The sorted lists made me think binary search immediately, which was the right call for finding the next valid departure.
Clarify the problem constraints and edge cases first, then propose an efficient algorithm using binary search to find the earliest valid departure in each list. Simulate the round trips by updating the current time and repeating for the given number of missions, analyzing time and space complexity.
Pro tip: Discuss how to handle the case where no valid departure exists in one direction, and mention that pre-sorting or using binary search ensures O(log n) per lookup, which is optimal for large lists.
Ask about input sizes, whether lists are sorted, if times are in minutes or hours, and what to do if no flight is available. Confirm the number of missions and initial current time.
Since lists are sorted, use binary search (e.g., bisect in Python) to find the earliest departure >= current time in each direction. This gives O(log n) per lookup.
For each mission, find departure from A to B, update current time to that departure (or arrival? clarify), then find departure from B to A, update time again. Repeat for the given number of missions.
If no valid departure exists in either direction, the simulation cannot continue. Decide whether to return an error, stop early, or return the current time.
Time complexity: O(m log n) for m missions and n flights per list. Space: O(1) extra. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.