Automated Resource Scheduling for Construction Projects Using Genetic Algorithm

被引:0
作者
Moharram, Raghda M. [1 ]
Essawy, Yasmeen A. S. [1 ,2 ]
Hosny, Osama S. [1 ]
机构
[1] Amer Univ Cairo, Dept Construct Engn, New Cairo, Egypt
[2] Ain Shams Univ, Dept Struct Engn, Cairo, Egypt
来源
PROCEEDINGS OF THE CANADIAN SOCIETY OF CIVIL ENGINEERING ANNUAL CONFERENCE 2022, VOL 1, CSCE 2022 | 2023年 / 363卷
关键词
Automated resource scheduling; Genetic algorithm; Construction projects;
D O I
10.1007/978-3-031-34593-7_32
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Site advance methods for planning and scheduling are essential for effective project management. Typically, construction managers tend to produce schedules based on two constraints: (1) Minimize Project Duration and (2) Allocate Minimum Resources. Moreover, deficits in the cash flow result in reduced profits at the end of the project and delays if financing problems arise, which results in damages (usually additional costs). Traditional scheduling tools like the Critical Path Method (CPM) and Time Constrained Project Scheduling Problem (TCPSP) do not show high efficiency in achieving the required objectives, as scheduling gets complicated. Advanced planning and scheduling methods will be used to produce a feasible schedule that meets specific objectives. This paper proposes amulti-objectivemodel, (1) minimum project duration, (2) resource availability, and (3) minimum cash flow deficit. The model is divided into two modules. The first module produces an optimized, automated schedule achieving the objective of minimum project duration. The second module applies an optimized resource constraint scheduling using user input data of the available resources to allocate the resources on each activity while maintaining the maximum number of resources on site. The model is optimized using an evolutionary algorithm, namely: Genetic Algorithm (GA).
引用
收藏
页码:513 / 522
页数:10
相关论文
共 50 条
  • [41] A new genetic algorithm for resource-constrained project scheduling problem
    Luo Ronggui
    Chen Xiaoming
    Huang Minmei
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON INNOVATION & MANAGEMENT, VOLS 1 AND 2, 2006, : 1595 - 1599
  • [42] Solving Resource-Constrained Project Scheduling Problem by Genetic Algorithm
    Kadam, Sachin U.
    Kadam, Narendra S.
    2014 2ND INTERNATIONAL CONFERENCE ON BUSINESS AND INFORMATION MANAGEMENT (ICBIM), 2014,
  • [43] A competitive Genetic Algorithm for resource-constrained project scheduling problem
    Wang, H
    Lin, D
    Li, MQ
    Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 2945 - 2949
  • [44] A genetic algorithm for the resource constrained multi-project scheduling problem
    Goncalves, J. F.
    Mendes, J. J. M.
    Resende, M. G. C.
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2008, 189 (03) : 1171 - 1190
  • [45] Tailoring Genetic Algorithm for Resource Scheduling in Many-Core Processors
    Hu, Xiande
    Li, Jingming
    Cheng, Jiaxing
    PROCEEDINGS OF THE 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER ENGINEERING AND ELECTRONICS (ICECEE 2015), 2015, 24 : 465 - 471
  • [46] Research on Resource Scheduling in Cloud Computing Based on Firefly Genetic Algorithm
    Chen, Jiyu
    INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2016, 9 (07): : 141 - 148
  • [47] Fuzzy Flexible Resource Constrained Project Scheduling Based on Genetic Algorithm
    查鸿
    张连营
    Transactions of Tianjin University, 2014, (06) : 469 - 474
  • [48] Efficient Genetic Algorithm for Resource-Constrained Project Scheduling Problem
    王宏
    李同玲
    林丹
    Transactions of Tianjin University, 2010, 16 (05) : 376 - 382
  • [49] Efficient genetic algorithm for resource-constrained project scheduling problem
    Wang H.
    Li T.
    Lin D.
    Transactions of Tianjin University, 2010, 16 (5) : 376 - 382
  • [50] GASolver-A Solution to Resource Constrained Project Scheduling by Genetic Algorithm
    Madan, Mamta
    Madan, Rajneesh
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2013, 4 (02) : 210 - 217