Modelling activity times by hybrid synthetic method

Abstract Uncertain (manual) activity times impact a number of manufacturing system modules: plant and layout design, capacity analysis, operator assignment, process planning, scheduling and simulation. Direct observation cannot be used for non-existent production lines. A hybrid direct observation/synthetic method derived from Method Time Measurement available in industry is proposed. To determine accurate activity times required by heuristics and metaheuristics optimisation, manufacturing system modules are modelled by MILP and operator efficiency parameters are used for time standardisation. Among human factors considered are skill and ergonomics. Application to the sterilisation of reusable medical devices is extensively described. Experimental data taken from observation on the field and a worst-case date have shown the model direct applicability for professionals also to non-manufacturing cases.

[1]  R. Chenhall Management control systems design within its organizational context: findings from contingency-based research and directions for the future , 2003 .

[2]  S. H. Choi,et al.  Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach , 2012, Comput. Ind. Eng..

[3]  Baoding Liu,et al.  Project scheduling problem with stochastic activity duration times , 2005, Appl. Math. Comput..

[4]  Michele Lanzetta,et al.  Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines , 2014 .

[5]  Michael Lewis,et al.  Change management in the public sector: the use of cross-functional teams , 2013 .

[6]  Andrea Rossi,et al.  Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships , 2014 .

[7]  Michele Lanzetta,et al.  Heuristics for scheduling a two-stage hybrid flow shop with parallel batching machines: application at a hospital sterilisation plant , 2013 .

[8]  Abbas Afshar,et al.  Fuzzy-based MOGA approach to stochastic time-cost trade-off problem , 2009 .

[9]  Ahmet B. Keha,et al.  Impact of permutation enforcement when minimizing total weighted tardiness in dynamic flowshops with uncertain processing times , 2007, Comput. Oper. Res..

[10]  M.-L. Espinouse,et al.  Optimizing the makespan of washing operations of medical devices in hospital sterilization services , 2010, 2010 IEEE Workshop on Health Care Management (WHCM).

[11]  Eric Marcon,et al.  Optimization of hospital sterilization costs proposing new grouping choices of medical devices into packages , 2008 .

[12]  Abbas Afshar,et al.  Stochastic time–cost optimization using non-dominated archiving ant colony approach , 2011 .

[13]  Agneta Larsson,et al.  The accuracy of surgery time estimations , 2013 .

[14]  Yves Dallery,et al.  Manufacturing flow line systems: a review of models and analytical results , 1992, Queueing Syst. Theory Appl..

[15]  A new approach to two-machine flow shop problem with uncertain processing times , 2006 .

[16]  Waldemar Karwowski,et al.  Application of Standardized Motions in Temporal Analysis of Work Activity , 2015 .

[17]  W. DeKeseredy,et al.  Future directions , 2005, Psychiatric Quarterly.

[18]  Jean-Paul Arnaout Rescheduling of parallel machines with stochastic processing and setup times , 2014 .

[19]  I. Grossmann,et al.  A novel branch and bound algorithm for scheduling flowshop plants with uncertain processing times , 2002 .

[20]  Michele Lanzetta,et al.  Native metaheuristics for non-permutation flowshop scheduling , 2014, J. Intell. Manuf..

[21]  Mitsuo Gen,et al.  An Effective Multi-objective EDA for Robust Resource Constrained Project Scheduling with Uncertain Durations , 2014, Complex Adaptive Systems.

[22]  Antonio Freitas Rentes,et al.  A new value stream mapping approach for healthcare environments , 2016 .

[23]  Michel Gourgand,et al.  A contribution to the stochastic flow shop scheduling problem , 2003, Eur. J. Oper. Res..

[24]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[25]  Mao-Jiun J. Wang,et al.  Motion generation from MTM semantics , 2009, Comput. Ind..

[26]  Jafar Razmi,et al.  Developing a specific predetermined time study approach: an empirical study in a car industry , 2008 .

[27]  D. C. Krueger,et al.  Six Sigma implementation: a qualitative case study using grounded theory , 2014 .

[28]  Maria Di Mascolo,et al.  A generic simulation model to assess the performance of sterilization services in health establishments , 2013, Health care management science.

[29]  Reha Uzsoy,et al.  Executing production schedules in the face of uncertainties: A review and some future directions , 2005, Eur. J. Oper. Res..

[30]  Maarten P. D. Schadd,et al.  Optimizing sterilization logistics in hospitals , 2008, Health care management science.

[31]  Andrzej Jaszkiewicz,et al.  Fuzzy project scheduling system for software development , 1994 .

[32]  N. Raman,et al.  FMS planning decisions, operating flexibilities, and system performance , 1995 .

[33]  G M Buxey,et al.  Production Flow Line System Design–A Review , 1973 .

[34]  Michele Lanzetta,et al.  Hybrid stage shop scheduling , 2015, Expert Syst. Appl..

[35]  Sanja Petrovic,et al.  A new approach to two-machine flow shop problem with uncertain processing time , 2003, Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. ISUMA 2003..

[36]  Armin Scholl,et al.  A survey on problems and methods in generalized assembly line balancing , 2006, Eur. J. Oper. Res..

[37]  Mehmet A. Begen,et al.  A branch and bound based heuristic for makespan minimization of washing operations in hospital sterilization services , 2014, Eur. J. Oper. Res..

[38]  Fardin Ahmadizar,et al.  Group shops scheduling with makespan criterion subject to random release dates and processing times , 2010, Comput. Oper. Res..

[39]  Makespan minimisation on parallel batch processing machines with non-identical job sizes and release dates , 2012 .