**Novel Integration of Discrete Event Simulation with Other Modeling Techniques**

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**Chapter 4** 

© 2012 Brito et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Discrete Event Simulation Combined** 

**Methodology in Complex Logistics Systems** 

Discrete Event Simulation (DES) is a decision support tool that is extensively used to solve logistics and industrial problems. Indeed, the scope of DES is now extremely broad in that it includes manufacturing environments, supply chains, transportations systems and computer information systems [1]. However, although its usage spreads dramatically, few authors, practitioners or users are able to fully understand and apply the methodology in

While alone, the DES methodology is a tool that improves user comprehension of a system, it has sometimes been incorrectly stigmatized as a method, a "crystal ball." Indeed, a DES model should not be built to accurately predict the behavior of a system, but rather used to allow decision makers to fully understand and respond to the behavior of the variables (elements, resources, queues, etc.) of the system and the relations between those variables. However, depending on the complexity of the system, a deeper analysis and evaluation of the system behavior and variable tradeoff analyses may be a complicated task, since logistics problems, by nature, are composed of several elements interacting among themselves simultaneously, influencing each other in a complex relationship network, often under conditions that involve randomness. Further, the observation and evaluation of numerous decision criteria is required, led by multiple goals (often intangible and even antagonistic) and commonly running across long time horizons where the risks and uncertainties are

**with Multiple Criteria Decision** 

**Analysis as a Decision Support** 

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50107

order to derive its full potential.

**1. Introduction** 

salient elements.

Thiago Barros Brito, Rodolfo Celestino dos Santos Silva, Edson Felipe Capovilla Trevisan and Rui Carlos Botter
