Feature issue: Neural networks and operations research/management scienceTraditional heuristic versus Hopfield neural network approaches to a car sequencing problem
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Cited by (40)
Optimizing real-time vehicle sequencing of a paint shop conveyor system
2015, Omega (United Kingdom)Citation Excerpt :Consequently, they attempt to solve a very large integer programming assignment problem with side constraints. Because these problems are NP-hard, they are usually solved with heuristic techniques, such as local search (e.g., [12–15]), tabu search (e.g., [16]), simulated annealing (e.g., [17,18]), and ant colony optimization (e.g., [19,20]). In certain instances, they are solved with exact branch-and-bound algorithms (e.g., [21,22]).
Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line
2010, International Journal of Production EconomicsCitation Excerpt :Prior to the 1980s, the main goal was (1) to smooth the workload at each workstation through the line. This goal seeks to reduce line stoppages or inefficiencies like work congestion or utility work (see Mitsumori, 1969 or Xiaobo and Ohno, 1997 for optimal models and for heuristics, see Thomopoulos, 1967; Macaskill, 1973; Sumichrast et al., 1992; Smith et al., 1996; Gottlieb et al., 2003). The emerging JIT concept by the mid 1980s has raised a second goal that consists in keeping a constant rate of part usage to avoid large inventories.
A review of Hopfield neural networks for solving mathematical programming problems
2009, European Journal of Operational ResearchAnt colony optimization with a specialized pheromone trail for the car-sequencing problem
2009, European Journal of Operational ResearchSequencing mixed-model assembly lines: Survey, classification and model critique
2009, European Journal of Operational ResearchIterated tabu search for the car sequencing problem
2008, European Journal of Operational Research