Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SC74

2 - Air Force Warfighting Integration Center (AFWIC) Analyses Mark Gallagher, US Air Force, Arlington, VA, United States, Doug Fullingim The Air Force has established an Air Force Warfighting Integration Center (AFWIC) to “drive innovation and transformation to ensure the AF provides the world’s most ready, lethal, and dominant air force to the joint fight. We discuss the steps between strategy and budget, and how we are using wargaming, analyses, and assessments to support the decisions at each of those steps. Much of our analytical challenges are modeling the concepts and ideas impact on future warfare integrated view. 3 - A New Framework for Naval Inventory Models Javier Salmeron, Naval Postgraduate School, 1411 Cunningham Road, Monterey, CA, 93943, United States, Duncan Ellis Naval Supply Systems Command, Weapons Systems Support sets inventory policy across all echelons of supply. In 2014, it partnered with the Naval Postgraduate School to develop an in-house suite of inventory models: Wholesale Inventory Optimization Model, currently in production; Site Level Inventory Optimization Model, sets reorder points and quantities at Naval Air Stations and Distribution Depots; Naval Aviation Readiness Based Sparing Model, sets inventory levels for aviation weapon systems to achieve operational availability. The models introduce novel features, provide defendable, auditable, and transparent policy, and help to maintain critical skills within the civil service. 4 - A Comparative Assessment of Network Solution Methodologies for the Comprehensive Core Capability Risk Assessment Framework Alex M. Marshall, Analyst, The Perduco Group, 3610 Pentagon Blvd, Suite 110, Beavercreek, OH, 45431, United States, Paul F. Auclair Given an enterprise-level risk model consisting of a weighted, directed, cyclic network, we describe an experimental approach to examining the suitability of four network solution methods for estimating the effect of risk propagating across the network. We also compare results obtained from detailed activity-level networks to those obtained from aggregated capability-level networks and suggest adjustments to eliminate observed aggregation bias and divergence from notional composite truth solutions. n SC76 West Bldg 212C Performance Modeling and Optimization in Manufacturing Systems General Session Chair: James M. Smith, University of Massachusetts-Amherst, Amherst, MA, 01002, United States 1 - Scheduling Policies in Flexible Bernoulli Lines with Dedicated Finite Buffers Jingshan Li, University of Wisconsin-Madison, 1513 University Avenue, 3222 Mechanical Engineering Building, Madison, WI, 53706, United States, Kyungsu Park, Shaw Feng This presentation is devoted to studying scheduling policies in flexible serial lines with two Bernoulli machines and dedicated finite buffers. Priority, cyclic and work-in-process (WIP)-based scheduling policies are investigated. 2 - Stochastic Models for Multi-resource Allocation in Biomanufacturing Yasemin Limon, University of Wisconsin-Madison, Madison, WI, 53705, United States, Ananth Krishnamurthy We analyze multi-resource allocation decisions for protein purification projects that need to meet strict yield and purity requirements. The purification project can involve multiple steps, each step improving the purity of the sample, although resulting in some yield loss in the process. These purification projects are completed by scientists with different capabilities and capacities. We analyze allocation strategies of projects to scientists using queueing models. Exact solutions are obtained by applying Matrix-Geometric solution algorithms and numerical examples are presented to explore the impact of different strategies on the system performance. 3 - A Branch and Bound Procedure for Chance Constrained Stochastic Assembly Line Balancing Raik Stolletz, PhD, University of Mannheim, Mannheim, Germany, Johannes Schnitzler We analyze the assembly line balancing problem where tasks have to be assigned to stations with the goal of minimizing the number of stations used. Task times are stochastic, leading to the possibility of incomplete work pieces. Therefore, we consider a constraint on the probability of finishing a work piece within the given cycle time. A sampling model is developed to account for generally distributed task times. We present a bidirectional branch and bound procedure in order to solve the model. A numerical study compares the performance of the algorithm to the solution with standard solvers.

n SC74 West Bldg 212A Joint Session MCDM/Practice Curated: MCDM in Practice Sponsored: Multiple Criteria Decision Making Sponsored Session Chair: Lorraine Gardiner, Dalton State College 1 - Multi-Objective Delivery Allocation Jonathon Leverenz, Systems Planning and Analysis, Inc, Alexandria, VA, 22302, United States, Stephanie Diane Brown, Chris Grubb The Multi-Objective Delivery Allocation problem assigns delivery vehicles to jobs delivering equipment to a set of locations. Each vehicle is assigned a job and each job contains multiple locations. Locations can be assigned to multiple jobs and grouped together in various ways resulting in multiple options for each vehicle. Vehicle-location pairs are assigned a probability representing the chance of a successful delivery. Assignments are made under competing objectives that include cost, location coverage, probability of success, and expected number of completed deliveries. A network-with-gains model is used to make assignments and investigate tradeoffs between the various objectives. 2 - Blockchain’s Impact on Digital Supply Chain: Contributions from MCDM Birsen Karpak, Distinguished Professor, Youngstown State University, One University Plaza, WCBA 3303, Youngstown, OH, 44555, United States, Valerio A. Salomon Digital Supply Chain (DSC) is about the way how supply chain processes are managed with a wide variety of innovative technologies, e.g. internet of things, big data, cloud computing, among others. Blockchain is the new-comer not explored even in the frameworks offered very recently. This study explores the impact of blockchain on digital supply chain and reports the findings of the implementation of a proposed DSC framework into an industrial real-case. Authors see a potential contribution from multiple criteria decision approaches. 3 - Weighting Criteria for Agriculture Planning: An Application of the Analytical Hierarchy Process Jay Parsons, University of Nebraska-Lincoln, 103B Filley Hall, P.O. Box 830922, Lincoln, NE, 68583-0922, United States, Kathleen Brooks A statewide farm financial health survey of Nebraska farmers and ranchers was conducted in 2016 to assess farm and ranch financial stress as a result of low commodity prices. In the survey, producers were asked a series of questions to assess the importance of different factors in their planning process. The analytical hierarchy process is used to identify the importance of the factor criteria for all respondents as a whole and for various segments of the sample population. 4 - Survey on the Practice and Implementation of MCDM Lorraine R. Gardiner, Dalton State College, Wright School of Business, 650 College Drive, Dalton, GA, 30720, United States The purpose of the research survey is to document and classify published accounts describing the practice of MCDM. The survey includes peer-reviewed articles that describe the actual use of one or more MCDM methods by decision makers in an organization. The author summarizes results by general problem area, organizational type, decision level, degree of organizational involvement and MCDM method category.

n SC75 West Bldg 212B Service-oriented Enterprise Analysis Sponsored: Military and Security Sponsored Session

Chair: Mark Gallagher, US Air Force, Arlington, VA, United States, 1 - Strategy-to-Task Hierarchy for a Strategy through System Requirements Framework Mark Gallagher, US Air Force, Arlington, VA, United States, Michael Moss, Doug Fullingim The Air Force Warfighting Center (AFWIC) is developing a functional linkage that maps strategy themes through our concept documents to our capabilities and systems. This framework enables us to justify requirements in terms of achieving the strategy and concepts that are driving them. We present the current version of this strategy-to-task framework.

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