不确定环境下的多项目动态调度研究_王伟鑫_9787564365554

前言

PREFACE

随着客户需求的不断提高，多项目调度往往处于不确定环境下，导致实际调度方案不能完全按照基准调度方案执行或需重新安排调度计划。不确定环境下的多项目调度问题需要充分考虑调度环境中的不确定性因素，如天气原因造成的工期延误或设备故障造成的停工等，而这种不确定往往是不可避免且无法预测的。因此，不确定环境下的多项目调度问题的研究有重要的理论价值与实际意义。针对多项目调度问题中各类不确定因素扰动初始调度计划等问题展开研究，可恢复或避免扰动对多项目调度计划的影响，以确保多项目顺利完工。

项目执行过程中存在诸多不确定性，对于多项目调度而言，由于任务数量庞大，涉及跨组织的资源调度，故而不确定环境下的多项目调度问题具有复杂性和不确定性等特征，增加了调度计划的制订难度，从而为多项目调度的管理带来了困难和挑战。因此，针对不确定环境下的多项目调度问题，如何在基准调度计划制订阶段考虑调度过程中存在的不确定因素，合理优化项目结构，制订符合客户动态需求的调度方案，已成为项目调度领域的研究热点和前沿课题。

作者长期从事项目调度和网络优化等课题的研究，跟踪国内外的研究动态，本书是作者多年研究的总结，同时也吸取了国内外的最新研究成果。本书以不确定环境下的多项目调度优化问题为研究对象，探索动态客户需求与项目任务调度的最佳配置方式。为此，搭建多项目动态调度的研究框架，构建多项目动态调度优化模型，从系统和全局视角研究不确定环境下的多项目动态调度优化问题，丰富了多项目调度的研究方法，以期为不确定环境下的多项目动态调度问题提供理论与方法上的指导。

全书共7章。第1章介绍了不确定环境下多项目动态调度优化问题理论与方法研究的背景和基本研究内容。第2章主要分析不确定环境下多项目调度的基本属性及国内外研究现状。第3章针对不确定环境下多项目调度的工期不确定问题展开研究，构建不确定环境下的多项目调度工期-鲁棒性均衡优化模型，鉴于问题的复杂性，设计多级遗传算法求解此问题。第4章深入分析不确定因素对多项目调度内部资源使用率的影响，按照活动的可执行性将其拆分为若干子活动，并执行非抢占式执行模式，在提高多项目调度资源利用率的基础上，降低不确定因素对多项目调度系统的影响。以工期-成本-质量-鲁棒性为优化目标，利用效用函数建立多目标均衡优化模型，并设计云遗传算法对问题进行求解。第5章阐释了不确定因素对工期、客户满意度、成本和资源可用量的影响，构建面向多干扰的多项目调度模型，并设计双种群遗传算法求解问题模型。第6章介绍了多项目前摄性调度问题，并建立不确定环境下多项目前摄性调度模型，结合模型的特点设计遗传模拟退火算法求解此问题。第7章介绍了多项目调度的未来发展趋势。

本书的撰写得到了教育部人文社科项目资助（15XJC630007、14YJC630038），重庆市教委科学技术研究项目资助（KJ1500702）、重庆市社会科学规划博士项目资助（2016BS080）、博士后科学基金资助（2017M612951）、重庆市基础与前沿研究项目资助（cstc2016jcyjA0160）、重庆市教委人文社会科学研究项目（17SKG073）、四川外国语大学学术专著后期资助（sisu201785）。同时，本书还得到四川外国语大学国际商学院物流管理专业各位同仁的帮助与大力支持。在本书出版之际，向他们表示真诚的感谢。

本书在创作过程中借鉴了许多专家的观点，并参考了大量的文献资料，虽力图详尽列出相关资料的来源，但难免有疏忽之处，还请读者海涵。书中也有不少创新之处，但作者的某些看法可能还不成熟，可能会存在考虑不周之处，加之作者能力所限，还望各位专家给予批评指正。

王伟鑫

2018年5月

CONTENTS

1 概述································································································· 001

1.1 研究背景····································································· 001

1.2 研究意义····································································· 002

1.3 不确定环境下多项目调度属性分析······································ 003

1.3.1 不确定环境下多项目调度资源属性分析·························· 003

1.3.2 不确定环境下多项目调度环境分析······························· 004

2 不确定环境下多项目调度问题研究综述··················································· 006

3 工期不确定的多项目调度研究································································ 011

3.1 问题描述····································································· 011

3.2 模型变量及参数···························································· 012

3.3 工期不确定的多项目调度鲁棒性-工期优化分析······················· 013

3.4 建立鲁棒性-工期均衡优化模型·········································· 015

3.5 求解算法研究······························································· 016

3.5.1 多级遗传算法介绍··················································· 016

3.5.2 多级遗传算法设计··················································· 017

3.5.3 修复运算子··························································· 020

3.5.4 适应度计算··························································· 021

3.5.5 种群规模和停止运算准则··········································· 021

3.6 仿真试验····································································· 022

3.6.1 算例分析······························································ 022

3.6.2 结果分析······························································ 027

3.7 本章小结····································································· 028

4 活动不确定的多项目调度研究································································ 029

4.1 问题描述····································································· 029

4.2 示例·········································································· 030

4.3 模型变量及相关概念······················································· 032

4.3.1 模型变量······························································ 032

4.3.2 相关概念······························································ 034

4.4 活动不确定的多项目调度模型··········································· 035

4.5 求解算法研究······························································· 037

4.5.1 基于优先权规则的非抢占式多项目调度算法···················· 037

4.5.2 云遗传算法··························································· 039

4.6 仿真试验····································································· 041

4.6.1 算例分析······························································ 041

4.6.2 非抢占式与抢占式操作模式对比分析····························· 043

4.6.3 算法对比分析························································· 044

4.6.4 结果分析······························································ 046

4.7 本章小结····································································· 047

5 面向多干扰的多项目调度研究································································ 048

5.1 问题描述····································································· 048

5.2 扰动恢复策略······························································· 050

5.3 模型变量及假设···························································· 051

5.4 多项目调度的扰动分析···················································· 053

5.4.1 多项目调度的扰动因素分析········································ 054

5.4.2 扰动的度量··························································· 055

5.5 面向多干扰的多项目调度模型··········································· 058

5.6 设计双种群遗传算法······················································· 060

5.7 仿真试验····································································· 065

5.8 本章小结····································································· 070

6 不确定环境下的多项目前摄性调度研究··················································· 071

6.1 问题描述····································································· 071

6.2 模型变量及参数···························································· 073

6.3 前摄性调度优化目标分析················································· 075

6.4 多项目前摄性调度模型···················································· 076

6.5 遗传模拟退火算法设计···················································· 077

6.6 仿真试验····································································· 080

6.7 本章小结····································································· 083

7 多项目动态调度的未来发展趋势····························································· 084

7.1 大数据驱动的多项目调度特征分析······································ 084

7.2 大数据驱动的多项目调度跨组织资源共享模式分析·················· 085

参考文献·································································································· 088

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