Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

TA80

5 - Retailers’ Order Placement Timing under Competition and Demand Uncertainty Ozgun Caliskan Demirag, Associcate Professor of O&SCM, Penn State Erie, The Behrend College, Black School of Business, 5101 Jordan Road Rm Burke 259, Erie, PA, 16563, United States, Weili Xue We consider a supply chain with two retailers and a common supplier. Retailers sell differentiated but substitutable products over a limited selling period and face demand uncertainty. We investigate the retailers’ timing of their order placement with the supplier in relation to demand realization and identify the equilibrium strategies. Surprisingly, we find that asymmetric order placement strategies can arise as an equilibrium decision for symmetric retailers. n TA80 Hyatt, Curtis B Practice- Supply Chain Management VI Contributed Session Chair: Mike Sherwin, Mississippi State University, 1767 Independence Way, Valencia, PA, 16059, United States 1 - Should the Manufacturer Offer an Emergency Order Opportunity with Uncertain Customer Demand Meimei Zheng, Shanghai Jiao Tong University, Dongchuan Road 800,, Minhang District, Shanghai, 200240, China, Xue-Ming Yuan We consider a manufacturer-retailer supply chain in the presellingand selling seasons, where the manufacturer can offer theretailer an emergency order opportunity with limited commitment quantity, in addition to the regular order from the retailer before the selling season. Through mathematically modeling and analyzing the supply chain, it is found that when the emergency order opportunity is provided, the manufacturer might be worse off although the retailer is always better off. We derive the conditions where both manufacturer and retailer can benefit from the emergency order, and the supply chain profit can be maximized. 2 - Advertising in a Capital-constrained Supply Chain with Retailers’ Different Market Shares Haijun Wang, Shanghai Jiao Tong University, Huashan Road 1954, Xuhui, Shanghai, 200030, China We consider a supply chain with a manufacturer (leader) and two retailers (followers), where both retailers advertise to stimulate ad-related demand. One retailer is capital-constrained and has access to bank financing. The retailer goes bankruptcy if he cannot pay off his loan obligation. Each retailer has its own market share and unmet demand in one retailer’s market share turn to the other retailer to purchase the product. We formulate the problem as a game theory model with the manufacturer and retailers’ objective to maximize their own expected profits. Numerical examples are provided to illustrate the impact of advertising on retailers’ capital allocation, and their expected profits. 3 - Sourcing Under Multiple Attributes: Cost Sharing Mechanisms Shivam Gupta, University of Nebraska Lincoln, P.O. Box 880491, Lincoln, NE, 68588, United States, Milind Dawande, Ganesh Janakiraman, Shouqiang Wang We propose a simple cost-sharing mechanism for a principal procuring service to complete a project under information asymmetry on the agent’s cost and non-cost estimate. We establish its near-optimality, and provide valuable insights into the nature of cost-sharing for its use in practice. 4 - An Optimized Resource Allocation Approach to Identify and Mitigate Supply Chain Risks using Fault Tree Analysis Mike Sherwin, Mississippi State University, Mississippi State, MS, United States, Hugh Medal, Cameron MacKenzie Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. In this research, we develop novel approaches that provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains.

n TA81 Hyatt, Phoenix East Optimization I Contributed Session Chair: Lakmali Weerasena, University of Tennessee-Chattanooga, 615 McCallie Ave, Chattanooga, TN, 37403, United States 1 - Community Resilience Planning Alternatives Generation Kenneth Harrison, Operations Research Analyst, National Institute of Standards and Technology (NIST), 8701 Susanna Lane, Chevy Chase, MD, 20815-4713, United States Math programming (MP) models will be presented that underpin a decision support tool in development at the National Institute of Standards and Technology (NIST). Most broadly, the models address the search for mitigation/preparedness actions at the community scale that shorten the recovery time from natural hazard events while addressing cost, social and political feasibility. Among the phenomena captured are the stochastic nature of disasters and the linkages between the mitigation decisions and post-event decisions. Important inputs include “interdependency networks”, including a lines-of- defense, cascading failure, and recovery network. 2 - Sustainable Energy Supply Planning Under the Impact of Climate Change Cansu Agrali, PhD Student, Purdue University, West Lafayette, IN, United States, Seokcheon Lee, Roshanak Nateghi Renewable sources (RS) are one of the main part of energy management environment. RS are known for their intermittent nature caused by weather and climate. If RS systems are able to adapt to and even take advantage of the vital climate change, it may be possible to greatly rely on renewable energy sources in the near future. In this study, we investigate a long-term climate-driven energy supply planning model. A mixed integer model is developed and data set is created by using forecasting methodologies. Because of long-term model nature, a heuristic algorithm is needed. Case studies will be conducted with both MILP model and a heuristic algorithm. 3 - Offline Smart Appliance Scheduling in Smart Grid System Nihat Oner, PhD Candidate, TOBB University of Economics and Technology, Ankara, Turkey, Cansu Agrali Home energy management systems are seen as a need to reduce the electricity cost and make efficient the energy management. Coupled with the possibility to generate power at a residential level and the energy storage systems, the management of all devices has been getting more complex. In this work, a smart appliance scheduling in isolated smart grid problem is studied. In this network, renewable generators and energy storage systems are included. As a solution technique, 3 algorithms are designed and tested with benchmarks. The main contribution of this study is a cutting plane algorithm which yields an optimal solution in a reasonable time period. 4 - Weighted P-Dispersion Problem Saeed Chavoshi, NC State University, 3100 Kings Court, Apartment H, Raleigh, NC, 27606, United States, Yahya Fathi Given a collection of p facilities with a weight wl associated with each facility l, and a collection of n locations (n=p) with a symmetric distance dij between each pair of locations i and j, we seek to establish a location for each facility so as to maximize the minimum weighted distance between all pairs of established facilities. We formulate an integer programming model for this problem and propose an effective method for solving the problem by exploiting its relationship with the well known node packing problem. We also present the results of a limited computational experiment. n TA82 Hyatt, Phoenix West Health Care I Contributed Session Chair: Salar Ghamat, Richard Ivey Business School, London, ON, N6H 0C4, Canada 1 - The End User Trust Acceptance Model of Genetic Testing Services Xinyu Wei, University of North Texas, Denton, TX, United States, Heng Xie, Xianghui (Richard) Peng, Victor R. Prybutok This study investigates end user’s perspectives on genetic health risk (GHR) testing services. GHR testing services have expanded dramatically and there is a need to study the trust challenges to end user acceptance of genetic testing services. We develop and test an integrated end user trust model that examines the relationships between trusting bases, trusting beliefs, and trusting intention.

272