A comparison of a genetic algorithm with an experimental design technique in the optimization of a production process

Backhouse, P G; Fotheringham, Alexander; Allan, G

doi:10.1038/sj.jors.2600362

Cited by 2 publications

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“…There is, therefore, a considerable synergistic relationship between them. The identification of this synergy provides a good example of the merits of a systematic statistical approach, in contrast to the “one‐factor‐at‐a‐time” approach often employed 13, 15…”

Section: Introductionmentioning

confidence: 99%

The influence of fiber processing parameters on the structural properties of as‐spun polypropylene fibers: A factorial design approach

Yang

Mather

Fotheringham

2004

J of Applied Polymer Sci

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ABSTRACT:The structures of polypropylene fibers, spun according to a factorial experimental design, have been studied with the aid of wide angle X-ray diffraction and birefringence measurements. From statistical analysis of the results, the fibers have been characterized in terms of their crystallographic order and the overall orientation of their constituent polymer chains. These properties have been quantitatively assessed as responses to seven specially selected process control parameters in the extrusion equipment used to process the fibers. For both crystallographic order and overall orientation, the metering pump speed (MPS) at which the fibers are extruded and the speed (WS) at which the extruded fibers are wound exert significant effects. Moreover, the interaction, WS ϫ MPS, between these two control parameters also significantly influences orientation. For crystallographic order, two further significant parameters are the melt flow index (MFI) of the grade of polypropylene used and the temperature (ST) at which the polymer melt passes through the spinneret. The roles of these two factors in the development of crystallites within the fibers are discussed. No interaction effects, however, appear to be significant for crystallographic order. Models that specify the direction of change of the significant parameters for increasing or reducing both responses are given.

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Section: Introductionmentioning

confidence: 99%

The influence of fiber processing parameters on the structural properties of as‐spun polypropylene fibers: A factorial design approach

Yang

Mather

Fotheringham

2004

J of Applied Polymer Sci

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“…Finally, evolutionary computation applications have been reported for the problems of vehicle distribution scheduling [263], warehouse scheduling [264], sequencing optimization in automotive manufacturing industries [265], optimal control of spinning processes [266], design of flexible electronic assembly systems [267], optimization of textile processes [268], and optimization of area loss in flat glass cutting [269].…”

Section: G Various Applicationsmentioning

confidence: 99%

Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

Dimopoulos

Zalzala

2000

IEEE Trans. Evol. Computat.

229

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The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.

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A comparison of a genetic algorithm with an experimental design technique in the optimization of a production process

Cited by 2 publications

References 0 publications

The influence of fiber processing parameters on the structural properties of as‐spun polypropylene fibers: A factorial design approach

The influence of fiber processing parameters on the structural properties of as‐spun polypropylene fibers: A factorial design approach

Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

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