Development of an algorithm and a system for deductive prediction and analysis of movment of basketball referees

Докторска дисертација припада области информационих система, са јасним акцентом на употребу неуронских мрежа за решавање проблема вишеструких зависних временских серија  који је у  овом докторату дефинисан.Основни циљ дисертације је креирање система у форми едукативног софтвера путем којег ће се обучавати младе кошаркашке судије Један од кључих елемената овог  доктората јесте симулација хоризонталног видног поља на основу којег се  утврђује да ли је резоновано кретање кошаркашких судија било адекватно  или није. Стога развијени софтвер поседује споменуту едукативну примену. Како би се реализовао споменути софтвер спроведено је истраживање које је обухватило обучавање великог броја традиционалних вишеслојних перцептрона као и формирање посебне  LTR  –  MDTS структуре неуронске мреже за коју се сматра да је погодна за решавање постојећег проблема. За реализацију симулације хоризонталног видног поља разматрано је више алгоритама из области рачунарске графике  а  Sweep and Prune  алгоритам је парцијално пружио основу за развијени и тренутно  имплементирани алгоритам.Doktorska disertacija pripada oblasti informacionih sistema, sa jasnim akcentom na upotrebu neuronskih mreža za rešavanje problema višestrukih zavisnih vremenskih serija  koji je u  ovom doktoratu definisan.Osnovni cilj disertacije je kreiranje sistema u formi edukativnog softvera putem kojeg će se obučavati mlade košarkaške sudije Jedan od ključih elemenata ovog  doktorata jeste simulacija horizontalnog vidnog polja na osnovu kojeg se  utvrđuje da li je rezonovano kretanje košarkaških sudija bilo adekvatno  ili nije. Stoga razvijeni softver poseduje spomenutu edukativnu primenu. Kako bi se realizovao spomenuti softver sprovedeno je istraživanje koje je obuhvatilo obučavanje velikog broja tradicionalnih višeslojnih perceptrona kao i formiranje posebne  LTR  –  MDTS strukture neuronske mreže za koju se smatra da je pogodna za rešavanje postojećeg problema. Za realizaciju simulacije horizontalnog vidnog polja razmatrano je više algoritama iz oblasti računarske grafike  a  Sweep and Prune  algoritam je parcijalno pružio osnovu za razvijeni i trenutno  implementirani algoritam.Doctoral dissertation belongs to the field of information systems, with a clear  emphasis on the use of neural networks for solving the problem of multiple dependent time series, which is defined in this doctorate. The main objective of the  thesis is to create a system in the form of educational software that will be used druring the training of young basketball referees.One of the key elements of this doctorate is a simulation of a horizontal field of  vision  on the basis of which it is determined whether the movement of  reasoned  basketball referees was adequate   or not.  Therefore developed software has  aforementioned educational use. In order  to realize the aforementioned software, a  research  was conducted that included training of a large number of traditional multilayer perceptron neural networks and the  formation of special LTR  -  MDTS  neural network  structure which is considered to be  suitable  for solving the presented problem. For the realization of the simulation  of the horizontal field of vision a large number of algorithms in the field of computer graphis was considered  and Sweep and Prune algorithm partially provided the basis for the developed and  currently implemented algorithm

Empirical study of the effect of stochastic variability on the performance of human-dependent flexible flow lines

Manufacturing systems have developed both physically and technologically, allowing production of innovative new products in a shorter lead time, to meet the 21st century market demand. Flexible flow lines for instance use flexible entities to generate multiple product variants using the same routing. However, the variability within the flow line is asynchronous and stochastic, causing disruptions to the throughput rate. Current autonomous variability control approaches decentralise the autonomous decision allowing quick response in a dynamic environment. However, they have limitations, e.g., uncertainty that the decision is globally optimal and applicability to limited decisions. This research presents a novel formula-based autonomous control method centered on an empirical study of the effect of stochastic variability on the performance of flexible human-dependent serial flow lines. At the process level, normal distribution was used and generic nonlinear terms were then derived to represent the asynchronous variability at the flow line level. These terms were shortlisted based on their impact on the throughput rate and used to develop the formula using data mining techniques. The developed standalone formulas for the throughput rate of synchronous and asynchronous human-dependent flow lines gave steady and accurate results, higher than closest rivals, across a wide range of test data sets. Validation with continuous data from a real-world case study gave a mean absolute percentage error of 5%. The formula-based autonomous control method quantifies the impact of changes in decision variables, e.g., routing, arrival rate, etc., on the global delivery performance target, i.e., throughput, and recommends the optimal decisions independent of the performance measures of the current state. This approach gives robust decisions using pre-identified relationships and targets a wider range of decision variables. The performance of the developed autonomous control method was successfully validated for process, routing and product decisions using a standard 3x3 flexible flow line model and the real-world case study. The method was able to consistently reach the optimal decisions that improve local and global performance targets, i.e., throughput, queues and utilisation efficiency, for static and dynamic situations. For the case of parallel processing which the formula cannot handle, a hybrid autonomous control method, integrating the formula-based and an existing autonomous control method, i.e., QLE, was developed and validated.InnovateU