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.
HardTechnical
0 practiced
You have an IP with poor solve performance due to complex coupling constraints. Propose advanced techniques to improve solve time and solution quality: Benders decomposition, Dantzig-Wolfe (column generation), problem reformulation, cover cuts, variable fixing heuristics, and warm-starting. For each technique explain when it applies, how to implement it, and the main trade-offs.
HardTechnical
0 practiced
Outline and implement (pseudocode or illustrative Python modules) an end-to-end pipeline that: ingests hourly demand forecasts, runs a constrained production optimization (MIP) hourly with re-optimization, logs inputs/outputs for audit, handles infeasible solves by auto-relaxation or heuristic fallback, and exposes a REST API for downstream consumers. Describe module responsibilities, error handling, unit/integration testing, and operational monitoring.
EasyTechnical
0 practiced
Define a binding constraint and a non-binding (slack) constraint in optimization. Provide a manufacturing example where a machine-hour constraint is binding while a raw-material constraint has slack. Explain the business implications of binding constraints for prioritizing investments and process improvements.
HardTechnical
0 practiced
A revenue-maximizing LP repeatedly produces allocations that disproportionately disadvantage a set of regions. Describe how you'd add fairness constraints (e.g., minimum allocation per region, ratio bounds) to the LP, how to measure the revenue-fairness trade-off (compute Pareto frontier), test for unintended consequences, and present recommendations and KPIs to stakeholders to monitor fairness and performance drift.
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).
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