Resource allocation assigns limited resources among competing activities while respecting constraints. Linear programming turns that decision into an optimization model that maximizes strategic value without exceeding available budgets or capacity. section 8.1.1 section 1.1.3
Implement a constrained allocation model by:
- Define decision variables for each candidate activity; here, each feature is binary: selected (1) or not selected (0). section 8.2.2
- Define an objective function that maximizes priority scores, representing value relative to effort. section 8.1.3
- Add constraints for yearly budget, total lifecycle cost, resource availability, and strategic balance. section 8.1.3 section 8.1.4
- Solve the model, verify success, and inspect selected activities, costs, and scores before interpreting the allocation. section 8.2.2
Selecting strategic features
An organization evaluates features across three strategic epics. It requires three or four selections per epic, at least nine and at most twelve selections overall, a yearly investment no greater than $700,000, and a lifecycle cost no greater than $1.1 million. The optimizer chooses the feasible combination with the highest total priority score. section 8.1.4
Table 8.1 Features aligned with the Enhance Data Infrastructure epic
|
Feature name
|
First-year cost
|
Total cost
|
Priority score
|
|---|---|---|---|
|
Automated Data Cleaning Pipeline
|
$50,000
|
$80,000
|
1.23
|
|
Real-time Data Ingestion
|
$70,000
|
$120,000
|
1.29
|
|
Data Privacy and Compliance Tools
|
$80,000
|
$130,000
|
1.19
|
|
Real-time Data Monitoring and Alerts
|
$60,000
|
$100,000
|
1.38
|
|
Integration with External Data Sources
|
$50,000
|
$85,000
|
1.42
|
|
Feature Engineering Automation
|
$55,000
|
$90,000
|
1.45
|
Use the feature table to compare candidate activities by first-year cost, total cost, and priority score before constructing the optimization model. section 8.1.4
