> For the complete documentation index, see [llms.txt](https://mayanktyagi3111.gitbook.io/interview-prep/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mayanktyagi3111.gitbook.io/interview-prep/priority-queue/minimum-number-of-refueling-stops.md).

# Minimum Number of Refueling Stops

A car travels from a starting position to a destination which is `target` miles east of the starting position.

Along the way, there are gas stations.  Each `station[i]` represents a gas station that is `station[i][0]` miles east of the starting position, and has `station[i][1]` liters of gas.

The car starts with an infinite tank of gas, which initially has `startFuel` liters of fuel in it.  It uses 1 liter of gas per 1 mile that it drives.

When the car reaches a gas station, it may stop and refuel, transferring all the gas from the station into the car.

What is the least number of refueling stops the car must make in order to reach its destination?  If it cannot reach the destination, return `-1`.

Note that if the car reaches a gas station with 0 fuel left, the car can still refuel there.  If the car reaches the destination with 0 fuel left, it is still considered to have arrived.

**Example 1:**

```
Input: target = 1, startFuel = 1, stations = []
Output: 0
Explanation: We can reach the target without refueling.
```

**Example 2:**

```
Input: target = 100, startFuel = 1, stations = [[10,100]]
Output: -1
Explanation: We can't reach the target (or even the first gas station).
```

**Example 3:**

```
Input: target = 100, startFuel = 10, stations = [[10,60],[20,30],[30,30],[60,40]]
Output: 2
Explanation: 
We start with 10 liters of fuel.
We drive to position 10, expending 10 liters of fuel.  We refuel from 0 liters to 60 liters of gas.
Then, we drive from position 10 to position 60 (expending 50 liters of fuel),
and refuel from 10 liters to 50 liters of gas.  We then drive to and reach the target.
We made 2 refueling stops along the way, so we return 2.
```

**Note:**

1. `1 <= target, startFuel, stations[i][1] <= 10^9`
2. `0 <= stations.length <= 500`
3. `0 < stations[0][0] < stations[1][0] < ... < stations[stations.length-1][0] < target`

```java
class Solution {
    // Priority Queue O(NlogN)
    public int minRefuelStops(int target, int cur, int[][] s) {
        Queue<Integer> pq = new PriorityQueue<>((a, b) -> b - a);
        int i = 0, stops = 0;
        while (cur < target) {
            // Finding all the stations within current range
            // Choose the station with max fuel overall
            while (i < s.length && s[i][0] <= cur)
                pq.add(s[i++][1]);
            if (pq.isEmpty())
                return -1;
            // Remember our PQ is common for multiple loops
            // so lets say we take max from a loop and cannot reach anywhere after that,
            // then we will also take a stop at 2nd highest value and so on until
            // either our PQ becomes empty or our range reaches somewhere
            cur += pq.poll();
            stops++;
        }
        return stops;
    }
}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://mayanktyagi3111.gitbook.io/interview-prep/priority-queue/minimum-number-of-refueling-stops.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
