Abstract The purpose of this study is to deepen the knowledge and understanding of the strategies “just in time” and “freight pooling” in logistics management in the food industry. In order to answer the purpose, we had two different research questions. The questions are about what advantages and disadvantages there are with these strategies in the food industry. The study is conducted in a qualitative way where the interviews and observations are used to deepen the knowledge of how an organization in food industries uses the strategies “just in time” and “freight pooling”. The previous science shows that there are some advantages with these strategies but also disantvages that the organization should have in mind and work with so that they do not happen. The study shows that the organization this study was conducted on where very successful in implementing these strategies and they experienced many advantages by using “just in time” and “freight pooling”. The organization this study was conducted on were really cautious with the possible setbacks that the strategies can so they emphasized that they need to work really close with all the involved parties so that they can minimize the risk for the disadvantages to happen.  Keywords: just in time, pooling, logistic strategies, transport optimization, food supply chain

In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index. Most experimenters will be less familiar with the Gumbel bivariate logistic regression model than with the univariate logistic regression models which comprise its marginals. Therefore, the optimal designs based on the Gumbel model are evaluated based on univariate logistic regression D-efficiencies; conversely, designs derived from the univariate logistic regression model are evaluated with respect to the Gumbel optimality criteria. Further practical considerations motivate an exploration of designs providing a maximum compromise between the three Gumbel-based criteria D, Q and T. Finally, 5-point designs which can be generated by fitted equations are proposed as a practical option for experimental use. / Ph. D.

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The most common mode of levee failure, breach due to overtopping, is generally considered as a function of a complex set of contributing factors. The goal of this research is to enhance the state of the art and practice for performing levee overtopping risk assessment. For this purpose, a dataset of levee overtopping event records within the portfolio of levee systems maintained by the U.S. Army Corps of Engineers (USACE) is presented. The dataset is utilized with logistic regression analysis to develop a probabilistic model to calculate system response probabilities and assess risk related to levee overtopping. The presented dataset can be used for identifying key factors controlling overtopping behavior, validation of model results, and providing new insight into the phenomenon of levee overtopping. The proposed model offers a practical yet robust tool for levee risk analysis and can be readily employed by engineers and other stakeholders.

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