ArticlePeerJ. Computer science2021
A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling.Scientific reports · 2025Article
- Application of Levy flight-based harmony search algorithm for the flexible job shop scheduling.Scientific reports · 2025Article
- Optimizing resource allocation for IoT applications in the edge cloud continuum using hybrid metaheuristic algorithms.Scientific reports · 2025Article
- Multicriteria scheduling of two-subassembly products with batch availability and precedence constraints.PeerJ. Computer science · 2025Article
- HSMVS: heuristic search for minimum vertex separator on massive graphs.PeerJ. Computer science · 2024Article
- Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm.PeerJ. Computer science · 2023Article
- Novel variable neighborhood search heuristics for truck management in distribution warehouses problem.PeerJ. Computer science · 2023Article
- A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem.Computational intelligence and neuroscience · 2022Article
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6 authors.
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Abstract
The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.
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