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Solve Maximum Flight Time Scheduler using Go Lang Language

Solve Maximum Flight Time Scheduler using Go Lang to enhance your skills with go lang coding practice , master coding concepts, and prepare for interviews with practical exercises and detailed solutions.

Maximum Flight Time Scheduler

Difficulty : Hard

Categories :

  • Sorting algorithms

You are given a list of airplane tickets with departure and arrival times. Each ticket is represented as a tuple [departure_time, arrival_time]. Times are in 24-hour format "HH:MM".

A passenger wants to take multiple flights to maximize total flight time while satisfying these conditions:

  • Must wait at least K minutes between consecutive flights
  • Cannot take overlapping flights
  • Can take flights in any order
  • All flights must occur within the same day (00:00 to 23:59)

Return the maximum possible total minutes spent in flight.

Constraints:

  • 1 ≤ tickets.length ≤ 10^4
  • 30 ≤ K ≤ 120
  • Times are valid in 24-hour format
  • departure_time < arrival_time for each ticket

Examples:

Input: tickets = [["09:00","10:30"],["11:30","13:00"],["12:00","14:00"]], K = 60
Output: 180
Explanation:
Optimal schedule:
09:00-10:30 (90 minutes)
11:30-13:00 (90 minutes)
Total = 180 minutes
Input: tickets = [["08:00","09:00"],["09:30","10:30"],["11:00","12:00"]], K = 30
Output: 180
Explanation:
Can take all flights as gap between each is ≥ 30 minutes
Total = 60 + 60 + 60 = 180 minutes

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Choose from the following categories

Strings A sequence of characters used to represent text, with problems focused on manipulation, searching, and pattern matching.

Arrays A collection of elements stored in contiguous memory locations, allowing efficient random access and manipulation.

Linked Lists A linear data structure where each element (node) points to the next, offering dynamic memory allocation and efficient insertions/deletions.

Trees A hierarchical data structure with nodes connected by edges, used in applications like databases, file systems, and parsing.

Graphs A collection of nodes (vertices) connected by edges, representing relationships, widely used in networking, routing, and social networks.

Dynamic Programming An optimization technique that solves problems by breaking them into overlapping subproblems and storing their results.

Backtracking A problem-solving method that explores all possibilities recursively, backtracking when a solution is not feasible.

Sorting Algorithms Techniques for arranging elements in a specific order, such as quicksort, mergesort, and bubble sort, essential for data organization.

Searching Algorithms Methods for finding elements in a dataset, such as binary search, linear search, and graph traversal techniques.

Mathematics A field of study involving numbers, calculations, and logical reasoning, forming the foundation for algorithms and problem-solving.

Recursion A programming technique where a function calls itself to solve smaller instances of the same problem, often used in divide-and-conquer approaches.

Bit Manipulation Operations performed directly on binary representations of data, used for efficient computations and optimization tasks.