Operator Assignment Decisions in a Highly Dynamic Cellular Environment

ALHAWARI, OMAR IBRAHIM, M.S. November 2008, Industrial and Systems Engineering Operator Assignment Decisions in a Highly Dynamic Cellular Environment (130 pp.) Director of Thesis: Gursel A. Suer Operators are assigned to operations in labor-intensive manufacturing cells using two assignment approaches: Max-Min and Max. The major concern is to see how these two approaches impact operators’ skill levels, makespan and total processing time among cells. The impact is discussed under chaotic environment where sudden changes in product mix with different operation times are applied and under non-chaotic environment where same product mix is run period after period. In this thesis, operator’s skill levels are affected by learning and forgetting rates. Max-Min improved operators’ skill levels more significantly than Max in this multi-period study; particularly in chaotic environment. This eventually improved makespan and total processing times as well. Approved: ___________________________________________________________ Gursel A. Suer Professor of Industrial and Systems Engineering

[1]  Hidefumi Wakamatsu,et al.  Dynamic Co-Operative Scheduling Based on HLA , 2002, DIISM.

[2]  Eric Molleman,et al.  Cross-training in a cellular manufacturing environment , 2005, Comput. Ind. Eng..

[3]  Thomas N. McDonald,et al.  Analysis of Worker Assignment Policies on Production Line Performance Utilizing a Multi-skilled Workforce , 2004 .

[4]  Gürsel A. Süer.  Optimal operator assignment and cell loading in labor-intensive manufacturing cells , 1996 .

[5]  Gürsel A. Süer,et al.  Effects of different fuzzy operators on fuzzy bi-objective cell loading problem in labor-intensive manufacturing cells , 2009, Comput. Ind. Eng..

[6]  Gürsel A. Süer,et al.  Intra-cell manpower transfers and cell loading in labor-intensive manufacturing cells , 2005, Comput. Ind. Eng..

[7]  Gürsel A. Süer,et al.  Multi-period operator assignment considering skills, learning and forgetting in labour-intensive cells , 2008 .

[8]  Charles R. Standridge,et al.  Modeling and Analysis of Manufacturing Systems , 1993 .

[9]  Ip-Shing Fan,et al.  The effects of allocation of functions on the long-term performance of manufacturing cells—A case study , 1997 .

[10]  Gürsel A. Süer,et al.  Models for cell loading and product sequencing in labor-intensive cells , 2009, Comput. Ind. Eng..

[11]  Jing Huang,et al.  Heuristic procedures and mathematical models for cell loading and scheduling in a shoe manufacturing company , 2009, Comput. Ind. Eng..

[12]  Muh-Cherng Wu,et al.  A project scheduling and staff assignment model considering learning effect , 2006 .

[13]  Gürsel A. Süer,et al.  Bi-objective cell loading problem with non-zero setup times with fuzzy aspiration levels in labour intensive manufacturing cells , 2008 .

[14]  Gürsel A. Süer,et al.  Evolutionary programming for minimizing the average flow time in the presence of non-zero ready times , 2003, Comput. Ind. Eng..

[15]  J. Slomp,et al.  Assignment Flexibility in a Cellular Manufacturing System-Machine Pooling versus Labor Chaining-Jos , 2004 .

[16]  Shahrukh A. Irani.  Handbook of cellular manufacturing systems , 1999 .

[17]  Peter Nyhuis,et al.  Influences of Human Operators on the Logistics of Manufacturing Cells , 2003, APMS.

[18]  D A Nembhard,et al.  Heuristic approach for assigning workers to tasks based on individual learning rates , 2001 .

[19]  Bopaya Bidanda,et al.  Human related issues in manufacturing cell design, implementation, and operation: a review and survey , 2005, Comput. Ind. Eng..

[20]  Mustafa Uzumeri,et al.  Experiential learning and forgetting for manual and cognitive tasks , 2000 .

[21]  Geetha Srinivasan,et al.  Static and dynamic operator allocation problems in cellular manufacturing systems , 1997 .

[22]  Hanif D. Sherali,et al.  Linear Programming and Network Flows , 1977 .

[23]  Hidefumi Wakamatsu,et al.  Dynamic Co-Operative Scheduling Based on HLA , 2002 .

[24]  David A. Nembhard,et al.  Task complexity effects on between-individual learning/forgetting variability , 2002 .

[25]  Hanif D. Sherali,et al.  Linear programming and network flows (2nd ed.) , 1990 .

[26]  Hiroyuki Hiraoka,et al.  A New Approach Manufacturing Cell Scheduling based on Skill-based Manufacturing Integrated to Genetic Algorithm , 2007 .

[27]  John W. Fowler,et al.  Modelling inherent worker differences for workforce planning , 2007 .

[28]  S. E. Stephanou,et al.  The manufacturing challenge : from concept to production , 1992 .

[29]  Harold J. Steudel,et al.  A study of labor assignment flexibility in cellular manufacturing systems , 2005, Comput. Ind. Eng..