Job shop scheduling problem optimization by means of graph-based algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F21%3A43919551" target="_blank" >RIV/62156489:43110/21:43919551 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/21:PU139924

Výsledek na webu

<a href="https://doi.org/10.3390/app11041921" target="_blank" >https://doi.org/10.3390/app11041921</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app11041921" target="_blank" >10.3390/app11041921</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Job shop scheduling problem optimization by means of graph-based algorithm

Popis výsledku v původním jazyce

In this paper we introduce the draft of a new graph-based algorithm f or optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm&apos;s classic implementation. The acquired results of these experiments were compared by each algorithm&apos;s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promis-ing convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

Název v anglickém jazyce

Job shop scheduling problem optimization by means of graph-based algorithm

Popis výsledku anglicky

In this paper we introduce the draft of a new graph-based algorithm f or optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm&apos;s classic implementation. The acquired results of these experiments were compared by each algorithm&apos;s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promis-ing convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

1921

Kód UT WoS článku

000632101900001

EID výsledku v databázi Scopus

2-s2.0-85101967299