Integrating a SMT Solver based Local Search in Ant Colony Optimization for Solving RCMPSP

被引：0
|
作者：
Gomez Sanchez, Mariam [1 ]
Fernandez Gil, Alejandro [1 ]
Castro, Carlos [1 ]
机构：
[1] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile
来源：
2019 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI) | 2019年
关键词：
Ant Colony Optimization; Combinatorial Optimization; Multi Project Scheduling Problem; Local Search; SMT Solver; PROJECT; ALGORITHM; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The project scheduling problem has been widely studied given the practical importance it has and the complexity to find efficient solutions. In this work we address an extension of the same problem based on the Resource-Constrained Multi-Project Scheduling Problem, taking into account that each project has a set of own resources available for consumption. We propose a hybrid algorithm based on Ant Colony Optimization and a Local Search procedure based on Hill-Climbing First Improvement supported by an SMT Solver as a movement satisfaction verifier. Our approach was tested with a set of 12 instances belonging to the MPSPLIB datasets. We have obtained near-optimal solutions for several instances of the problem.
引用
收藏
页码：106 / 111
页数：6
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