2015 Informs Annual Meeting

SB01

INFORMS Philadelphia – 2015

SA79 79-Room 302, CC

3 - Capacity Allocation with Demand Competition: Uniform, Proportional, and Lexicographic Mechanisms Niu Yu, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, China, nyu@hust.edu.cn, Jianbin Li, Zhixin Liu We examine capacity allocation mechanisms in a two-echelon supply chain comprising a monopoly supplier and duopoly retailers with asymmetric market powers and demand competition.The supplier allocates limited capacity to retailers according to uniform, proportional, or lexicographic mechanisms.Our results show that regardless of whether retailer market powers are symmetric or asymmetric, lexicographic allocation, regardless of order priority by retailer, is the best choice for the supplier. 4 - Approximate Dynamic Programming and Real Options Approaches for Sourcing Strategies under Risks Purushottam Meena, Assistant Professor, NYIT, School of Management, Old Westbury NY 11568, United States of America, pmeena@nyit.edu This paper demonstrates the use of real options for the valuation of different sourcing strategies for supply disruptions risk management. The Approximate Dynamic Programming (ADP) approach is used to solve the problem. The results of ADP are compared with backward recursion to benchmark the performance of ADP. SA78 78-Room 301, CC Managing Supply Chain Disruptions Contributed Session Chair: Hsin-Tsz Kuo, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan - ROC, d01741003@ntu.edu.tw 1 - Topological Resilience Analysis of Supply Networks under Random Disruptions and Targeted Attacks Wenjun Wang, University of Iowa, S283 Pappajohn Business Building, Iowa City, IA, 52242, United States of America, wenjun-wang@uiowa.edu, Nick Street, Renato De Matta We exploit the resilience embedded in the supply-network topology by investigating the multiple-path reachability of each demand node to other nodes, and propose a novel network resilience metric. We also develop new supply- network growth strategies that reflect the heterogeneous roles of different types of nodes in the network. We demonstrate the validity of our resilience metric and experimentally show the effectiveness of our growth model. 2 - Post-disaster Disruptive Crop Supply Chain Resilience with Stockpile Hoarding Hsin-Tsz Kuo, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan - ROC, d01741003@ntu.edu.tw, Jiuh-biing Sheu This paper presents a conceptual model to analyze the members’ opportunistic behavior on post-disaster disruptive crop supply chain resilience. In this study, a post-disaster supplier psychology theory is proposed to investigate the antecedents of stockpile hoarding that may be revealed in a disruptive supply chain. Moreover, we examine the decisions of the suppliers with government intervention by selling stockpiled crops to the crop supply chain members for crop market stabilization. 3 - Empirical Research on Supply Chain Resilience Factors Jingjing Li, Student, Huazhng University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan, China, 1471058151@qq.com Supply chain resilience is an effective index to measure the ability of the supply chain to deal with emergencies , including the ability to resist interruptions and self-healing after emergencies . The conceptual model of supply chain resilience is established on the basis of theory , including five factors. According to the empirical study using SEM . The impact of various factors on the supply chain resilience is quantized. It has substantial significance in improving supply chain resilience.

Software Demonstration Cluster: Software Demonstrations Invited Session 1 - Microsoft Power BI – Power BI: Bring your Data to Life Jen Underwood, Microsoft Senior Program Manager, Microsoft In this tutorial session, you will explore Power BI with Microsoft engineering product team members. Feel free to bring a sample of your own data to create your own personalized dashboards. You will learn: • Data preparation: Discover, transform and combine data from various sources. • Data models: Create relationships, categorize your data and author business calculations. • Interactive visual reports: Author professional reports to slice/dice data to discover insights. • Power BI Dashboards: Ask language questions, create real-time dashboards and share insights. • Power BI Mobile Apps: Interact with dashboards from iOS, Android or Windows devices. SB01 01-Room 301, Marriott Network Mapping Sponsor: Military Applications Sponsored Session Chair: Andrew Hall, COL, U.S. Army, 4760 40th St N, Arlington, VA, United States of America, AndrewOscarH@aol.com 1 - Network Inference from Grouped Data Charles Weko, Senior Reserve Manpower Analyst, US Army, 2812 Nobel Fir Court, Woodbridge, VA, 22192, United States of America, charles.w.weko.mil@mail.mil In many fields, network structure is not directly observed. Inferring implicit network structure requires a probabilistic model of grouped data. Grouped data records the manner in which a population forms subsets. In the existing literature, network inference from grouped data is performed using descriptive statistics. This presentation defines stochastic models for modeling group formation and applies the parameters to the famous 18th century Chinese novel, Dream of the Red Chamber. 2 - A Graph Comparison Approach to Network Attack and Defense Jonathan Roginski, Applied Mathematics, NPS, Monterey, United States of America, jwrogins1@nps.edu The promise of network science is to provide a foundation that supports understanding the large, dynamic, complex networks that characterize life today. In the Department of Defense, we create, manage, and defend against change in these complex networks. However, the scientists and analysts providing decision support are constrained by an acknowledged lack of tools for proper modelling of network topology and quantification of network change. In this research, we begin with the well-known problem of graph isomorphism and abstract it to the idea of similarity between graphs, rather than graph “sameness.” We use structure rather than statistics to quantify similarity between graphs by introducing a matrix that captures more graph topological information than existing metrics or descriptive statistics. We show that where current structural metrics fail, the newly introduced distance-k matrix enables discrimination between graphs. We also show a methodology through which a network may be “triaged” to identify vertices which are potentially influential on the network’s topology. Those candidate vertices are then analyzed using the distance-k matrix to determine the “distance between graphs,” thereby quantifying the change in the network under vertex removal. The result is a “target set” of vertices a decision maker can choose from, based upon mission requirements, desired effect, and resources available- supporting attack, defense, and stability operations. 3 - A Framework for Comparing Networks Ralucca Gera, Naval Postgraduate School, Department of Applied Mathematics, Monterey, CA, United States of America, rgera@nps.edu A challenging problem in studying large networks is that networks data is generally incomplete and often impossible to observe all members and interactions within the network. Researches try to infer as much as possible of a network and study its structure and function. Our approach is to create a ground truth topology, infer the topology using several inference algorithms, and then compare the inferred to the true topology, using synthetic and real networks. Sunday, 11:00am - 12:30pm

65