Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Abstract
This paper addresses a distribution problem involving a set of different products that need to be distributed among a set of geographically disperse retailers and transported from the single warehouse to the aforementioned retailers. The disfribution and transportation are made in order to satisfy retailers' demand while satisfying storage limits at both the warehouse and the retailers, transportation limits between the warehouse and the retailers, and other operational constraints. This problem is combinatorial in nature as it involves the assignment of a discrete finite set of objects, while satisfying a given set of conditions. Hence, we propose a genetic algorithm that is capable of finding good quality solutions. The genetic algorithm proposed is used to a real case study involving the disfribution of eight products among 108 retailers from a single warehouse. The results obtained improve on those of company's current practice by achieving a cost reduction of about 13%.

Abstract
This work proposes a multi-criteria decision-making approach to select suppliers in the olive oil sector. Besides several performance criteria required to the supplier, olive oil characteristics such as colour, smell, and density, as well as organoleptic tests are used. Hence, the assessment and selection of suppliers assumes a major importance and needs to be done yearly. The process of finding a set of suppliers to choose from involves two sequential stages, namely identification and elimination. The identification stage consists of finding a set of potential suppliers. Then, in the elimination stage, suppliers that are not able to meet the thresholds associated with some technical indicators are disregarded. Thus, only a small set of very promising suppliers need to be assessed. The assessment was performed by resorting to the Macbeth approach, resulting in a ranking. The results obtained were validated through sensitivity and robustness analyses.

Abstract
This paper proposes a mixed integer linear programming model for a multi-product ordering in a three-echelon supply chain network, where multiple manufacturers supply multiple warehouses with multiple products, which in turn distribute the products to the multiple retailers involved. The model considers practical production constraints such as production capacity, backorder allowances, and economically-viable minimum order quantities. Numerical computations show that the model can efficiently solve small-sized problem instances. © 2019, IEOM Society International.

Abstract
We fit an immune response model to data reporting the CD4(+) T cell numbers from the 28 days following the infection of mice with lymphocytic choriomeningitis virus LCMV. We used an ODE model that was previously used to describe qualitatively the behaviour of CD4(+) T cells, regulatory T cells (Tregs) and interleukine-2 (IL-2) density. The model considered two clonotypes of T cells in order to fit simultaneously the two time series for the gp61 and NP309 epitopes. We observed the proliferation of T cells and, to a lower extent, Tregs during the immune activation phase following infection and subsequently, during the contraction phase, a smooth transition from faster to slower death rates. The six parameters that were optimized were: the beginning and ending times of the immune response, the growth rate of T cells, their capacity, and the two related with the homeostatic numbers of T cells that respond to each epitope. We showed that the ODE model was able to be calibrated thus providing a quantitative description of the data.

Abstract
In this work, we introduce the concept of maximum curvature to separate the low from high reinfection levels. For each temporary immunity transition rate, the threshold value is the infection rate where the positive curvature of the endemic stationary state attains its maximum value. Hence, the maximum curvature reinfection threshold can be interpreted as the moment when the graph of the stationary state of infected attains the maximum change in its direction. When the temporary immunity transition rate tends to zero, the limiting point of the maximum curvature reinfection threshold coincides with the Gomes' reinfection threshold and the curvature blows up to infinity.