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[126930] Artykuł:

Web Based Application for Probability Job Scheduling

Czasopismo: International Conference on Distributed Computing and Artificial Intelligence [DCAI]  
Opublikowano: 2023
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3
Grupa
przynależności
Dyscyplina
naukowa
Procent
udziału
Liczba
punktów
do oceny pracownika
Liczba
punktów wg
kryteriów ewaluacji
Tomasz Michno orcid logo WEAiIKatedra Systemów Informatycznych *Takspoza "N" jednostkiInformatyka techniczna i telekomunikacja33.00.00  
Roman Deniziak orcid logo WEAiIKatedra Systemów Informatycznych *Takzaliczony do "N"Informatyka techniczna i telekomunikacja33.00.00  
Aleksandra Michno Niespoza "N" jednostkiInformatyka techniczna i telekomunikacja33.00.00  

Grupa MNiSW:  Recenzowany referat w materiałach konferencyjnych w języku angielskim
Punkty MNiSW: 0


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Keywords:

job scheduling  probability  web application 



Abstract:

The problem of job scheduling has been studied by various researchers since the early fifties of the 20th century. It can be defined as finding the most optimal assignment of different jobs to some resources such as machines, e.g. to find the lowest cost, which may be time. One of the most common problems in preparing the job scheduling is the precise definition of the time required to complete the task. In some situations this can be difficult because there may be many factors with uncertainties. In this paper we have focused on this problem from a practical point of view, which may be useful for e.g. project managers. For such situation, solutions using fuzzy sets or probability distributions may be relevant, but unfortunately they may be not easy to understand and use by people without advanced mathematical background. This paper presents a tool for probabilistic job scheduling. The web-based application has been prepared as well as a job scheduling approach using probability and four types of results showing the most probable total time, least probable, maximum and minimum. This can be useful for decision making by e.g. project managers. As a result, a web application for probabilistic job scheduling was created using Python, HTML, CSS and Flask framework.