Modeling, analysis, and design of supply chain networks with the integration of nonlinear Cost of Quality Functions

Due to the complexity of the supply chain, sourcing and distribution activities within the supply chain require a fair deal of orchestrating in order to eliminate delays and other inefficiencies. For this reason, researchers have worked effortlessly to incorporate a wide range of parameters in the modeling of the supply chain. The parameters integrated have touched many important issues. As important, issues pertaining to quality are of great importance in organizations. Some literature has discussed quality from the perspective of the supply chain and acknowledged the lack of a consistent vision pertaining to quality throughout the supply chain. With many industries today on the quest of improving their quality systems, finding ways to reduce nonconformities and failure of products is crucial. In industries such as the aerospace industry, the variable production cost is considerably high; hence producing extra parts to compensate for defectives would be a costly option. While Cost of Quality (COQ) is a very good indicator of how much poor quality is costing a company, the literature lacks a work that aims at integrating COQ into Supply Chain Network Design (SCND). This thesis aims at exploring the challenges in doing so and introduces a comprehensive supply chain model that minimizes a series of costs, in which COQ is integrated. The inclusion of COQ is done through the integration of quadratic quality function. The overall supply chain is mathematically modeled producing a nonlinearity in the objective function and in the constraints. Hence this thesis solves a constrained binary nonlinear programming problem. Further, this work integrates binary entities, to allow for assignable/set-up costs, into the model and introduces seven solution procedures to solve the model. A real life supply chain network is used to extract relevant results. The real life supply chain is in the domain of the aerospace industry and has an n level Bill of Material (BOM). Heuristics have been introduced to solve Binary Quadratic Programming (BQP) problems before. A majority of these heuristics are geared towards unconstrained problems where feasibility might not be a concern. Alternatively in the COQ model, constraints bind the objective function making feasibility a criterion for optimality. Therefore, the seven solution procedures entertain a feasibility check mechanism and one of the seven solution procedures is a hybrid solution procedure formulated to tailor for the special topography of the feasible solution region of the proble

Forecasting and optimization stock predictions: Varying asset profile, time window, and hyperparameter factors

Machine learning has made significant progress in various fields, including financial markets. Numerous studies have applied different machine learning algorithms to predict stock market behavior, but these studies often face challenges in terms of data acquisition and preparation, algorithm design, hyperparameter optimization, and feature selection, as well as the inherent volatility of stocks. In this work, our aim is to review the literature for comprehensive studies that address these challenges and enhance the state-of-the-art by introducing novel factors, such as multi-time windows, training batch size, stopping criteria, training data ratio, and financial technical indicators. We observe statistical significance when varying the training period window, with a p-value lower than 0.0001. However, genetic-based hyperparameter optimization brings about a significant 40% improvement compared to random-grid search. Concerning the inclusion of technical indicators, we see little improvement in terms of prediction accuracy, but there is some improvement in directional prediction accuracy across several stocks. Overall, the results show high variation with respect to the time window chosen for conducting a study. Additionally, we discover that the characteristics of the stock and the time period, including the length of the time period and the specific start and end dates, significantly impact prediction accuracy