2007Unpublished venueRequires access

Multi-criteria scheduling optimization with genetic algorithms

Igor Bernik, Mojca Bernik

Open publisher page 3 citations

Abstract

Multi-criteria scheduling optimization with genetic algorithms is described. Scheduling optimization methodology provides the planner with a quick and efficient scheduling method and enables him/her to experiment and decide which of the suitable solutions will become the production plan. The scheduling system is composed of a business information system - a database, a discrete event simulation model and a scheduling algorithm. The purpose of the integrated system is to help operative management personnel with production scheduling and planning. By comparing various scheduling methods, we established that the system utilizing genetic algorithms and simulation yielded from 5% to 15% better scheduling within a shorter time compared to manual scheduling.

About this research paper

What this paper is about

Multi-criteria scheduling optimization with genetic algorithms is described. Scheduling optimization methodology provides the planner with a quick and efficient scheduling method and enables him/her to experiment and decide which of the suitable solutions will become the production plan. The scheduling system is composed of a business information system - a database, a discrete event simulation model and a scheduling algorithm. The purpose of the integrated system is to help operative management personnel with production scheduling and planning. By comparing various scheduling methods, we established that the system utilizing genetic algorithms and simulation yielded from 5% to 15% better scheduling within a shorter time compared to manual scheduling.

Why it matters

OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Multi-criteria scheduling optimization with genetic algorithms is described. Scheduling optimization methodology provides the planner with a quick and efficient scheduling method and enables him/her to experiment and decide which of the suitable solutions will become the production plan. The scheduling system is composed of a business information system - a database, a discrete event simulation model and a scheduling algorithm. The purpose of the integrated system is to help operative management personnel with production scheduling and planning. By comparing various scheduling methods, we established that the system utilizing genetic algorithms and simulation yielded from 5% to 15% better scheduling within a shorter time compared to manual scheduling.

Key concepts: Fair-share scheduling, Two-level scheduling, Rate-monotonic scheduling, Dynamic priority scheduling, Computer science, Genetic algorithm scheduling, Flow shop scheduling, Lottery scheduling

Related papers

Back to paper searchBrowse research topicsOriginal source
Multi-criteria scheduling optimization with genetic algorithms — Research Paper | ScholarLens