Multi-objective balancing of assembly lines by population heuristics
From MaRDI portal
Publication:3533194
DOI10.1080/00207540600988089zbMath1151.90427OpenAlexW1991139131MaRDI QIDQ3533194
Publication date: 23 October 2008
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207540600988089
Pareto optimalityevolutionary algorithmsdifferential evolutionassembly line balancingmulti-objective optimisationmanufacturing optimisation
Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59) Mathematical geography and demography (91D20)
Related Items
Optimisation of a multi-objective two-dimensional strip packing problem based on evolutionary algorithms, Assembly line balancing: what happened in the last fifteen years?, A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem, SALSA: combining branch-and-bound with dynamic programming to smoothen workloads in simple assembly line balancing, A HYBRID METAHEURISTIC FOR THE SINGLE-MACHINE TOTAL WEIGHTED TARDINESS PROBLEM, Multiple-criteria decision-making in two-sided assembly line balancing: a goal programming and a fuzzy goal programming model
Cites Work
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- Population set-based global optimization algorithms: some modifications and numerical studies
- A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines
- State-of-the-art exact and heuristic solution procedures for simple assembly line balancing
- A numerical study of some modified differential evolution algorithms
- A Survey of Exact Algorithms for the Simple Assembly Line Balancing Problem
- A knowledge-based system for solving multi-objective assembly line balancing problems
- Using simulated annealing to solve a multiobjective assembly line balancing problem with parallel workstations
- Multi-objective meta-heuristics: An overview of the current state-of-the-art