Contributions to Reverse Logistics with Game theoretic Applications

The last two decades witnessed an increasing emphasis on reverse logistics (RL). Our thesis attempts to investigate two research problems in RL and explore game theoretic applications in this field. In Chapter 1, we introduce SCM, RL, relevant game theoretic applications, and the organizational structure of this thesis. In Chapter 2, we address a newsvendor problem with resalable returns. We develop a basic model with order quantity as the single decision variable and conduct concavity analysis. We also develop a general model in which the retailer determines both order quantity and two inter-period inventory thresholds. We use simulation to investigate the timing effect of both customer demands and returns on the retailer's decision making. In Chapter 3, we explore the application of game theoretic models with incomplete information in inventory management. Games with incomplete information may provide a more realistic modeling framework. We hope this exposition be helpful to researchers interested in applying game theoretic models and computing equilibriums in their specific problems in SCM and RL. In Chapter 4 we consider a remanufacturing competition problem between an original equipment manufacturer (OEM) and a pure remanufacturer (REM) with the OEM's incomplete information on the REM's unit cost. We apply the type-III model in Chapter 3 for formulation and derive the closed-form Bayesian Nash equilibrium. We use sensitivity analysis to investigate the effect of such incomplete information on both competitors' decision making. We summarize in Chapter 5 and provide a general direction for future research on game theoretic applications in RL.Doctor of Philosophy (PhD

A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion

Hub-and-spoke networks are widely applied in a variety of industries such as transportation, postal delivery, and telecommunications. Although they are designed to exploit economies of scale, hub-and-spoke networks are known to favour congestion, jeopardizing the performance of the entire system. This paper looks at incorporating congestion and capacity decisions in the design stage of such networks. The problem is formulated as a nonlinear mixed-integer program (NMIP) that explicitly minimizes congestion, capacity acquisition, and transportation costs. Congestion at hubs is modeled as the ratio of total flow to surplus capacity by viewing the hub-and-spoke system as a network of M/M/1 queues. To solve the NMIP, we propose a Lagrangean heuristic where the problem is decomposed into an easy subproblem and a more difficult nonlinear subproblem. The nonlinear subproblem is first linearized using piecewise functions and then solved to optimality using a cutting plane method. The Lagrangean lower bound is found using subgradient optimization. The solution from the subproblems is used to find a heuristic solution. Computational results indicate the efficiency of the methodology in providing a sharp bound and in generating high-quality feasible solutions in most cases. </jats:p

AGProto: Adaptive Graph ProtoNet towards Sample Adaption for Few-Shot Malware Classification

Traditional malware-classification methods reliant on large pre-labeled datasets falter when encountering new or evolving malware types, particularly when only a few samples are available. And most current models utilize a fixed architecture; however, the characteristics of the various types of malware differ significantly. This discrepancy results in notably inferior classification performance for certain categories or samples with uncommon features, but the threats of these malware samples are of equivalent significance. In this paper, we introduce Adaptive Graph ProtoNet (AGProto), a novel approach for classifying malware in the field of Few-Shot Learning. AGProto leverages Graph Neural Networks (GNNs) to propagate sample features and generate multiple prototypes. It employs an attention mechanism to calculate the relevance of each prototype to individual samples, resulting in a customized prototype for each case. Our approach achieved optimal performance on two few-shot malware classification datasets, surpassing other competitive models with an accuracy improvement of over 2%. In extremely challenging scenarios&mdash;specifically, 20-class classification tasks with only five samples per class&mdash;our method notably excelled, achieving over 70% accuracy, significantly outperforming existing advanced techniques

A generalized Lorenz system-based initialization method for deep neural networks

Deep neural networks (DNNs) are a powerful tool for solving complex problems. The effectiveness of DNNs largely depends on the initialization technique used. This research develops a new initialization method for DNNs that uses chaotic sequences from the generalized Lorenz system to improve their performance. The proposed method, termed Generalized Lorenz Initialization (GLI), has been compared with two established initialization methods (Kaiming and Xavier) across four different DNN architectures: Informer, Neural Basis Expansion Analysis for Interpretable Time Series, Long Short-Term Memory, and NeuRewriter. The performance of these methods has been evaluated on seven time series forecasting datasets and one combinatorial optimization dataset. Results show that the GLI method improved forecasting accuracy by up to 86.47% compared to the Kaiming method and 88.86% compared to the Xavier method across all time series datasets. For the combinatorial optimization task, the GLI method reduced computational time by up to 9.24% with the better solution quality. These indicate the superiority of the GLI method over the two representative initialization methods for different DNN architectures across different problem domains