{"title":"离散时间仿真","authors":"Jiangjun Tang, George Leu, H. Abbass","doi":"10.1002/9781119527183.ch8","DOIUrl":null,"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":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Discrete Time Simulation\",\"authors\":\"Jiangjun Tang, George Leu, H. Abbass\",\"doi\":\"10.1002/9781119527183.ch8\",\"DOIUrl\":null,\"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\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Simulation and Computational Red Teaming for Problem Solving\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119527183.ch8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Simulation and Computational Red Teaming for Problem Solving","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119527183.ch8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.