Typical practical logistics problems are characterized by complexity and dynamics. Because of the complexity, it is often difficult or even impossible to solve such problems mathematically. Because of the dynamics involved in logistics systems, mathematical calculations for today's system can be out of date tomorrow. Simulation models that carefully 'mimic' the characteristics and dynamics of the system can provide the answer. The methodology of simulation allows an organization to analyze the behavior of complex systems in a flexible and detailed manner. Simulation also allows for a quick implementation of adjustments in the modeled system, making it possible to analyze different alternative solutions in a relatively short time.

**Simulation** is (Shannon, 1975):

"the process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or evaluating various strategies (within the limits imposed by a criterion or set of criteria) for the operation of the system".

This broad definition of simulation emphasizes the steps in a problem-solving process; first analyzing the current situation, before moving to experimenting with alternative solutions. With the availability of strong and fast simulation software tools, simulation is increasingly becoming an independent research method for solving problems, not only in natural sciences, but in social sciences as well. Simulation in the sense of 'imitating' or 'pretending' is an age-old concept. During the post-war advent of methods and techniques for operations research (OR), many considered simulation as a last resort when no analytical solution could be found. One uses a model that imitates the problem situation and start to experiment with this model. Nowadays, simulation is often a first step to take, especially in designing complex logistics and transportation systems. With all its hard to understand interrelations, a container port is for instance a logical candidate for simulation studies in each phase of design and development.

Simulation must be considered as an approach for following a structured process using modeling techniques to solve problems of the type described above. In this approach, modeling and simulation techniques that allow researching the dynamics of the process are used. Examples include system dynamics, continuous simulation, and discrete-event simulation. System dynamics is a qualitative analysis method, which enables quantitative modeling and analysis to design the system structure and control. In continuous and discrete-event simulation, a problem situation is simulated to gain an understanding of this problem and to find possible solutions. Discrete or continuous relates to the way in which the process is imitated, with time steps based on events or as a continuous process – e.g. based on differential equations – respectively. In supply chain and logistical studies, we often deal with discrete-event simulation, which means that we are interested at the state of the system studies at discrete points in time, e.g. at the points in time when goods are ordered, sent, or received.

Two main types of simulation can be identified:

**Computer simulation**: Using a simulation program, different simulation models can be made that reflect the real situation. In the rest of this chapter, all references to the word 'simulation' mean 'computer simulation'. Classical computer simulations run in 'what-if' mode and are used by one analyst at a time to answer one question by changing the parameters of the simulation model.- We talk about
**interactive computer simulation**(sometimes also called games or gaming simulation) when users can control the process during the simulation. The users are 'actively' taking part in the simulation and influence the simulation during the run. In many cases, the users in an interactive computer simulation play a certain 'role', and interact or communicate with other roles, i.c. other players.

Literature: R.E. Shannon. *Systems Simulation: the art and science*. Prentice-Hall, 1975.