Anti-Human BAD (Phospho-Ser136) polyclonal antibody

In the service market targeting fleets of industrial assets (e.g., aircraft, jet engines, wind turbines, etc.), original equipment manufacturers and service providers compete with one another through offers covering day-to-day service as well as major maintenance and repairs over. Since decision-making is highly guided by reliability models, it is safe to say that services profitability dependents on the ability to understand the complex stochastic interactions between operating conditions and component capability. Unfortunately, factors such as aggressive mission mixes introduced by operators, exposure to a harsh environment, inadequate maintenance, and problems with mass production can lead to large discrepancies between predicted and observed useful lives. This paper focuses on the quantification of the infant mortality impact on fleets of industrial assets. A numerical experiment is used to study how the number of failure observations and fleet size impacts the modeling of fleet reliability. Dynamic Bayesian networks implementing physics-based models are used to model fleet unreliability considering the effects of bad batch of materials.The results demonstrate that material capability, penetration of bad batch of material in the fleet, and fleet size drastically influence the model accuracy. Therefore, small fleet operators, which naturally observe a low-number of failures, have to deal with larger uncertainties when quantifying infant mortality. This negatively impacts their ability to allocate resources such as inventory, labor, and account for the loss of productivity while servicing their fleet. With large fleet operators, on the other hand, large number of failure observations can cause high financial burden. Nevertheless, it also allows for reduced uncertainty in building/updating the reliability models, which can help their ability to forecast future failures and make provisions for service and maintenance. Finally, the results also show that measures such as recommissioning of the fleet and inspection campaigns can mitigate the effects of fleet-wide early life problems.

We present a general method for obtaining strong bounds for discrete optimization problems that is based on a concept of branching duality. It can be applied when no useful integer programming model is available, and we illustrate this with the minimum bandwidth problem. The method strengthens a known bound for a given problem by formulating a dual problem whose feasible solutions are partial branching trees. It solves the dual problem with a "worst-bound" local search heuristic that explores neighboring partial trees. After proving some optimality properties of the heuristic, we show that it substantially improves known combinatorial bounds for the minimum bandwidth problem with a modest amount of computation. It also obtains significantly tighter bounds than depth-first and breadth-first branching, demonstrating that the dual perspective can lead to better branching strategies when the object is to find valid bounds.