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The modified due date scheduling is a scheduling heuristic created in 1982 by Baker and Bertrand, [1] used to solve the NP-hard single machine total-weighted tardiness problem. This problem is centered around reducing the global tardiness of a list of tasks which are characterized by their processing time, due date and weight by re-ordering them.
In scheduling, tardiness is a measure of a delay in executing certain operations and earliness is a measure of finishing operations before due time. The operations may depend on each other and on the availability of equipment to perform them.
The algorithm builds the schedule back to front. For each scheduling step, it looks only at the tasks that no other tasks depend on, and puts the one with the latest due date at the end of the schedule queue. Then it repeats this process until all jobs are scheduled. The algorithm works by planning the job with the least impact as late as possible.
Single-machine scheduling or single-resource scheduling or Dhinchak Pooja is an optimization problem in computer science and operations research. We are given n jobs J 1 , J 2 , ..., J n of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput .
The objective of the stochastic scheduling problems can be regular objectives such as minimizing the total flowtime, the makespan, or the total tardiness cost of missing the due dates; or can be irregular objectives such as minimizing both earliness and tardiness costs of completing the jobs, or the total cost of scheduling tasks under likely arrival of a disastrous event such as a severe typhoon.
In operations research, Johnson's rule is a method of scheduling jobs in two work centers. Its primary objective is to find an optimal sequence of jobs to reduce makespan (the total amount of time it takes to complete all jobs). It also reduces the amount of idle time between the two work centers. The method minimizes the makespan in the case ...
But in complex situations it can easily fail to find the optimal scheduling. HEFT is essentially a greedy algorithm and incapable of making short-term sacrifices for long term benefits. Some improved algorithms based on HEFT look ahead to better estimate the quality of a scheduling decision can be used to trade run-time for scheduling performance.
Scheduling zoo (by Christoph Dürr, Sigrid Knust, Damien Prot, Óscar C. Vásquez): an online tool for searching an optimal scheduling problem using the notation. Complexity results for scheduling problems (by Peter Brucker, Sigrid Knust): a classification of optimal scheduling problems by what is known on their runtime complexity.