Determining an optimal order-up to level on (T, S) policy by search methods with multi-item consumer product inventory

Name: Natphapas Archareewongpaisal

keyword: Inventory optimization

; Retail inventory

; (T, S) inventory policy

; Grid search method

; Golden section search method

Abstract: The IP Multiprotocol Label Switching (IP/MPLS) network is a complex system comprising switches, routers, DWDM (Dense Wavelength Division Multiplexing) devices, Network Management System (NMS) servers, and various other components. Managing such large-scale networks requires multiple tools and advanced network management techniques. Due to the intricate architecture and interconnectivity of IP/MPLS, identifying and resolving network issues, particularly chain failures, is a challenging task. In chain failures, a single issue can cascade, affecting multiple interconnected devices. To address these issues, network operators rely on NMS, event or alarm signals from network devices, and frequently perform manual operational commands for further diagnostics. Given this complexity, a centralized approach is crucial for efficient network management. This article proposes a Multi-Purpose System that leverages Machine Learning and Case-Based Reasoning to enhance network operations and troubleshoot IP/MPLS networks. The system comprises several components: a Message Broker for real-time streaming of different message types (e.g., SNMP Traps, Syslog), a Log Template Generation service for standardizing logs, an Event Identification Service for classifying network events, a Node Chain Lookup Service for identifying impacted devices, and a Node Test Service for running diagnostic commands. Additionally, the system includes a Case-Based Fault Identification Service, which acts as a knowledge repository of historical fault cases and expert knowledge, a Notification Service for sending alerts through modern communication channels, and a Dashboard to provide network operators with root cause analysis and troubleshooting guidance. The proposed system aims to improve network availability and operational efficiency utilizing scenarios from the Provincial Electricity Authority of Thailand (PEA). We assessed its performance using event messages from various NMS. The results illustrate the system's efficacy regarding accuracy and performance, providing a reliable option for automating network troubleshooting and management.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Tai, Pham Duc

Role: advisor

Created: 2024

Modified: 2568-05-15

Issued: 2025-04-13

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.103

eng

Level: Master's Degree

Grantor: Thammasat University

DegreeName: Master of Engineering

Descipline: Logistics and Supply Chain Systems Engineering

©copyrights Thammasat University

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