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考虑模糊工序工期的重复性项目时间费用权衡优化

邹鑫 陈丹昊 张立辉

工业工程2024,Vol.27Issue(4):150-160,170,12.
工业工程2024,Vol.27Issue(4):150-160,170,12.DOI:10.3969/j.issn.1007-7375.230104

考虑模糊工序工期的重复性项目时间费用权衡优化

Time-Cost Tradeoff Optimization for Scheduling Repetitive Projects with Fuzzy Activity Durations

邹鑫 1陈丹昊 1张立辉2

作者信息

  • 1. 华北电力大学 经济管理系,河北 保定 071003
  • 2. 华北电力大学 经济与管理学院,北京 102206
  • 折叠

摘要

Abstract

In order to reduce the impact of uncertainty on project performance objectives and improve the robustness of scheduling,the discrete time-cost tradeoff problem for repetitive projects with fuzzy activity durations is investigated.A fuzzy chance-constrained programming model considering the risk preferences of decision makers is developed by means of fuzzy risk measurement to determine the optimal execution modes for all activities(i.e.,mode list),thereby to minimize the project budget while meeting the pre-specified risk levels of project delays and cost overruns.Given a known mode list,a forward recursive process for calculating the membership functions of fuzzy project duration and fuzzy total cost is proposed,while an improved genetic algorithm based on electromagnetic mechanism(GA-EM)for searching the optimal mode list is designed accordingly.The effectiveness of the algorithm is verified using a real-life engineering case,and the computational performance of the algorithm is analyzed via numerical experiments.Results show that GA-EM can provide a fuzzy schedule that satisfies the given levels of schedule delays and cost overrun risks,with the average and maximum percentage deviations in the budget not exceeding 0.096%and 0.239%,respectively.

关键词

时间费用权衡/重复性项目/模糊机会约束规划/遗传算法

Key words

time-cost tradeoff/repetitive projects/fuzzy chance-constrained programming/genetic algorithm

分类

管理科学

引用本文复制引用

邹鑫,陈丹昊,张立辉..考虑模糊工序工期的重复性项目时间费用权衡优化[J].工业工程,2024,27(4):150-160,170,12.

基金项目

国家自然科学基金资助项目(72171081,71701069) (72171081,71701069)

河北省自然科学基金资助项目(G2022502001) (G2022502001)

中央高校基本科研业务费专项资金资助项目(2023MS153) (2023MS153)

工业工程

OACHSSCDCSTPCD

1007-7375

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