2015 Informs Annual Meeting

MC49

INFORMS Philadelphia – 2015

2 - Value of Dynamic Pricing in Congestible Systems Jeunghyun Kim, University of Southern California, Marshall School of Business, Bridge Hall 401, Los Angeles, CA, 90089, United States of America, jeunghyun.kim.2015@marshall.usc.edu, Ramandeep Randhawa From UBER to express lanes on highways, dynamically changing the premium for access to limited resources based on congestion is prevalent. Our research question is: what is the value of dynamic pricing over static pricing in such systems. By modeling a firm that caters to price- and delay-sensitive customers, we analytically prove that the value can be significant and a simple dynamic scheme of using only two price points reaps most of this value. 3 - Price Competition with Customer Search in Congested Environments Laurens Debo,Associate Professor, Dartmouth College, 100 Tuck Hall, Hanover NH 03755, United States of America, laurens.g.debo@tuck.dartmouth.edu, Varun Gupta, Luiyi Yang We study how firms compete in service rate when congestion-sensitive customer search, at some cost, for the firm with the shortest line. We find that decreasing search costs increases search and intensifies service rate competition, which reduces firms’ equilibrium profits. Firms can get around by inflicting random costs on customers. 4 - Learning and Earning for Congestion-prone Service Systems Philipp Afeche, Associate Professor, University of Toronto, 105 St. George Street, Toronto, ON, M5S3E6, Canada, afeche@rotman.utoronto.ca, Bora Keskin We consider a firm that sells a service in a congestion-prone system to price- and delay-sensitive customers. The firm faces Bayesian uncertainty about the consumer demand for its service and can dynamically make noisy observations on the demand. We characterize the structure and performance of the myopic Bayesian policy and well-performing variants. Our results show that capacity constraints have an important effect on performance. MC47 47-Room 104B, CC Energy Operations and Energy Efficiency Sponsor: Manufacturing & Service Oper Mgmt/Sustainable Operations Sponsored Session Chair: Nur Sunar, Assistant Professor, University of North Carolina, Kenan-Flagler School of Business, Chapel Hill, NC, United States of America, Nur_Sunar@kenan-flagler.unc.edu 1 - A Unifying Framework for Consumer Surplus under Stochastic Demand Georgia Perakis, MIT, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, georgiap@mit.edu, Maxime Cohen, Charles Thraves We present a general extension of the consumer surplus for stochastic demand under several capacity rationing rules. We derive this extension from a graphical approach as well as from a utility maximization perspective. We then use this definition to study the impact of demand uncertainty on consumers in interesting applications including the electric vehicle market. We show that in many cases demand uncertainty may actually hurt consumers. 2 - Optimal Utilization of Energy Storage for Energy Shifting Gilvan (Gil) Souza, Professor, Indiana University, Kelley School of Business, Bloomington, IN, 47405, United States of America, gsouza@indiana.edu, Shanshan Hu, Shanshan Guo Batteries may be used for energy shifting in the power system: storing electricity when the power supply is abundant and cheap, and releasing electricity when the supply is tight and more expensive. Both permanent capacity loss and useful life of a battery are affected by discharge decisions in energy shifting. This paper studies the optimal discharge decisions that maximize the total profit of energy shifting in a battery’s entire life. 3 - Do Profitable Carbon Emission Reduction Opportunities Decrease Over Time? A Perspective Based on CDP Christian Blanco, University of California-Los Angeles, Los Angeles, CA, United States of America christian.noel.blanco@gmail.com, Felipe Caro, Charles Corbett Using climate change-related surveys collected by CDP (formerly the Carbon Disclosure Project), we investigate if firms experience decreasing opportunities for profitable initiatives to reduce greenhouse gas emissions. We also characterize payback and marginal abatement costs of these energy efficiency investments over time.

4 - Strategic Commitment to a Production Schedule with Supply and Demand Uncertainty: The Renewable Power Nur Sunar, Assistant Professor, University of North Carolina, Kenan-Flagler School of Business, Chapel Hill, United States of America, Nur_Sunar@kenan-flagler.unc.edu, John Birge How should a renewable power producer strategically commit to a production schedule in a day-ahead electricity market? How does this commitment affect the day-ahead price? Motivated by these important questions, we introduce and analyze via the ODE theory a supply function competition model with demand and supply uncertainty. Using our novel equilibrium characterization, we study the implications of different penalty schemes and subsidy for equilibrium production schedules and market outcomes. Emerging Topics in Supply Chain Management Sponsor: Manufacturing & Service Oper Mgmt/Supply Chain Sponsored Session Chair: Hakjin Chung, Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI, United States of America, hakjin@umich.edu Co-Chair: Kun Soo Park, Assistant Professor, KAIST College of Business, 85 Hoegi-ro, Dongdaemun-gu, Seoul, 130722, Korea, Republic of, kunsoo@kaist.ac.kr 1 - The Newsvendor under Demand Ambiguity: Combining Data with Moment and Tail Information Data-driven approaches typically assume that the planner has no information beyond the evolving history of demand observations. The planner may, however, have partial information about the demand distribution in addition to demand observations. We propose a non-parametric, maximum-entropy based technique, termed SOBME (Second Order Belief Maximum Entropy), which allows the planner to effectively combine demand observations with partial distributional information. 2 - Managing The Supply-demand Mismatch with Complementary Product Flow Options Alexander Angelus, University of Texas, To address the pervasive supply-demand mismatch in multi-stage supply chains with stochastic demand, we use the option to expedite shipments downstream to manage excess demand, and allow for returns of stock upstream to deal with excess inventory. We identify the optimal policy that decomposes this multi- dimensional problem into single-dimensional subproblems. Our numerical studies of supply chains with both expediting and returns of stock find those two product flow options to be complementary. 3 - Capacity Investment with Demand Learning Anyan Qi, Assistant Professor, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, United States of America, axq140430@utdallas.edu, Amitabh Sinha We study a firm’s strategy to adjust its capacity using information learned from observed demand. We characterize the firm’s optimal policy and develop an easily-implementable and data-driven heuristic about when and by how much the firm should adjust its capacity. We also numerically validate the performance of our heuristic. 4 - Sequential Capacity Allocation under Order Manipulation: Efficiency and Fairness Soroush Saghafian, Harvard University, 79 JFK Street, Cambridge, MA, 02138, United States of America, Soroush.Saghafian@asu.edu, Brian Tomlin Jindal School of Management, Dallas, TX, United States of America, alexandar.angelus@utdallas.edu, Ozalp Ozer MC49 49-Room 105B, CC

Kun Soo Park, Assistant Professor, KAIST(Korea Advanced Institute of Science and Technology), 410 Supex Bldg, 85 Hoegiro, Dongdaemun-g, Seoul, Korea, Republic of, kunsoo@business.kaist.ac.kr, Seyed Iravani, Bosung Kim

We analyze the strategic behaviors of the supplier and manufactures in sequential capacity allocations when the manufacturers’ order strategy is not necessarily truthful to the supplier. We show how an allocation changes under order manipulation and consider two directions to improve sequential allocation mechanisms under order manipulation from the perspective of efficiency and fairness of an allocation.

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