Crummer SunTrust Portfolio Recommendations: Crummer Investment Management [2014]

On balance, we believe the market will move modestly higher in the short-term. As we believe the economy is still recovering, we recommend over weighting the portfolio in cyclical sectors and underweighting countercyclical sectors. Like all soothsayers, we can only hope our glass is half‐full

Synthesis and Verification of Digital Circuits using Functional Simulation and Boolean Satisfiability.

The semiconductor industry has long relied on the steady trend of transistor scaling, that is, the shrinking of the dimensions of silicon transistor devices, as a way to improve the cost and performance of electronic devices. However, several design challenges have emerged as transistors have become smaller. For instance, wires are not scaling as fast as transistors, and delay associated with wires is becoming more significant. Moreover, in the design flow for integrated circuits, accurate modeling of wire-related delay is available only toward the end of the design process, when the physical placement of logic units is known. Consequently, one can only know whether timing performance objectives are satisfied, i.e., if timing closure is achieved, after several design optimizations. Unless timing closure is achieved, time-consuming design-flow iterations are required. Given the challenges arising from increasingly complex designs, failing to quickly achieve timing closure threatens the ability of designers to produce high-performance chips that can match continually growing consumer demands. In this dissertation, we introduce powerful constraint-guided synthesis optimizations that take into account upcoming timing closure challenges and eliminate expensive design iterations. In particular, we use logic simulation to approximate the behavior of increasingly complex designs leveraging a recently proposed concept, called bit signatures, which allows us to represent a large fraction of a complex circuit's behavior in a compact data structure. By manipulating these signatures, we can efficiently discover a greater set of valid logic transformations than was previously possible and, as a result, enhance timing optimization. Based on the abstractions enabled through signatures, we propose a comprehensive suite of novel techniques: (1) a fast computation of circuit don't-cares that increases restructuring opportunities, (2) a verification methodology to prove the correctness of speculative optimizations that efficiently utilizes the computational power of modern multi-core systems, and (3) a physical synthesis strategy using signatures that re-implements sections of a critical path while minimizing perturbations to the existing placement. Our results indicate that logic simulation is effective in approximating the behavior of complex designs and enables a broader family of optimizations than previous synthesis approaches.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61793/1/splaza_1.pd

Surveillance of Complex Auction Markets: a Market Policy Analytics Approach

The dissertation consists of four essays that investigates the merits of big data-driven decision-making in the surveillance of complex auction markets. In the first essay, Avci and her co-researchers examine the aggregate-level bidding strategies and market efficiency in a multi-time tariff setting by using parametric and semi parametric methods. In the second essay, they address three key forecasting challenges; risk of selection of an inadequate forecasting method and transparency level of the market and market-specific multi-seasonality factors in a semi-transparent auction market. In the third essay, they demonstrate the effect of information feedback mechanisms on bidders’ price expectations in complex auction markets with the existence of forward contracts. They develop a research model that empirically tests the impact of bidders’ attitudes on their price expectation through their trading behavior and tested their hypotheses on real ex-ante forecasts, evaluated ex-post. In the fourth essay, they investigate characterization of bidding strategies in an oligopolistic multi-unit auction and then examine the interactions between different strategies and auction design parameters. This dissertation offers important implications to theory and practice of surveillance of complex auction markets. From the theoretical perspective, this is, to our best knowledge, the first research that systematically examines the interplay of different informational and strategic factors in oligopolistic multi-unit auction markets. From the policy perspective, Avci’s research shows that integration of big data analytics and domain-specific knowledge improves decision-making in surveillance of complex auction markets

Modelling and Optimizing Supply Chain Integrated Production Scheduling Problems

Globalization and advanced information technologies (e.g., Internet of Things) have considerably impacted supply chains (SCs) by persistently forcing original equipment manufacturers (OEMs) to switch production strategies from make-to-stock (MTS) to make-to-order (MTO) to survive in competition. Generally, an OEM follows the MTS strategy for products with steady demand. In contrast, the MTO strategy exists under a pull system with irregular demand in which the received customer orders are scheduled and launched into production. In comparison to MTS, MTO has the primary challenges of ensuring timely delivery at the lowest possible cost, satisfying the demands of high customization and guaranteeing the accessibility of raw materials throughout the production process. These challenges are increasing substantially since industrial productions are becoming more flexible, diversified, and customized. Besides, independently making the production scheduling decisions from other stages of these SCs often find sub-optimal results, creating substantial challenges to fulfilling demands timely and cost-effectively. Since adequately managing these challenges asynchronously are difficult, constructing optimization models by integrating SC decisions, such as customer requirements, supply portfolio (supplier selection and order allocation), delivery batching decisions, and inventory portfolio (inventory replenishment, consumption, and availability), with shop floor scheduling under a deterministic and dynamic environment is essential to fulfilling customer expectations at the least possible cost. These optimization models are computationally intractable. Consequently, designing algorithms to schedule or reschedule promptly is also highly challenging for these time-sensitive, operationally integrated optimization models. Thus, this thesis focuses on modelling and optimizing SC-integrated production scheduling problems, named SC scheduling problems (SCSPs). The objective of optimizing job shop scheduling problems (JSSPs) is to ensure that the requisite resources are accessible when required and that their utilization is maximally efficient. Although numerous algorithms have been devised, they can sometimes become computationally exorbitant and yield sub-optimal outcomes, rendering production systems inefficient. These could be due to a variety of causes, such as an imbalance in population quality over generations, recurrent generation and evaluation of identical schedules, and permitting an under-performing method to conduct the evolutionary process. Consequently, this study designs two methods, a sequential approach (Chapter 2) and a multi-method approach (Chapter 3), to address the aforementioned issues and to acquire competitive results in finding optimal or near-optimal solutions for JSSPs in a single objective setting. The devised algorithms for JSSPs optimize workflows for each job by accurate mapping between/among related resources, generating more optimal results than existing algorithms. Production scheduling can not be accomplished precisely without considering supply and delivery decisions and customer requirements simultaneously. Thus, a few recent studies have operationally integrated SCs to accurately predict process insights for executing, monitoring, and controlling the planned production. However, these studies are limited to simple shop-floor configurations and can provide the least flexibility to address the MTO-based SC challenges. Thus, this study formulates a bi-objective optimization model that integrates the supply portfolio into a flexible job shop scheduling environment with a customer-imposed delivery window to cost-effectively meet customized and on-time delivery requirements (Chapter 4). Compared to the job shop that is limited to sequence flexibility only, the flexible job shop has been deemed advantageous due to its capacity to provide increased scheduling flexibility (both process and sequence flexibility). To optimize the model, the performance of the multi-objective particle swarm optimization algorithm has been enhanced, with the results providing decision-makers with an increased degree of flexibility, offering a larger number of Pareto solutions, more varied and consistent frontiers, and a reasonable time for MTO-based SCs. Environmental sustainability is spotlighted for increasing environmental awareness and follow-up regulations. Consequently, the related factors strongly regulate the supply portfolio for sustainable development, which remained unexplored in the SCSP as those criteria are primarily qualitative (e.g., green production, green product design, corporate social responsibility, and waste disposal system). These absences may lead to an unacceptable supply portfolio. Thus, this study overcomes the problem by integrating VIKORSORT into the proposed solution methodology of the extended SCSP. In addition, forming delivery batches of heterogeneous customer orders is challenging, as one order can lead to another being delayed. Therefore, the previous optimization model is extended by integrating supply, manufacturing, and delivery batching decisions and concurrently optimizing them in response to heterogeneous customer requirements with time window constraints, considering both economic and environmental sustainability for the supply portfolio (Chapter 5). Since the proposed optimization model is an extension of the flexible job shop, it can be classified as a non-deterministic polynomial-time (NP)-hard problem, which cannot be solved by conventional optimization techniques, particularly in the case of larger instances. Therefore, a reinforcement learning-based hyper-heuristic (HH) has been designed, where four solution-updating heuristics are intelligently guided to deliver the best possible results compared to existing algorithms. The optimization model furnishes a set of comprehensive schedules that integrate the supply portfolio, production portfolio (work-center/machine assignment and customer orders sequencing), and batching decisions. This provides numerous meaningful managerial insights and operational flexibility prior to the execution phase. Recently, SCs have been experiencing unprecedented and massive disruptions caused by an abrupt outbreak, resulting in difficulties for OEMs to recover from disruptive demand-supply equilibrium. Hence, this study proposes a multi-portfolio (supply, production, and inventory portfolios) approach for a proactive-reactive scheme, which concerns the SCSP with complex multi-level products, simultaneously including unpredictably dynamic supply, demand, and shop floor disruptions (Chapter 6). This study considers fabrication and assembly in a multi-level product structure. To effectively address this time-sensitive model based on real-time data, a Q-learning-based multi-operator differential evolution algorithm in a HH has been designed to address disruptive events and generate a timely rescheduling plan. The numerical results and analyses demonstrate the proposed model's capability to effectively address single and multiple disruptions, thus providing significant managerial insights and ensuring SC resilience

2017-18 Graduate Bulletin

After 2003 the University of Dayton Bulletin went exclusively online. This copy was downloaded from the University of Dayton\u27s website in March 2018.https://ecommons.udayton.edu/bulletin_grad/1048/thumbnail.jp

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

2002-2003 Bulletin

Volume 112, Number 1 Scanned from the copy held in the Registrar\u27s Office.https://ecommons.udayton.edu/bulletin_grad/1031/thumbnail.jp