Facilitating the Analysis of a UK National Blood Service Supply Chain Using Distributed Simulation

被引:53
|
作者
Mustafee, Navonil [1 ]
Taylor, Simon J. E. [1 ]
Katsaliaki, Korina [2 ]
Brailsford, Sally [3 ]
机构
[1] Brunel Univ, Sch Informat Syst Comp & Math, Uxbridge UB8 3PH, Middx, England
[2] Middlesex Univ, Sch Business, London N11 1QS, England
[3] Univ Southampton, Sch Management, Southampton SO17 1BJ, Hants, England
来源
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL | 2009年 / 85卷 / 02期
关键词
discrete-event simulation; supply chain simulation; commercial simulation packages; distributed simulation; high level architecture; standards;
D O I
10.1177/0037549708100530
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial off-the-shelf (COTS) discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard, the High Level Architecture (HLA), the de facto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance.
引用
收藏
页码:113 / 128
页数:16
相关论文
共 46 条
  • [41] Analysis of dual-bus metropolitan area networks using distributed quantitative stochastic simulation
    Yau, V
    Pawlikowski, K
    SIMULATION, 2000, 75 (03) : 157 - 169
  • [42] Design of Supply Chain Transportation Pooling Strategy for Reducing CO2 Emissions Using a Simulation-Based Methodology: A Case Study
    Jerbi, Abdessalem
    Jribi, Haifa
    Aljuaid, Awad M.
    Hachicha, Wafik
    Masmoudi, Faouzi
    SUSTAINABILITY, 2022, 14 (04)
  • [43] A Discrete Event Simulation Analysis of the Bullwhip Effect in a Multi-Product and Multi-Echelon Supply Chain of Fast Moving Consumer Goods
    Ali, Ramsha
    Bin Khalid, Ruzelan
    Qaiser, Shahzad
    PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH, 2020, 16 (03) : 561 - 576
  • [44] Time analysis of the containerized cargo flow in the logistic chain using simulation tools: the case of the Port of Seville (Spain)
    Ruiz-Aguilar, J. J.
    Turias, I. J.
    Cerban, M.
    Jimenez-Come, M. J.
    Gonzalez, M. J.
    Pulido, A.
    EFFICIENT, SAFE AND INTELLIGENT TRANSPORT, 2016, 18 : 19 - 26
  • [45] Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)
    Mirzaaliyan, Maede
    Hajian Heidary, Mojtaba
    Amiri, Maghsoud
    JOURNAL OF SIMULATION, 2024, 18 (05) : 851 - 867
  • [46] Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic
    Tagimacruz, Toni
    Cepoiu-Martin, Monica
    Marshall, Deborah A.
    HEALTH SYSTEMS, 2025, 14 (01) : 1 - 11