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

The flexible job shop scheduling problem is one of the most important optimization problems in the field of production scheduling,due to its complexity and practical applications in real life.Most studies focus on only one objec— tive—the makespan that is the total time required to complete al jobs.However,a single objective may be insufficient for real applications.Therefore,a new hybrid multi-objective algorithm was proposed for solving the flexible job shop scheduling problem(FJSP)with three objectives,including the makespan,total workload,and critical workload.Efec— tive chromosome representation and genetic operators are introduced.The nondominated neighbor immune algorithm is used to search for Pareto optimal solutions.To improve the search performance,three different local search strategies were designed and combined in the multi—objective algorithm.The computational results on several data sets show that the proposed algorithm outperforms other representative algorithms in genera1.In addition,the experiments validate the effectiveness of local search strategies.

混合多目标算法用于柔性作业车间调度问题 (Hybrid Multi-objective Algorithm for Solving Flexible Job Shop Scheduling Problem).