A Framework for Designing of Optimization Software Tools by Commercial API Implementation

The customized software development aims to release a software product tailored to the specific user needs. The goal of such software is to best fit the business requirements in an efficient system. This is especially true when business should perform optimization of the used resources. The existence of application programming interfaces in many commercial mathematical programming systems makes it possible to develop custom tailored tools using proven over the years effective methods and algorithms for optimization. In the current paper, a framework for designing of customized software based on the implementation of LINDO API is described. A developed prototype is used for numerical testing of real life optimization problem. The testing results show the applicability of the proposed framework for designing of effective customized optimization tools

Predictive Maintenance Sensors Placement by Combinatorial Optimization

The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential problem in predictive maintenance monitoring is the optimal sensor placement. The paper addresses that problem by using mixed integer linear programming tasks solving. The proposed optimal sensors location approach is based on the difference between sensor information if sensor is present and information calculated by linear interpolation if sensor is not present. The tasks results define the optimal sensors locations for a given number of sensors. The results of chosen sensors locations give as close as possible repeating the curve of structure dynamic response function. The proposed approach is implemented in an algorithm for predictive maintenance and the numerical results indicate that together with intelligent signal processing it could be suitable for practical application