{"title":"Discrete Time Simulation","authors":"Jiangjun Tang, George Leu, H. Abbass","doi":"10.1002/9781119527183.ch8","DOIUrl":"https://doi.org/10.1002/9781119527183.ch8","url":null,"abstract":"This chapter discusses the state, input, and output variables of discrete time systems along with the methods for building discrete time simulation (DTS). It also discusses comparison of DTS and discrete event simulation (DES). The chapter describes the ‘sample path’ – also known as ‘trajectory’ – a useful technique to extract the relevant dataset and conduct analysis. A vending machine example is used to demonstrate that DTS is a general case of DES, being able to model and simulate a discrete event system. However, DES can simplify the modelling process for a discrete event system, and is easy to understand. DTS is more suitable for systems in which the states change at uniformly distributed time points, as in the car‐following example. Finally, the chapter concludes with an example that simulates microscopic traffic – a car‐following model. If the system is event driven, DES is better, whereas if the system is time driven, DTS is better.","PeriodicalId":394470,"journal":{"name":"Simulation and Computational Red Teaming for Problem Solving","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Language of Abstraction and Representation","authors":"Jiangjun Tang, George Leu, H. Abbass","doi":"10.1002/9781119527183.ch5","DOIUrl":"https://doi.org/10.1002/9781119527183.ch5","url":null,"abstract":"This chapter focuses on the representation side of the process of building a simulation. Selecting how to represent the real system in a model is essential for the resultant simulation. There are various ways to represent a real system in a model – a diagram, an equation, a text script or others. The chapter discusses the most important representation paradigms, and uses finite‐state machine as an example to show how to abstract a system into a model under certain assumptions. It describes all major approaches to representation, including informal methods and formal methods for representation, which are especially relevant to computer simulation. While numerous semi‐formal methods are available for representing real‐world systems the authors only present diagramming and rule‐based representation, which are most relevant to computer simulation. A form of representation can be natural language description, such as a story book or a script given to actors to perform in a film.","PeriodicalId":394470,"journal":{"name":"Simulation and Computational Red Teaming for Problem Solving","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115716579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}