2016 INFORMS Annual Meeting Program

TC52

INFORMS Nashville – 2016

TC52

context - external knowledge inputs, encouragement of creativity, and high autonomy - are positively related to external commercialization. Sufficient resources are negatively related to external commercialization. 2 - Managing External Intellectual Capital In New Product Development: The Case For Ontological Semantic Analysis Of Patent Data Charles Weber, Portland State University, webercm@pdx.edu, Farshad Madani, Nitin Mayande Historically, intellectual capital from outside the firm has been derived from patent metadata. This paper presents a potentially much more effective approach, which automatically analyzed the body text of patents. 3 - Logistics Service Performance In Nova Scotia: Facilitators, Barriers, And Measurement M. Ali Ulku, Rowe School of Business, Dalhousie University, Halifax, NS, B3H3S7, Canada, ulku@dal.ca, Horand I. Gassmann, Michael Foster Logistics plays a pivotal service role in efficient management of supply chains. Building on the extant literature and company-survey results, we explore the facilitators and barriers logistics companies face in the province of Nova Scotia, Canada. We also propose key metrics for measuring the performance of regional logistics services. 4 - Management Of Service And R&D Portfolios Kai Basner, PhD Candidate, Copenhagen Business School, Solbjerg Plads 3, Frederiksberg, DK-2000, Denmark, kba.om@cbs.dk, Thomas Frandsen, Jawwad Raja, Juliana Hsuan Managing technological innovation is critical to the continued success of industrial companies, which in recent years have been observed to expand their business models by complementing their products with services. For manufacturers with a strong focus on product technology, we explore the challenges of introducing service innovation in R&D portfolios. TC54 Music Row 2- Omni Service Science: Best Cluster Paper Presentation Sponsored: Service Science Sponsored Session Chair: Paul Maglio, University of California, Merced, 5200 N Lake Rd, Merced, CA, 95343, United States, pmaglio@ucmerced.edu 1 - Scheduling Promotion Vehicles To Boost Profits Lennart Baardman, Massachusetts Institute of Technology, Cambridge, MA, United States, baardman@mit.edu, Maxime Cohen, Georgia Perakis, Kiran Panchamgam, Segev Danny With our collaborators from Oracle, we model how to schedule promotion vehicles to maximize profits using ideas from the non-linear bipartite matching problem. Promotion vehicles should be assigned to time periods, subject to capacity constraints. We introduce a class of models for which the boost effects of vehicles on demand are multiplicative. We show that the problem is computationally intractable and develop a greedy method as well as a (1-epsilon)- approximation using an IP of polynomial size. We analyze our methods as well as validate them on actual data and finally, quantify their impact. 2 - Optimizing Precision In Machine Learning Models For Actionable Predictions Of Revenue Change Abhinav Maurya, Carnegie Mellon University, 5634 Stanton Avenue, Apt 306, Pittsburgh, PA, 15206, United States, ahmaurya@cmu.edu, Aly Megahed, Ray Strong, Jeanette Blomberg, Alaa Elwany Predicting changes in account revenues is of vital importance to a business in order to take action on accounts predicted to shrink, and learn success stories of offerings that led to revenue growth. However, the corresponding datasets are often imbalanced, and so optimizing prediction accuracy, as the majority of classifiers do, yields poor results in this case. We present a Gaussian Process-based method that directly maximizes precision subject to a minimum recall level, yielding actionable results without sacrificing much accuracy. Numerical experiments show very promising results.

214-MCC Urban Transportation and Logistics in Public Sector OR II Sponsored: Public Sector OR Sponsored Session

Chair: Sung Hoon Chung, Binghamton University, PO Box 6000, Binghamton, NY, 13902, United States, schung@binghamton.edu 1 - A New Fast Algorithm For The Time-expanded Network Of Dynamic Building Evacuation Dong-jin Noh, Postech, Pohang, Korea, Republic of, visionph@postech.ac.kr, Chang Hyup Oh, Young Myoung Ko, Byung-In Kim Time-expanded network models have been widely used in many evacuation studies. Linear programming and heuristics algorithms have been applied for the models. In this presentation, we propose a new fast exact algorithm for a time- expanded network model arisen in a dynamic building evacuation. The proposed algorithm takes advantage of the characteristic that there exist no cycle in the time-expanded network model. Experimental results demonstrate the efficiency of the proposed approach. The proposed algorithm can be applied for real-world dynamic building evacuation problems. 2 - Human In The Loop Optimization For Recovery From Extreme Events Aybike Ulusan, Northeastern University, Boston, MA, 02115, United States, ulusan.a@husky.neu.edu, Ozlem Ergun We consider the problem faced by contractors of collecting debris from a transportation network to the disposal facilities in the aftermath of a disaster. The problem has a multi-objective nature which embodies implementable division of a service region among subcontractors such that the assigned workload among different subcontractors is balanced and time to complete debris collection operations is minimized. In this study, we explore the potential of having humans collaborate with algorithms, and the use of game-based experiments to build a decision support tool. We investigate how to exploit human input to achieve a performance that can not be achieved by human or computer itself individually. 3 - Clearing Roads And Collecting Debris By Integrating Remote Sensing Technique And Vrp Eunsu Lee, New Jersey City University, ELee3@njcu.edu Every natural disaster generates large amount of debris on roads. Clearing the debris from roads is crucial to recover mobility and accessibility to support humanitarian aids and normal life. This study investigates an integrated approach using remote sensing technology and vehicle routing problem to find, clear, and dispose the items quickly and efficiently. 4 - Disaster Relief Routing under Uncertainty Sung Hoon Chung, Binghamton University, 4400 Vestal Parkway East, Binghamton, NY, 13902, United States, schung@binghamton.edu, Yinglei Li We propose a robust optimization approach for vehicle routing problems (VRPs) under uncertainty for humanitarian logistics. In addition to classical cost- minimizing and route-minimizing objectives of the VRPs, we employ alternative objectives such as minimizing the average arrival time, the latest arrival time, and the demand weighted arrival time, as it is critical for deliveries to be fast and fair in routing for relief efforts. We show the use of the proposed approach using benchmark problems and identify instances in which solutions of the VRP variants are significantly different than ones of conventional VRPs. TC53 Music Row 1- Omni Management of Product and Service Innovation Sponsored: Technology, Innovation Management & Entrepreneurship Sponsored Session Chair: Juliana Hsuan, Professor, Copenhagen Business School, Solbjerg Plads 3, B.5.27, Frederiksberg, DK-2000, Denmark, jh.om@cbs.dk 1 - Linking The Firm’s Internal Innovation Context With Commercialization Choices Lee Davis, Copenhagen Business School, ld.ino@cbs.dk, Karin Hoisl, Jerome Davis This paper investigates the linkages between the firm’s internal innovation context and how the innovation is commercialized (by the firm itself or an external third party). By analyzing these linkages at the level of the individual innovation, we add to the literature on how firms profit from R&D. We base our study on original survey data comprising 3,773 commercialized innovations from 23 countries in all major industries. We find that three aspects of the innovation

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