Clarify the problem constraints and edge cases, then describe a single-pass greedy algorithm that maintains a current ship and a rejected list. Walk through the logic step-by-step, emphasizing O(n) time and O(1) extra space (excluding output), and discuss how to handle oversized items.
Pro tip: Explicitly state that you're treating the input as a stream and that the algorithm is online, which is crucial for logistics systems like Flexport's where items arrive sequentially and decisions must be made immediately.
Ask about input format, whether item IDs are unique, if ships have a maximum item count, and how to handle empty input or items exactly equal to capacity. Confirm that order must be preserved and that rejected items are excluded from ships.
Explain that you'll iterate through items once, maintaining a current ship and its remaining capacity. For each item, if it fits, add it; if not, close the current ship, start a new one, and add the item if it fits; if the item alone exceeds capacity, reject it.
Use a small example (e.g., weights [2,3,4,5], C=5) to demonstrate how ships are formed and items rejected. Show the state of the current ship and rejected list at each step to validate the logic.
State that time complexity is O(n) because each item is processed once, and space complexity is O(n) for the output (or O(1) extra space if output is not counted). Discuss why a greedy approach is optimal here and mention that no backtracking is needed.
Mention edge cases like an item exactly equal to capacity, consecutive oversized items, or an empty input. Optionally, discuss how to handle multiple ships in parallel or if items could be reordered (which would change the problem).
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