2016 INFORMS Annual Meeting Program

SD25

INFORMS Nashville – 2016

SD25 110A-MCC

to practice, where large numbers of EVs need to be charged. We prove the theoretical optimal conditions that must hold in order to have maximum social welfare in the grid. We validate our mechanism on real-world data and find both peak demand and delay reduction. 2 - Truthful Approximation Mechanisms for Knapsack Bidders Martin Bichler, Soeren Merting, Technische Universitat Munchen, Munich, Germany. bichler@in.tum.de In markets such as digital advertising markets, bidders want to maximize value for impressions subject to a budget constraint. This type of utility function is typically implemented in bidding agents, but it differs from quasilinear utility functions in important ways. We refer to such bidders as knapsack bidders. We study the offline mechanism design problem and analyze truthful approximation mechanisms to maximize social welfare. Serial dictatorship mechanisms are shown to be strategy-proof and Pareto-optimal, but they can have low welfare. We propose a randomized mechanism with an approximation ratio of 4. Our mechanism draws on a fractional deferred acceptance algorithm and randomized rounding, and it illustrates how the relax-and-round principle can be implemented in an important non-quasilinear environment. 3 - Modelling Electricity Balancing Market Prices And Premiums: A Non-parametric Non-linear Approach Ezgi Avci-Surucu, PhD Student, Rotterdam School of Management, Rotterdam, Netherlands, avcisurucu@rsm.nl Wolfgang Ketter, Gerhard Wilhelm-Weber In smart electricity markets, the increased penetration of renewable sources reveals the need for decision support systems. For developing reasonable bidding strategies, market participants need intelligent agents to make informed decisions about the trade-off between sales in the day-ahead market or in the balancing market. In this paper, by considering a detailed system-level data; firstly we examine the market efficiency by fractal analysis to understand the level of price predictability. Further, due the invalidity of normality and linearity assumptions, we propose non-parametric non-linear models to provide strategic tools for policy makers and market participants. SD27 201A-MCC Empirical Research in Finance and Operations Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: William Schmidt, Cornell University, United States, ws366@cornell.edu 1 - Optimal Timing Of Inventory Decisions Under Price Uncertainty Nikolay Osadchiy, Emory University, nikolay.osadchiy@emory.edu, Vishal Gaur, Sridhar Seshadri, Marti Subrahmanyam We study the problem of optimal inventory order timing when the selling price and demand are random and their forecasts improve with time. We show that the optimal timing of inventory ordering decision follows a simple threshold policy in the price variable with a possible option of non-purchasing, and is independent of the demand. Given this policy structure, we evaluate the benefits of timing flexibility using the best pre-committed order timing policy as the benchmark. 2 - Wisdom Of Crowds: Forecasting Using Prediction Markets Ruomeng Cui, Kelley School of Business, Indiana University, Bloomington, IN, 47401, United States, cuir@indiana.edu Achal Bassamboo, Antonio Moreno-Garcia Prediction markets are virtual markets created to aggregate predictions from the crowd. We examine data from a public prediction market and internal prediction markets run at three corporations. We study the efficiency of these markets in extracting information from participants. We show that the distribution forecasts, such as sales and commodity prices predictions, generated by the crowds are perfectly calibrated. In addition, we run a field experiment to study drivers of forecast accuracy. 3 - Linking Operational Performance To Financial Distress In The U.S. Airline Industry Yasin Alan, Vanderbilt University, Nashville, TN, United States, yasin.alan@owen.vanderbilt.edu, Michael A Lapre We study the impact of four areas of operational performance -revenue management, operational efficiency, service quality and operational complexity- on financial distress in the U.S. airline industry using quarterly data from 1988 through 2013. Our findings suggest that operational metrics convey useful information regarding future financial distress even after controlling for financial ratios that predict bankruptcies.

Managing Uncertainties in Projects Invited: Project Management and Scheduling Invited Session Chair: Janne Kettunen, The George Washington University, Washington, DC, United States, jkettune@gwu.edu 1 - Zooming In On The Innovator’s Bias Within Organizations Fabian Sting, Erasmus University Rotterdam, Rotterdam School of Management, fsting@rsm.nl, Christoph Fuchs, Maik Schlickel Firms in competitive industries strive for process innovations, and one source of such ideas is the firm’s workforce. In the selection process, firms rely on input from ideating employees - input that might contain systematic errors (biases) and/or unsystematic errors (noise). We study such errors by analyzing the process innovation ideas considered by an automotive manufacturer. Our data set is unique in that it includes information on idea generation, employee evaluation, standardized value calculation, selection, and implementation. Overall, our findings contribute to a more differentiated yet theoretically coherent understanding of the innovator’s bias in organizations. 2 - To Better Manage Risks In New Product Development Portfolio Selection – Be Risk Neutral Janne Kettunen, Assistant Professor, The George Washington University, 2201 G Street, NW, Washington, DC, 20052, United States, jkettune@gwu.edu, Shivraj Kanungo We investigate trade-offs between risk and return in multi-period new product development (NPD) portfolio selection problems, where new development projects become periodically available. Our analytical and computational results show that, paradoxically, a risk-neutral NPD portfolio selection approach provides higher return and lower risk than a risk-averse selection approach. This result can explain why leading innovators tend to employ a risk-neutral NPD selection approach. The risk of the NPD portfolio can be mitigated by (i) reviewing portfolios more frequently and (ii) increasing the proportion of derivative products instead of platform products. 3 - Project Portfolio Selection – A Behavioral Study Sebastian Schiffels, Technical University of Munich, Munich, 80333, Germany, sebastian.schiffels@wi.tum.de, Thomas Fliedner, Rainer Kolisch Choosing the right set of projects is a key driver of success and failure in new product development. We conducted experimental studies based on the knapsack problem to address the question which decision rules individuals apply to select a portfolio as well as how cognitive limitations influence their selection. Grounded in portfolio selection practice, we investigate subjects’ adherence to four heuristics. Decision making is partially explained by adherence to two simple rules, but problem complexity limits the application of such rules as subjects apply a local search. Furthermore, decision maker prefer projects with low risk resulting in portfolios with few high risk high impact projects. 4 - Initiating Supplier New Product Development Projects: A Behavioral Investigation

David Wuttke, EBS University, Wiesbaden, Germany, david.wuttke@ebs.edu, Karen Donohue, Enno Siemsen

Using a combination of analytical models and laboratory experiments, we study the effectiveness of buyer contract mechanisms, including breach penalties and profit sharing, on incentivizing product innovation at the supplier level. Our results provide insight into how the mechanisms can be altered to better account for supplier-specific behavior.

SD26 110B-MCC Auctions and Trading Agents

Invited: Auctions Invited Session Chair: Wolfgang Ketter, Rotterdam School of Management, Rotterdam, Netherlands, wketter@rsm.nl 1 - Using Optimal Grid Resources For Coordinating Electric Vehicle Charging Konstantina Valogianni, IE Business School, Madrid, Spain, konstantina.valogianni@ie.edu, Alok Gupta, Wolfgang Ketter, Soumya Sen, Eric F Van Heck We propose a social welfare maximization mechanism to optimally schedule EV charging, ensuring the lowest overall delay for the EV owners. At the same time, our mechanism creates electricity peak demand reduction which is important for improving sustainability in the grid. Our solution has lower computational complexity, compared to state of the art mechanisms, making it easily applicable

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