优化软件之间的比较,以解决双目标部门化问题
1Universidade Católica Portuguesa, Católica Porto Business School, Centro de Estudos em Gestão e Economia Porto, Portugal.
Heliyon
|August 14, 2023
概括
本研究评估了针对双目标部门化问题的优化软件. 在解决方案中,GAMS表现最好,而Pymoo的元启发学则有效地提供了可行的解决方案.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 部门分类问题是复杂的,通常涉及多个目标.
- 现有的优化方法与非线性,双目标模型面临挑战.
研究的目的:
- 为了比较各种优化软件和metaheuristics对双目标部门化问题的性能.
- 引入一种新的方法,将双目标模型转化为单目标模型,使用理想和反理想点.
主要方法:
- 一种新的方法,将双目标部门化模型转化为单目标模型.
- 优化软件的实现和比较:Pulp,Lingo,CPLEX和GAMS. 这些软件的实现和比较.
- 应用Pymoo库中的metaheuristics用于单个和双目标版本.
主要成果:
- 商业解决方案的表现不令人满意,GAMS产生了最好的结果.
- 在合理的时间框架内,Pymoo的元启发有效地找到可行的解决方案.
- 分析模型线性化及其对变量和约束的影响.
结论:
- 建议使用GAMS来解决被测试的解决者之间的部门化问题.
- 超听觉学为有效地获得实际解决方案提供了一个可行的替代方案.
- 进一步的研究应该探索类似复杂的优化任务的增强方法.
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