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

Galankashi, Masoud Rahiminezhad; Fallahiarezoudar, Ehsan; Moazzami, Anoosh; Yusof, Noordin Mohd; Helmi, Syed Ahmad

doi:10.1016/j.advengsoft.2015.10.004

Cited by 24 publications

(14 citation statements)

References 45 publications

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“…Chen and Ou (2011) simulated five confirmation experiments and proved that the factor levels selected were significant and suitable. Galankashi et al (2016) also simulated five confirmation tests, where all the values for the runs were within a 95% prediction interval built from the previous runs. Sharma and Garg (2013) used the optimal level of the design parameters to predict and verify the improvement of the quality characteristic, and observed that the performance increased using the optimal levels.…”

Section: Discussionmentioning

confidence: 99%

“…Concerning the type of design chosen for experimentation, most authors were very clear in explaining their reasons for selecting one ED or another. The authors that opted for full factorial designs usually mentioned the importance of this type of design for studying all the possible combinations of controlled factors and their respective interactions (Antony et al, 2014;Chackelson et al, 2013;Chuang and Oliva, 2015;Galankashi et al, 2016). On the other hand, Taguchi designs were mostly chosen when the authors needed matrices to test controlled and uncontrolled factors (Dehlendorff et al, 2011;Holcomb, 1994;Raajpoot et al, 2008;Ree et al, 2014;Shahin et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…As presented in Section 3.2, 58.6% of the studies either did not replicate the experiments or failed to provide such information, which means that the majority of the papers did not follow CSF 7. Additionally, only five papers reported confirmatory experiments (Chen and Ou, 2011;Galankashi et al, 2016;Krishnamoorthy and Kapadia, 1999;Ree et al, 2014;Sharma and Garg, 2013) as suggested in CSF10. Finally, the other CSFs were not explicitly presented in the analysed papers.…”

Section: Critical Success Factorsmentioning

confidence: 99%

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Design of experiments in the service industry: a critical literature review and future research directions

Antony

Viles

Torres

et al. 2020

TQM

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PurposeThe purpose of the article is to present the results of a critical literature review (CLR) on Design of experiments (DoE) in the service industry.Design/methodology/approachA critical review of existing literature review across various databases including Scopus, Web of Science, Google Scholar and Emerald Insight were searched for the identification of relevant papers. The authors searched relevant journal articles for a time period of 25 years (1994–2019).FindingsA total of 29 industry case studies of DoE applications were identified spanning healthcare, retail, logistics, education, marketing, after sales and catering business. The industrial experimentation strategies adopted by the case study organisations were screening, factorial designs, Taguchi, response surface method and split-plot. It was apparent that there are only a handful number of papers showing the applications of DoE across the service sector and this motivates for pursuing further research into this topic by the authors.Practical implicationsThe findings of the study can be very useful for middle and senior managers to understand the benefits of implementing this powerful technique for increased understanding of service processes, as well as for optimising service performance. Moreover, the paper presents some of the fundamental challenges, as well as skills needed for the successful application of DoE.Originality/valueTo the best of our knowledge, this is the first CLR on DoE and its applications in the service sector. The findings of the study can be beneficial to both academic and industrial communities to understand some of the challenges and fundamental gaps which need to be tackled in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Critical Success Factorsmentioning

confidence: 99%

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Design of experiments in the service industry: a critical literature review and future research directions

Antony

Viles

Torres

et al. 2020

TQM

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“…Negahban and Smith (2014) provided a comprehensive review of discrete event simulation publications with a particular focus on applications in manufacturing. Galankashi et al (2016) developed an integrated simulation-design of experiments model to optimize a petrol station queuing system and sales rate. They used Witness 2014 simulation software for performance evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and simulation analysis of a bulk queueing system

Nithya

Haridass

2019

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Purpose The purpose of this paper is to provide simulation modelling for bulk arrival bulk service queueing system involved in a textile industry and analyze the performance metrics. Design/methodology/approach This paper describes the simulation modelling of a bulk queueing system with limited number of admissions and multiple vacations. The model is developed for the proposed queueing system using Flexsim 2017, and it is explained through an application observed in a textile industry involving the process of cone winding. Findings In this paper, the simulation model has been developed to study the behaviour of queues at different resources in a production system. Various performance measures such as average components, average waiting time, total number of inputs and outputs, processing time and idle time involved in a textile industry are evaluated using simulation and justified through numerical illustration. Practical implications The proposed simulation model may be used in various scenarios wherever a real time situation exists related to bulk queueing system. The results produced in this paper can be used by the manufacturing industries to enhance the need-based accuracy. It is worth pointing out that the findings are of direct practical relevance and can be successfully used for a number of industrial applications. Originality/value The approach suggested in this paper attempts to deal with the queueing system involved in a textile industry and provides numerical results in less time with less computer resources. It provides a reasonably good approximation for simple and complex queueing models where it is difficult to find closed form of theoretical results.

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“…The use of simulation modeling is increasing for solving healthcare, manufacturing, and supply chain problems (Dehghanimohammadabadi & Keyser, 2017, Barenji et al, 2016. The useful tools for the practitioners and researchers to perform experimentation without disturbing operations include statistics, lean six-sigma, mathematical modeling, and simulation (Galankashi et al, 2016, Habidin et al, 2016, Silva et al, 2015, Tako & Kotiadis 2015, Galankashi et al, 2013. However, the development of simulation models of real world problems and then solving using DOE techniques are challenging (Amiri & Mohtashami 2012, Ajdari & Mahlooji, 2014.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Throughput, WIP and Cycle Time: A Case Study of Utensils Manufacturing Shop Floor

Shafiq

Waqas

Shahzad

et al. 2020

JER

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The development of simulation models for improving performances is trendy in manufacturing industries. This paper presents simulation model for evaluating and improving performances of a utensils manufacturing plant. Internal benchmarking is employed for comparison under exactly similar conditions. The data of all input variables is collected and statistical models for each process is developed using Arena input analyzer. The simulation model is then developed using SIMIO to optimize throughput, work-in-process, and cycle time. The experiments have been performed under various scenarios i.e. at different values of the input parameters. The areas for improvement have been highlighted on the basis of results. Furthermore, best and worst case scenarios have been discussed in detail to provide insights for managerial performance improvements by: (1) optimizing inter-arrival time, (2) increasing production rate, and (3) reducing the number of rejected parts without disturbing existing set up of the manufacturing facility.

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

Cited by 24 publications

References 45 publications

Design of experiments in the service industry: a critical literature review and future research directions

Design of experiments in the service industry: a critical literature review and future research directions

Modelling and simulation analysis of a bulk queueing system

Optimizing Throughput, WIP and Cycle Time: A Case Study of Utensils Manufacturing Shop Floor

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