کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1133753 1489087 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Selection, grouping, and assignment policies with learning-by-doing and knowledge transfer
ترجمه فارسی عنوان
سیاست های انتخاب، گروه بندی و تخصیص با یادگیری از طریق انجام و انتقال دانش
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
چکیده انگلیسی


• We examine relative impacts of selection, grouping and assignment policies.
• We develop an empirical model and distribution of learning and knowledge transfer.
• Worker selection tended to have greater impacts than grouping or assignment.
• Policies based on individual knowledge transfer parameter perform well.

This paper investigates the allocation of workers to tasks based on individual learning characteristics in order to improve system throughput. We model worker productivity to include skill knowledge obtained by learning-by-doing and learning-by-transfer. The allocation problem is composed of the selection of workers from a pool, grouping workers based on individual characteristics, and assignment of groups to tasks. We examine several related heuristic approaches for each phase with interest in identifying useful measures and policies, as well as interactions among these policies. The performance of each heuristic is compared to a baseline policy and to an upper bound obtained by solving a non-linear math-programming problem. Results are examined in the context of both parallel and serial systems. Current results demonstrate that heuristics based on estimated output, ability to learn from other workers, and asymptotic steady state productivity are useful for selecting, grouping and assigning workers to tasks, and these policies perform well with respect to objective gap relative to an upper bound.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Industrial Engineering - Volume 79, January 2015, Pages 175–187
نویسندگان
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