Primjena genetičkih algoritma kod problema raspoređivanja tehnoloških operacija

The basic enterprise task is to satisfy customer requirements: due date, price and quality. Based on experiences from engineers practice of work in Croatian enterprises it could be concluded that enterprises are not able to fulfil obligations to the customer in a way of due dates. One of the basic reasons lies in inappropriate scheduling model that has not had possibility to make plan variants. The paper shows how genetic algorithm could be successfully applied in scheduling model to solve the problem of plan variant. As a basic selection in the paper 3-tournament steady-state selection has been applied.Danas je osnovni zadatak poduzeća zadovoljiti kupčeva zahtjeve u pogledu roka isporuke, cijene i kvalitete. Na osnovi iskustava inženjerske prakse iz hrvatskih proizvodnih poduzeća može se zaključiti kako poduzeća nisu u mogućnosti ispuniti obveze prema krajnjem kupcu u pogledu roka isporuke. Jedan od osnovnih razloga za takva odstupanja leži u neadekvatnom modelu raspoređivanja koji nemaju mogućnost izrade varijanti planova. U radu je prikazana mogućnost primjene genetičkog algoritma kod problema raspoređivanja tehnoloških operacija u svrhu dobivanje viševarijantnog raspoređivanja. Primijenjeni genetički algoritam baziran je na 3-turnirskoj eliminacijskoj selekciji bez duplikata

Primjena neuronskih mreža u planiranju održavanja metalurške opreme

Maintenance of metallurgical equipment is very complex and demanding job. Condition Based Maintenance (CBM) is used for complex and significant equipment. The paper shows element selection that will be used in CBM planning as a location on which will be applied neural network. The paper presents few different neural network algorithms that have been used for different prediction problems and review of achieved results. Data structure that has been used in researching problem is part of Information System and its Equipment Maintenance subsystem that was developed for enterprise Aluminij d.d. Mostar.Održavanje metalurških postrojenja predstavlja složen i zahtjevan posao. Strategija održavanja po stanju predstavlja strategiju koja se primjenjuje za složeniju i značajniju opremu. U radu se daje jedan od pristupa odabira elementa za koji se provodi istraživanje s ciljem primjene neuronskih mreža u planiranju održavanja po stanju. Istraživano je nekoliko različitih algoritama neuronskih mreža za rješavanje problema predikcije, te se u radu daje pregled ostvarenih rezultata istraživanja. Kao pretpostavka za istraživanje korišteni su podaci iz podsustava održavanja postrojenja Informacijskog sustava razvijenog za potrebe poduzeća Aluminij d.d. Mostar

Primjena neuronskih mreža u planiranju održavanja metalurške opreme

Maintenance of metallurgical equipment is very complex and demanding job. Condition Based Maintenance (CBM) is used for complex and significant equipment. The paper shows element selection that will be used in CBM planning as a location on which will be applied neural network. The paper presents few different neural network algorithms that have been used for different prediction problems and review of achieved results. Data structure that has been used in researching problem is part of Information System and its Equipment Maintenance subsystem that was developed for enterprise Aluminij d.d. Mostar.Održavanje metalurških postrojenja predstavlja složen i zahtjevan posao. Strategija održavanja po stanju predstavlja strategiju koja se primjenjuje za složeniju i značajniju opremu. U radu se daje jedan od pristupa odabira elementa za koji se provodi istraživanje s ciljem primjene neuronskih mreža u planiranju održavanja po stanju. Istraživano je nekoliko različitih algoritama neuronskih mreža za rješavanje problema predikcije, te se u radu daje pregled ostvarenih rezultata istraživanja. Kao pretpostavka za istraživanje korišteni su podaci iz podsustava održavanja postrojenja Informacijskog sustava razvijenog za potrebe poduzeća Aluminij d.d. Mostar

Application of genetic algorithm to the technological operations scheduling problem

The basic enterprise task is to satisfy customer requirements: due date, price and quality. Based on experiences from engineers practice of work in Croatian enterprises it could be concluded that enterprises are not able to fulfil obligations to the customer in a way of due dates. One of the basic reasons lies in inappropriate scheduling model that has not had possibility to make plan variants. The paper shows how genetic algorithm could be successfully applied in scheduling model to solve the problem of plan variant. As a basic selection in the paper 3-tournament steady-state selection has been applied

Comparison of meat quality of tench, Tinca tinca, reared in extensive and semi-intensive culture systems

The aim of the study was to examine the production parameters and chemical composition of fillets in tench, Tinca tinca L., farmed in one of two systems: an extensive system based on only natural food (NF) available in the fish ponds; and a semi-intensive system based on natural food plus the addition of formulated feeds supplemented with various oils: fish (FO), rapeseed (RO), soybean (SO), or linseed oil (LO). Proper pond preparation resulted in a favourable amount and structure of natural food in all ponds. The rearing system had a significant influence on the tench yield, muscle lipid contents and fatty acid composition, and the supplemented feed influenced the fatty acid composition. The percentages of eicosapentaenoic (EPA) and docosahexaenoic (DHA) acid and total highly unsaturated n-3 fatty acids (n-3 HUFA) were higher in the fish oil and the natural food groups than in the others. The n-3/n-6 ratio in the five dietary treatments was notably variable, and in any case higher in fillets than in the diets. In conclusion, the present study suggests that RO, SO and LO represent effective lipid sources for tench fed formulated diets. On the other hand, tench that fed on naturally available pond foods had a high content of n-3 HUFA in their muscle lipids, but poor growth parameters and low yields