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The uncertainty in the timing and severity of disaster events makes the long‐term planning of mitigation and recovery actions both critical and extremely difficult. Planners often use expected values for hazard occurrences, leaving communities vulnerable to worse‐than‐usual and even so‐called "black swan" events. This research models disasters in terms of their best‐case, most‐likely, and worst‐case damage estimates. These values are then embedded in a fuzzy goal programming model to provide community planners and stakeholders with the ability to strategize for any range of events from best‐case to worst‐case by adjusting goal weights. Examples are given illustrating the modeling approach, and an analysis is provided to illustrate how planners might use the model as a planning tool. [ABSTRACT FROM AUTHOR]

Sport venue naming rights agreements represent monumental long-term marketing investments to increase brand awareness and improve a firm's image. These agreements require enormous expenditures, highlighting the importance of understanding their impact on the firm's market value. Questions of the wisdom of such investments today, which types of sport venues are most appropriate, and whether full corporate names or truncated ones are equally successful are of more than academic interest. Prior research has presented only a limited window into the efficacy of these deals. Results from the well-known event study methodology show improved stock market returns based on 122 announcements of naming rights deals, although it is found that market reactions have soured since 2001, when several firms investing in naming rights experienced insolvency. Investments in naming major league baseball or multi-sport venues have been particularly wise. Somewhat surprisingly, we observe that longer sport venue names are preferred to other name lengths. [ABSTRACT FROM AUTHOR]

An essential condition necessary for the implementation of the Japanese just-in- time (JIT) technique with Kanbans is small setup times relative to processing times. Without this condition bottlenecks occur at workcentres which delay production. However, the Japanese have developed a variation of the strict JIT technique that employs a special type of Kanban, referred to as a `signal Kanban', at workcentres with relatively high setup times. While a normal Kanban triggers the production of larger than normal lots this altered version of the JIT technique provides an alternative approach for firms that desire to use the JIT technique, but are unable to reduce setup times at all workstations. The purpose of this paper is to describe the signal Kanban technique and demonstrate two versions of an integer mathematical programming approach for determining the optimal lotsizes to be used in conjunction with signal Kanbans. A simulation model is subsequently employed to test the effectiveness of the integer programming models and to explore some of the characteristics and conditions a firm might encounter when implementing a JIT system. [ABSTRACT FROM AUTHOR]

Japanese production environments exhibit a number of characteristics not present in many American firms that contribute to the success of the just-in-time (JIT) system with Kanbans. However, because of the advantages of a JIT system with Kanbans many firms have attempted implementation without the existence of the appropriate production environment to do so. The purpose of this paper is first to identify those factors that will influence the number of Kanbans required at a workcenter for the production manager implementing a JIT system in an uniquely American production environment. The factors that will be identified include the throughput velocity (i.e. the rate at which items flow through a workcenter machine), the coefficient of variation in processing times (i.e. the degree of variability of processing times), the machine utilization (i.e. the availability of slack time on a machine), and. autocorrelation of processing times (the degree to which successive processing times on a specific machine are related to each other). For example, considering just one of these factors, the coefficient of variation, it is demonstrated for a sample shop that reducing the coefficient of variation by a factor of three will reduce the number of Kanbans required by a factor of six. This type of analysis of these factors by the potential Kanban user can provide the user with some insight into the viability of a Kanhan-type system for a particular shop. The impact of these factors will be analyzed via simulation. The second part of the paper will describe a methodology for determining the number of Kanbans to use at a workcenter given a dynamic production environment. This methodology will also he demonstrated via simulation. [ABSTRACT FROM AUTHOR]

Describes a network simulation model that integrates the material requirements planning-based shop control level with the shop level in a manufacturing system. Multiple-level product structures; Job and assembly operations; Product demand.

The histologic evaluation of gastrointestinal (GI) biopsies is the standard for diagnosis of a variety of GI diseases (e.g., inflammatory bowel disease [IBD] and alimentary lymphoma [ALA]). The World Small Animal Veterinary Association (WSAVA) Gastrointestinal International Standardization Group proposed a reporting standard for GI biopsies consisting of a defined set of microscopic features. We compared the machine classification accuracy of free-text microscopic findings with those represented in the WSAVA format with a diagnosis of IBD and ALA. Unstructured free-text duodenal biopsy pathology reports from cats (n = 60) with a diagnosis of IBD (n = 20), ALA (n = 20), or normal (n = 20) were identified. Biopsy samples from these cases were then scored following the WSAVA guidelines to create a set of structured reports. Three supervised machine-learning algorithms were trained using the structured and then the unstructured reports. Diagnosis classification accuracy for the 3 algorithms was compared using the structured and unstructured reports. Using naive Bayes and neural networks, unstructured information-based models achieved higher diagnostic accuracy (0.90 and 0.88, respectively) compared to the structured information-based models (0.74 and 0.72, respectively). Results suggest that discriminating diagnostic information was lost using current WSAVA microscopic guideline features. Addition of free-text features (number of plasma cells) increased WSAVA auto-classification performance. The methodologies reported in our study represent a way of identifying candidate microscopic features for use in structured histopathology reports. [ABSTRACT FROM AUTHOR]

Much effort has been invested in standardizing medical terminology for representation of medical knowledge, storage in electronic medical records, retrieval, reuse for evidence-based decision making, and for efficient messaging between users. We only focus on those efforts related to the representation of clinical medical knowledge required for capturing diagnoses and findings from a wide range of general to specialty clinical perspectives (e.g., internists to pathologists). Standardized medical terminology and the usage of structured reporting have been shown to improve the usage of medical information in secondary activities, such as research, public health, and case studies. The impact of standardization and structured reporting is not limited to secondary activities; standardization has been shown to have a direct impact on patient healthcare. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA–IBD, ALA–normal, and IBD–normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. [ABSTRACT FROM AUTHOR]

The desirability of a merger/acquisition alternative depends in part on the perceptions of the decision maker. What sources of information are "useful" to the decision maker? Does the set of useful information remain constant for all decision makers; if not, do individuals using similar information sets have similar information processing characteristics? Do these sets vary as feedback is obtained during the decision process? To answer these questions, graduate students participated in a modified Delphi experiment, and the resulting data were analyzed by the two-way aligned-ranks nonparametric test. These test results affirm that in a merger/acquisition scenario, decision makers with different cognitive styles prefer different sets of information and these sets vary dynamically as feedback is incorporated in the decision-making process. Furthermore, information that contains worker and community welfare considerations is identified as "useful" five times more frequently by decision makers with a "feeling" cognitive style than those with a "thinking" style. [ABSTRACT FROM AUTHOR]

The Japanese "just-in-time with kanban" technique reduces in-process inventory to absolute minimal levels, in concert with the Japanese belief that inventory is an unnecessary evil. Due to the success of Japanese firms that employ this type of system, American firms would like to import this technique and emulate Japanese successes. But this Japanese success may be attributable not only to the just-in-time with kanban technique but also to the production environment in which the technique is employed. This paper simulates the just-in-time with kanban technique for a multiline, multistage production system in order to determine its adaptability to an American production environment that might include such characteristics as variable processing times, variable master production scheduling, and imbalances between production stages. The results have practical implications for those firms considering adoption of the Japanese technique. [ABSTRACT FROM AUTHOR]

HEADNOTE The multi-commodity network flow problem has frequently been formulated as a linear programming model In this problem several commodities are distributed through a network of distribution points (i.e., stores, outlets, terminals, etc.) in order to meet supply end demand constraints at these distribution points at the minimum shipping coat. However, frequently cost minimization ia not the sole or even the most important objective of the firm. Other objectives often exist related to preferred customer demand, inventory conditions, and shipping arrangements end contracts. In this article a linear goal programming model for the multi-commodity network flow problem with multiple objectives is formulated and illustrated via a case example. The example and model are complicated by the fact that shipments occur over several periods. The model solution is analyzed and sensitivity analysis is performed to ascertain the impact of alternative priority structures and goal levels on the solution. [ABSTRACT FROM AUTHOR]

In a Just-in-Time (JIT) production system with Kanbans, as used by the Japanese, the number of Kanbans employed at each workcenter remains relatively constant from month to month even though demand may change. This occurs because of the unique (and stable) conditions inherent in the production environment of Japanese firms. However, a number of American firms have considered or attempted the implementation of a JIT system without a similar production environment, thus, necessitating that the number of Kanbans at each workcenter be adjusted periodically. In this paper, a procedure for dynamically adjusting the number of Kanbans at workcenters in an unstable production environment is presented and analyzed. The adjustment methodology is presented in a step- by-step manner. This is followed by three examples presented within the context of a simulation model of a hypothetical shop. The first example will illustrate the methodology while the last two examples will demonstrate how well the methodology reacts to the dynamics of the shop operation. [ABSTRACT FROM AUTHOR]

This paper describes a new procedure for obtaining satisfactory solutions to multiple-response, multiple-input simulation models. A modified version of response surface methodology is incorporated to obtain input values which meet user specified goals for the responses. The approach is illustrated with three examples which demonstrate the method. The desirability of incorporating this approach into an interactive computer mode is also discussed. [ABSTRACT FROM AUTHOR]