Flow shop scheduling with makespan objective and general position weighted learning effects

Clicks: 19
ID: 333547
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #33 of 42 articles by views in The Computer Journal

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract This article addresses the permutation flow shop problem of minimizing the makespan of all jobs with general position weighted learning effects. Since this problem is NP-hard, we propose two simple heuristics and analyze their worst-case bounds. The lower and upper bounds are given, and a branch-and-bound algorithm and two complex heuristics (including simulated annealing and tabu search) are proposed to solve this problem. Computational results show that our algorithms can generate effective and efficient schedules.
Reference Key
openalex_W7223260961 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li-Han Zhang, Dan‐Yang Lv, Ji‐Bo Wang
Are you one of these authors? Claim your profile
Journal The Computer Journal
Year 2026
DOI
10.1093/comjnl/bxag111
URL
https://doi.org/10.1093/comjnl/bxag111
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.