English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Planning and scheduling are as decision making processes which they have important roles in production systems and industries. According that, job shop scheduling is one of NPhard problems to solve multi-objective decision making approaches. So, the problem is known as uncertain with many variables in optimal solution view. Finding optimal solutions are essential task in scheduling of jobs between machines in the industries. In this paper, we present classical sum weighted (WS) method and non-dominated sorting genetic algorithm II (NSGA-II) to solve flexible job shop scheduling problem (FJSSP) with multiple objectives and find Paretofronts: minimizing completion time of jobs and maximizing machine employment. To generate Pareto-fronts, a search algorithm uses mechanism of variable weights and random selection to change directions in search spaces. The experiment results indicate that NSGA-II solve the problem more acceptable than WS method with considering computing time and consuming memory.

Pareto- Front Generation by Classical and Meta-heuristic Methods in Flexible Job Shop Scheduling with Multiple Objectives