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Optimizacija montažnega in strežnega sistema delavniške proizvodnje s pomočjo digitalnega dvojčka
Grgurič, Jurij (Author), Herakovič, Niko (Mentor) More about this mentor... This link opens in a new window

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Abstract
Za optimiziranje procesov, ki so v današnji industriji čedalje kompleksnejši in vedno bolj avtomatizirani, se ne moremo več zanašati na preproste izračune in izkušnje. Zato vedno pogosteje uporabljamo diskretne simulacije, ki predstavljajo digitalni dvojček realnega sistema. S pomočjo simulacije lahko preizkušamo različne scenarije, tudi povsem hipotetične, ne da bi motili realni sistem. Problem delavniške proizvodnje (angl. JSSP) predstavlja elementarno težavo planiranja zaporedja proizvodnje različnih naročil. Cilj optimizacije je bil najti zaporedje naročil, v katerem bodo vsi proizvodi končani v cim krajšem času. Naš 10-dimenzionalni problem smo optimizirali naključno in z genetskim algoritmom. Z obema metodama smo pridobili enak najboljši rezultat (2 d 8 h 44 min), kvalitativno pa smo lahko vrednotili le konvergiranega, ki ga je pridobil genetski algoritem. Ta se je izkazal kot dobro orodje simulacijske optimizacije, saj v kratkem času preveri veliko število permutacij, s pomočjo mutacij in križanja pa relativno hitro in z zadostno stopnjo gotovosti najde aproksimacijo optimalne vrednosti. Prav zaradi enostavnosti, hitrosti in relativne natančnosti ostaja med vodilnimi algoritmi optimizacije aplikativnih simulacij.

Language:Slovenian
Keywords:optimizacija, digitalni dvojčki, simulacije, delavniška proizvodnja, genetski algoritmi
Work type:Final paper (mb12)
Tipology:2.11 - Undergraduate Thesis
Organization:FS - Faculty of Mechanical Engineering
Year:2019
Publisher:[J. Grgurič]
Number of pages:XIII, 27 f., [8] f. pril.
UDC:004.896:658.5(043.2)
COBISS.SI-ID:16607515 Link is opened in a new window
Views:482
Downloads:235
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Secondary language

Language:English
Title:Optimisation of assembling and feeding system of job shop production using a digital twin
Abstract:
Todays industrys increasingly complex and automated processes cannot be optimised efficiently only using simple equations and experiences. Therefore the use of discrete simulations is on the rise, presenting real systems with digital twins. Using a simulation we can test different scenarios, even hypothetical ones, without disturbing the real system. Job shop scheduling problem represents a basic production planning problem of different sets of products. The goal of optimisation was finding the sequence of orders that equates in the shortest possible production time. Our 10 dimensional problem was optimised using pseudo-random generated sequences and using genetic algorithm. Both methods produced same optimisation result (2d 8h 44min), but we were only able to qualitatively assess the converging result, that of genetic algorithm. The algorithm has proved itself to be very useful solving simulation optimisation problems, with its fast calculations including mutations and cross-overs. It produces approximate results of the optimal solution, which is usually sufficient for real life applications. Its simplicity, speed and relative accuracy place it amongst the most used simulation optimisation algorithms.

Keywords:optimisation, digital twins, simulations, job shop production, Tecnomatix Plant Simulation, genetic algorithms

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