The Computer Journal· 2026Q2
Flow shop scheduling with makespan objective and general position weighted learning effects
- 0citations
- Q2SCImago
- 2026year
Short summary
Two simple heuristics and a branch-and-bound algorithm were developed to solve the NP-hard permutation flow shop problem with general position weighted learning effects, achieving effective and efficient schedules.
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Key points
- Developed two simple heuristics and a branch-and-bound algorithm for permutation flow shop scheduling.
- The problem incorporates general position weighted learning effects and aims to minimize makespan.
- Worst-case bounds were analyzed for the simple heuristics.
- Simulated annealing and tabu search were also employed as complex heuristics.
- Computational results confirm the effectiveness and efficiency of the proposed algorithms.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. The Computer Journal, 2026 · DOI ↗
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Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering