Optimizacija maloserijske proizvodnje dveh tipov elektromotorjev z upoštevanjem stohastičnih dogodkov : diplomsko delo Visokošolskega strokovnega študijskega programa I. stopnje Strojništvo - Projektno aplikativni program

Volk, Andraž (author), Rihar, Lidija (mentor), Jenko, Marjan (comentor)

Cilj diplomske naloge je simulirati proizvodnjo dveh tipov elektromotorjev, pri čemer bomo upoštevali podatke o delovnih operacijah, času izvedbe ter vhodnih in izhodnih materialih, vključno z njihovo težo in volumnom in preteklimi podatki o nepredvidenih dogodkih v proizvodnem procesu v zadnjih dveh letih. S pomočjo simulacijskega modela bomo optimizirali postavitev delovnih mest in logistiko materiala, pripravili načrt za povečanje proizvodnje ter analizirali vpliv stohastičnih dogodkov na proizvodni proces. Model bo vključeval nominalne značilke proizvodnje in evidentirane stohastične dogodke, kar nam bo omogočilo doseči optimalen proces z minimalnimi odstopanji v pretočnih časih. S simulacijami bomo izboljšali sistem in dosegli optimalno proizvodnjo, za katero smo postavili hipotezo.

• Language: Slovenian
• Keywords: stohastični proces, proizvodnja, motnje v proizvodnji, FlexSim, optimizacija, verjetnostne porazdelitve
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FS - Faculty of Mechanical Engineering
• Publisher: [A. Volk]
• Year: 2024
• Number of pages: XX, 54 str.
• PID: 20.500.12556/RUL-160854
• UDC: 658.51:519.216:004.94(043.2)
• COBISS.SI-ID: 211151363
• Publication date in RUL: 05.09.2024

Secondary language

• Language: English
• Title: Optimisation of two electric motor types series production with stochastic events

Abstract

The objective of this thesis is to simulate the production of two types of electric motors, taking into account data on work operations, execution time, and input and output materials, including their weight and volume, as well as past data on unforeseen events in the production process over the past two years. Using the simulation model, we will optimize the layout of workstations and material logistics, prepare a plan to increase production, and analyze the impact of stochastic events on the production process. The model will include nominal production characteristics and recorded stochastic events, allowing us to achieve an optimal process with minimal deviations in flow times. Through simulations, we will improve the system and achieve optimal production, for which we have formulated a hypothesis.

• Keywords: stochastic process, production, production disruptions, FlexSim, optimisation, probability distributions