Tabitha L. James

A hybrid grouping genetic algorithm for the cell formation problem

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems.

A cooperative parallel tabu search algorithm for the quadratic assignment problem

In this study, we introduce a cooperative parallel tabu search algorithm (CPTS) for the quadratic assignment problem (QAP). The QAP is an NP-hard combinatorial optimization problem that is widely acknowledged to be computationally demanding. These characteristics make the QAP an ideal candidate for parallel solution techniques. CPTS is a cooperative parallel algorithm in which the processors exchange information throughout the run of the algorithm as opposed to independent concurrent search strategies that aggregate data only at the end of execution. CPTS accomplishes this cooperation by maintaining a global reference set which uses the information exchange to promote both intensification and strategic diversification in a parallel environment. This study demonstrates the benefits that may be obtained from parallel computing in terms of solution quality, computational time and algorithmic flexibility. A set of 41 test problems obtained from QAPLIB were used to analyze the quality of the CPTS algorithm. Additionally, we report results for 60 difficult new test instances. The CPTS algorithm is shown to provide good solution quality for all problems in acceptable computational times. Out of the 41 test instances obtained from QAPLIB, CPTS is shown to meet or exceed the average solution quality of many of the best sequential and parallel approaches from the literature on all but six problems, whereas no other leading method exhibits a performance that is superior to this.

Multistart Tabu Search and Diversification Strategies for the Quadratic Assignment Problem

The quadratic assignment problem (QAP) is a well-known combinatorial optimization problem with a wide variety of applications, prominently including the facility location problem. The acknowledged difficulty of the QAP has made it the focus of many metaheuristic solution approaches. In this paper, we show the benefit of utilizing strategic diversification within the tabu search (TS) framework for the QAP, by incorporating several diversification and multistart TS variants. Computational results for an extensive and challenging set of QAP benchmark test problems demonstrate the ability of our TS variants to improve on a classic TS approach that is one of the principal and most extensively used methods for the QAP. We also show that our new procedures are highly competitive with the best recently introduced methods from the literature, including more complex hybrid approaches that incorporate the classic TS method as a subroutine.

Determining the Intention to Use Biometric Devices: An Application and Extension of the Technology Acceptance Model

Protection of physical assets and digital information is of growing importance to society. As with any new technology, user acceptance of new software and hardware devices is often hard to gauge, and policies to introduce and ensure adequate and correct usage of such technologies are often lacking. Security technologies have widespread applicability to different organizational contexts that may present unusual and varied adoption considerations. This study adapts the technology acceptance model (TAM) and extends it to study the intention to use biometrics devices across a wide variety of organizational contexts. Due to the use of physiological characteristics, biometrics present unique adoption concerns. TAM is extended in this study to include constructs for perceived need for privacy, perceived need for security and perceived physical invasiveness of biometric devices as factors that influence intention to use. The model is shown to be a good predictor of intention to use biometric devices.

A hybrid grouping genetic algorithm for the registration area planning problem

With the growing use of mobile communication devices, the management of such technologies is of increasing importance. The registration area planning (RAP) problem examines the grouping of cells comprising a personal communication services (PCS) network into contiguous blocks in an effort to reduce the cost of managing the location of the devices operating on the network, in terms of bandwidth. This study introduces a hybridized grouping genetic algorithm (HGGA) to obtain cell formations for the RAP problem. The hybridization is accomplished by adding a tabu search-based improvement operator to a traditional grouping genetic algorithm (GGA). Results indicate that significant performance gains can be realized by hybridizing the algorithm, especially for larger problem instances. The HGGA is shown to consistently outperform the traditional GGA on problems of size greater than 19 cells.

Sequential and parallel path-relinking algorithms for the quadratic assignment problem

The quadratic assignment problem, a classical combinatorial optimization problem, has garnered much attention due to its many applications and solution complexity. This research represents the first use of parallelization for path relinking within the QAP setting. We used a simple form of path relinking to focus on the parallel implementations elements and to determine their impact when used with a method of this type. Our computational results demonstrate highly attractive outcomes despite the procedures simplicity and show in particular the value of a well-designed parallelization process in this context.

A Study of Communication and Coordination in Collaborative Software Development

Abstract A virtual software development team consists of members who may not be physically at the same location at the same time and use electronic modes of communication. We examine the communication, coordination, and satisfaction of members as they work with both co-located and remote members in virtual software development teams. We perform an interpretive evaluation of the qualitative comments. The results of this study indicate that virtual teams can have communication and coordination problems if not properly managed but successful virtual teams can work effectively despite the lean electronic communication. Members who use a lean communication mode are more likely to break communication with their team members and tend to be more critical of the contributions of their remote members. Our results suggest that successful teams communicate information that is perceived to have value and develop a shared context within the communication and coordination structure. We discuss the implications of this study for collaborative software development.

Grouping Genetic Algorithm for the Blockmodel Problem

Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the blockmodel problem. Testing on numerous examples from the literature indicates a GGA is an appropriate tool for solving this type of problem. Specifically, our GGA provides good solutions, even to large-size problems, in reasonable computational time.

Impact of HIPAA provisions on the stock market value of healthcare institutions, and information security and other information technology firms

Title 1 of the Health Insurance Portability and Accountability Act (HIPAA) was enacted to improve the portability of healthcare insurance coverage and Title II was intended to alleviate fraud and abuse. The development of a health information system was suggested in Title II of HIPAA as a means of promoting standardization to improve the efficiency of the healthcare system and ensure that electronic healthcare information is transferred securely and kept private. Since the legislation places the onus of providing the described improvements on healthcare institutions and part of these requirements relate to information technology (IT) and information security (IS), the process of complying with the legislation will necessitate acquiring products and services from IT/IS firms. From the viewpoint of stock market analysts, this increase in demand for IT/IS products and services has the potential to boost the profitability of public IT/IS firms, in turn positively enhancing their stock market valuation. Following the same logic, the legislations compliance burdens shared by healthcare firms are expected to require hefty costs, thus potentially reducing the profitability of healthcare firms and reflecting negatively on their stock price. The intent of this paper is to evaluate the stock market reaction to the introduction of HIPAA legislation by evaluating the abnormal movement in the price of the stock of public healthcare institutions, IT, and IS firms. We conduct event-study analyses around the announcement dates of the various provisions of HIPAA. An event study is a standard statistical methodology used to determine whether the occurrence of a specific event or events results in a statistically significant reaction in financial markets. The advantage of the event study methodology for policy analysis is that it provides an anchor for determining value, which eliminates reliance on ad hoc judgments about the impact of specific events or policies on stock prices. While event studies have been conducted that examine the market effect of security and privacy breaches on firms, none has attempted to determine the impact, in terms of resulting market reaction, of the HIPAA legislation itself. The results of the study confirm the logic above, while also providing insight into specific stages of the legislative path of HIPAA.

Platform impact on performance of parallel genetic algorithms: Design and implementation considerations

Many problems in the operations research field cannot be solved to optimality within reasonable amounts of time with current computational resources. In order to find acceptable solutions to these computationally demanding problems, heuristic methods such as genetic algorithms are often developed. Parallel computing provides alternative design options for heuristic algorithms, as well as the opportunity to obtain performance benefits in both computational time and solution quality of these heuristics. Heuristic algorithms may be designed to benefit from parallelism by taking advantage of the parallel architecture. This study will investigate the performance of the same global parallel genetic algorithm on two popular parallel architectures to investigate the interaction of parallel platform choice and genetic algorithm design. The computational results of the study illustrate the impact of platform choice on parallel heuristic methods. This paper develops computational experiments to compare algorithm development on a shared memory architecture and a distributed memory architecture. The results suggest that the performance of a parallel heuristic can be increased by considering the desired outcome and tailoring the development of the parallel heuristic to a specific platform based on the hardware and software characteristics of that platform.