2015 Informs Annual Meeting

SB27

INFORMS Philadelphia – 2015

SB28 28-Room 405, Marriott Contingent Mechanisms Cluster: Auctions Invited Session

2 - Optimizing Community Healthcare Coverage in Remote Liberia through an Integer Linear Programming Model Paige Von Achen, Northwestern University, Sheridan Road,

Evanston, IL, United States of America, PaigeVonAchen2014@u.northwestern.edu

Here we present a collaborative effort by the NGO, Last Mile Health, and Northwestern University to aid the expansion of healthcare accessibility throughout remote Liberia. Two integer linear programming models are developed that determine (1) the location assignments of healthcare workers and their supervisors and (2) the routing of the supervisors. We highlight the benefits of rigorous data collection and using a cross-disciplinary team to provide proper scoping and representation of a given problem. 3 - A Faster Algorithm for the Resource Allocation Problem with Convex Cost Functions Chao Qin, PhD Candidate, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, United States of America, chaoqin2019@u.northwestern.edu, Cong Shi, Huanan Zhang We revisit the classical resource allocation problem with general convex objective functions, subject to an integer knapsack constraint. This class of problems is fundamental in discrete optimization and arises in a wide variety of applications. In this paper, we propose a novel polynomial-time divide-and-conquer algorithm and prove that it has a computational complexity of O(n log n log N), which outperforms the best known polynomial-time algorithm with O(n (log N)^2). 4 - Integrated Optimization of Aircraft Utilization and On-time Performance Beril Burçak, Bilkent University, 1972. Sok. Melis Sit. D Blok No: 8, Ankara, Turkey, beril.burcak@gmail.com, Alaz Ata Senol, Hakan Sentörk, Ayça Karatepe, Osman Rauf Karaaslan, Dr. Kemal Güler, Kaan Yavuz This paper concerns the decision-support system created for Pegasus Airlines of Turkey, designed to improve the company’s two key performance indicators; aircraft utilization and on-time performance. A unique approach is introduced to tackle the tradeoff between these two indicators via mathematical modeling. Significant improvements in operational performance and customer satisfaction are achieved as the previously manually done flight scheduling process has been automatized. 5 - Routing Optimization of a Drone for Agricultural Inspections Kaan Telciler, Koc University, Rumelifeneri yolu Koç University, Main Campus Sariyer, Istanbul, Turkey, ktelciler@ku.edu.tr, Ezgi Karakas, Cagan Urkup Drones can be used in various areas with developing drone technologies. In order to provide an automized usage for drones, there is a need of routing approach. We developed a mathematical model and routing heuristic for drones which considers recharge stations, battery limit, wind changes, restricted regions and sequential routes. We used cluster first, route second approach for heuristic. In several datasets and cases, we obtained near-optimal routing in feasible times.

Chair: Rakesh Vohra, University of Pennsylvania, 3718 Locust Walk, Philadelphia, United States of America, rvohra@seas.upenn.edu 1 - Implementation with Contingent Contracts Rahul Deb, Assistant Professor, University of Toronto, 150 St. George St, Toronto, ON, m5s3g7, Canada, rahul.deb@utoronto.ca We study dominant strategy incentive compatibility with contingent contracts where the payoff of each agent is observed by the principal and can be contracted upon. We characterize outcomes implementable by linear contracts and provide a foundation for them by showing that, in finite type spaces, every social choice function (SCF) that can be implemented using a more general nonlinear contingent contract can also be implemented using a linear contract. 2 - Market Selection and the Information Content of Prices Mehmet Ekmekci, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, United States of America, ekmekci@bc.edu, Alp Atakan In an economy where buyers with unit demand decide to purchase one of two possible goods which are traded in two distinct markets. The goods traded within each market are identical, common-value objects and the price formation process as a large uniform-price auction. imperfectly informed bidders choose to participate in one of the markets. If market frictions lead to uncertain gains from trade in any of the two markets, then there is no equilibrium where prices aggregate information. 3 - Contingent Mechanisms with Endogenous Information Yunan Li, University of Pennsylvania, 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA, 19104, United States of America, yunanli0202@gmail.com I study the auction design problem when buyers can make payments contingent on their ex-post returns. An example is selling a company using securities like shares. I consider settings where buyers can covertly acquire information at a cost before the auction. I find that auctions using steeper securities provide lower incentives for agents to acquire information, and thus may generate lower revenues. I also study the design of the optimal linear contingent mechanism with endogenous information. 4 - Participation and Unbiased Pricing in CDS Settlement Mechanisms Ahmad Peivandi, Participation and Unbiased Pricing in CDS Settlement Mechanisms, Georgia State University, 35 Broad St, Atlanta, GA, United States of America, apeivandi@gsu.edu Credit default swaps are insurance contracts on default. Currently, there are over 20 trillion USD worth of outstanding CDS contracts. These contracts are settled through a centralized market that has been criticized for underpricing the asset. In this paper, I take a mechanism design approach and characterize robust settlement mechanisms that deliver an unbiased price for the asset. A second contribution of my paper is a new notion of the core for games of incomplete information. This is particularly relevant here because participation in the settlement mechanism cannot be compelled. SB29 29-Room 406, Marriott A Collection of State of the Art Analytics Models and Methods Chair: Michael Katehakis, Professor And Chair, Rutgers University, 100 Rockafeller Rd., Piscataway, NJ, 08854, United States of America, mnk@rutgers.edu 1 - The [ Map(t)/ Ph(t)/ Inf ] k Queueing System and Network Ira Gerhardt, Manhattan College, 4513 Manhattan College Parkway, Riverdale, NY, 10471, United States of America, ira.gerhardt@manhattan.edu, Michael Taaffe, Barry Nelson We generalize a numerically exact method for evaluating time-dependent moments of the entities in a Ph(t) /Ph(t) / \Inf queueing system to the MAP(t) /Ph(t) / \Inf queueing system, and show that these same results can be used to analyze the multiclass [MAP(t) /Ph(t) / \Inf]^K queueing network system. Finally we show that the covariance of the number of entities at different nodes and times may be described by a single closed differential equation. Sponsor: Analytics Sponsored Session

SB27 27-Room 404, Marriott

Multi-objective Choice Problems Sponsor: Multiple Criteria Decision Making Sponsored Session

Chair: Ozlem Karsu, Bilkent University, Bilkent Universitesi, Endustri Muhendisligi, Ankara, 06800, Turkey, ozlemkarsu@bilkent.edu.tr 1 - Two Approaches for Inequity-averse Sorting

Ozlem Karsu, Assistant Professor, Bilkent University, Bilkent, Ankara, Turkey, ozlemkarsu@yahoo.co.uk

We consider multi-criteria sorting problems where the decision maker(DM) has equity concerns.In such problems each alternative represents an allocation of an outcome over multiple entities. We propose two sorting algorithms that are different from the ones in the current literature in the sense that they apply to cases where the DM’s preference relation satisfies anonymity and convexity properties. We illustrate their use by sorting countries into groups based on their income distributions. 2 - A Preference-based Approach to Multi-objective Feature Selection Muberra Ozmen, Middle East Technical University, Industrial Engineering Department, Ankara, 06800, Turkey, mozmen@metu.edu.tr, Gulsah Karakaya, Murat Koksalan In feature selection problems, one or more subsets of available features that best characterize the output of interest are selected. In this study, we develop a preference-based approach for the multi-objective feature selection problems considering objectives such as maximizing classification performance and minimizing the number of selected features. We test the approach on several instances.

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