GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation

Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despite the traditional machine learning models, Graph Neural Networks (GNNs), by design, can understand complex relations like similarity between products. However, in contrast to their wide usage in retrieval tasks and their focus on optimizing the relevance, the current GNN architectures are not tailored toward maximizing revenue-related objectives such as Gross Merchandise Value (GMV), which is one of the major business metrics for e-Commerce companies. In addition, defining accurate edge relations in GNNs is non-trivial in large-scale e-Commerce systems, due to the heterogeneity nature of the item-item relationships. This work aims to address these issues by designing a new GNN architecture called GNN-GMVO (Graph Neural Network - Gross Merchandise Value Optimizer). This model directly optimizes GMV while considering the complex relations between items. In addition, we propose a customized edge construction method to tailor the model toward similar item recommendation task and alleviate the noisy and complex item-item relations. In our comprehensive experiments on three real-world datasets, we show higher prediction performance and expected GMV for top ranked items recommended by our model when compared with selected state-of-the-art benchmark models.Comment: 9 pages, 3 figures, 43 citation

Sequential decision making and simulation-optimization for the design of complex engineering systems

In this dissertation, we create a novel simulation-based design platform to determine the optimal design of engineered systems. We develop resilient, reliable, and flexible design solutions that account for system uncertainties within the optimization algorithm. The purpose of this dissertation is to study simulation-optimization and sequential decision-making strategies for the design of complex engineering systems. Simulation optimization and sequential decision-making frameworks are developed in order to optimize the design of complex engineering systems in four different studies: designing a resilient wind turbine system for risk-averse decision-makers, improving the reliable design of airfield concrete pavement, incorporating flexibility into the design of a hybrid renewable energy system, and finding the optimal policy for the design of engineering systems using reinforcement learning. In chapter 2, a framework is developed to incorporate risk aversion into a firm’s design decisions for a resilient wind turbine system. In chapter 3, a reliability-based design optimization framework is developed for airfield concrete pavement design. Chapter 4 presents a multi-stage simulation-optimization algorithm for the flexible design of a hybrid renewable energy system. In chapter 5, a new framework is developed to find the optimal policy for the design of engineering systems operating under uncertainty

Sequential decision making and simulation-optimization for the design of complex engineering systems

In this dissertation, we create a novel simulation-based design platform to determine the optimal design of engineered systems. We develop resilient, reliable, and flexible design solutions that account for system uncertainties within the optimization algorithm. The purpose of this dissertation is to study simulation-optimization and sequential decision-making strategies for the design of complex engineering systems. Simulation optimization and sequential decision-making frameworks are developed in order to optimize the design of complex engineering systems in four different studies: designing a resilient wind turbine system for risk-averse decision-makers, improving the reliable design of airfield concrete pavement, incorporating flexibility into the design of a hybrid renewable energy system, and finding the optimal policy for the design of engineering systems using reinforcement learning. In chapter 2, a framework is developed to incorporate risk aversion into a firm’s design decisions for a resilient wind turbine system. In chapter 3, a reliability-based design optimization framework is developed for airfield concrete pavement design. Chapter 4 presents a multi-stage simulation-optimization algorithm for the flexible design of a hybrid renewable energy system. In chapter 5, a new framework is developed to find the optimal policy for the design of engineering systems operating under uncertainty.</p

Sequential decision making and simulation-optimization for the design of complex engineering systems

In this dissertation, we create a novel simulation-based design platform to determine the optimal design of engineered systems. We develop resilient, reliable, and flexible design solutions that account for system uncertainties within the optimization algorithm. The purpose of this dissertation is to study simulation-optimization and sequential decision-making strategies for the design of complex engineering systems. Simulation optimization and sequential decision-making frameworks are developed in order to optimize the design of complex engineering systems in four different studies: designing a resilient wind turbine system for risk-averse decision-makers, improving the reliable design of airfield concrete pavement, incorporating flexibility into the design of a hybrid renewable energy system, and finding the optimal policy for the design of engineering systems using reinforcement learning. In chapter 2, a framework is developed to incorporate risk aversion into a firm’s design decisions for a resilient wind turbine system. In chapter 3, a reliability-based design optimization framework is developed for airfield concrete pavement design. Chapter 4 presents a multi-stage simulation-optimization algorithm for the flexible design of a hybrid renewable energy system. In chapter 5, a new framework is developed to find the optimal policy for the design of engineering systems operating under uncertainty

Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach

Engineering system design, viewed as a decision-making process, faces challenges due to complexity and uncertainty. In this paper, we present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of engineering systems. We outline a step-by-step framework for optimizing engineering system designs. The goal is to find policies that maximize the output of a simulation model given multiple sources of uncertainties. The proposed algorithm handles linear and non-linear multi-stage stochastic problems, where decision variables are discrete, and the objective function and constraints are assessed via a Monte Carlo simulation. We demonstrate the effectiveness of our proposed framework by solving two engineering system design problems in the presence of multiple uncertainties, such as price and demand.This is a preprint from Giahi, Ramin, Cameron A. MacKenzie, and Reyhaneh Bijari. "Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach." arXiv preprint arXiv:2312.17284 (2023). doi: https://doi.org/10.48550/arXiv.2312.17284. Copyright the Authors 2023. CC BY

Design Optimization under Long-Range Uncertainty

Flexibility in engineering system design: •Flexibility in system design and implications for aerospace systems (Saleh et.al 2003) •A flexible and robust approach for preliminary engineering design based on designer's preference (Nahmet.al, 2007) •A real options approach to hybrid electric vehicle architecture design for flexibility (Kang et.al 2016) •Our research: •Simulation optimization •Long range uncertainty •Add flexibility and robustness to designThis presentation is from The Institute of Industrial and Systems Engineers Annual Conference & Expo, Orlando, Florida; Giahi, R., MacKenzie,C., Hu,C.,; Design Optimization under Long-Range Uncertainty. Posted with permission. </p