Yield Optimization & Constraint-Based Modeling Questions
Techniques for optimizing yield and performance under constraints using constraint-based modeling, including linear programming, integer programming, and related optimization methods, applied to operations, manufacturing, supply chain, and product optimization.
MediumTechnical
0 practiced
Explain methods to model cardinality constraints (limiting the number of selected items) in optimization. Provide at least two formulations: a linear MIP formulation using binaries and big-M linkage, and a continuous surrogate such as l1 regularization for relaxed problems. Discuss pros/cons and numeric stability issues (e.g., choice of big-M).
HardTechnical
0 practiced
As a senior data scientist leading deployment of an optimization system that requires operational changes across procurement and manufacturing, describe your plan for stakeholder engagement and change management. Cover identifying stakeholders and champions, pilot design, KPIs and success criteria, training for operations teams, rollback strategies, monitoring for model decay after go-live, and a continuous improvement loop.
HardTechnical
0 practiced
Propose a robust pricing optimization model where demand elasticity is uncertain and known to lie within confidence intervals. Show how to formulate a robust counterpart (e.g., box or ellipsoidal uncertainty sets) and derive a tractable convex reformulation (LP or SOCP depending on set). Explain solution approaches and computational considerations.
MediumTechnical
0 practiced
How would you use sensitivity analysis from an LP to decide whether to expand capacity of a particular machine? Describe which outputs you would inspect (dual values/shadow prices, allowable increase/decrease, reduced costs), how to convert a shadow price into a dollar value to compare against investment cost, and what caveats exist for large capacity changes.
EasyTechnical
0 practiced
In Python using PuLP (or another Python LP library), write code that solves this LP: maximize 3*x + 5*y subject to 2*x + y <= 100, x + 2*y <= 80, x >= 0, y >= 0. Provide the code, solver call, and report the optimal x, y values and the objective value. Also mention how you'd change the model to require integer solutions for x and y.
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