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Activity or Task Scheduling Problem

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Activity or Task Scheduling Problem

This is the dispute of optimally scheduling unit-time tasks on a single processor, where each job has a deadline and a penalty that necessary be paid if the deadline is missed.

A unit-time task is a job, such as a program to be rush on a computer that needed precisely one unit of time to complete. Given a finite set S of unit-time tasks, a schedule for S is a permutation of S specifying the order in which to perform these tasks. The first task in the schedule starts at time 0 and ends at time 1; the second task begins at time 1 and finishes at time 2, and so on.

The dispute of scheduling unit-time tasks with deadlines and penalties for each processor has the following inputs:

  • a set S = {1, 2, 3…..n} of n unit-time tasks.
  • a set of n integer deadlines d1 d2 d3…dn such that di satisfies 1≤ di ≤ n and task i is supposed to finish by time di and
  • a set of n non-negative weights or penalties w1 w2….wn such that we incur a penalty of wi if task i is not finished by time di, and we incurred no penalty if a task finishes by its deadline.

Here we find a schedule for S that minimizes the total penalty incurred for missed deadlines.

A task is late in this schedule if it finished after its deadline. Otherwise, the task is early in the schedule. An arbitrary schedule can consistently be put into early-first form, in which the first tasks precede the late tasks, i.e., if some new task x follows some late task y, then we can switch the position of x and y without affecting x being early or y being late.

An arbitrary schedule can always be put into a canonical form in which first tasks precede the late tasks, and first tasks are scheduled in order of nondecreasing deadlines.

A set A of tasks is independent if there exists a schedule for the particular tasks such that no tasks are late. So the set of first tasks for a schedule forms an independent set of tasks ‘l’ denote the set of all independent set of tasks.

For any set of tasks A, A is independent if for t = 0, 1, 2…..n we have Nt(A) ≤ t where Nt(A) denotes the number of tasks in A whose deadline is t or prior, i.e. if the tasks in A are expected in order of monotonically growing deadlines, then no task is late.

Example: Find the optimal schedule for the following task with given weight (penalties) and deadlines.

1 2 3 4 5 6 7
di 4 2 4 3 1 4 6
wi 70 60 50 40 30 20 10

Solution: According to the Greedy algorithm we sort the jobs in decreasing order of their penalties so that minimum of penalties will be charged.

In this problem, we can see that the maximum time for which uniprocessor machine will run in 6 units because it is the maximum deadline.

Let Ti represents the tasks where i = 1 to 7

Activity or Task Scheduling Problem

T5 and T6 cannot be accepted after T7 so penalty is

w5 + w6 = 30 + 20 = 50 (2 3 4 1 7 5 6)  

Other schedule is

Activity or Task Scheduling Problem

(2 4 1 3 7 5 6)

There can be many other schedules but (2 4 1 3 7 5 6) is optimal.

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