Multi-response simulation optimization approach for the performance optimization of an Alarm Monitoring Center

Abstract This study offers a multi-response simulation–optimization approach to optimize an Alarm Monitoring Center’s performance. In this paper, the multi-response simulation–optimization application is firstly addressed in the Alarm Monitoring Center. Five performance criteria affect the performance of Alarm Monitoring Center and five factors, each of which has three control levels, are identified. The data belonging to the performance criteria, which are determined, are obtained with the help of the running scenarios combining with the factor levels using Taguchi design. Then, signals to the noise ( S / N ) ratios are calculated for these which are obtained from the performance data. A decision matrix is generated with S / N ratios; the TOPSIS method is used to transfer the multi-response problems into the single-response problems. The system improvement rate is also determined by finding the levels of factors to optimize the system using Taguchi’s single response optimization methodology.

[1]  Hong Chul Lee,et al.  The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System , 2009 .

[2]  R. H. Myers,et al.  STAT 319 : Probability & Statistics for Engineers & Scientists Term 152 ( 1 ) Final Exam Wednesday 11 / 05 / 2016 8 : 00 – 10 : 30 AM , 2016 .

[3]  Yusuf Tansel İç,et al.  A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete , 2013 .

[4]  Yu Guo,et al.  A New Simulation Optimisation System for the Parameters of a Machine Cell Simulation Model , 2003 .

[5]  Harun Resit Yazgan,et al.  A new algorithm and multi-response Taguchi method to solve line balancing problem in an automotive industry , 2011 .

[6]  Terry P. Harrison,et al.  A stochastic programming model for scheduling call centers with global Service Level Agreements , 2010, Eur. J. Oper. Res..

[7]  Ali Azadeh,et al.  A unique fuzzy multi-criteria decision making: computer simulation approach for productive operators’ assignment in cellular manufacturing systems with uncertainty and vagueness , 2011 .

[8]  Farhad Azadivar,et al.  Optimization of discrete variable stochastic systems by computer simulation , 1986 .

[9]  N. Suresh Kumar,et al.  Simulation-based metamodels for the analysis of scheduling decisions in a flexible manufacturing system operating in a tool-sharing environment , 2010 .

[10]  Qing Chang,et al.  Data driven production modeling and simulation of complex automobile general assembly plant , 2011, Comput. Ind..

[11]  Iraj Mahdavi,et al.  Designing a model of fuzzy TOPSIS in multiple criteria decision making , 2008, Appl. Math. Comput..

[12]  Ashraf Elfasakhany,et al.  Design and Development of a House-Mobile Security System , 2011 .

[13]  Mauricio A. Valle,et al.  Job performance prediction in a call center using a naive Bayes classifier , 2012, Expert Syst. Appl..

[14]  M. Yurdakul *,et al.  Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches , 2005 .

[15]  Anna Syberfeldt,et al.  Multi-Objective Evolutionary Simulation-Optimization of a Real-World Manufacturing Problem , 2008 .

[16]  Gül Gürkan,et al.  Response surface methodology with stochastic constraints for expensive simulation , 2009, J. Oper. Res. Soc..

[17]  Shankar Chakraborty,et al.  Applications of the MOORA method for decision making in manufacturing environment , 2011 .

[18]  M. Yurdakul,et al.  Application of correlation test to criteria selection for multi criteria decision making (MCDM) models , 2009 .

[19]  Mostafa Jafarian,et al.  A fuzzy multi-attribute approach to select the welding process at high pressure vessel manufacturing , 2012 .

[20]  Rob Alexander,et al.  Supporting systems of systems hazard analysis using multi-agent simulation , 2013 .

[21]  María Teresa Lamata,et al.  On rank reversal and TOPSIS method , 2012, Math. Comput. Model..

[22]  E. Stanley Lee,et al.  An extension of TOPSIS for group decision making , 2007, Math. Comput. Model..

[23]  Douglas C. Montgomery,et al.  A Nonlinear Programming Solution to the Dual Response Problem , 1993 .

[24]  Yu-Min Chiang,et al.  The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing , 2009, Comput. Ind. Eng..

[25]  Yves Dallery,et al.  Queueing models for full-flexible multi-class call centers with real-time anticipated delays , 2009 .

[26]  Shane G. Henderson,et al.  Call Center Staffing with Simulation and Cutting Plane Methods , 2004, Ann. Oper. Res..

[27]  Heeseok Lee,et al.  FMS design model with multiple objectives using compromise programming , 2001 .

[28]  R. J. Kuo,et al.  Simulation optimization using particle swarm optimization algorithm with application to assembly line design , 2011, Appl. Soft Comput..

[29]  Catherine M. Harmonosky,et al.  A simulation optimization method that considers uncertainty and multiple performance measures , 2007, Eur. J. Oper. Res..

[30]  Enrique del Castillo,et al.  Calculation of an optimal region of operation for dual response systems fitted from experimental data , 1999, J. Oper. Res. Soc..

[31]  Oualid Jouini,et al.  Call centers with hyperexponential patience modeling , 2013 .

[32]  Taho Yang,et al.  Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method , 2005, Math. Comput. Simul..

[33]  Hung-Chang Liao,et al.  A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method , 2004, Comput. Ind. Eng..

[34]  Jack P. C. Kleijnen,et al.  A methodology for fitting and validating metamodels in simulation , 2000, Eur. J. Oper. Res..

[35]  Rita Gamberini,et al.  A fuzzy multi-attribute model for risk evaluation in workplaces. , 2009 .

[36]  Taho Yang,et al.  Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines , 2002, J. Oper. Res. Soc..

[37]  Taho Yang,et al.  The use of a grey-based Taguchi method for optimizing multi-response simulation problems , 2008 .

[38]  Ling Rothrock,et al.  Performance Measurement and Evaluation in Human-in-the-Loop Simulations , 2011 .

[39]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[40]  R. H. Myers,et al.  Response Surface Techniques for Dual Response Systems , 1973 .

[41]  Berna Dengiz,et al.  Computer simulation of a PCB production line: metamodeling approach , 2000 .

[42]  Berna Dengiz,et al.  Redesign of PCB production line with simulation and Taguchi design , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[43]  Hsing-Pei Kao,et al.  Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming , 2010, Comput. Ind. Eng..

[44]  Ran Huang,et al.  Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures , 2011 .

[45]  Mehmet Cakmakci,et al.  A feasibility study using simulation-based optimization and Taguchi experimental design method for material handling—transfer system in the automobile industry , 2012 .

[46]  Pierre L'Ecuyer,et al.  Staffing Multiskill Call Centers via Linear Programming and Simulation , 2008, Manag. Sci..

[47]  Namhun Kim,et al.  Performance assessment in an interactive call center workforce simulation , 2011, Simul. Model. Pract. Theory.

[48]  Jesus R. Artalejo,et al.  Applications of maximum queue lengths to call center management , 2007, Comput. Oper. Res..

[49]  Armen Bagdasaryan,et al.  Discrete dynamic simulation models and technique for complex control systems , 2011, Simul. Model. Pract. Theory.

[50]  Mehrdad Tamiz,et al.  Combining simulation and goal programming for healthcare planning in a medical assessment unit , 2009, Eur. J. Oper. Res..

[51]  Seyed Taghi Akhavan Niaki,et al.  Multi-response simulation optimization using genetic algorithm within desirability function framework , 2006, Appl. Math. Comput..