Application of survival function in robust scheduling of production jobs

Scheduling production jobs in the real production system requires considering a number of factors which may prove to exert a negative effect on the production processes. Hence the need for the identification and compensation of potential disruptions as early as at the production planning stage. The aim of this paper is to employ the survival and the hazard function to anticipate potential disruptions of the schedule so that they could be absorbed to produce a robust job schedule.

[1]  Li-Chen Fu,et al.  Using dispatching rules for job shop scheduling with due date-based objectives , 2007 .

[2]  Sławomir Kłos,et al.  Improving Manufacturing Processes Using Simulation Methods , 2015 .

[3]  Tadeusz Nowicki,et al.  The Maintenance Management in the Macro-Ergonomics Context , 2017 .

[4]  Arkadiusz Gola,et al.  Application of Fuzzy Logic in Assigning Workers to Production Tasks , 2016, DCAI.

[5]  Dariusz Mazurkiewicz,et al.  The use of stationary tests for analysis of monitored residual processes , 2015 .

[6]  Richard F. Hartl,et al.  A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer , 2013 .

[7]  Arkadiusz Gola,et al.  Risk-based estimation of manufacturing order costs with artificial intelligence , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[8]  Pawel Sitek,et al.  A Hybrid Approach to the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) , 2014, Recent Advances in Automation, Robotics and Measuring Techniques.

[9]  Tarek Y. ElMekkawy,et al.  Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm , 2011 .

[10]  Gordon Johnston,et al.  Statistical Models and Methods for Lifetime Data , 2003, Technometrics.

[11]  Peter Nielsen,et al.  An Empirical Investigation of Lead Time Distributions , 2014, APMS.

[12]  J. Christopher Beck,et al.  Slack-based Techniques for Robust Schedules , 2014 .

[13]  Sinan Gürel,et al.  An anticipative scheduling approach with controllable processing times , 2010, Comput. Oper. Res..

[14]  Li-Chen Fu,et al.  Using dispatching rules for job shop scheduling with due date-based objectives , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.

[16]  Jorge Puente,et al.  Improved Local Search for Job Shop Scheduling with uncertain Durations , 2009, ICAPS.

[17]  Deepu Philip.  ROBUST SCHEDULES AND DISRUPTION MANAGEMENT FOR JOB SHOPS , 2008 .

[18]  B. Gajdzik,et al.  Optymisation of equipment setup processes in enterprises , 2012 .

[19]  J. Herrmann.  A HISTORY OF DECISION-MAKING TOOLS FOR PRODUCTION SCHEDULING , 2005 .

[20]  Mikkel T. Jensen,et al.  Robust and Flexible Scheduling with Evolutionary Computation , 2001 .

[21]  Robert H. Storer,et al.  Robustness Measures and Robust Scheduling for Job Shops , 1994 .

[22]  Arkadiusz Gola,et al.  Computer-Aided Production Task Scheduling , 2015 .