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Model constraints allow us to capture real-world limitations in our supply chain. These limitations might include production capacity, transportation restrictions, storage limits, etc.
By default, Cosmic Frog allows us to incorporate the following constraint types for Neo optimization models:
Each set of constraints has its own table in the Cosmic Frog data schema.
Flow Constraints introduce a limit on the amount of flow (product) that can pass between an origin/destination pair in our supply chain.
In the example below, we limit the number of pencils that the Ashland production site can ship to the Chicago customer to a maximum of 20 per period.
Note that we can adjust the unit of measure of our constraints as needed. For example, we might be interested in limiting the total weight of product shipped between locations, instead of the total count of product.
Inventory constraints limit the amount of product that can be stored at a facility. They can represent both minimum and maximum inventory limits.
In the example below we limit the capacity of the Bakersfield distribution center such that it can hold at most 500 pens. We are also requiring that the DC stores at least 100 pencils.
Production constraints limit the amount of product a supplier or a production facility can produce. Like inventory constraints, you can set both minimum and maximum production limits.
In the example below, the Memphis production can produce at most 250 pens in each period. Additionally, the Ashland site has a fixed production constraint, which requires it to always produce exactly 400 pencils in each period.
Facility count constraints limit the number of facilities that can be used in a Cosmic Frog model. These constraints are often used in facility location and selection problems.
For many of the “count” constraints, we want to apply our constraint across a number of different supply chain elements (e.g. across all facilities). To do this, the group functionality of Cosmic Frog is particularly useful, and often necessary. For more detailed information on using groups in constraints, see Using Groups to Write Model Constraints.
In the following example we want to design a supply chain with exactly 2 facilities, and we want to know the optimal location of these 2 facilities. First, we set the status of candidate locations in our Facilities table to “consider”. Without making this change, our model will be forced to include all facilities where Status = “Include” in our network.
Then, we create a constraint requiring the model to select exactly 2 facilities from the “ProductionSites” group.
Flow count constraints limit the number of flows of a particular product. Flow count constraints are particularly useful in problems where the available transportation resources are limited.
In the following example we are implementing a “single-sourced” rule where each customer is served by exactly one production site. For each customer (enumeration) we look across all production sites (aggregation) and set the flow count to be exactly 1.
Production count constraints allow us to manage the number of product types that are produced at different facilities.
In the example below we add two production count constraints. First, we require that only one production site produces pens. Second, we require that each production site makes at most one product from the “Products” group.
Inventory count constraints allow us to limit the number of product types that are stored at our facilities. In the example below we require the Bakersfield DC to hold at most one type of product.
Custom Columns can be added to any input table directly through Cosmic Frog. To add a custom column to a table, open the table and then select Grid > Create Custom Column
This will open a window on the right-hand side of the screen where you can configure your custom column. Each of the required entries are used as follows:
This will be the name of the custom column. Please note that column names will only be allowed to contain alphanumeric characters and underscores. Spaces are not allowed in column names, and you will not be able to create a column name that begins with a number. If you enter any sort of an invalid column name, you will not be able to save the column and a message will be displayed noting the issues with the name currently entered.
Pseudo typing allows for the data entered into this column to accept all sorts of input formatting without strictly adhering to the Data Type selected for the custom column. For example, if the Data Type for a given custom column is set to numeric and you attempt to import a string into the field, Pseudo Typed = TRUE will allow for this import to succeed. If Pseudo Typed = FALSE, then this import would fail as the field would only accept numbers. This also applies for editing the field within the grid directly.
For reference, all fields within the standard Anura schema are pseudo typed. We will allow for all entries into the data fields and then cast the entries to their appropriate data type for use within maps, dashboards and the application of scenario items.
Select one of the available data types that the data in your custom column will be formatted in. Depending on the selection chosen for the Pseudo Typed setting, this data type will be strictly enforced (Pseudo Typed = FALSE) or be relaxed and only used when generating visuals or applying scenario items (Pseudo Typed = TRUE).
This setting informs Cosmic Frog if the custom column should be used as a primary key for the table when evaluating file imports. If True, the custom column will be included in addition to the table’s default primary key columns.
When data is imported into a Cosmic Frog model through the Import File action, data is loaded using Upsert logic. This means that rows which already exist in a table will be updated, and rows which do not exist will be inserted. Existing rows will be identified using the table’s primary key – all input tables have a preconfigured set of columns that represent the primary key of the input table. For information on which columns represent the primary key of any given table, please reference Downloadable Anura Data Structure – Inputs | Optilogic.
Once all of the settings have been filled out, click Save and your table will refresh with the custom column now being displayed on the far-right of the table. You’ll notice that the column name has been down cased – this is done to align with standard naming convention for PSQL.
If any issues arise during the Custom Column creation, please reach out to support@optilogic.com for further assistance.
