Minimum Number of Chairs in a Waiting Room

Minimum Number of Chairs in a Waiting Room - Problem

String Simulation Easy

Imagine you're managing a busy waiting room where people constantly enter and leave throughout the day. Your goal is to determine the minimum number of chairs needed so that every person who enters always finds an available seat.

You are given a string s representing events that happen second by second:

'E' - A person enters the waiting room and needs a chair
'L' - A person leaves the waiting room, freeing up a chair

The waiting room starts completely empty. You need to find the minimum number of chairs required to ensure no one ever has to stand.

Example: If events are "EELLEL", you need 2 chairs because at most 2 people are present simultaneously.

Input & Output

example_1.py — Basic Case

$ Input: s = "EELLEL"

› Output: 2

💡 Note: At second 1, we have 2 people in the room (after EE), which is the maximum occupancy. We need 2 chairs to handle this peak.

example_2.py — Sequential Events

$ Input: s = "ELELEL"

› Output: 1

💡 Note: People enter and leave alternately, so at most 1 person is present at any time. Only 1 chair is needed.

example_3.py — All Enters First

$ Input: s = "EEELLL"

› Output: 3

💡 Note: All 3 people enter first (EEE), creating maximum occupancy of 3, then all leave. We need 3 chairs for the peak period.

Constraints

1 ≤ s.length ≤ 50
s consists only of characters 'E' and 'L'
The number of 'L' events will never exceed 'E' events at any point (no one can leave an empty room)

Visualization

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Understanding the Visualization

Track Current Occupancy

Keep a running count of people currently in the waiting room

Monitor Peak Usage

Record the highest number of simultaneous occupants

Determine Chair Requirement

The peak occupancy tells us exactly how many chairs we need

Key Takeaway

🎯 Key Insight: The maximum number of people present simultaneously determines the minimum chairs needed - no need to track individual people, just the running count!

Asked in

G Google 25 a Amazon 18 Apple 12 ⊞ Microsoft 15

The optimal solution uses a simple one-pass simulation approach. Track the current number of people in the room and the maximum occupancy seen so far. For each 'E', increment the count and update the maximum. For each 'L', decrement the count. The maximum occupancy determines the minimum chairs needed. Time: O(n), Space: O(1).

Common Approaches

Approach	Time	Space	Notes
✓ One-Pass Simulation (Optimal)	O(n)	O(1)	Track running count of people, update maximum as we go
Brute Force (Track All Events)	O(n²)	O(n)	Store all enter/leave events and simulate every timestamp

One-Pass Simulation (Optimal) — Algorithm Steps

Initialize current_people = 0 and max_chairs = 0
Iterate through each character in the string
If 'E': increment current_people, update max_chairs if needed
If 'L': decrement current_people
Return max_chairs

Visualization

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Step-by-Step Walkthrough

Process 'E'

Increment count, update maximum if needed

Process 'L'

Decrement count (no need to update maximum)

Continue

Repeat for all events, tracking maximum

Code -

solution.c — C

#include <stdio.h>
#include <string.h>

int minChairs(char* s) {
    int currentPeople = 0;
    int maxChairs = 0;
    int n = strlen(s);
    
    for (int i = 0; i < n; i++) {
        if (s[i] == 'E') {
            currentPeople++;
            if (currentPeople > maxChairs) {
                maxChairs = currentPeople;
            }
        } else if (s[i] == 'L') {
            currentPeople--;
        }
    }
    
    return maxChairs;
}

int main() {
    char s[1000];
    scanf("%s", s);
    
    // Remove quotes if present
    int len = strlen(s);
    if (s[0] == '"' && s[len-1] == '"') {
        for (int i = 0; i < len-2; i++) {
            s[i] = s[i+1];
        }
        s[len-2] = '\0';
    }
    
    printf("%d\n", minChairs(s));
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single pass through the string, each operation is O(1)

✓ Linear Growth

Space Complexity

O(1)

Only using a few integer variables regardless of input size

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

One-Pass Simulation (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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