## What are the applications of job sequencing?

Sequencing techniques plays a vital role in the field of networking, such as road network, railway network and airline network etc. These are the real applications where optimal routing has to be required.

### Which algorithm can be used to solve the job sequencing problem?

The job sequencing problem can be solved using the binary search approach using sets.

#### How many machines are used for processing job sequencing with deadlines problem?

Step 4: Find the total processing time as per the sequence determined and also determine idle time associated with machines. There are three machines M1, M2 and M3. Each job has to go through three machines in the order M1, M2 and M3.

**What is the time complexity of job sequencing with deadlines?**

Job sequencing problems has the time complexity of O(n2).

**What is greedy method explain with example?**

Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. So the problems where choosing locally optimal also leads to global solution are best fit for Greedy. For example consider the Fractional Knapsack Problem.

## What is the time complexity of job sequencing with deadlines using greedy algorithm?

Keep track of maximum profit among all feasible subsets. The time complexity of this solution is exponential. This is a standard Greedy Algorithm problem.

### What is job sequencing with deadlines problem in data structure and algorithm?

Problem Statement In job sequencing problem, the objective is to find a sequence of jobs, which is completed within their deadlines and gives maximum profit.

#### What is the time complexity for job sequencing with deadlines?

**What is sequence problem?**

The selection of an appropriate order for finite number of different jobs to be done on a finite number of machines is called sequencing problem. In a sequencing problem we have to determine the optimal order (sequence) of performing the jobs in such a way so that the total time (cost) is minimized.

**How do you approach greedy problems?**

To make a greedy algorithm, identify an optimal substructure or subproblem in the problem. Then, determine what the solution will include (for example, the largest sum, the shortest path, etc.). Create some sort of iterative way to go through all of the subproblems and build a solution.

## Which of the following problems can be solved efficiently using the greedy programming approach?

Which of the following problems is NOT solved using dynamic programming? Explanation: The fractional knapsack problem is solved using a greedy algorithm. 10. Which of the following problems should be solved using dynamic programming?

### What is job sequencing problem with Deadline?

In this tutorial we will learn about Job Sequencing Problem with Deadline. This problem consists of n jobs each associated with a deadline and profit and our objective is to earn maximum profit. We will earn profit only when job is completed on or before deadline. We assume that each job will take unit time to complete.

#### How to solve job sequencing problem using greedy algorithm?

The greedy algorithm described below always gives an optimal solution to the job sequencing problem- Sort all the given jobs in decreasing order of their profit. Check the value of maximum deadline. Draw a Gantt chart where maximum time on Gantt chart is the value of maximum deadline. Pick up the jobs one by one.

**What is the minimum deadline for a job?**

Every job will take a single unit of time, so the minimum deadline for a job is 1. If only one job can be scheduled at a time, then maximize the profit. To solve this problem, all subset of the set of jobs are generated to check whether the individual subset is feasible or not.

**What is the time complexity of the job search algorithm?**

To solve this problem, all subset of the set of jobs are generated to check whether the individual subset is feasible or not. Also, keep track on maximum profit for all feasible subset that has generated. The time complexity of this algorithm is O (n^2) Input: A list of jobs with job id, deadline and profit.