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Clarify the problem constraints and define the objective function, then model it as a combinatorial optimization problem such as bin packing or interval scheduling. Discuss possible algorithms (greedy, DP, matching) and analyze their time/space complexity, and finally test with edge cases.
Pro tip: Start by asking clarifying questions about constraints (e.g., order sizes, shopper capacity, time windows) to show you think about real-world applicability. Then, relate the problem to a known NP-hard problem and discuss approximation algorithms or heuristics if exact solutions are infeasible.
Ask questions to understand the exact constraints: order sizes, shopper capacity, time windows, and whether orders can be split. Define what 'minimum number of shoppers' means precisely.
Abstract the problem into a known combinatorial optimization model, such as bin packing (if orders are items and shoppers are bins) or interval scheduling (if time windows matter).
Select an appropriate algorithm based on problem size and constraints: greedy for simple cases, dynamic programming for small inputs, or integer programming for exact solutions. Discuss trade-offs.
Explain the time and space complexity of your chosen algorithm and argue its correctness. If the problem is NP-hard, discuss approximation ratios or heuristics.
Walk through a small example to validate the approach, including edge cases like empty orders or shoppers with zero capacity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.