A four-Phase Meta-Heuristic Algorithm for Solving Large Scale Instances of the Shift Minimization Personnel Task Scheduling Problem

2018 ◽  
Author(s):  
Sebastian Nechita ◽  
Laura Diosan
Keyword(s):  
Task Scheduling ◽  
Heuristic Algorithm ◽  
Large Scale ◽  
Scheduling Problem

scholarly journals Uniform Parallel Machine Scheduling Problem with Controllable Delivery Times

2015 ◽  
Vol 3 (6) ◽  
pp. 525-537
Author(s):  
Kai Li ◽  
Hui Li ◽  
Bayi Cheng ◽  
Qing Luo
Keyword(s):  
Resource Allocation ◽  
Heuristic Algorithm ◽  
Large Scale ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Scheduling Problem ◽  
Delivery Times ◽  
Parallel Machine Scheduling Problem ◽  
Uniform Parallel Machine Scheduling

AbstractThis paper considers the uniform parallel machine scheduling problem with controllable delivery times, which assumes that the delivery times of jobs are linear decreasing functions of the consumed resource. It aims to minimize the maximum completion time under the constraint that the total resource consumption does not exceed a given limit. For this NP-hard problem, we propose a resource allocation algorithm, named RAA, according to the feasible solution of the uniform parallel machine scheduling problem with fixed delivery times. It proves that RAA algorithm can obtain the optimal resource allocation scheme for any given scheduling scheme inO(nlogn)time. Some algorithms based on heuristic algorithm LDT, heuristic algorithm LPDT and simulated annealing are proposed to solve the uniform parallel machine scheduling problem with controllable delivery times. The accuracy and efficiency of the proposed algorithms are tested based on those data with problem sizes varying from 40 to 200 jobs and 2 to 8 machines. The computational results indicate that the SA approach is promising and capable of solving large-scale problems in a reasonable time.

scholarly journals Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2377
Author(s):  
Yixin Huang ◽  
Zhongcheng Mu ◽  
Shufan Wu ◽  
Benjie Cui ◽  
Yuxiao Duan
Keyword(s):  
Reinforcement Learning ◽  
Task Scheduling ◽  
Large Scale ◽  
Gradient Algorithm ◽  
Experimental Simulation ◽  
Scheduling Problem ◽  
Policy Gradient ◽  
Scheduling Method ◽  
Satellite Scheduling ◽  
Metaheuristic Optimization Algorithms

Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.

A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽  
2021 ◽  
pp. 102442
Author(s):  
Lin Zhou ◽  
Lu Zhen ◽  
Roberto Baldacci ◽  
Marco Boschetti ◽  
Ying Dai ◽  
...  
Keyword(s):  
Heuristic Algorithm ◽  
Real World ◽  
Design Problem ◽  
Large Scale ◽  
Territory Design
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