A Mixed-Integer Linear Programming Model and a Simulated Annealing Algorithm for the Long-Term Preventive Maintenance Scheduling Problem

2018 ◽  
pp. 144-153
Author(s):  
Roberto D. Aquino ◽  
Jonatas B. C. Chagas ◽  
Marcone J. F. Souza
Keyword(s):  
Preventive Maintenance ◽  
Mixed Integer Linear Programming ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Maintenance Scheduling ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Integer Linear Programming Model ◽  
Annealing Algorithm

scholarly journals Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Feifeng Zheng ◽  
Zhaojie Wang ◽  
Yinfeng Xu ◽  
Ming Liu
Keyword(s):  
Numerical Experiments ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Open Map ◽  
Annealing Algorithm ◽  
Mapreduce Scheduling

Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume that all the operations cannot be processed in parallel, and the machine settings are unrelated machines. For solving the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function. We then propose a genetic algorithm, a simulated annealing algorithm, and an L-F algorithm to solve this problem. Numerical experiments show that L-F algorithm has better performance in solving this problem.

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