2015 Informs Annual Meeting

SC76

INFORMS Philadelphia – 2015

SC77 77-Room 300, CC Supply Chain Management III Contributed Session Chair: Pengyu Chen, PhD Student, Huazhong University of Science &Technology, 1037 Luoyu Road, Wuhan, China, andychen@hust.edu.cn 1 - Control and Emergence in Supply Networks Anand Nair, Michigan State University, 632 Bogue St., East Lansing, MI, United States of America, nair@broad.msu.edu, Ilaria Giannoccaro, Thomas Choi In this paper we consider the supply network of Honda as the focal firm and develop an empirically-informed NK simulation model to examine how varying levels of control influence performance of supply networks. 2 - A Study on Internet of Things and Supply Chain Agility Bo Li, Ashland University, 401 College Ave, Ashland, OH, United States of America, bli@ashland.edu Internet of Things (IoT) changes our daily life and business world, thus supply chain managers must answer the question about how IoT can improve their supply chain performances. This research develops a conceptual model with simulation demonstration, to study the relationship among IoT, supply chain agility, and supply chain performances. 3 - Orders and Reciprocity in the Technology Supply Chain Heejong Lim, Purdue University, 403 W. State Street, West Lafayette, IN, United States of America, limh@purdue.edu, Ananth Iyer Motivated by the semiconductor and the LCD industry, we incorporate the reciprocal game in a dyadic (buyer-supplier) supply channel. In our model, the buyer’s anticipated reciprocal behavior influences the seller’s order so as to protect the seller during the oversupply period. We provide an analysis under different level of reciprocity following accepted economic models in order to explore the impact on order size and channel coordination. Insights from practice are then provided. 4 - Vertical Integration in Two-tier Decentralized Supply Chain Pengyu Chen, PhD Student, Huazhong University of Science &Technology, 1037 Luoyu Road, Wuhan, China, andychen@hust.edu.cn, He Xu We study a supply chain with three types of firms (i.e. suppliers, manufacturers and integrated firms). Integrated firms can sell parts and final products while suppliers and manufacturers can sell only one of them. We find the conditions under which integrated firms sell parts to other manufacturers and investigate how prices and social wealths are affected. We also answer the question when two individual firms prefer to vertically merging. Chair: Lisa Yeo, Assistant Professor, Loyola University Maryland, 4501 N. Charles St., Sellinger Hall 306, Baltimore, MD, 21210, United States of America, mlyeo@loyola.edu 1 - Perspectives on Supply Chain Corruption and Risk Management Xiaojing Liu, PhD Candidate, The University of Auckland, 12 Grafton Road, Auckland City, Auckland, 1010, New Zealand, xiaojing.liu@auckland.ac.nz, Tiru Arthanari Research on supply chain risk management is still immature and of increasing importance. Corruption from the perspective of supply chains largely lacks research. We propose a conceptual framework for managing corruption risk in supply chains. Case studies help verify and establish factors. System dynamics modelling clarifies complex relationships. We anticipate exploring how corruption risks modify supply chain risk factors and performance, and how to safeguard supply chains against such risks. 2 - An Examination of the Impact Supplier Flexibility and Reliability on Supply Chain Resilience Masoud Kamalahmadi, Student, North Carolina A&T State University, 1601 E. Market St, Greensboro, NC, 27411, United States of America, Mkamalah@aggies.ncat.edu, Mahour Mellat Parast In this paper, the impact of supplier flexibility and reliability on supply chain resilience is examined. We develop a mathematical model to minimize the impact of two types of supply chain disruptions (supply disruption and environmental disruption), and propose strategies for supplier selection and allocation to minimize supply chain risks and costs. Risk of delivery reliability is also examined in this study. SC78 78-Room 301, CC Supply Chain Risk Management I Contributed Session

4 - Initiative Scheduling Mode for Physical Internet-based Manufacturing System Jun-Qiang Wang, Professor, Northwestern Polytechnical University, Box 554, No. 127 West Youyi Road, Department of Industrial Engineering, Xi’an, 710072, China, wangjq@nwpu.edu.cn, Guo-qiang Fan, Shu-dong Sun, Ying-feng Zhang We categorize scheduling as passive scheduling and initiative scheduling, depending on who takes the initiative and controls the decision-making of scheduling in physical internet-based manufacturing systems. Focusing on initiative scheduling mode, we model a p-shaped framework for scheduling elements, analyze the decentralized operation mode, explore the individual and organizational scheduling behaviors among enabling units. Finally we discuss some future research directions. SC76 76-Room 204C, CC Design of Experiments and Statistical Analysis for Simulation Sponsor: Simulation Sponsored Session Chair: Feng Yang, Associate Professor, West Virginia University, P.O.Box 6070, Morgantown, United States of America, Feng.Yang@mail.wvu.edu Co-Chair: Hong Wan, Associate Professor, Purdue University, 315 N. Grant Street, GRIS 327, West Lafayette, United States of America, hwan@purdue.edu 1 - Simulation Experiments Involving Stochastic Optimization Models for Disaster Relief Susan Sanchez, Professor, Naval Postgraduate School, Operations Research Dept, 1411 Cunningham Rd, Monterey, CA, 93943, United States of America, smsanche@nps.edu, Emily Craparo, Maxine Gardner Stochastic optimization approaches have been used for determining effective disaster relief operations, but may not fully capture the uncertainty that is inherent in these disasters and the demands that result. We use large-scale design of experiments to more fully investigate the effect of variability on the solution of a two-stage mixed-integer stochastic optimization model that explores using UAVs as logistics assets when planning the Navy’s logistics response to natural disasters. 2 - Sequential Experimental Designs for Stochastic Kriging Xi Chen, Assistant Professor, Industrial and Systems Egnineering at Virginia Tech, 1145 Perry St., Blacksburg, VA, 24061, United States of America, xchen.ise@vt.edu In this work, we establish a sequential experimental design framework for applying Stochastic Kriging (SK) to predicting performance measures of complex stochastic systems. We show how the integrated mean squared error of prediction decreases as additional simulation runs are allocated sequentially, and derive some efficient sequential design strategies from these analytic results. The performance of the proposed sequential design strategies is demonstrated through numerical examples. 3 - Block-coordinate Strong for Large Scale Simulation Optimization Hong Wan, Associate Professor, Purdue University, 315 N. Grant Street, GRIS 327, West Lafayette, United States of America, hwan@purdue.edu, Wenyu Wang STRONG is a response surface methodology (RSM) based algorithm that iteratively constructs linear or quadratic fitness model to guide the searching direction within the trust region. For high-dimensional problem, this paper modify the Block Coordinate Descent framework to fit the STRONG algorithm, and propose an algorithm which guarantees to converge to a stationary point. This is the first result establishing the BCD framework in high dimensional simulation optimization problems.

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