Predictive push logistic using runtime monitoring of hidden and visible data

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NPS-CS-18-001.pdf (203.4 KB)
Authors
Drusinsky, Doron
Subjects
Predictive logistics
Hidden Markov Models
Advisors
Date of Issue
2018-02
Date
February 2018
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
This report described a general purpose predictive logistics software package based on three primary components: 1. A spreadsheet of ship related orders. 2. A Probabilistic Temporal Finite State-Machine (PTFSM) automatically learned from that data. 3. (Optional) Expert rules written in English. The deliverable tool predicts orders per Ship-ItemType pairs. It has two main prediction modes: a. Predict a probability of an order (for one or more Ship-ItemType pairs) to be required in n weeks. b. Predict the number of weeks required for the probability of an order (for one or more Ship-ItemType pairs) to exceed some given value p.
Type
Technical Report
Description
URI
https://hdl.handle.net/10945/57696
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
NPS-CS-18-001
Sponsors
Prepared for: OPNAV/N1
Funder
NPS Naval Research Program
Format
25 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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