Effects of operator learning on production output: a Markov chain approach

We develop a Markov chain approach to forecast the production output of a human-machine system, while encompassing the effects of operator learning. This approach captures two possible effects of learning: increased production rate and reduced downtime due to human error. In the proposed Markov chain, three scenarios are possible for the machine at each time interval: survival, failure, and repair. To calculate the state transition probabilities, we use a proportional hazards model to calculate the hazard rate, in terms of operator-related factors and machine working age. Given the operator learning curves and their effect on reducing human error over time, the proposed approach is considered to be a non-homogeneous Markov chain. Its result is the expected machine uptime. This quantity, along with production forecasting at various operator skill levels, provides us with the expected production output.

[1]  Dragan Banjevic,et al.  INTERPRETATION OF INSPECTION DATA EMANATING FROM EQUIPMENT CONDITION MONITORING TOOLS: METHOD AND SOFTWARE , 2005 .

[2]  Itzhak Venezia.  On the statistical origins of the learning curve , 1985 .

[3]  Timothy D. Fry,et al.  The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops , 1993 .

[4]  H. S. Blanks.  Quality and reliability research into the next century , 1994 .

[5]  Balbir S. Dhillon,et al.  Human error in maintenance: a review , 2006 .

[6]  Andrew K. S. Jardine,et al.  Proportional hazards analysis of diesel engine failure data , 1989 .

[7]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[8]  Tim Horberry,et al.  Human Factors for the Design, Operation, and Maintenance of Mining Equipment , 2010 .

[9]  Waltraud Kahle,et al.  A general repair, proportional-hazards, framework to model complex repairable systems , 2003, IEEE Trans. Reliab..

[10]  Marvin Rausand,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2003 .

[11]  R. Fritzsche.  Cost adjustment for single item pooling models using a dynamic failure rate: A calculation for the aircraft industry , 2012 .

[12]  Simon French,et al.  Statistical Analysis of Reliability Data , 1992 .

[13]  Huan Neng Chiu,et al.  A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem , 2009, Comput. Ind. Eng..

[14]  G. Kent Webb,et al.  Integrated circuit (IC) pricing , 1994 .

[15]  Viliam Makis,et al.  Optimal replacement policy and the structure of software for condition‐based maintenance , 1997 .

[16]  Martin Crowder,et al.  Statistical Analysis of Reliability Data , 1991 .

[17]  Dragan Banjevic,et al.  Using principal components in a proportional hazards model with applications in condition-based maintenance , 2006, J. Oper. Res. Soc..

[18]  Louis E. Yelle.  THE LEARNING CURVE: HISTORICAL REVIEW AND COMPREHENSIVE SURVEY , 1979 .

[19]  S. M. Seyed Hosseini,et al.  Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique , 2006, Reliab. Eng. Syst. Saf..

[20]  Dirk Biskup,et al.  A state-of-the-art review on scheduling with learning effects , 2008, Eur. J. Oper. Res..

[21]  Christos Koulamas,et al.  Throughput‐dependent periodic maintenance policiesfor general production units , 1999, Ann. Oper. Res..

[22]  P C Cacciabue,et al.  Human error risk management methodology for safety audit of a large railway organisation. , 2005, Applied ergonomics.

[23]  Ying Peng,et al.  A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction , 2011 .

[24]  Yu-Hern Chang,et al.  Significant human risk factors in aircraft maintenance technicians , 2010 .

[25]  T.C.E. Cheng,et al.  AN ECONOMIC PRODUCTION QUANTITY MODEL WITH LEARNING AND FORGETTING CONSIDERATIONS , 1994 .

[26]  Nima Safaei,et al.  Choosing the optimal intervention method to reduce human-related machine failures , 2014, Eur. J. Oper. Res..

[27]  Francine D. Blau,et al.  International Differences in Male Wage Inequality: Institutions versus Market Forces , 1994, Journal of Political Economy.

[28]  Bernhard Reer.  A probabilistic method for analyzing the reliability effect of time and organizational factors , 1994 .

[29]  A. H. Christer,et al.  Incorporating the potential for human error in maintenance models , 2003, J. Oper. Res. Soc..

[30]  Natasa S. Vidic.  DEVELOPING METHODS TO SOLVE THE WORKFORCE ASSIGNMENT PROBLEM CONSIDERING WORKER HETEROGENEITY AND LEARNING AND FORGETTINGDEVELOPING METHODS TO SOLVE THE WORKFORCE ASSIGNMENT PROBLEM CONSIDERING WORKER HETEROGENEITY AND LEARNING AND FORGETTING , 2008 .

[31]  Jin Wang,et al.  Modified failure mode and effects analysis using approximate reasoning , 2003, Reliab. Eng. Syst. Saf..

[32]  Nima Safaei,et al.  Integrating human reliability analysis into a comprehensive maintenance optimization strategy , 2010 .

[33]  Waldemar Karwowski,et al.  Economics of human performance and systems total ownership cost. , 2012, Work.

[34]  William J. Kolarik,et al.  Human performance reliability: on-line assessment using fuzzy logic , 2004 .

[35]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[36]  James E. Ward,et al.  Effect of Learning and Forgetting on Batch Sizes , 2011 .

[37]  Emmanuel Adamides,et al.  Model-based assessment of military aircraft engine maintenance systems , 2004, J. Oper. Res. Soc..

[38]  Viliam Makis,et al.  Optimal component replacement decisions using vibration monitoring and the proportional-hazards model , 2002, J. Oper. Res. Soc..

[39]  J. Dutton,et al.  Treating Progress Functions as a Managerial Opportunity , 1984 .