<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Papers on Qian Jia</title><link>https://qian-jia.github.io/papers/</link><description>Recent content in Papers on Qian Jia</description><generator>Hugo -- 0.145.0</generator><language>en</language><lastBuildDate>Sun, 30 Mar 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://qian-jia.github.io/papers/index.xml" rel="self" type="application/rss+xml"/><item><title>A Dynamic Patching Policy for Enterprise Information Systems</title><link>https://qian-jia.github.io/papers/dynamic-patching-policy/</link><pubDate>Sun, 30 Mar 2025 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/dynamic-patching-policy/</guid><description>The present research proposes a novel dynamic patching policy (DPP) to minimize the total system cost.</description></item><item><title>Enabling Ultra-Fast Online Order Fulfillment: Efficient Inventory Management for Micro-Fulfillment Centers</title><link>https://qian-jia.github.io/papers/mfc-inventory-management/</link><pubDate>Sun, 30 Mar 2025 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/mfc-inventory-management/</guid><description>In this paper, we propose an effective threshold-based policy to help retailers make well-informed decisions, by formulating the MFC inventory decision problem as a Markov decision process.</description></item><item><title>Optimal Live Selling Strategies for Brands: The Role of Effort Costs</title><link>https://qian-jia.github.io/papers/live-selling-strategy/</link><pubDate>Sat, 15 Mar 2025 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/live-selling-strategy/</guid><description>This study present an analytical model to identify the optimal live selling strategy for maximizing brand profits. Published in Information Technology and Management.</description></item><item><title>Enterprise Security Patch Management with Deep Reinforcement Learning</title><link>https://qian-jia.github.io/papers/security-patch-rl-icis/</link><pubDate>Sun, 15 Dec 2024 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/security-patch-rl-icis/</guid><description>This study addresses the patching decision problem by proposing a novel deep reinforcement learning-based approach. Presented in ICIS 2024.</description></item></channel></rss>