2015 Informs Annual Meeting

WA26

INFORMS Philadelphia – 2015

WA24 24-Room 401, Marriott Robustness and Approximation in Markov Decision Processes Sponsor: Artificial Intelligence Sponsored Session Chair: Marek Petrik, IBM, 1101 Kitchawan Rd., Yorktown Heights, NY, Mohammad Ghavamzadeh, Senior Analytics Researcher, Adobe Research, 321 Park Ave., E7412, San Jose, CA, 95126, United States of America, mohammad.ghavamzadeh@inria.fr In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. We consider variance-related and percentile-based risk- sensitive criteria. For each criterion, we devise algorithms for estimating its gradient and updating the policy parameters in the descent direction. We establish the convergence of our algorithms and demonstrate their usefulness in a variety of control problems. 2 - Ambiguous Joint Chance Constraints with Conic Dispersion Measures Grani A. Hanasusanto, École Polytechnique Fédérale de Lausanne, EPFL-CDM-MTEI-RAO, Station 5, Lausanne, 1015, Switzerland, grani.hanasusanto@epfl.ch, Daniel Kuhn, Wolfram Wiesemann, Vladimir Roitch We analyse the complexity of a class of ambiguous joint chance constrained programs where the uncertain parameters are described through their mean values and through upper bounds on general dispersion measures. We derive explicit conic reformulations for tractable problem classes and suggest efficiently computable conservative approximations for intractable ones. We illustrate the effectiveness of our reformulation in numerical experiments in project management and image denoising problems. 3 - Learning the Uncertainty in Robust Markov Decision Processes Xu Huan, Assistant Professor, National University of Singapore, 9 Engineering Drive 1, Singapore, Singapore, mpexuh@nus.edu.sg, Shie Mannor, Shiau Hong Lim Robust MDP models the parameter uncertainty as arbitrary element of uncertainty sets, and seeks the minimax policy. A crucial problem of robust MDP is how to find appropriate uncertainty. We address this using an online learning approach: we devise an algorithm that, without knowing the true uncertainty model, is able to adapt its level of protection to uncertainty, and in the long run performs as good as the minimax policy knowing the uncertainty model. 4 - Robust Approximate Dynamic Programming Marek Petrik, IBM, 1101 Kitchawan Rd., Yorktown Heights, NY, 10598, United States of America, mpetrik@us.ibm.com I describe how robust MDPs can be used to improve solution quality of both on- policy and policy approximate dynamic programming methods. The robustness addresses both model and sampling error. Finally, I show the utility of robust optimization when computing implementable policies in MDPs. 10598, United States of America, mpetrik@us.ibm.com 1 - Algorithms for Risk-sensitive Optimization in MDPS

2 - Coordinating Co-opetition: Insights from Open-source Cloud Software Development Yash Raj Shrestha, ETH Zurich, Weinbergstrasse 56/58, Zurich, Switzerland, yshrestha@ethz.ch, Shiko Ben-Menahem, Georg Von Krogh Using longitudinal data from OpenStackóan open-source cloud computing software development platform, this study explores why some participating firms exhibit greater success than others in their ability to coordinate activities in a co- opetitive ecosystem. Focusing on patterns of strategic task allocation and completion by firms facing strong competition for highly skilled developers, our study advances understanding on co-opetition and coordination in new organizational forms. 3 - The Movement of Open Source Communities Georg J.P. Link, University of Nebraska Omaha, 6001 Dodge St, Omaha, NE, 68182, United States of America, glink@unomaha.edu, Matt Germonprez In 2010, Oracle acquired OpenOffice during its acquisition of Sun Microsystems. At that time, community members formed the LibreOffice fork under The Document Foundation. And in the following year, Oracle transferred ownership of OpenOffice to the Apache Foundation. We explore thresholds for communal movement and how the transfer of open source projects affects the community. We find that understanding communities as movable reveals their nature as commoditized and consolidated objects of value. WA26 26-Room 403, Marriott Production and Scheduling II Contributed Session Chair: Katariina Kemppainen, School of Business, Aalto University, Runeberginkatu 22-24, Helsinki, 00076 Aalt, Finland, katariina.kemppainen@aalto.fi 1 - A Capacitated Multi-Item Lot-Sizing Problem with Stochastic Setup Times We introduce uncertainty with respect to the setup times in the standard capacitated lot sizing problem. The company fixes a production plan (i.e. timing and level of the production quantities). The company can use overtime if the given capacity is not sufficient due to the specific realizations of the setup times. We develop an efficient procedure to evaluate the expected overtime assuming a specific probability distribution. We also present several MIP-based heuristics to solve this problem. 2 - Issues in Batch Flowshop and Lot Streaming Problems Ramakrishna Govindu, Instructor, University of South Florida, 8350 N Tamiami Tr, SMC-C263, Sarasota, FL, 34243, United States of America, rgovindu@sar.usf.edu, Anurag Agarwal The lot streaming problem attempts to find sublots to reduce the makespan. We treat this problem as a multiobjective problem that attempts to strike a balance between makespan and cost of handling the sublots. We propose some heuristics and properties of the problem. 3 - Routing and Spectrum Assignment in Rings We present a theoretical study of the routing and spectrum assignment (RSA) problem in ring networks. We show that the RSA problem with fixed-alternate routing in general topology networks is a special case of a multiprocessor scheduling problem. We then investigate two problems: the spectrum assignment problem under the shortest path assumption and the general RSA whereby a routing decision must be made jointly with spectrum allocation. We then develop a suit of heuristic algorithms. 4 - Cutting Stock with Sequence Dependent Set-up Times: An Application to a Large Scale Industry Problem David Wuttke, EBS University, EBS University, ISCM, Burgstr. 5, Oestrich-Winkel, 65375, Germany, david.wuttke@ebs.edu, Sebastian Heese, Florian Gojny We consider a two-dimensional cutting stock problem with sequence dependent set-up times and tolerances as witnessed in textile and fiber-composite industries. To solve real-life-instances we provide a decomposition heuristic that first identifies optimal cutting patterns and then optimizes their sequence by minimizing the number of knife relocations. Raf Jans, Professor, HEC Montreal, 3000 Chemin de la Cote-St-Catherine, Montreal, QC, H3T 2A7, Canada, raf.jans@hec.ca, Michel Gendreau, Duygu Tas, Ola Jabali Sahar Talebi, North Carolina State University, Operations Research and Computer Science, Raleigh, NC 27695, United States of America, stalebi@ncsu.edu

WA25 25-Room 402, Marriott Managing Sustained Participation in Online Communities Sponsor: Information Systems Sponsored Session

Chair: Pratyush Nidhi Sharma, Assistant Professor, University of Delaware, 010 Purnell Hall, University of Delaware, Newark, DE, 19716, United States of America, pnsharma@udel.edu 1 - The Impact of Person-organization Fit and Psychological Ownership on Turnover in Open Source Software Projects Tingting Rachel Chung, Chatham University, 106 Woodland Road, Pittsburgh, PA, United States of America, Rchung@chatham.edu, Pratyush Nidhi Sharma, Sherae Daniel Open source software projects represent an alternate form of software production by relying on voluntary contributions. Most projects fail to sustain their development due to high turnover. Using 574 survey responses from GitHub, we examined the impact of Person-Organization fit and psychological ownership on developers’ turnover intentions. Results show value and demands-abilities fit negatively impact turnover intentions and that psychological ownership moderates these effects.

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