Kk2.pdf

statements. The bank has a policy that customers should not wait tor more than five minutes in the majority of cases (generally interpreted as 99 per cent). Develop a conceptual model tor this problem outlining the objectives, model outputs and inputs, model scope and level of detail, and assump­ tions and simplifications. E6.2 Take a typical operations system, preferably one that can be observed (c.g. a supermarket or airport), and identify in broad terms at least three conceptual models that could be developed of the system. For each model identity the objectives and model content. E6.3 Obtain and read some simulation case studies (sec Exercise E l.3 in Chapter 1 tor potential sources). Identify the objectives of the simula­ tion study, the model outputs and inputs, and any assumptions and sim­ plifications that were made. As far as possible try to identity the scope and level of detail modelled. Can you identify any means for improving upon the simulation model that was developed (c.g. more or less detail, different assumptions)? E6.4 For each of the following situations identity a methexi of simplification that could be employed in a simulation model o f the system: a) A factory that produces a range of products: 80 per cent of orders are for two product types, 18 per cent for five prcxluct types and the remaining 2 per cent for 17 product types. b) A bottling plant that tills, caps and labels bottles at a rate o f five bot­ tles a second. c) An automotive final assembly line that is experiencing problems with the supply of seven key components (out of hundreds that are sup­ plied to the line). The company wishes to investigate the inventory polio' for these components. d ) Modelling weather conditions at a port. e) A supply chain for the manufacture, warehousing, distribution and retailing of wooden doors. f) A model representing the splitting of trains as they arrive at a rail yard and the forming of trains ready for departure. g) A ceramics factory in which crockery is processed by pallet loads through the glazing and firing processes. E6.5 Design a conceptual model for the simple queue model case described in Appendix 1 (Section A 1.2.1). Identify' the m(xklling objectives, mtxlel inputs and outputs, model content and any assumptions and simplifications. E6.6 Design a conceptual model for the Wardeon Cinema case described in Appendix 2 (Section A2.2.1). Identify the imxlelling objectives, model inputs and outputs, model content and any assumptions and simplifications.

simplifications and it may be useful to seek advice from such people before employing a particular simplification. The second approach is to test the simplification in the computer model; a form of prototyping. The modeller develops two computer models, one with and one without the simplification. It is then possible to compare the results from the two models to see the effect on accuracy. This, o f course, provides much greater certainty over the appropriateness of a simplification, but the advantage of faster model development is lost. Apart from maintaining a sufficient level o f accuracy (validity), a good sim­ plification should not compromise credibility either. Although the aim of sim­ plification is to improve the transparency of the model, over simplification can make a model less transparent, reducing its credibility. Take, tor example, the use of black-box modelling. Although a black-box may provide a sufficiently accurate representation of part of an operations system, the details of the representation arc not transparent. For some clients this may be satisfactory, but for others it may be necessary to provide a more detailed representation to give the model credibility. It is sometimes necessary to include a greater scope and level of detail than is required to assure the accuracy of the model, in order to assure the model's credibility. A poor simplification is one that causes a client to lose confidence in a model. Indeed, there are occasions when it is necessary to reverse the concept of simplification and actually increase the complexity (scope and level of detail) of the model, simply to satisfy the requirement for credibility.

6.4

Summary

The issue of how to develop a conceptual model is discussed from two stand­ points. The first, by presenting a framework for conceptual modelling, enabling a modeller to design a conceptual model from scratch. The second, by describing a number of methods for simplifying an existing conceptual model. The frame­ work is illustrated with reference to an example of a fast-food restaurant. The framework is further illustrated by the mini case studies at the end of the book, a simple queue model (Appendix 1, Section A 1.2), Wardeon Cinema (Appendix 2, Section A2.2) and Panorama Televisions (Appendix 3, Section A3.2). A final issue that has not been discussed is the validation of the conceptual model. This is covered in Section 12.4.1 as part of a more general discussion on the verification and validation of simulation models.

Exercises E6.1 A bank is planning its requirements for ATMs (automated teller machines) in a new branch. There are spaces for up to six ATMs, not all o f which have to be used. Three types of ATM can be purchased: general ATMs (giving cash, balances, mini statements and PIN change facilities), ATMs for paving money into accounts and ATMs that provide full account

| Model A [

Figure 6.3

Splitting models

one run of a combined model, because of reduced processing ar the C-phase; assuming the three-phase method is being employed (Section 2.2.2). This is a result of there being fewer conditional events in each sub model. In a com­ bined model, even' C-event would need to be checked whenever an event occurs somewhere in the model, leading to a lot of redundant processing. Another advantage of splitting models is that it is possible to speed develop­ ment time by having separate modellers develop each model in parallel. Where splitting models is not so successful is when there is feedback between the models. For instance, if model B cannot receive entities, because the first buffer is lull, then it is not possible to stop model A outputting that entity, although in practice this is what would happen. It is best, therefore, to split models at a point where there is minimal feedback, for instance, there is a large buffer. There is much interest in running simulations in parallel on separate com­ puters, with the aim of gaining run-speed advantages. If split models are run in parallel, then it should be possible to model feedback effects and so overcome the difficulty described above. At present, however, there are a number of obstacles to the use of parallel computing for simulation, not least developing efficient mechanisms for synchronising the models as they run. For a practical example see Mustafee et al. (2009). 6.3.7

What is a Good Simplification ?

Although model simplifications are beneficial, a poor choice of simplification, or over-simplifying a model, may seriously affect the accuracy of the simu­ lation. A good simplification is one that brings the benefits of faster model development and run-speed (feasibility and utility), while maintaining a suffi­ cient level of accuracy (validity) and credibility. How can a modeller determine whether a simplification is good or not? There are two broad approaches. The first is to use judgement in deciding whether a simplification is likely to have a significant impact on model accuracy. This should be determined by discussion between the modeller, client and other members of the simula­ tion project team. The project specification and conceptual model representa­ tion (Section 5.6), especially a list of simplifications, is a useful mechanism for explaining and discussing the efficacy of proposed simplifications. Of course, this approach provides no certainty over whether a simplification is appropriate or not. An expert modeller is likely to have much experience in applying model