Grouping and Sequencing of Machining Operations for High Volume Transfer Lines | Zendy

Soumitra Bhale | Zendy; Fazle Baki | Zendy; Ahmed Azab | Zendy

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Grouping and Sequencing of Machining Operations for High Volume Transfer Lines

Author(s) -

Soumitra Bhale,

Fazle Baki,

Ahmed Azab

Publication year - 2014

Publication title -

procedia cirp

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.683

H-Index - 65

ISSN - 2212-8271

DOI - 10.1016/j.procir.2014.02.047

Subject(s) - limit (mathematics) , integer programming , process (computing) , machining , volume (thermodynamics) , integer (computer science) , set (abstract data type) , transfer (computing) , linear programming , mathematical optimization , product (mathematics) , computer science , engineering , time limit , transfer function , function (biology) , component (thermodynamics) , industrial engineering , mechanical engineering , mathematics , systems engineering , physics , geometry , electrical engineering , thermodynamics , quantum mechanics , evolutionary biology , parallel computing , biology , programming language , operating system , mathematical analysis

Transfer lines are costly assets used for manufacturing of voluminous quantities of a dedicated discrete product following a pre- defined process plan. This paper deals with the problem of optimal grouping, sequencing and utilization of the dedicated machinery that goes into such transfer lines. A time objective function has been utilized. Handling time, which is an important component of cycle time, is assumed to be comprised of tool change time, time required to reposition and transport the workpiece and refixturing & reloading time. A new mixed integer linear programming model is developed to solve the problem with the aforementioned objectives while respecting a set of constraints, which include machine loading, tool allocation, tool magazine limit, takt time limit and precedence & inclusion constraints. A numerical case study is utilized to illustrate the functionality of the model

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