Informs 2015

INFORMS Philadelphia – 2015

432

WC17

17-Franklin 7, Marriott

Optimization Network

Contributed Session

Chair: Charles Nicholson, Assistant Professor, University of Oklahoma,

202 W. Boyd St., Room 116-H, Norman, OK, 73071,

United States of America,

cnicholson@ou.edu

1 - 1 Refueling Location Problem on a Continuous Tree Network

Sang Jin Kweon, Ph. D. Student, The Pennsylvania State

University, 232 Leonhard Building, The Pennsylvania State

University, University Park, PA, 16802, United States of America,

svk5333@psu.edu

, Jose A. Ventura, Seong Wook Hwang

In this talk, we consider a location problem for 1-refueling station on a tree

network. We aim to locate the station anywhere on the network, including along

the edge. Our objective is to locate a 1-refueling station to maximize the total

traffic flow covered in round trips. For this, we derive reduction properties

regarding the problem size and optimality conditions. Then, we develop an exact

algorithm to determine the set of optimal locations for the refueling station.

2 - Near Optimal Design of Wavelength Routed Optical Networks

Prahalad Venkateshan, Associate Professor, Indian Institute of

Management, Ahmedabad, Vastrapur, Ahmedabad, India,

prahalad@iimahd.ernet.in

, Yogesh Agarwal

The problem of designing a wavelength routed optical transport network is

considered. Valid inequalities are used to augment traditional network design

formulations. The resulting network is optimal for a majority of the problem

instances tested and in those cases where it is not, a trial-and-error method finds

near-optimal solutions. Computational tests are reported on relatively larger

problem sizes than have been reported in literature on the wavelength routing

problem.

3 - Metrics of Compactness: The Benefits of a Cluster-first,

Route-second Approach to the Min-max K WRPP

Oliver Lum, University of Maryland, 3103 Mathematics Building,

University of Maryland, College Park, MD, 20742, United States

of America,

oliver@math.umd.edu

, Bruce Golden, Edward Wasil,

Carmine Cerrone

In practice, it is often desirable for the routes of vehicles to exhibit certain

properties that are not included in the objective function. Two such properties are

compactness and separation. We discuss existing metrics that attempt to capture

these traits, propose a new metric that combines these intuitions into a single

value, and compare computational results for route-first, and cluster-first

heuristics for the Min-Max K Windy Rural Postman.

4 - Optimal Flow Analysis, Prediction and Application

Charles Nicholson, Assistant Professor, University of Oklahoma,

202 W. Boyd St., Room 116-H, Norman, OK, 73071,

United States of America,

cnicholson@ou.edu

, Weili Zhang

The fixed charge network flow problem is a classic NP-hard problem with many

applications. To the best of our knowledge, this is the first paper that employs

statistical learning technique to analyze the characteristics of optimal solutions.

We develop an accurate propensity model based on this analysis to predict which

arcs will have positive flow in an optimal solution. This propensity score allows

for multiple applications such as identification of critical components in complex

networks.

WC18

18-Franklin 8, Marriott

Optimization Robust I

Contributed Session

Chair: Zhi Chen, Department of Decision Sciences, PhD B1-02 Biz2

Building, NUS Business School, Singapore, 117592, Singapore,

chenzhi@u.nus.edu

1 - Competitive Difference Analysis of One-way Trading with

Limited Information

Yingjie Lan, Peking University, Guanghua School of

Management, Beijing, 100871, China,

ylan@pku.edu.cn

Wei Wang

We consider robust one-way trading with limited information on price as a game.

Our analysis finds the best guarantee of difference from the optimal performance.

We provide closed-form solution, and reveal for the first time all possible worst

case scenarios. Numerical experiments show that our policy is more tolerant of

information inaccuracy than Bayesian policies, and can earn higher average

revenue than other robust policies while keeping a lower standard deviation.

2 - A Robust Optimization Inventory Model with Uncertain Demand

and Lead Time

Mohammad Rahdar, Iowa State University, 133 University

Village, Unit F, Ames, IA, 50010, United States of America,

rahdar@iastate.edu

, Lizhi Wang, Guiping Hu

This study aims to improve the efficiency of a supply chain system by optimizing

the ordering strategy in a manufacturing facility with uncertain demand and lead-

time. A robust optimization model which has tri-level structure is proposed to

explicitly address the uncertainty, and make planning decisions which are robust

against all scenarios. We propose an exact algorithm for the tri-level programming

model and report the numerical results.

3 - An Optimal Solution to a PCA Based Multi-Response Problem

Nasser Fard, Associate Professor, Northeastern University, 334

Snell Engineering Center, 360 Huntington Avenue, Boston, MA,

02115, United States of America,

n.fard@neu.edu,

Huyang Xu,

Yuanchen Fang

It is shown that each eigenvalue obtained from the application of PCA in multi-

response optimization has a set of eigenvectors, and different eigenvectors

corresponding to each eigenvalue will lead to different results. A method for

determining the optimal eigenvector combination is proposed. These eigenvectors

are used to calculate the multi-response performance indices, and then the

optimal factor-level combination will be uniquely determined.

4 - Distributionally Robust Optimization with Semi-infinite

Ambiguity Sets

Zhi CHEN, Department of Decision Sciences, PhD B1-02 Biz2

Building, NUS Business School, Singapore, 117592, Singapore,

chenzhi@u.nus.edu,

Melvyn Sim, Xu Huan

We investigate semi-infinite ambiguity sets that involve infinite expectation

constraints besides moments and support. We study the associated intractable

distributionally robust optimization by considering relaxed ambiguity sets with

finite constraints. Based on worst-case distribution, we demonstrate an algorithm

that tightens the relaxation gradually. We present expressive examples of these

ambiguity sets, and show examples where the worst-case distribution can be

relatively easy verified.

5 - Robust Optimization for a Location Problem with Codependent

Uncertain Constraints

Juan Carlos Espinoza Garcia, ESSEC Business School,

Av Bernard Hirsch, CS 50105, Cergy-Pontoise, 95021, France,

juancarlos.espinoza@essec.edu

, Laurent Alfandari

We consider an uncertain problem where the same parameter is liable to

variation in both constraints and objective function. We model this problem

under the Gamma-Robustness paradigm adapting the framework to include

codependency between the uncertain equations. We apply this model to a

location problem with uncertain demands and conduct computational

experiments on a housing problem.

WC19

19-Franklin 9, Marriott

Heuristics

Sponsor: Computing Society