constrained Problems
Chris Beck, University of Toronto, 5 King’s College Rd, University
of Toronto, Toronto, On, M5S3G8, Canada,
Wen-Yang Ku
Inspired by the geometric reasoning exploited in discrete ellipsoid-based search
(DEBS) from the communications literature, we develop a constraint
programming (CP) approach to solve problems with convex quadratic constraints.
Such constraints appear in numerous applications. We strengthen the key aspects
of DEBS and implement them as combination of a global constraint and
variable/value ordering heuristics. Preliminary experiments on a variety of
benchmark instances show promising results.
2 - Strengthening Convex Relaxations with Bound Tightening for
Power Network Optimization
Pascal Van Hentenryck, University of Michigan, 1205 Beal
Avenue, Ann Arbor, MI, 48109, United States of America,
, Carleton Coffrin, Hassan Hijazi
Convexification is a fundamental technique in (mixed-integer) nonlinear
optimization and many convex relaxations are parametrized by variable bounds,
i.e., the tighter the bounds, the stronger the relaxations. This paper studies how
bound tightening can improve convex relaxations for power network
optimization. It adapts traditional constraint-programming concepts to a
relaxation framework and shows how bound tightening can dramatically improve
power network optimization.
3 - Improved Constraint Propagation via Lagrangian Decomposition
Andre Augusto Cire, Assistant Professor, University of Toronto
Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4,
Canada, , David Bergman,
Willem-jan Van Hoeve
Constraint propagation is inherently restricted to the local information that is
available to each propagator. We propose to improve the communication between
constraints by encoding them as decision diagrams and applying a Lagrangian
Decomposition scheme, where penalty costs are incurred when the variable
assignments in each constraint do not correspond to one another. We show that
propagating Lagrangian cost information can help improve the quality of bounds
as well as the solution time.
4 - General Bounding Mechanism for Constraint Programs
Louis-Martin Rousseau, CIRRELT Ecole Polytechnique de
Montréal, CP 6079 Succ Centre-Ville, Montréal, H3C3A7,
Canada, Claude-guy Quimper,
Hoang Minh Ha
This paper presents an approach based on ideas from Lagrangian decomposition
(LD) that establishes a general bounding scheme for any CP. We provide an
implementation for optimization problems that can be formulated with knapsack
and regular constraints, and we give comparisons with pure CP approaches.
WC26
26-Room 403, Marriott
Project Management I
Contributed Session
Chair: Ted Klastorin, Professor, University of Washington,
Foster School of Business, Box 353226, Seattle, WA, 98195-3226,
United States of America,
1 - Project Management for IT-driven Development of Disadvantaged
Communities in the Developing World
Devendra Potnis, Assistant Professor, University of Tennessee at
Knoxville, 1345 Circle Park Drive, Communications Bldg.,
Suite 451, Knoxville, TN, 37996, United States of America,
Inability of researchers to collect rich data from disadvantaged communities leads
to the failure of the projects aiming to develop information systems for the
development of disadvantaged communities. This study demonstrates the specific
ways in which researchers can contextualize project management principles to
address data collection barriers by managing scope, time, cost, human resources,
quality, communications, and risks related to “IT for development projects” in the
developing world.
2 - On Coordinating Contracts in Decentralized Stochastic Projects
Ted Klastorin, Professor, University of Washington,
Foster School of Business, Box 353226, Seattle, WA, 98195-3226,
United States of America, Tony Chen,
Michael Wagner
We study a decentralized project where a client organization contracts individual
tasks to independent subcontractors. We show that a simple linear incentive
contract coordinates the project while maximizing the expected project profit and
minimizing two risk measures. We show how this contract can be implemented in
both bidding and auction environments.
3 - Stochastic Resource-constrained Project Scheduling using
Approximate Dynamic Programming
Haitao Li, Associate Professor, University of Missouri - St. Louis,
229 ESH, St. Louis, MO, 63121, United States of America,
, Keith Womer
We present an effective and efficient approximate dynamic programming (ADP)
algorithm for the well-known stochastic resource-constrained project scheduling
problems. Our approach features a hybrid ADP framework that integrates both
the rollout look-ahead policy and the look-back policy via lookup table to
enhance solution performance.
4 - Resource Allocation Patterns in a Multi-project Portfolio of
Engineering Projects
Vishwanath Hegde, California State University East Bay, 25800
Carlos Bee Blvd, Hayward, CA, 94542, United States of America,
Zinovy Radovilsky
Using historic resource loading data in a multi-project setting, we show that
resource distribution patterns can be captured by parametric regression models,
which can forecast resource distribution during project lifetime using project due
date and attributes. Forecast based on historical data instead of priory assumption
can improve the accuracy of resource planning. This macro estimation approach
can be deployed easily by project portfolio managers.
WC27
27-Room 404, Marriott
Multicriteria Decision Making I
Contributed Session
Chair: Sune Lauth Gadegaard, PhD fellow, Department of Economics
and Business, Aarhus University, Fuglesangs AllÈ 4, bygning 2622,
lokale 315a, Aarhus V, 8210, Denmark,
1 - A Multi-Objective Decision Making Model based on Uncertain
Preference Sequence Information
Xiao Liu, Mr., Huazhong University of Science and Technology,
No. 1037, Road Luoyu, Wuhan, 430074, China,
, Huimin Ma
In this paper, we put forward a multi-objective decision making model for two-
sided matching based on uncertain preference sequence information. We first
design a mechanism to get preference ordinal value in uncertain sequence. Then
multiple optimal objectives on two-sided matching make up a mixed 0-1 integer
linear programming model. At last comparing with the typical two-sided
matching optimization approaches, we discuss their characteristics and
performance in different evolution criteria.
2 - Preferences Modeling in Goal Programming Model through
Satisfaction Functions
Belaid Aouni, Associate Dean, Qatar University,
College of Business and Economics, P.O. Box 2713, Doha, Qatar,
The concept of Satisfaction Functions was developed by Martel and Aouni in
1991 to explicitly integrate the Decision-Maker’s preferences within the Goal
Programming Model. Through this concept several criticism of the Goal
Programming model were addressed. This concept has been widely applied to
different Goal Programming variants and utilized in solving several application
cases. The aim of this paper is to present an analysis of different applications of
this concept with a guideline.
3 - Balancing Faculty and Student Preferences in the Assignment of
Students to Groups
Richard Forrester, Associate Professor Of Mathematics, Dickinson
College, College and Louther Streets, Carlisle, PA, 17013,
United States of America, Kevin Hutson
We develop a practical approach for assigning students to groups where the goal is
to create diversity within the groups while considering the preferences of the
students. A motivating application is the assignment of students to seminars.
Faculty want seminars that are balanced with regards to gender and ethnicity,
while students want to be assigned to one of their higher ranked seminars. Our
model is a multi-objective convex quadratic program that can be solved using a
standard solver.
WC24