VTechWorks Repository :: Browsing by Author "Russell, Roberta S."

Browsing by Author "Russell, Roberta S."

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Applying Best Supply Chain Practices to Humanitarian Relief
Russell, Roberta S.; Hiller, Janine S. (Penn State, 2015-05)
With the growth in length and breadth of extended supply chains, more companies are employing risk management techniques and resilience planning to deal with burgeoning and costly supply chain disruptions. As companies can learn from humanitarian groups, so can humanitarian groups learn from industry how to respond, recover, and prepare for these disruptive events. This paper looks at industry leaders in supply chain risk management and explores how humanitarian supply chains can learn from industry best practices.
Cellular and functional production environments: design methodology and comparison
Sarper, Hüseyin (Virginia Polytechnic Institute and State University, 1988)
A hybrid methodology was developed to fairly compare functional and cellular production environments with respect to the production of machined parts which constitute the indivisible components of some final products. The methodology provides a means of designing each production environment at the lowest possible cost and then comparing the two environments with respect to cost and non-cost performance measures. The results show that the long-held belief that the cellular manufacturing or group technology method of production may be superior to that of the traditional functional or job shop layout may not be correct. A detailed comparison using four problem sets with different job and machine mixes failed to indicate a clear case in which the cellular environment performed better than the functional. The methodology consists of two stages. Stage one has six hierarchical steps which systematically determine machine requirements and layout planning of each environment through mathematical modelling. External and internal operation constraints and inputs such as stochastic daily demand and operation times were considered. Stochastic programming was used in handling uncertain daily demand and operation times by specifying a desired minimum probability of meeting the demand for each job type in both environments. The MPSIII package was used in solving large mixed integer problems that resulted once nonlinear terms, due to the chance-constrained nature of the segments of the models, were linearized. Because of the large problem sizes, MPSIII input files had to be created using FORTRAN codes. In stage two, the SIMAN simulation language was used to determine the feasibility of stage one decisions and to obtain other system information. In simulation, some approximations were made to implement stage one decisions. For example, jobs received an average processing time in each operation class area rather than the exact operation time of the specific machine type to which the jobs were assigned in stage one. The effect of material handling distances and the use of limited number of work-in-process carriers were considered. Although the methodology was mainly developed for the comparison of the two production environments, it is readily usable for individual design of either production environment. In addition to the two main stages of development, this research also required the development of two other procedures: unitizing daily demands and the modifying the previously available job/cell grouping methods.
Cellular manufacturing: applicability and system design
Leu, Yow-yuh (Virginia Tech, 1991-08-07)
As competition has intensified, many American manufacturers have sought alternatives to rejuvenate their production systems. Cellular manufacturing systems have received considerable interest from both academics and practitioners. This research examines three major issues in cellular manufacturing that have not been adequately addressed: applicability, structural design, and operational design. Applicability, in this study, is concerned with discerning the circumstances in which cellular manufacturing is the system of choice. The methodology employed is simulation and two experimental studies are conducted. The objective of Experiment I, a 2 x 3 x 3 factorial design, is to investigate the role of setup time and move time on system performance and to gain insight into why and how one layout could outperform another. The results of Experiment I suggest that move time is a significant factor for job shops and that workload variation needs to be reduced if the performance of cellular manufacturing is to be improved. Experiment II evaluates the impact of setup time reduction and operational standardization on the performance of cellular manufacturing. The results of Experiment II suggest that cellular manufacturing is preferred if the following conditions exist: (1) well balanced workload, (2) standardized products, (3) standardized operations, and (4) setup times independent from processing times.
Collection-and-Delivery-Points: A Variation on a Location-Routing Problem
Savage, Laura Elizabeth (Virginia Tech, 2019-09-20)
Missed deliveries are a major issue for package carriers and a source of great hassle for the customers. Either the carrier attempts to redeliver the package, incurring the additional expense of visiting the same house up to three times, or they leave the package on the doorstep, vulnerable to package thieves. In this dissertation, a system of collection-and-delivery-points (CDPs) has been proposed to improve customer service and reduce carrier costs. A CDP is a place, either in an existing business or a new location, where the carrier drops any missed deliveries and the customers can pick the packages at their convenience. To examine the viability of a CDP system in North America, a variation on a location-routing problem (LRP) was created, a mixed-integer programming model called the CDP-LRP. Unlike standard LRPs, the CDP-LRP takes into account both the delivery truck route distance and the direct customer travel to the CDPs. Also, the facilities being placed are not located at the beginning and ending of the truck routes, but are stops along the routes. After testing, it became clear that, because of the size and complexity of the problem, the CDP-LRP is unable to be solved exactly in a reasonable amount of time. Heuristics developed for the standard LRP cannot be applied to the CDP-LRP because of the differences between the models. Therefore, three heuristics were created to approximate the solution to the CDP-LRP, each with two different embedded modified vehicle routing problem (VRP) algorithms, the Clark-Wright and the Sweep, modified to handle the additional restrictions caused by the CDPs. The first is an improvement heuristic, in which each closed CDP is tested as a replacement for each open CDP, and the move that creates the most savings is implemented. The second begins with every CDP open, and closes them one at a time, while the third does the reverse and begins will only one open CDP, then opens the others one by one. In each case, a penalty is applied if the customer travel distance is too long. Each heuristic was tested for each possible number of open CDPs, and the least expensive was chosen as the best solution. Each heuristic and VRP algorithm combination was tested using three delivery failure rates and different data sets: three small data sets pulled from VRP literature, and randomly generated clustered and uniformly distributed data sets with three different numbers of customers. OpenAll and OpenOne produced better solutions than Replacement in most instances, and the Sweep Algorithm outperformed the Clark-Wright in both solution quality and time in almost every test. To judge the quality of the heuristic solutions, the results were compared to the results of a simple locate-first, route-second sequential algorithm that represents the way the decision would commonly be made in industry today. The CDPs were located using a simple facility location model, then the delivery routes were created with the Sweep algorithm. These results were mixed: for the uniformly distributed data sets, if the customer travel penalty threshold and customer density are low enough, the heuristics outperform the sequential algorithm. For the clustered data sets, the sequential algorithm produces solutions as good as or slightly better than the sequential algorithm, because the location of the potential CDP inside the clusters means that the penalty has less impact, and the addition of more open CDPs has less effect on the delivery route distances. The heuristic solutions were also compared to a second value – the route costs incurred by the carrier in the current system of redeliveries, calculated by placing additional customers in the routes and running the Sweep algorithm – to judge the potential savings that could be realized by implementing a CDP system in North America. Though in some circumstances the current system is less expensive, depending on the geographic distribution of the customers and the delivery failure rate, in other circumstances the cost savings to the carrier could be as high as 27.1%. Though the decision of whether or not to set up a CDP system in an area would need to be made on a case-by-case basis, the results of this study suggest that such a system could be successful in North America.
Competitive determinants of technology diffusion in the wood household furniture industry
West, Cynthia D. (Virginia Tech, 1990)
Adoption of manufacturing technologies have been cited as an important competitive strategy for successful firms. This study assessed the wood household furniture industry for its current level of technology adoption, examined the impact of competitive variables on technology adoption and strategy formation, as well as, characteristics of innovators or early adopters within the industry. The results provide both insight into the technological direction of this industry and factors influencing the adoption of innovations by industrial organizations. The U.S. wood household furniture industry was surveyed concerning their recent equipment purchases, future purchase plans, and adoption of a list of 21 innovative technologies. Respondents listed recent equipment purchases within the finish machining area of the mill, particularly with automatic controls, as providing them with the most important benefits of increased efficiency and product quality. Respondents indicated that the functional areas of finish machining and the rough mill will receive the majority of new equipment over the next five years with automatic controls increasing in importance over time. A competitive-policy contingent model of technology adoption was developed and empirically tested. Innovativeness of firms was accessed by the number of technologies adopted from a set developed by industry experts. Empirical results suggest that organizational policy is dependent on the competitive conditions under which it was formed and that policy has an important effect on the innovativeness of an organization. Communication variables (signal frequency, cosmopliteness, and professionalization) were found to exhibit greater direct and indirect effects on innovation than industry structural variables with the exception of firm size. Characteristics of early adopters were contrasted with those of later adopters of technologies within the furniture industry based upon their adoption of thirteen processing technologies. Early adopters were found to differ significantly from later adopters on firm size, technological expertise, technological progressiveness, opinion leadership, information sources, and cosmopolitanism of the decision making group. The influence of technology push versus marketing pull strategies on firms was examined in an empirical study. Results of cluster analysis indicate that firms do align themselves along these strategic dimensions and can be contrasted on key characteristics; such as, demographics, company performance and environmental uncertainty.
Computational Studies in Multi-Criteria Scheduling and Optimization
Martin, Megan Wydick (Virginia Tech, 2017-08-11)
Multi-criteria scheduling provides the opportunity to create mathematical optimization models that are applicable to a diverse set of problem domains in the business world. This research addresses two different employee scheduling applications using multi-criteria objectives that present decision makers with trade-offs between global optimality and the level of disruption to current operating resources. Additionally, it investigates a scheduling problem from the product testing domain and proposes a heuristic solution technique for the problem that is shown to produce very high-quality solutions in short amounts of time. Chapter 2 addresses a grant administration workload-to-staff assignment problem that occurs in the Office of Research and Sponsored Programs at land-grant universities. We identify the optimal workload assignment plan which differs considerably due to multiple reassignments from the current state. To achieve the optimal workload reassignment plan we demonstrate a technique to identify the n best reassignments from the current state that provides the greatest progress toward the utopian solution. Solving this problem over several values of n and plotting the results allows the decision maker to visualize the reassignments and the progress achieved toward the utopian balanced workload solution. Chapter 3 identifies a weekly schedule that seeks the most cost-effective set of coach-to-program assignments in a gymnastics facility. We identify the optimal assignment plan using an integer linear programming model. The optimal assignment plan differs greatly from the status quo; therefore, we utilize a similar approach from Chapter 2 and use a multiple objective optimization technique to identify the n best staff reassignments. Again, the decision maker can visualize the trade-off between the number of reassignments and the resulting progress toward the utopian staffing cost solution and make an informed decision about the best number of reassignments. Chapter 4 focuses on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Each product receives a certification at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the make span while ensuring that technicians are available when needed to complete in-process and at-completion inspections We first formulate a mixed-integer programming model (MILP) to identify the optimal solution to this problem using IBM ILOG CPLEX Interactive Optimizer 12.7. We also present a genetic algorithm (GA) solution that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the relative merits of the MILP and GA solution approaches across a number of scenarios.
Computer integrated machining parameter selection in a job shop using expert systems and algorithms
Gopalakrishnan, B. (Virginia Polytechnic Institute and State University, 1988)
The research for this dissertation is focused on the selection of machining parameters for a job shop using expert systems and algorithms. The machining processes are analyzed in detail and rule based expert systems are developed for the analysis of process plans based on operation and work-material compatibility, the selection of machines, cutting tools, cutting fluids, and tool angles. Data base design is examined for this problem. Algorithms are developed to evaluate the selection of machines and cutting tools based on cost considerations. An algorithm for optimizing cutting conditions in turning operations has been developed. Data framework and evaluation procedures are developed for other machining operations involving different types of machines and tools.
Cost Modeling Based on Support Vector Regression for Complex Products During the Early Design Phases
Huang, Guorong (Virginia Tech, 2007-08-09)
The purpose of a cost model is to provide designers and decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling cost. The cost models developed in the design phases are the most important and the most difficult to develop. Therefore it is necessary to identify appropriate cost drivers and employ appropriate modeling techniques to accurately estimate cost for directing designers. The objective of this study is to provide higher predictive accuracy of cost estimation for directing designer in the early design phases of complex products. After a generic cost estimation model is presented and the existing methods for identification of cost drivers and different cost modeling techniques are reviewed, the dissertation first proposes new methodologies to identify and select the cost drivers: Causal-Associated (CA) method and Tabu-Stepwise selection approach. The CA method increases understanding and explanation of the cost analysis and helps avoid missing some cost drivers. The Tabu-Stepwise selection approach is used to select significant cost drivers and eliminate irrelevant cost drivers under nonlinear situation. A case study is created to illustrate their procedure and benefits. The test data show they can improve predictive capacity. Second, this dissertation introduces Tabu-SVR, a nonparametric approach based on support vector regression (SVR) for cost estimation for complex products in the early design phases. Tabu-SVR determines the parameters of SVR via a tabu search algorithm improved by the author. For verification and validation of performance on Tabu-SVR, the five common basic cost characteristics are summarized: accumulation, linear function, power function, step function, and exponential function. Based on these five characteristics and the Flight Optimization Systems (FLOPS) cost module (engine part), seven test data sets are generated to test Tabu-SVR and are used to compare it with other traditional methods (parametric modeling, neural networking and case-based reasoning). The results show Tabu-SVR significantly improves the performance compared to SVR based on empirical study. The radial basis function (RBF) kernel, which is much more robust, often has better performance over linear and polynomial kernel functions. Compared with other traditional cost estimating approaches, Tabu-SVR with RBF kernel function has strong predicable capability and is able to capture nonlinearities and discontinuities along with interactions among cost drivers. The third part of this dissertation focuses on semiparametric cost estimating approaches. Extensive studies are conducted on three semiparametric algorithms based on SVR. Three data sets are produced by combining the aforementioned five common basic cost characteristics. The experiments show Semiparametric Algorithm 1 is the best approach under most situations. It has better cost estimating accuracy over the pure nonparametric approach and the pure parametric approach. The model complexity influences the estimating accuracy for Semiparametric Algorithm 2 and Algorithm 3. If the inexact function forms are used as the parametric component of semiparametric algorithm, they often do not bring any improvement of cost estimating accuracy over the pure nonparametric approach and even worsen the performance. The last part of this dissertation introduces two existing methods for sensitivity analysis to improve the explanation capability of the cost estimating approach based on SVR. These methods are able to show the contribution of cost drivers, to determine the effect of cost drivers, to establish the profiles of cost drivers, and to conduct monotonic analysis. They finally can help designers make trade-off study and answer “what-i” questions.
Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters
Chacko, Josey; Rees, Loren P.; Zobel, Christopher W.; Rakes, Terry R.; Russell, Roberta S.; Ragsdale, Cliff T. (Elsevier, 2016-07-01)
This paper discusses a new mathematical model for community-driven disaster planning that is intended to help decision makers exploit the synergies resulting from simultaneously considering actions focusing on mitigation and efforts geared toward long-term recovery. The model is keyed on enabling long-term community resilience in the face of potential disasters of varying types, frequencies, and severities, and the approach’s highly iterative nature is facilitated by the model’s implementation in the context of a Decision Support System. Three examples from Mombasa, Kenya, East Africa, are discussed and compared in order to demonstrate the advantages of the new mathematical model over the current ad hoc mitigation and long-term recovery planning approaches that are typically used.
Development of an object-oriented modeling environment for prototyping heterogeneous simulation models
Gupta, Vikas (Virginia Tech, 1990-12-05)
Computer simulation modeling is currently the most flexible method of manufacturing system analysis. Unfortunately, current simulation frameworks do not support the modular specification of homogeneous and heterogeneuos models. A Simulation Program Generator (SPG) for prototyping heterogeneuos simulation models is developed. Objects in the model represent elements within a flexible assembly system. These elements are the robot, the conveyor, the part, the schedule plan and the robot program. These objects were modeled using the GIBSS simulation framework. A model base and an user interface is developed to allow the construction and execution of simulation models. Instances of the objects are created and stored into the model base. These are retrieved later to construct a model The user interface is provided with an extensive set of tools for model creation and execution. Icons. representing objects, are selected and placed on the terminal screen and then connected together by interaction lines to create the complete model. The attributes of the objects can be altered and viewed on their respective panes. The SPG is a step forward in the development of computer-aided manufacturing system design environments for prototyping heterogeneous models. It allows the rapid generation and execution of simulation models.
Disruption Information, Network Topology and Supply Chain Resilience
Li, Yuhong (Virginia Tech, 2017-07-17)
This dissertation consists of three essays studying three closely related aspects of supply chain resilience. The first essay is "Value of Supply Disruption Information and Information Accuracy", in which we examine the factors that influence the value of supply disruption information, investigate how information accuracy influences this value, and provide managerial suggestions to practitioners. The study is motivated by the fact that fully accurate disruption information may be difficult and costly to obtain and inaccurate disruption information can decrease the financial benefit of prior knowledge and even lead to negative performance. We perform the analysis by adopting a newsvendor model. The results show that information accuracy, specifically information bias and information variance, plays an important role in determining the value of disruption information. However, this influence varies at different levels of disruption severity and resilience capacity. The second essay is "Quantifying Supply Chain Resilience: A Dynamic Approach", in which we provide a new type of quantitative framework for assessing network resilience. This framework includes three basic elements: robustness, recoverability and resilience, which can be assessed with respect to different performance measures. Then we present a comprehensive analysis on how network structure and other parameters influence these different elements. The results of this analysis clearly show that both researchers and practitioners should be aware of the possible tradeoffs among different aspects of supply chain resilience. The ability of the framework to support better decision making is then illustrated through a systemic analysis based on a real supply chain network. The third essay is "Network Characteristics and Supply Chain Disruption Resilience", in which we investigate the relationships between network characteristics and supply chain resilience. In this work, we first prove that investigating network characteristics can lead to a better understanding of supply chain resilience behaviors. Later we select key characteristics that play a critical role in determining network resilience. We then construct the regression and decision tree models of different supply chain resilience measures, which can be used to estimate supply chain network resilience given the key influential characteristics. Finally, we conduct a case study to examine the estimation accuracy.
Distribution Planning for Rail and Truck Freight Transportation Systems
Feng, Yazhe (Virginia Tech, 2012-06-21)
Rail and truck freight transportation systems provide vital logistics services today. Rail systems are generally used to transport heavy and bulky commodities over long distances, while trucks tend to provide fast and flexible service for small and high-value products. In this dissertation, we study two different distribution planning problems that arise in rail and truck transportation systems. In the railroad industry, shipments are often grouped together to form a block to reduce the impact of reclassification at train yards. We consider the time and capacity constrained routing (TCCR) problem, which assigns shipments to blocks and train-runs to minimize overall transportation costs, while considering the train capacities and shipment due dates. Two mathematical formulations are developed, including an arc-based formulation and a path-based formulation. To solve the problem efficiently, two solution approaches are proposed. The sequential algorithm assigns shipments in order of priority while considering the remaining train capacities and due dates. The bump-shipment algorithm initially schedules shipments simultaneously and then reschedules the shipments that exceed the train capacity. The algorithms are evaluated using a data set from a major U.S. railroad with approximately 500,000 shipments. Industry-sized problems are solved within a few minutes of computational time by both the sequential and bump-shipment algorithms, and transportation costs are reduced by 6% compared to the currently used trip plans. For truck transportation systems, trailer fleet planning (TFP) is an important issue to improve services and reduce costs. In this problem, we consider the quantities and types of trailers to purchase, rent, or relocate among depots to meet time varying demands. Mixed-integer programming models are developed for both homogeneous and heterogeneous TFP problems. The objective is to minimize the total fleet investment costs and the distribution costs across multiple depots and multiple time periods. For homogeneous TFP problem, a two-phase solution approach is proposed. Phase I concentrates on distribution costs and determines the suggested fleet size. A sweep-based routing heuristic is applied to generate candidate routes of good quality. Then a reduced mathematical model selects routes for meeting customer demands and determines the preferred fleet size. Phase II provides trailer purchase, relocation, and rental decisions based on the results of Phase I and relevant cost information. This decomposition approach removes the interactions between depots and periods, which greatly reduces the complexity of the integrated optimization model. For the heterogeneous TFP problem, trailers with different capacities, costs, and features are considered. The two-phase approach, developed for the homogeneous TFP, is modified. A rolling horizon scheme is applied in Phase I to consider the trailer allocations in previous periods when determining the fleet composition for the current period. Additionally, the sweep-based routing heuristic is also extended to capture the characteristics of continuous delivery practice where trailers are allowed to refill products at satellite facilities. This heuristic generates routes for each trailer type so that the customer-trailer restrictions are accommodated. The numerical studies, conducted using a data set with three depots and more than 400 customers, demonstrate the effectiveness of the two-phase approaches. Compared to the integrated optimization models, the two-phase approaches obtain quality solutions within a reasonable computational time and demonstrate robust performance as the problem sizes increase. Based on these results, a leading industrial gas provider is currently integrating the proposed solution approaches as part of their worldwide distribution planning software.
An evaluation of scheduling policies in a dual resource constrained assembly shop
Russell, Roberta S. (Virginia Polytechnic Institute and State University, 1983)
Research in job shop scheduling has concentrated on sequencing simple, single component jobs that require no coordination of multiple parts for assembly. However, since most jobs in reality involve some assembly work, scheduling multiple component jobs through an assembly shop, where both serial and parallel operations take place, represents a more realistic and practical problem. The scheduling environment for multiple component jobs in terms of routing, sequencing, and the pacing of common components may be quite complex, and, as such, requires special scheduling considerations. The purpose of this research is to evaluate scheduling policies for the production of assembled products in a job shop environment, termed "assembly shop". The specific scheduling policies examined include duedate assignment procedures, labor assignment procedures, and item sequencing rules. The sensitivity of these policies to product structure is also addressed.
Exploratory and Empirical Analysis of E-Marketplaces for Truck Transportation Services Procurement
Collignon, Stephane Eric (Virginia Tech, 2016-08-11)
In the late 1990s, early 2000s, academic literature considered electronic marketplaces as a game changer in truck transportation services procurement. Early enthusiasm was followed by skepticism regarding e-marketplaces' usefulness and the popularity of e-marketplaces appeared to wane both in industry and in academic literature. However, recent sources argue that almost half of the freight currently transported by truck in the USA is subject to transactions conducted in e-marketplaces. This dissertation intends to fill a gap in the academic literature by showing that truck transportation e-marketplaces necessitate renewed dedicated research efforts, by exploring the strategies implemented by e-marketplaces in this specific industry and by linking these strategies to marketplaces' performance. First, transportation and non-transportation e-marketplaces are compared in chapter 2 with regard to their usage of mechanisms designed to generate trust among users. Results show that truck transportation e-marketplaces use these trust mechanisms differently than non-transportation e-marketplaces, which supports a call for research on e-marketplaces in the specific context of truck transportation services procurement. In chapter 3, a database inventorying the usage of 141 features by 208 e-marketplaces is then created to initiate the empirical exploration of these specific e-marketplaces. Thanks to that database, a new typology (a way of classifying objects based on several simultaneous classification criteria) is developed in chapter 4 that identifies three main truck transportation e-marketplace strategies (two with sub-divided into two sub-strategies). The typology provides a state of industry and puts in perspective the specificity of truck transportation e-marketplaces with regard to their structure along 11 dimensions known to the general e-marketplace literature. Finally, the link between e-marketplace strategies and performance is investigated in chapter 5. Performance is measured with three traffic metrics: number of unique visitors per day, number of page views per day, and website ranking. Results show that third-party-owned e-marketplaces that provide auction mechanisms with a fairly high level of user decision and transaction support are more successful than other e-marketplaces. This dissertation provides a picture of existing e-marketplaces for the procurement of truck transportation services, challenges components of existing theories and provides ground for further research.
Extracting the Wisdom of Crowds From Crowdsourcing Platforms
Du, Qianzhou (Virginia Tech, 2019-08-02)
Enabled by the wave of online crowdsourcing activities, extracting the Wisdom of Crowds (WoC) has become an emerging research area, one that is used to aggregate judgments, opinions, or predictions from a large group of individuals for improved decision making. However, existing literature mostly focuses on eliciting the wisdom of crowds in an offline context—without tapping into the vast amount of data available on online crowdsourcing platforms. To extract WoC from participants on online platforms, there exist at least three challenges, including social influence, suboptimal aggregation strategies, and data sparsity. This dissertation aims to answer the research question of how to effectively extract WoC from crowdsourcing platforms for the purpose of making better decisions. In the first study, I designed a new opinions aggregation method, Social Crowd IQ (SCIQ), using a time-based decay function to eliminate the impact of social influence on crowd performance. In the second study, I proposed a statistical learning method, CrowdBoosting, instead of a heuristic-based method, to improve the quality of crowd wisdom. In the third study, I designed a new method, Collective Persuasibility, to solve the challenge of data sparsity in a crowdfunding platform by inferring the backers' preferences and persuasibility. My work shows that people can obtain business benefits from crowd wisdom, and it provides several effective methods to extract wisdom from online crowdsourcing platforms, such as StockTwits, Good Judgment Open, and Kickstarter.
Firms' Resilience to Supply Chain Disruptions
Baghersad, Milad (Virginia Tech, 2018-07-16)
This dissertation consists of three papers related to firms' resiliency to supply chain disruptions. The first paper seeks to evaluate the effects of supply chain disruptions on firms' performance by using a recent dataset of supply chain disruptions. To this end, we analyzed operating and stock market performances of over 300 firms that experienced a supply chain disruption during 2005 to the end of 2014. The results show that supply chain disruptions are still associated with a significant decrease in operating income, return on sales, return on assets, sales, and a negative performance in total assets. Supply chain disruptions are also associated with a significant negative abnormal stock return on the day of the supply chain disruption announcements. These results are in line with previous findings in the literature. In the second paper, in order to provide a more detailed characterization of negative impacts of disruptions on firms' performance, we develop three complementary measures of system loss: the initial loss due to the disruption, the maximum loss, and the total loss over time. Then, we utilize the contingent resource-based view to evaluate the moderating effects of operational slack and operational scope on the relationship between the severity of supply chain disruptions and the three complementary measures of system loss. We find that maintaining certain aspects of operational slack and broadening business scope can affect these different measures of loss in different ways, although these effects are contingent on the disruptions' severity. The third paper examines relationships between the origin of supply chain disruptions, firms' past experience, and the negative impacts of supply chain disruptions on firms' performance. This third study shows that the impact of external and internal supply chain disruptions on firms' performance can be different when firms do and do not have past experience with similar events. For example, the results show that past experience significantly decreases initial loss, recovery time, and total loss over time experienced by firms after internal disruptions, although past experience may not decrease initial loss, recovery time, and total loss over time in the case of external disruptions.
A framework for the performance-based design of flexible manufacturing cells
Rao, Polarouthu Chandrasekhar (Virginia Tech, 1989-04-05)
A conceptual framework for the design and performance evaluation of flexible manufacturing cells (FMCS) based on the strategic objectives of firms was developed. Four different types of manufacturing task profiles were identified based on the primary manufacturing task, product characteristics, and manufacturing system characteristics of a strategic business unit (SBU). Performance measures were discussed for each of the manufacturing task profiles, and the task profiles of firms likely to implement FMCs were identified. A methodology, based on the analytic hierarchy process (AHP), introduced by Saaty, was developed to prioritize the manufacturing objectives of an FMC. The implications of each of the manufacturing objectives for an FMC were hypothesized and related performance measures identified. An interactivecomputer-based model, based on the theory of closed network-of–queues, was then developed to aid in the preliminary design and evaluation on an FMC. Field work was carried out to determine the practical applicability of the conceptual framework. Visits to a company in the Southeastern United States were made and an analysis of the FMC being developed in the Department of Industrial Engineering and Operations Research, at Virginia Tech was conducted. The framework developed in this research was used to determine the manufacturing task profile of the company, identify key performance measures, and exercise the AHP methodology for one cell. Operational measures were then calculated for the FMC, using the computer-based model.
GIBSS: a framework for the multi-level simulation of manufacturing systems
De Meter, Edward Christopher (Virginia Polytechnic Institute and State University, 1989)
A systems approach for manufacturing system design calls for the division of a system design into sub-designs, and their specification over multiple levels of detail. Through an iterative design and evaluation process, a system design progresses from an abstraction to an implemental specification. To facilitate the evaluation process, models of sub-designs must be applicable to modular assembly, even if the sub-designs are heterogeneously specified. Computer simulation modeling is currently the most flexible method of manufacturing system analysis. When used in the multi-level design process, two forms of simulation models are encountered, uni-level and multi-level. A simulation model of a manufacturing system is considered uni-level if objects of equivalent type within the system are modeled at the same level of detail. On the other hand, a model is considered multi-level if objects of equivalent type are not modeled at the same level of detail. Unfortunately, current simulation frameworks do not integrate modular construction with the various discrete event and continuous simulation techniques needed to support multi-level modeling. This dissertation describes GIBSS (Generalized Interaction Based Simulation Specification), a simulation framework which supports the modular construction of uni-level and multi-level simulation models. Under GIBSS, the mechanisms and attributes of a manufacturing system simulation are distributed among various classes of independent sub-models. These classes are passive, internal interaction, external interaction, and master simulation. GIBSS describes the mechanics of each of these classes, as well as their method of synchronization. Using GIBSS, sub-models are created, executed, and validated independently, and then brought together to execute in parallel or near parallel fashion. As a result, uni-level and multi-level system simulation models are assembled from multiple sub-models. GIBSS eliminates a barrier to the rapid evaluation of manufacturing system designs. It facilitates the multi-level design process, and is the basis of a research effort, dedicated to the development of a new generation of computer-aided manufacturing system design environments.
The Human Factor in Supply Chain Risk Management
Kwaramba, Shingirai C. (Virginia Tech, 2019-02-04)
In a three paper essay series we address the human impact in SCRM from the microeconomic and macroeconomic perspectives. First, using a positivist theory building approach, we synthesize behavioral risk management and supply chain risk management theory to propose behavioral supply chain risk management as a new topic area. This paper is microeconomic in nature and focuses mostly on individuals as the unit of analysis in a SCRM context. Second, we introduce cross-impact analysis as a scenariobased supplier selection methodology. We demonstrate how cross-impact analysis can be used to provide supply chain decision-makers with probability estimates of the future viability of the members of a given set of possible suppliers in a backdrop of macroeconomic risk. The third and final paper in the series incorporates the probability estimates resulting from a cross-impact analysis exercise into a hybrid stochastic mixed-integer programming (SMIP) technique CIA-SMIP. We demonstrate how the CIA-SMIP approach can be utilized as a single-source supplier selection model. In its totality, this dissertation represents a step towards the theoretical framing of the human impact on SCRM into two main distinguishable areas: microeconomic and macroeconomic.
Multidimensional Visualization of Process Monitoring and Quality Assurance Data in High-Volume Discrete Manufacturing
Teets, Jay Marshall (Virginia Tech, 2007-01-19)
Advances in microcomputing hardware and software over the last several years have resulted in personal computers with exceptional computational power and speed. As the costs associated with microcomputer hardware and software continue to decline, manufacturers have begun to implement numerous information technology components on the shop floor. Components such as microcomputer file servers and client workstations are replacing traditional (manual) methods of data collection and analysis since they can be used as a tool for real-time decision-making. Server-based and web-based shop floor data collection and monitoring software applications are able to collect vast amounts of data in a relatively short period of time. In addition, advances in telecommunications and computer interconnectivity allow for the remote access and sharing of this data for additional analysis. Rarely, however, does the method by which a manager reviews production and quality data keep pace with the large amount of data being collected and thus available for analysis. Visualization techniques that allow the decision maker to react quickly, such as the ability to view and manipulate vast amounts of data in real-time, may provide an alternative for operations managers and decision-makers. These techniques can be used to improve the communication between the manager using a microcomputer and the microcomputer itself through the use of computer-generated, domain-specific visualizations. This study explores the use of visualization tools and techniques applied to manufacturing systems as an aid in managerial decision-making. Numerous visual representations that support process and quality monitoring have been developed and presented for evaluation of process and product quality characteristics. These visual representations are based on quality assurance data and process monitoring data from a high-volume, discrete product manufacturer with considerable investment in both automated and intelligent processes and information technology components. A computer-based application was developed and used to display the visual representations that were then presented to a sample group of evaluators who evaluated them with respect to their ability to utilize them in making accurate and timely decisions about the processes being monitored. This study concludes with a summary of the results and provides a direction for future research efforts.

Browsing by Author "Russell, Roberta S."

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