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We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision‐making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.

More attention must be paid to the health of the nursing workforce to positively influence the quality of patient care and patient safety and to control costs.

Kadilar and Cingi [Ratio estimators in simple random sampling, Appl. Math. Comput. 151 (3) (2004), pp. 893-902] introduced some ratio-type estimators of finite population mean under simple random sampling. Recently, Kadilar and Cingi [New ratio estimators using correlation coefficient, Interstat 4 (2006), pp. 1-11] have suggested another form of ratio-type estimators by modifying the estimator developed by Singh and Tailor [Use of known correlation coefficient in estimating the finite population mean, Stat. Transit. 6 (2003), pp. 655-560]. Kadilar and Cingi [Improvement in estimating the population mean in simple random sampling, Appl. Math. Lett. 19 (1) (2006), pp. 75-79] have suggested yet another class of ratio-type estimators by taking a weighted average of the two known classes of estimators referenced above. In this article, we propose an alternative form of ratio-type estimators which are better than the competing ratio, regression, and other ratio-type estimators considered here. The results are also supported by the analysis of three real data sets that were considered by Kadilar and Cingi.ratio-type estimators, mean square error (MSE), transformation, efficiency,

We review and evaluate empirical research in more than 150 papers published in Production and Operations Management (POM) during 1992 to 2005 to assess how far the papers' authors have met the journaľs stated objective of promoting empirical research. We also assess the diversity of articles in terms of the purposes of research, data collection approaches, and data analysis techniques. We classify the empirical research articles based on their primary purpose (theory building, theory verifying, application, and providing evidence), data collection approach (case study, qualitative research, archival research, survey‐based research, laboratory research, and field research), data analysis technique (descriptive statistics, various multivariate statistical techniques, and mathematical modeling), and operations topics (strategy, quality, and supply chain management). We also discuss directions for future empirical research in operations management. During 1992 to 2005, the articles based on empirical data have increased substantially from 30 to 50 percent of all articles published in POM. During 1992 to 1998, about three‐fourths of the empirical‐research‐based articles published in POM focused on the manufacturing industry, but recently the gap between the numbers of manufacturing‐ and service‐focused articles published in POM has almost disappeared. While a previous assessment of articles published in a range of operations management journals showed that almost all of the empirical articles were based on either surveys or case studies, our results indicate that POM has published articles that were based on a much wider and more diverse range of data collection approaches. Production and Operations Management has clearly established itself as a leading outlet for publishing empirical research in operations management.

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