2015 Informs Annual Meeting

WE03

INFORMS Philadelphia – 2015

2 - Addressing Complexity of Hurricane Sandy with Innovative Kingdon’s Model Eliot Evans, Lt Col & Graduate Student, George Mason University School of Public Policy, Government, & International Affairs, 12308 Cicero Drive, Alpharetta, GA, 30022, United States of America, eliotevans11@gmail.com Losses due to the impact of Hurricane Sandy in 2012 raise the concern of the effectiveness of disaster management and its operations. FEMA’s Hurricane Sandy After-Action Report revealed three significant problems 1) lack of collaboration 2) inadequate survivors’ needs met, 3) shortage of an agile, professional emergency management workforce. This research aims to analyze the complexity of Hurricane Sandy and its problems, to propose an agenda and alternatives, and to recommend public policies. 3 - throughput Analysis of Reserve Component Mobilization T raining Capacity Katharina Best, Associate Operations Researcher, The RAND Corporation, 1200 S. Hayes St, Arlington, VA, 22202, United States of America, kbest@rand.org, Jeremy Eckhause, Igor Mikolic-torreira, Michael Linick Army Reserve Component units require administrative processing and varying amounts of high-quality training at specialized installations before deploying to contingency locations. Capacity at such facilities is limited and opening bases quickly can be problematic. We present a mixed-integer programming model that optimizes training schedules under different assumptions about training time, facilities ramp-up, unit type prioritization, demand timing, and Active/Reserve Component force mix. 4 - Optimal Multi-stage Allocation via Approximate Dynamic Programming Darryl Ahner, Asst Professor, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, 45433-7765, United States of America, darryl.ahner@afit.edu, Carl Parson We consider the optimal allocation of resources over multiple stages to a collection of tasks with the objective of maximizing the reward for completing tasks where the task arrivals follow a known distribution, namely stochastic weapon-target assignment. Simulation and mathematical programming are used within a dynamic programming framework to update functional approximations representing future rewards using subgradient information and thereby determine allocation strategies. Chair: Majid Algwaiz, Engineering Specialist, Saudi Aramco Oil Company, P.O. Box 19422, Dhahran, 31311, Saudi Arabia, majid.gwaiz@gmail.com 1 - A Genetic Algorithm for the Resource Leveling Problem with Generalized Precedence Relations Hongbo Li, Shanghai University, School of Management, Shangda Road 99, Shanghai, 200444, China, ishongboli@gmail.com, Yinbin Liu, Li Xiong We present a bi-chromosome based genetic algorithm (BGA) for the resource leveling problem with generalized precedence relations. In the BGA, a solution is represented by a bi-chromosome that consists of two parts: a random key vector and a percentage based shift vector. To demonstrate the effectiveness of our BGA, we conduct extensive computational experiments on a set of benchmarks with up to 500 activities and compare the BGA with two best metaheuristics in the literature. 2 - Results on throughput Maximization with Limited Advance Information We consider throughput maximization, given limited advance information. For problems where job lengths are equal, we propose an on-line algorithm whose competitive ratio improves as the duration of the advance information increases. Further, the performance of this on-line algorithm asymptotically approaches that of the off-line algorithm. More importantly, we help to identify the structure of the worst case instances – those that correspond to the competitive ratios. 3 - A Lower Bound Analysis for the Flowshop Scheduling Problem with Makespan Minimization Carlos Ernani Fries, Professor, Federal University of Santa Catarina, Caixa Postal 5185, Florianopolis, SC, 88040-970, Brazil, carlos.fries@ufsc.br, Bruno De Souza Alves This paper deals with a lower bound (LB) analysis for makespan measure of FSP. The LB measures are compared with solutions obtained with exact models and WE02 02-Room 302, Marriott Scheduling V Contributed Session Ishwar Murthy, Professor, Indian Institute of Management Bangalore, Bannerghatta Road, Bangalore, 560076, India, ishwar@iimb.ernet.in

the popular CDS heuristic. Simulations varying the number of jobs, machines and processing times show that solutions discrepancies tend to increase until N less than M and decrease for N greater than M, with largest discrepancy observed for N equal M. The divergences tend to be larger when greater variability on processing times is considered. 4 - Stochastic Patient Scheduling by Chance Constraint Programming Bulent Erenay, PhD Candidate, Wilkes University,

5667 Barney Lane, Columbus, OH, 43235, United States of America, be977209@ohio.edu

A stochastic patient scheduling problem is studied by using chance constraint programming. The time patient stays at the hospital is considered as probablistic. 5 - Optimizing Ship Loading Schedules for Oil and Gas Terminals

Majid Algwaiz, Engineering Specialist, Saudi Aramco Oil Company, P.O.Box 19422, Dhahran, 31311, Saudi Arabia, majid.gwaiz@gmail.com, Abdulaziz Nutaifi

We consider in this paper an oil and gas firm that owns its entire hydrocarbon supply chain with many production facilities and ship loading terminals. Customers make purchases a month in advance but only provide a four day notice on the specific pickup times and the requested products and quantities. We present a MILP formulation to manage the hydrocarbon network and assign ships to berths on an hourly basis. Our objective is to minimize the ship waiting times along with the demurrage fees.

WE03 03-Room 303, Marriott Inventory Management - Inventory Policies Contributed Session

Chair: Jim Shi, Assistant Professor, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, United States of America, jshi@njit.edu 1 - Stochastic Integrated Location-inventory Up-to-S Model in Distribution System Maxim Bushuev, Assistant Professor, Kent State University - Geauga, 1835 Beacon Hill Cir #21, Cuyahoga Falls, OH, 44221, United States of America, mbushuev@kent.edu A stochastic integrated location-inventory problem with up-to-S policy is discussed. Simple proportional allocation rule is proposed which allows defining and solving the problem as convex optimization. This is the first stochastic model in the area of integrated location-inventory problems. 2 - An Extension of the Stochastic Dynamic Lot-Size Model of Vargas to a Model with Uncertain Production Hendrik Vermuyten, PhD Student, KU Leuven, Warmoesberg 26, Brussel, 1000, Belgium, hendrik.vermuyten@kuleuven.be We derive the optimal solution for the production planning for a single product for every period in the planning horizon, when demand is stochastic and non- stationary and the achievable production per period is stochastic as well. The model is an adaption of the stochastic dynamic lot-size model of Vargas without production restrictions. Simulation studies show a significant improvement in expected costs for this model compared to the model of Vargas in case of uncertain production capacity. 3 - Extended MIP Formulations for the Stochastic Lot-sizing Problem Huseyin Tunc, Hacettepe University, Institute of Population Studies, Sihhiye, Ankara, Turkey, huseyin.tunc@hacettepe.edu.tr We revisit the certainty equivalent mixed integer programming formulations of the stochastic lot-sizing problem under the static-dynamic uncertainty strategy, and develop extended formulations thereof. The extended formulations are far more time-efficient than the existing formulations in the literature. Also, instead of working with a pre-determined piece-wise linear approximation of the cost function, they can find a minimum cost solution by means of a novel dynamic cut generation procedure. 4 - Stockout Risk Control of a Continuous Production-inventory System Jim Shi, Assistant Professor, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, United States of America, Jshi@njit.edu This paper studies the stockout control problem pertaining to a single-product continuous-time production-inventory system with a constant replenishment rate. Our objective is to optimize the expected system cost subject to a predetermined stockout acceptance level.

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