We consider a supply chain on the territory of the USA comprising:
- DC in Topeka
- 100 customers
The demand for lamps is proportional to the population of the cities.
The network model may consider 3 DC facilities with different sizes:
- 1200 sq m (600000 lamps)
- 1800 sq m (900000 lamps)
- 2400 sq m (1200000 lamps)
Each DC has fixed investment, carrying, maintenance and throughput costs:
- Fixed investment costs depend on the DC size:
- IF DC capacity is 600 000, then the fixed investment costs constitute $1 000 000
- IF DC capacity is 900 000 then the fixed investment costs constitute $1 500 000
- IF DC capacity is 1 200 000 then the fixed investment costs constitute $2 000 000
- The carrying cost function is “stepped” and depends on the inventory:
- IF 0 < DC inventory < 300 000 pcs, then the carrying costs constitute $ 3 000 per month
- IF 300 000 < inventory < 600 000 pcs, then the carrying costs constitute $ 6 000 per month
- IF 600 000 < inventory < 900 000 pcs, then the carrying costs constitute $ 9 000 per month
- IF 900 000 < inventory < 1 200 000 pcs, then the carrying costs constitute $ 12 000 per month
- Warehouse maintenance costs depend on DC size:
- IF DC capacity is 600 000, then the maintenance costs constitute $ 72 000 per year
- IF DC capacity is 900 000, then the maintenance costs constitute $ 108 000 per year
- IF DC capacity is 1 200 000, then the maintenance costs constitute $ 144 000 per year
Note how constraints are defined.
Select the most suitable DC size to invest in.
The result of the experiment contains warehouse of 1 200 sq m that suits us.
The Named Expressions page shows the detailed statistics on the user-defined constraints within the supply chain.
As you can see on the screenshot below, the Carrying Costs peak at 6 000 in June.
The Optimization Results page is opened by default. It shows the result of the experiment
with all the possible combinations filtered per Profit (NetOpt) statistics column. The top record of the table is
the best one.
The data on other details is shown in the corresponding tables:
The following tables allow to define constraints:
Let us see how we can define custom constraints in the current task:
- The Linear Ranges table contains a set of conditions that will be further used.
- The Indicator Constraints table defines/binds values of the constraints.
E.g. The constraint of the highlighted record means that the fixed investment costs depend on the DC size. IF DC capacity is 600 000, then the fixed investment costs will constitute $1 000 000
- The carrying cost expressions are defined in the Custom Constraints table, where:
- Left-Hand Side - in our case contains the name assigned to the function that is defined in the Right-Hand Side.
- Right-Hand Side - defines the function and its type.
- Below you can see the defined function:
- Finally, we have set up everything to expand the objective function (Carrying Cost)
in the Objective Members table.
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