Parameter types

Parameter types model various aspects of time and variability in your simulation. They provide flexibility and control over how you define the start of the process, its duration and costs, and handle variability in your simulation model.

Duration parameters

Duration parameters allow you to define inter trigger timers, processing times, durations and properties using the ISO 8601 date and time format, with options for both a simple short format and a more detailed long format for greater control.

Short format

Short format in Cardanit allows you to define duration using a single unit of time, such as seconds, minutes, hours, days, weeks, months, or years. For example, you might define the processing time of an activity to take 3 hours or 2 weeks. In the XML model, this is expressed as “PT3H” or “P2W”.

Long format

Long format sets duration as multiple values, such as years, months, days, hours, minutes and seconds. This level of detail is useful for accurately modeling more complex durations, providing more granular control over timing. For instance, you might define the processing time of an activity to take 3 days, 2 hours, and 1 minute. In the XML model, this is expressed as “P0Y0M3DT2H1M0S”.

Distribution parameters

Processes often involve variability, and to simulate this, you can set parameter values using a distribution. This allows you to model a range of values instead of a fixed point. You can apply distributions to define inter trigger timers, processing times, fixed costs, unit costs and properties.

Beta

Beta distribution is a continuous probability distribution defined by two positive parameters. It’s often used to model random variables that are constrained within a range, such as percentages or probabilities.

Binomial

Binomial distribution models the number of successes in a fixed number of independent trials, each with the same probability of success. It is commonly used for scenarios with two possible outcomes, such as yes/no or success/failure.

Gamma

Gamma distribution is a continuous probability distribution defined by two positive parameters: shape and scale, and is useful for modeling processes that involve a series of events.

Log normal

Log normal distribution is a continuous probability distribution of a variable whose logarithm is normally distributed. It’s often used to model variables that are positively skewed.

Poisson

Poisson distribution is used to model the number of unpredictable events within a unit of time. It’s often used to model rare or random events.

Triangular

Triangular distribution is a continuous probability distribution shaped like a triangle, defined by a minimum, maximum, and mode. It’s often used when only limited information is available about the range and likely outcome of a process.

Truncated normal

Truncated normal distribution is a variation of the normal distribution that is restricted within a specific range. It’s useful for modeling processes where values outside a certain interval are not possible or realistic.

Uniform

Uniform distribution is a continuous probability distribution where all outcomes are equally likely within a specified range. It’s often used in simulations to model situations with no bias toward any specific outcome.

Weibull

Weibull distribution is a continuous probability distribution commonly used to model life data, reliability, and failure times. It’s defined by a shape parameter and a scale parameter, and it can model both increasing and decreasing failure rates.

Custom

Custom distribution allows you to define a specific set of weights (probabilities) or values based on your data or specific needs. It provides flexibility to model scenarios where standard distributions don’t apply or aren’t sufficient.

In a custom distribution, you define data points — the specific values or observations in your dataset that you want to model. Data points are expressed in minutes.

The distribution can be either discrete or continuous:

• Discrete: Specify distinct values such as 10 minutes or 15 minutes, along with their associated weights.

• Continuous: Define a range of values and their corresponding weights, which represent how likely values in that range are to occur. The minimum weight value is set to 0.

DateTime parameters

Dates and times are defined using the ISO 8601 date and time format. They allow you to define the start date, warmup and properties parameters.