Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study

A case study was simulated to provide a realistic depiction of an operating system.Investigated factors were number of pump, number of cashier and IATs.2-level full factorial experiments were used for further analysis of simulation.The performance of a service industry queuing system has been analyzed.The influential parameters on queue length and sales rate have been obtained. The main goal of this paper was to develop an integrated simulation-design of experiments (DOE) model to optimize a petrol station queuing system and sales rate. Initially, the petrol station operating system was simulated using Witness 2014 simulation software?. Then, the responses of simulation were deployed as the input of DOE. Two-level full factorial experiments with center points were performed where the simulated model parameter studied were number of pump, number of cashier and inter arrival times (IATs). The response variables analyzed were queue length and sales rate. The obtained model from experimental design revealed that number of cashier and inter arrival time were significant in determining the queue length while all the factors and their interaction were significantly affecting the sales rate.

[1]  Ali Fuat Guneri,et al.  A comprehensive review of emergency department simulation applications for normal and disaster conditions , 2015, Comput. Ind. Eng..

[2]  Aybars Ugur,et al.  An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms , 2009, Adv. Eng. Softw..

[3]  Keith Hurst Improving service efficiency and effectiveness: the resource implications. , 2014, International journal of health care quality assurance.

[4]  Kwasi Amoako-Gyampah,et al.  The operations management research agenda: An update , 1989 .

[5]  Zied Jemaï,et al.  A review on simulation models applied to emergency medical service operations , 2013, Comput. Ind. Eng..

[6]  R. J. Mayer,et al.  Using the Taguchi paradigm for manufacturing design using simulation experiments , 1992 .

[7]  Gilbert Laporte,et al.  A heuristic for the multi-period petrol station replenishment problem , 2008, Eur. J. Oper. Res..

[8]  Wei Xu,et al.  A framework for service enterprise workflow simulation with multi-agents cooperation , 2013, Enterp. Inf. Syst..

[9]  John W. Fowler,et al.  Simulation-based experimental design and statistical modeling for lead time quotation , 2015 .

[10]  Raphaël Duboz,et al.  The Virtual Laboratory Environment - An operational framework for multi-modelling, simulation and analysis of complex dynamical systems , 2009, Simul. Model. Pract. Theory.

[11]  Martin W. Mende,et al.  It’s all relative: how customer-perceived competitive advantage influences referral intentions , 2014, Marketing Letters.

[12]  Zoe J. Radnor,et al.  SimLean: Utilising simulation in the implementation of lean in healthcare , 2012, Eur. J. Oper. Res..

[13]  Pau Fonseca i Casas,et al.  Formal simulation model to optimize building sustainability , 2014, Adv. Eng. Softw..

[14]  S. Chuanga,et al.  Uniform design over general input domains with applications to target region estimation in computer experiments , 2009 .

[15]  Marin Varbanov Marinov,et al.  An event based simulation model to evaluate the design of a rail interchange yard, which provides service to high speed and conventional railways , 2015, Simul. Model. Pract. Theory.

[16]  Weihua Zhang,et al.  A holistic framework for engineering simulation platform development gluing open-source and home-made software resources , 2014, Adv. Eng. Softw..

[17]  Ali Ajdari,et al.  An Adaptive Exploration-Exploitation Algorithm for Constructing Metamodels in Random Simulation Using a Novel Sequential Experimental Design , 2014, Commun. Stat. Simul. Comput..

[18]  J. Kleijnen,et al.  Optimal design of experiments with simulation models of nearly saturated queues , 2000 .

[19]  Simon M. Hsiang,et al.  Incorporating the dynamics of epidemics in simulation models of healthcare systems , 2014, Simul. Model. Pract. Theory.

[20]  David Strutton,et al.  Measuring Customer Satisfaction with Logistics Services: An Investigation of the Motor Carrier Industry , 2015 .

[21]  Ashutosh Tiwari,et al.  State of the art in simulation-based optimisation for maintenance systems , 2015, Comput. Ind. Eng..

[22]  Noordin Mohd Yusof,et al.  Application of response surface methodology in optimization of electrospinning process to fabricate (ferrofluid/polyvinyl alcohol) magnetic nanofibers. , 2015, Materials science & engineering. C, Materials for biological applications.

[23]  Stewart Robinson,et al.  The application of discrete event simulation and system dynamics in the logistics and supply chain context , 2012, Decis. Support Syst..

[24]  Yuchuan Du,et al.  Microscopic simulation evaluation method on access traffic operation , 2015, Simul. Model. Pract. Theory.

[25]  Najmeh Madadi,et al.  Modeling and Simulation of a Bank Queuing System , 2013, 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation.

[26]  A. O. Moscardini,et al.  Interactive discrete event simulation without programming , 1982 .

[27]  V. C. Venkatesh,et al.  Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel , 2004 .

[28]  Alireza Khademi,et al.  Simulation, Modeling and Analysis of a Petrol Station , 2013 .

[29]  Virgilio Cruz-Machado,et al.  Supply chain redesign for resilience using simulation , 2012, Comput. Ind. Eng..

[30]  James M. McGree,et al.  Design of experiments for bivariate binary responses modelled by Copula functions , 2011, Comput. Stat. Data Anal..

[31]  Chi-Sheng Tsai,et al.  Evaluation and optimisation of integrated manufacturing system operations using Taguch's experiment design in computer simulation , 2002 .

[32]  Kathy Kotiadis,et al.  PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare , 2015, Eur. J. Oper. Res..

[33]  Desheng Dash Wu,et al.  A System Dynamics Modelling of Contagion Effects in Accounts Risk Management , 2014 .

[34]  Paul R. Harper,et al.  Simulation in health-care: lessons from other sectors , 2012, Oper. Res..

[35]  Neil Conway,et al.  Unit-level linkages between employee commitment to the organization, customer service delivery and customer satisfaction , 2015 .

[36]  Martina Vandebroek,et al.  An adjustment algorithm for optimal run orders in design of experiments , 2002 .

[37]  Xiaobing Pei An application of OR and IE technology in bank service system improvement , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[38]  Kash Barker,et al.  Sensitivity analysis for simulation-based decision making: Application to a hospital emergency service design , 2012, Simul. Model. Pract. Theory.

[39]  T. P. Young,et al.  A generic method to develop simulation models for ambulance systems , 2015, Simul. Model. Pract. Theory.

[40]  K. Ahmed Modeling drivers' acceleration and lane changing behavior , 1999 .

[41]  José F. Molina-Azorín,et al.  The effects of quality and environmental management on competitive advantage: A mixed methods study in the hotel industry , 2015 .

[42]  Ehsan Fallahiarezoudar,et al.  Influence of Process Factors on Diameter of Core (γ-Fe2O3)/Shell (Polyvinyl Alcohol) Structure Magnetic Nanofibers During Co-Axial Electrospinning , 2015 .

[43]  Parviz Ghoddousi,et al.  Verification of lean construction benefits through simulation modeling: A case study of bricklaying process , 2014 .

[44]  Ju-Hwan Cha,et al.  Combined discrete event and discrete time simulation framework and its application to the block erection process in shipbuilding , 2010, Adv. Eng. Softw..

[45]  Maghsoud Amiri,et al.  Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm , 2012 .

[46]  J.P.C. Kleijnen,et al.  Optimal design of simulation experiments with nearly saturated queues , 1995 .

[47]  J. López-Fidalgo,et al.  Optimal experimental designs for partial likelihood information , 2014, Comput. Stat. Data Anal..

[48]  Sayedeh Parastoo Saeidi,et al.  How does corporate social responsibility contribute to firm financial performance? The mediating role of competitive advantage, reputation, and customer satisfaction , 2015 .

[49]  Kuan Yew Wong,et al.  Comparison of Two Simulation Software for Modeling a Construction Process , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

[50]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..

[51]  Charles J. Malmborg,et al.  Simulation based experimental design to identify factors affecting performance of AVS/RS , 2010, Comput. Ind. Eng..

[52]  Juan P. Steibel,et al.  Optimizing design of two-stage experiments for transcriptional profiling , 2009, Comput. Stat. Data Anal..

[53]  Gertrude P. Pannirselvam,et al.  Operations management research: an update for the 1990s , 1999 .

[54]  Saeed Rahimpour Golroudbary,et al.  Traffic simulation of two adjacent unsignalized T-junctions during rush hours using Arena software , 2014, Simul. Model. Pract. Theory.

[55]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[56]  Biao Wu,et al.  A graph theoretic approach to simulation and classification , 2014, Comput. Stat. Data Anal..

[57]  Cathal Heavey,et al.  Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems , 2011, Comput. Ind. Eng..