Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SD50

2 - Introducing Swing Shifts to Dynamically Respond to Emergency Department Workload Uncertainty David L. Kaufman, University of Michigan - Dearborn, 19000 Hubbard Drive, Fairlane Center South, Dearborn, MI, 48126, United States, Kalyan Pasupathy, Daniel Cabrera, Mustafa Y. Sir A fundamental problem of emergency care is matching resources to uncertain patient demands. Staffing allocation decisions require good matching with workloads but also consider the needs of emergency providers at very high risk of burnout. Mayo Clinic Emergency Department recently introduced a “swing shift, which allows physicians to leave early depending on a workload threshold. While popular, swing shifts introduce several challenges: How to design a threshold mechanism? What is the optimal length of the furlough? When should these shifts start and what is their impact? We introduce an effective and tractable data- driven optimization model for a complex stochastic problem. 3 - Maximizing On-time Jobs for the Customer Order Scheduling Problem Hairong Zhao, Purdue University Northwest, 2200 169 Street, Hammond, IN, 46323, United States We consider the problem of scheduling multi-task jobs on parallel machines. Each job consists of one or more tasks. Each job has a release date and a due date. A task of a job can be processed by any one of the machines. Multiple machines can process the tasks of a job concurrently. The objective is maximizing the number of on-time jobs. We show that while the general problem is NP-hard, some special cases are solvable. For the general case, we develop some heuristics whose performance is evaluated by experimental results. 4 - Cost Allocation in Rescheduling with Machine Unavailable Period Zhixin Liu, University of Michigan-Dearborn, 19000 Hubbard Drive, Dearborn, MI, 48126-4100, United States, Liang Lu, Xiangtong Qi We study a rescheduling problem faced by multiple jobs owners sharing a single machine, where jobs need to be rescheduled, when the machine becomes unavailable for a period. We define a feasible schedule over which cost saving can be achieved by optimal rescheduling, and then formulate a cooperative game for job owners accordingly, to share the cost saving. Given that the optimization problem is computationally intractable, we find several optimal properties and develop an optimal pseudopolynomial time dynamic programming algorithm for rescheduling. We provide a simple closed form core allocation of the total cost saving for all the jobs, and provide the Shapley value of the game in a computable form. n SD52 North Bldg 231C Social Media for Socially Responsible Operations Emerging Topic: Social Media Analytics Emerging Topic Session Chair: Moravec Tricia, IN, United States Co-Chair: Alfonso Pedraza, Indiana University, Bloomington, IN, 47405, United States 1 - Transparency in Crowdfunding for Emergency Management Gloria Urrea, Indiana University, Bloomington, IN, 47408, United States, Jorge Mejia, Alfonso J. Pedraza-Martinez We study online crowdfunding as a tool to increase funding for emergency relief campaigns. Crowdfunding campaigns can use two tools to increase the transparency provided to potential donors: certification and online updates. Certification is a form of conventional transparency that ensures the campaign is benefiting a charitable purpose. Alternatively, updates are additional status posts and are a form of operational transparency when they communicate the work of the campaign. Using data from a large crowdfunding website, we show that work- related updates (operational transparency) have a stronger effect on increasing donations than certification (conventional transparency). 2 - Matching Donors to Projects on Charitable Giving Platform Yicheng Song, University of Minnesota, Minneapolis, MN, United States, Zhuoxin Li, Nachiketa Sahoo Matching donors with causes is critically important in philanthropy. We propose a donors-project match mechanism, considering donors’ preferences, budget, and cognitive limitations as well as the dynamic status and budgetary needs of the projects. The proposed model better captures donation behavior than several benchmarks. Using the estimated model, we design optimal recommendation policies to maximize fundraising success. By matching projects to donors, not only based on the donors’ preference, but also their budget, and their willingness to support projects with different odds of success, the optimal recommendation strategies increase the donations raised by about 22%.

n SD50 North Bldg 231A Joint Session Practice/Practice Curated: Edelman Reprise II Sponsored: INFORMS Section on Practice (formerly CPMS) Sponsored Session Chair: Anne G. Robinson, Verizon Wireless, Basking Ridge, NJ, 07920, United States Co-Chair: Carrie Beam, University of Arkansas, Fayetteville, AR, 94596, United States 1- Pediatric Heart Network Eva Lee, Georgia Tech, Industrial & Systems Engineering, Ctr for Operations Research in Medicine, Atlanta, GA, 30332-0205, United States The Pediatric Heart Network enlisted researchers with the Georgia Institute of Technology to create clinical practice guidelines (CPG) for pre-, intra-, and post- surgical care of patients with congenital heart defects (CHDs), the most common birth defect, impacting nearly 1 million children and 1.4 million adults in the U.S. Substantial variances in surgical practices to treat patients with CHDs among different healthcare centers were reflected in inconsistent surgical outcomes, some of which resulted in negative consequences for patients. By studying the nine leading U.S. pediatric centers, the researchers identified seven significant factors for influencing surgical outcome, and implemented a CPG that enables patients to be removed from breathing apparatuses earlier, lowered the rate of reintubation, and decreased the time patients need to remain in the intensive care unit. These guidelines also realized a cost savings of 27 percent, which translates to $13,500 per patient. 2 - Analytics Makes Inventory Planning A Lights-Out Activity at Intel Corporation Sean Willems, University of Tennessee, 617 Commodore Lane, Knoxville, TN, 37934, United States Intel, which employs more than 100,000 people in over 70 countries around the world and has an annual revenue of $60 billion, implemented a fully automated Multi-Echelon Inventory Optimization (MEIO) based inventory target-setting system managing $1 billion daily in finished goods inventory representing over $40B a year in sales. Algorithm-derived inventory targets at Intel are accepted by planners +99.5 percent of the time and have simultaneously driven higher customer service and lower inventory levels resulting in over $1.3B in gross profit since 2014. In addition, customers are delighted: since MEIO was implemented at all of Intel’s vendor managed inventory hubs in 2012, customer satisfaction has never been higher and Intel has landed in the top-10 of Gartner’s Supply Chain Top-25 every year. Faculty in the department of Business Analytics and Statistics at the University of Tennessee, Knoxville and the supply chain software company Logility also contributed to this project. n SD51 North Bldg 231B Joint Session OMS/Practice Curated: Applied Scheduling Emerging Topic: Project Management and Scheduling, in Memory of Joe Leung, Emerging Topic Session Chair: Zhixin Liu, University of Michigan - Dearborn, 19000 Hubbard Dr, Dearborn, MI, 48126, United States 1 - Scheduling Jobs on Mixed Batching Machines This paper considers a mixed batching model that is different from the parallel- batch and the serial-batch. The mixed batching machine can process at most b jobs simultaneously. The processing time of a batch is the weighted sum of the maximum processing time and the total processing time of the jobs in the batch. The objective is to minimize the makespan. We first prove that the Full Batch Longest Processing Time (FBLPT) algorithm yields an optimal schedule for the problem on a single mixed batching machine. Then we show the NP-hardness of the problem on parallel mixed batching machines. We analyze the worst-case ratio of FBLPT algorithm and a modified-FBLPT algorithm. Guoqiang Fan, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, SN 29, China, Junqiang Wang

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