2015 Informs Annual Meeting

TD15

INFORMS Philadelphia – 2015

TD15 15-Franklin 5, Marriott Capacity Management in Healthcare Operations Sponsor: Optimization in Healthcare Sponsored Session Chair: Sandeep Rath, PhD Candidate, UCLA Anderson, B501 Gold Hall, UCLA Anderson, Los Angeles, CA, 90024, United States of America, Sandeep.Rath.1@anderson.ucla.edu 1 - Workforce Optimization with Patient Volume Variations and Scheduling Pattern Generation for Hospital Xuanqi Zhang, Philips Research North America, 345 Scarborough Rd, Briarcliff Manor, NY, 10510, United States of America, Xuanqi.Zhang@philips.com, Jingyu Zhang A two-stage model is created to optimize hospitals’ workforce which directly affects hospital cost and patients satisfaction. The model uses simulation-based stochastic optimization and heuristics to reduce staffing cost, avoid understaffing and improve scheduling efficiency. The two-stage solution mechanism helps diminish the gap between staffing optimization research and hospital scheduling practice. Results were delivered to and tested in hospitals. 2 - Integer Programming Model to Solve Bloodmobiles Routing Problem Grisselle Centeno, Associate Professor, Univ. of South Florida, 4202 E. Fowler Ave., Tampa, FL, 33620, United States of America, gcenteno@usf.edu, Serkan Gunpinar Blood is a scarce and perishable resource. Approximately 80% of the blood donations are handled remotely via bloodmobiles. Blood center must determine the number of bloodmobile units to operate, and designate their daily location(s) to avoid shortfalls. In this study, a vehicle routing problem is developed using IP. Optimal routing for each bloodmobile is identified using CPLEX solver & column generation algorithm. Computational results will be discussed. 3 - A Binary Integer Programming Approximation for Vaccine Vial Distribution Zahra Azadi, Clemson University, 854 Issaqueena Trl. Apt#902, Central, SC, 29630, United States of America, zazadi@clemson.edu, Sandra Eksioglu One of the challenges faced by health care providers is designing an inventory replenishment policy for vaccines to ensure a successful immunization of patients while minimizing purchasing, inventory and wastage costs. Wastage incurs when doses are disposed from opened vials after their safe use time. We propose an (s, S) policy which determines the vial size, the reorder quantity, and the order up to point which optimizes system-wide costs. TD16 16-Franklin 6, Marriott Inverse Optimization Sponsor: Optimization/Linear and Conic Optimization Sponsored Session Chair: Daria Terekhov, Concordia University, 1455 De Maisonneuve Daria Terekhov, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Canada, dterekho@encs.concordia.ca, Taewoo Lee, Timothy Chan Using an analogy between regression and inverse optimization, we develop a framework for cost function estimation in linear optimization consisting of a general inverse optimization model and a corresponding goodness-of-fit metric. We propose several natural specializations of the framework that evaluate goodness-of-fit in both the space of decisions and objective value. 2 - Inverse Optimization for the Analysis of Competitive Markets Michael Pavlin, Wilfrid Laurier University, 75 University Ave, Waterloo, ON, Canada, mpavlin@wlu.ca, John Birge, Ali Hortacsu We consider use of inverse optimization as an empirical tool for uncovering unobservable parameters in competitive markets. In particular, we apply these techniques to the recovery of transportation and production cost parameters in natural gas markets. Blvd. W., Montreal, Canada, dterekho@encs.concordia.ca 1 - A Goodness-of-fit Measure for Inverse Optimization

3 - Three Newsvendor Models for Capacity Allocation Sam Choi, Assistant Professor, Shenandoah University, 1460 University Dr., Winchester, VA, 22601, United States of America, schoi@su.edu We propose three newsvendor models to allocate capacity under uncertainty: inverse newsvendor, sequential newsvendor, and inverse sequential newsvendor models. The inverse newsvendor model tries to find optimal demand size under capacitated environment. The sequential newsvendor model deals with optimal time durations given that demand sizes. Lastly, the inverse sequential newsvendor model determines optimal demand sizes given that time durations. We apply three models to healthcare settings. TD17 17-Franklin 7, Marriott Routing and Multidimensional Assignment Applications Sponsor: Optimization/Network Optimization Sponsored Session Chair: Jose Walteros, University at Buffalo, 342 Bell Hall, Buffalo, NY, United States of America, josewalt@buffalo.edu 1 - Gasoline Replenishment and Routing Problem with Variable Demands and Time Windows Yan Cheng Hsu, University at Buffalo (SUNY), 412 Bell Hall, Buffalo, NY, 14228, United States of America, yhsu8@buffalo.edu, Rajan Batta, Jose Walteros An iterative procedure is presented to tackle the gasoline replenishment problem of gas stations and vehicle routing problem. The problem is formulated as inventory model with a send-back cost due to the gasoline delivery property that gas stations should accept ordered quantity completely, to find order quantity and time window of gas stations, minimizing expected total cost, and as MIP model to resolve vehicle routing problem, maximizing total profit for transporters. 2 - Solving Multidimensional Assignment Problems with Combinatorial Decomposable Cost Functions Hadi Feyzollahi, State University of New York at Buffalo, 316 Bell Hall, Buffalo, NY, United States of America, hadifeyz@buffalo.edu, Jose Walteros We consider several variants of the multidimensional assignment problem (MAP) where the costs of the optimal assignments are calculated by solving combinatorial optimization problems. We focus our attention on the cases where the assignments represent optimal TSP tours, spanning trees, and cliques. We formulate these MAPs as set partitioning problems and solve them via branch and price. We develop specific algorithms for solving the corresponding subproblems for each of the aforementioned cases. 3 - Location-capacity-routing Problem of All-electric Delivery Vehicles Nan Ding, University of Buffalo, Buffalo, NY, United States of America, nanding@buffalo.edu, Rajan Batta All-electric truck adoption becomes one of the main addressees of green logistic activities, with challenges of limited driving range and long charging time to route these trucks. This problem aims to handle the challenges of congestion and waiting at the charging stations. A joint location-capacity-routing (LCR) problem to determine the optimal location and capacity of charging stations is formulated and a meta-heuristic approach is proposed to solve this LCR problem. 4 - Large-scale Neighborhood Search for the Multi-dimensional Assignment Problem Alla Kammerdiner, New Mexico State University, P.O. Box 30001, MSC 4230, Las Cruces, NM, 88003, United States of America, alla@nmsu.edu, Charles Vaughan The multi-dimensional assignment problem is an NP-hard problem in high- dimensional combinatorial optimization. This problem arises in the military surveillance applications (e.g. sensor data fusion and target tracking) and in healthcare for ranking exposures to falls. We propose and investigate a new large- scale search algorithm for this computationally difficult problem. We evaluate the performance of new algorithm on various instances and compare it to other state- of-the-art procedures.

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