Heuristics and augmented neural networks for task scheduling with non-identical machines

Obtaining dynamic scheduling policies with simulation and machine learning

“…Over the history of scheduling study, many works a empted to tackle the problem of scheduling with a wide range of approaches, covering from integer linear programming [3,11] to genetic algorithms [13,20] and neural networks [1,2]. Xhafa and Abraham [24] present a review of recent computational models and heuristics for scheduling in HPC platforms.…”

Genetic Algorithm with an Effective Dispatching Method for Unrelated Parallel Machine Scheduling with Sequence Dependent and Machine Dependent Setup Times

“…하지만 작업순서 의존적이고 기계 의존적인 이종병렬기 계 일정계획문제의 주요 연구들은 다음과 같이 제한적으로 연 구되어 왔다. Agarwal et al(2006) 본 연구에서는 순서 의존적이고 기계의존적인 작업 준비시 간과 작업 투입가능시점 및 완료요구시점이 주어진 상황에서 총 작업완료지연시간을 최소화하는 이종병렬기계 일정계획 문제를 다룬다. 순서 의존적인 작업준비시간을 고려한 동일 병렬기계의 총 작업완료시간 최소화 문제는 NP-hard로 증명되 어 있다 (Nait et al, 2003;Sveltana et al, 2001;Yalaoui and Chu, 2003 …”

GA Based Heuristic to Minimize Makespan in Single Machine Scheduling Problem with Uniform Parallel Machines

“…Panneerselvam and Kanagalingam [15] have given a heuristic to minimize the makespan for scheduling n independent jobs on m parallel processors with different speeds. Agarwal, Colak, Jacob and Pirkul [16] have proposed new heuristics along with an augmented-neural-netwrok (AugNN) formulation for solving the makespan minimization task-scheduling problem for the non-identical machine environment. They explored four task and three machine-priority rules, resulting in 12 combinations of single-pass heuristics.…”