2015 Informs Annual Meeting

TA54

INFORMS Philadelphia – 2015

4 - When to Hire the First Employee? Behavioral Evidence and Insights Beatrice Boulu-reshef, Behavioral Research Associate, Darden School of Business, 100 Darden Boulevard, Charlottesville, VA, 22903, United States of America, Boulu-ReshefB@darden.virginia.edu, Anton Ovchinnikov, Charles Corbett Effectively any entrepreneur shifts from doing all the work him/herself to hiring someone to do part of that work. We use an analytical model and behavioral experiments to study when entrepreneurs should and do hire their first employee. Understanding both the optimal timing/conditions of hiring and the deviations of the hiring patterns from optima have the potential to provide insights to a very broad spectrum of entrepreneurs at the critical early stage of their new venture formation process. TA54 54-Room 108A, CC Applying Machine Learning in Online Revenue Chair: David Simchi-Levi, Professor, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, dslevi@mit.edu 1 - Tutorial: Applying Machine Learning in Online Revenue Management David Simchi-Levi, Professor, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, dslevi@mit.edu In a dynamic pricing problem where the demand function is unknown a priori, price experimentation can be used for demand learning. In practice, however, online sellers are faced with a few business constraints, including the inability to conduct extensive experimentation, limited inventory and high demand uncertainty. In this talk we discuss models and algorithms that combine machine learning and price optimization that significantly improve revenue. We report results from live implementations at companies such as Rue La La, Groupon and a large European Airline carrier. TA55 55-Room 108B, CC Extensions of DEA Cluster: Data Envelopment Analysis Invited Session Chair: Endre Bjorndal, Associate Professor, Norwegian School of Economics, Helleveien 30, Bergen, 5045, Norway, Endre.Bjorndal@nhh.no 1 - Assessment of Alternative Approaches to Include Exogenous Variables in DEA Estimates Jose M. Cordero, Universidad de Extremadura, Av Elvas sn, Badajoz, Spain, jmcordero@unex.es, Daniel Santin The aim of this paper is to compare the performance of some recent methods developed in the literature to incorporate the effect of external variables into the estimation of efficiency measures such as the conditional approach developed by Daraio and Simar (2005, 2007) or the one-stage model proposed by Johnson and Kuosmanen (2012). To do this, we conduct a Monte Carlo experiment using a translog function to generate simulated data. 2 - Compensating for Exogenous Cost Drivers in the Regulation of Electricity Networks Endre Bjorndal, Associate Professor, Norwegian School of Economics, Helleveien 30, Bergen, 5045, Norway, Endre.Bjorndal@nhh.no, Maria Nieswand, Mette Bjørndal, Astrid Cullmann The present yardstick model used by the Norwegian regulator compensates, via two-stage DEA efficiency analysis, for a number of environmental factors. These factors are correlated with measured efficiency and company size. We compare conditional nonpararametric methods to current benchmarking model, and we discuss whether the choice of method affects the revenue caps of companies in a systematic manner. Management Cluster: Tutorials Invited Session

3 - Slacks-based Measure Variations Revisited Kaoru Tone, Professor, National Graduate Inst. for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo, 106-8677, Japan, tone@grips.ac.jp In Tone (2010), I developed four variants of the SBM model where main concerns are to search the nearest point on the efficient frontiers of the production possibility set. However, in the worst case, a massive enumeration of facets of polyhedron associated with the production possibility set is required. In this paper, I will present a new scheme for this purpose which requires a limited number of additional linear program solutions for each inefficient DMU. TA56 56-Room 109A, CC Execution Mode Choices for NPD Cluster: New Product Development Invited Session Chair: Pascale Crama, Singapore Management University, 50 Stamford Road, Singapore, 178899, Singapore, pcrama@smu.edu.sg 1 - Managing Exploration and Execution Nittala Lakshminarayana, University of California San Diego, 9256 Regents Road Apt. G, La Jolla, CA, 92037, United States of America, Lakshminarayana.Nittala@rady.ucsd.edu, Sanjiv Erat, Vish Krishnan We model Innovation as a multi-stage activity consisting of Exploration and Execution. Within this parsimonious model that mimics many contexts in Innovation, we consider the effect of incentives and several institutional features Sreekumar Bhaskaran, sbhaskar@mail.cox.smu.edu, Amit Basu Involving customers in the new product design can be a powerful means to achieve high levels of customer satisfaction and market success. However, the “co-design” process may require participating customers to commit significant time and effort, while facing the uncertainty that the firm may overprice the custom product. Since this reduces a customers incentive to commit effort up- front, co-design can be difficult to motivate. We develop analytical models that capture these various effects. 3 - Flexibility and Knowledge Development in Product Development: Insights from a Landscape Search Model Mohsen Jafari Songhori, Jsps Research Fellow, Tokyo Institute of Technology, J2 Bldg., Room 1704, 4259 Nagatsuta-cho,, Tokyo, 226-8502, Japan, mj2417@gmail.com, Majid Abdi, Takao Terano This study introduces a landscape model of Product Development (PD). The model captures different PD performance aspects (e.g. development time, quality and cost) and their trade-offs. Moreover, knowledge development dynamics and flexibility are incorporated in the model to investigate how strategies toward these, in PD process, are associated with the performance measures. TA57 57-Room 109B, CC Applications of Stochastic and Dynamic Programming in Energy Sponsor: ENRE – Energy II – Other (e.g., Policy, Natural Gas, Climate Change) Sponsored Session Chair: Andrew Liu, Assistant Professor, Purdue University, 315 N. Grant Street, West Lafayette, IN, 47907, United States of America, andrewliu@purdue.edu 1 - Approximate Dynamic Programming for Pricing-based Real-time Demand Management Ozgur Dalkilic, The Ohio State University, 205 Dreese Labs, 2015 Neil Ave, Columbus, OH, 43210, United States of America, dalkilic.1@buckeyemail.osu.edu, Atilla Eryilmaz, Antonio Conejo We consider the real-time demand management problem of a load aggregator that coordinates the consumer demand to match a predetermined daily load. The aggregator’s objective is to minimize its payment to the real-time market. Under uncertainty of the market prices, we derive dynamic pricing algorithms that approximate the optimal dynamic programming solution. We show via numerical investigations that the proposed algorithms coordinate flexible demand and achieve close to optimal allocation. on the optimal idea generation and development strategy. 2 - Customer Co-design: The Role of Product Lines

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