2015 Informs Annual Meeting

TC02

INFORMS Philadelphia – 2015

TC03 03-Room 303, Marriott Inventory Management I Contributed Session

3 - Retail Inventory Optimization for the U.S. Navy Javier Salmeron, Naval Postgraduate School, Operations Research Dept., GL-214, Monterey, CA, 93943, United States of America, jsalmero@nps.edu, Emily Craparo We present a mixed-integer linear model to guide retail (site-based demand level) inventory decisions for the Naval Supply Systems Command (NAVSUP), Weapons Systems Support. An (s,Q) model optimizes reorder points and order quantities for thousands of items in order to minimize deviations from target fill rates, with demand uncertainty, budget and order quantity constraints. We compare branch- and-bound with Lagrangian relaxation solutions, and our results with those from other NAVSUP tools. TC02 02-Room 302, Marriott Network Applications in Homeland Security Cluster: Homeland Security Invited Session Chair: Thomas Sharkey, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, United States of America, sharkt@rpi.edu 1 - Novel Bilevel Programming Approaches for Interdicting Multi-tiered Illegal Supply Chains N. Orkun Baycik, Rensselaer Polytechnic Institute, 110 8th Street, Center for Industrial Innovation, Troy, NY, 12180, United States of America, baycin@rpi.edu, Thomas Sharkey, Chase Rainwater We study a resource allocation problem faced by law enforcement in arresting criminals in the drug flow and information flow networks. The objective of law enforcement is to minimize the maximum amount of drugs reaching end users. There exist interdependencies between the networks which leads to a network interdiction problem with a discrete inner problem. We apply a novel dual-based reformulation technique to solve an equivalent single-level problem and present computational results. 2 - Bi-level Stochastic Network Interdiction Model for Deployment of Cyber-security Countermeasures Apurba Nandi, Mississippi State University, P.O. Box 9542, Mississippi State University, Mississippi State, MS, 39762, United States of America, akn77@msstate.edu, Hugh Medal We study how best to deploy cyber-security countermeasures to protect a cyber- network against attacks. We propose a bi-level stochastic network interdiction model capturing the interaction between the attacker and the defender as a sequential stackelberg game played on an attack graph. We consider that the attacker’s knowledge about the topology of the attack graph, and the attacker’s and the defender’s knowledge about each other’s actions are uncertain. We develop algorithm to solve the model. 3 - Vitality Measures for Multigraphs with Applications to Communications Networks Sarah Nurre, Assistant Professor, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH, 45433, United States of America, Sarah.Nurre@afit.edu, Christopher Hergenreter We consider the problem of determining the most vital arcs to protect within a multigraph, such as a communications network. Traditional vitality measures are insufficient as they often examine single arc failures which have no impact on multigraphs due to parallel arcs between pairs of nodes. Herein, we propose and examine set based vitality measures for multigraphs. We perform and present the results of promisting computational results multi-mode military communications networks. 4 - Efficient Resilience Optimization of Interdependent Networks Andres Gonzalez, Rice University, 6100 Main Street, MS-318, Ryon 203, Houston, TX, 77005, United States of America, andres.gonzalez@rice.edu, Leonardo Dueñas-osorio, Andres Medaglia, Mauricio Sánchez-silva MIP models of the Interdependent Network Design Problem (INDP) have proved to be effective tools to study and improve the resilience of systems of interdependent infrastructure networks. Nevertheless, these MIP models have limited scalability for large realistic systems. In this work, we present an enhanced methodology to optimize the resilience of interdependent networks, based on the joint use of decomposition techniques and efficient reformulations of the INDP models.

Chair: Jun-yeon Lee, California State University, Northridge, 18111 Nordhoff St, Northridge, CA, 91330-8378, United States of America, junyeon.lee@csun.edu 1 - Forecasting of Demand Tail Distribution for Inventory Optimzation Antony Joseph, Staff Data Scientist, Walmart Labs, 1001 National Avenue, Apt. 107, San Bruno, CA, 94066, United States of America, AJoseph0@walmartlabs.com We discuss a novel technique for forecasting demand tail distribution for items in the Walmart e-commerce inventory. The methodology first estimates the quantiles of the demand distribution, followed by fitting a parametric distribution using a suitable metric. The method is seen to be robust to high observed variability in demand. Performance of the proposed approach is assessed using Supply Chain metrics such as Weeks of Supply and Met Demand. 2 - Strategic Safety Stocks under Guaranteed Service and Constrained Service Models Ton De Kok, TU Eindhoven, Paviljoen E.04, P.O. Box 513, Eindhoven, Netherlands, a.g.d.kok@tue.nl In this presentation we discuss our findings on a set of real-life supply chains concerning the positioning of safety stocks in the supply chain. We compare the results from models under the guaranteed service assumption and under the constrained service assumption. The latter assumption follows the classical Clark and Scarf model for serial multi-echelon systems. Furthermore, we discuss some implications of the guaranteed service assumption in case of a bounded demand formulation. 3 - Scarcity Effect on Dual-channel Supply Chain Baoshan Liu, PhD Student, Huazhong University of Science and Technology, School of Management, 1037 Luoyu Road, Wuhan, China, liubaooshan@qq.com, Shihua Ma We consider manufacturer’s dual-channel sale system where customers get the product the direct channel with limited quantity. The limited quantity of the direct channel releases a scarcity message that consumers will increase their purchasing desire. Both the manufacturer and the retailer choose their own decision variable to maximize their respective profits considering scarcity effect. We model the problem using Stackelberg games and try to find the best solution from different perspectives. 4 - An Optimal Inventory Replenishment Considering Product Life Cycle Shunichi Ohmori, Assistant Professor, Waseda University, Room 51-15-05, Okubo 3-4-1, Shinjuku, Tokyo, 169-8555, Japan, ohmori0406@gmail.com, Kazuho Yoshimoto We consider a problem of determining initial and replenishment order quantities that minimize the cost of lost sales, inventory holding cost, fixed ordering cost, and obsolete inventory subject to stochastic demands. We model this problem as a multi-stage problem where the demands and prices of products lie in some uncertainty set depending on their life cycle. We develop a dynamic programming method to solve this problem. 5 - Vendor-managed Inventory with a Time-dependent Stockout Penalty Jun-yeon Lee, California State University, Northridge, CA, We examine the problem of designing a vendor-managed inventory (VMI) contract with a time-dependent stockout penalty in a stochastic (Q, r) inventory system, in which the supplier is charged a stockout penalty that depends on the length of the time during which stockouts occur at the customer. The customer chooses the stockout penalty and offers the VMI contract to the supplier, and the supplier can accept or reject the contract. We examine the optimal behaviors of the two parties. 18111 Nordhoff St, Northridge, CA, 91330-8378, United States of America, junyeon.lee@csun.edu

318