混合染色体非支配排序遗传算法在多项目调度问题中的多资源分配与均衡[j]

Nathaniel Alvin, I-Tung Yang
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引用次数: 0

摘要

多项目调度中的多资源分配与均衡(MR-AL-MP)是指在涉及多个资源的多项目环境中,在遵守所有优先级和资源可用性约束的情况下,试图产生一个项目工期最短、资源利用率最大的项目进度计划。本文提出了一个将资源配置模型和均衡模型整合到一个统一框架中的模型。本研究开发了非显性排序遗传算法II (NSGA-II)的改进版本,称为Hybrid-Chromosome NSGA-II,作为优化算法。为了验证混合染色体NSGA-II的优化性能,将其与多目标粒子群优化(MOPSO)和多目标共生生物搜索(MOSOS)两种基准元启发式算法进行了比较。结果表明,所提出的模型和算法能够产生一组代表目标之间可行权衡关系的非支配解。此外,杂交染色体NSGA-II在溶液质量、扩散和多样性方面优于MOPSO和MOSOS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MULTI-RESOURCE ALLOCATION AND LEVELING IN MULTI-PROJECT SCHEDULING PROBLEM WITH HYBRID-CHROMOSOME NON-DOMINATED SORTING GENETIC ALGORITHM II
Multi-resource allocation and leveling in multi-project (MR-AL-MP) scheduling refers to the attempt of producing a project schedule with minimum project duration and maximum resource utilization while complying with all precedence and resource availability constraints in a multi-project environment involving multiple resources. This study proposes a model that integrates both resource allocation and leveling models into a unified framework. This study develops a modified version of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-Chromosome NSGA-II, as the optimization algorithm. For validation purposes, the performance of Hybrid-Chromosome NSGA-II is compared with two benchmark metaheuristic algorithms which are Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Symbiotic Organisms Search (MOSOS) in optimizing a case study. It is shown that the proposed model and algorithm are able to produce a set of non-dominated solutions that represent the feasible trade-off relationships between the objectives. Furthermore, the Hybrid-Chromosome NSGA-II is superior to MOPSO and MOSOS in terms of the quality, spread, and diversity of the solutions.
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