Distributed task scheduling and allocation using genetic algorithms

被引:13
|
作者
Todd, D
Sen, P
机构
[1] Univ Newcastle Upon Tyne, Engn Design Ctr, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[2] Univ Newcastle Upon Tyne, Dept Marine Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
关键词
distributed; genetic algorithms; manufacturing; multiple criteria; planning; scheduling; systems; task allocation;
D O I
10.1016/S0360-8352(99)00021-2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
As complexity and size of projects increase so do the problems associated with the scheduling and management of the design, manufacturing and assembly processes. In the context of large projects the ability to optimise the scheduling and allocation of these processes can also aid in tendering for contract as well as the management of the project itself. Many large projects will be constructed across distributed sites, each with their own capabilities and specific areas of expertise. Multiple sources may be needed to provide skilled personnel, raw materials, specialised components or facilities for the project, even whole sub-systems within a complex project may be contracted out for financial or time reasons. This paper demonstrates how a computational intelligence technique know as the Genetic Algorithm can be used to optimise design, manufacturing and construction schedules for multiple objectives such as minimising cost and time and maximising utilisation. The system generates a number of near-optimal project scenarios from which a single solution can be selected and implemented by the project manager. (C) 1999 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:47 / 50
页数:4
相关论文
共 50 条
  • [1] Distributed task scheduling and allocation using genetic algorithms
    Engineering Design Centre, Dept. of Marine Technology, University of Newcastle, Newcastle-upon-Tyne NE1 7RU, United Kingdom
    [J]. Comput Ind Eng, 1 (47-50):
  • [2] Framework for task scheduling in heterogeneous distributed computing using genetic algorithms
    Page, AJ
    Naughton, T
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2005, 24 (3-4) : 415 - 429
  • [3] Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
    Andrew J. Page
    Thomas J. Naughton
    [J]. Artificial Intelligence Review, 2005, 24 : 415 - 429
  • [4] Using genetic algorithms for task allocation
    Alaoui, SM
    Bellaachia, A
    Bensaid, A
    Frieder, O
    [J]. INTELLIGENT SYSTEMS, 1997, : 67 - 70
  • [5] Improved genetic algorithms and list scheduling techniques for independent task scheduling in distributed systems
    Loukopoulos, Thanasis
    Lampsas, Petros
    Sigalas, Panos
    [J]. EIGHTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PROCEEDINGS, 2007, : 67 - +
  • [6] Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system
    Page, Andrew J.
    Keane, Thomas M.
    Naughton, Thomas J.
    [J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2010, 70 (07) : 758 - 766
  • [7] Review of Task Scheduling Algorithms Using Genetic Approach
    Sharma, Ashish
    Singh, Navdeep
    Hans, Abhinav
    Kumar, Kapil
    [J]. 2014 INNOVATIVE APPLICATIONS OF COMPUTATIONAL INTELLIGENCE ON POWER, ENERGY AND CONTROLS WITH THEIR IMPACT ON HUMANITY (CIPECH), 2014, : 169 - 172
  • [8] Task Scheduling in Distributed Environment Using Genetic Algorithm
    Sadeghzadeh, Mehdi
    [J]. AIC '09: PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON APPLIED INFORMATICS AND COMMUNICATIONS: RECENT ADVANCES IN APPLIED INFORMAT AND COMMUNICATIONS, 2009, : 118 - +
  • [9] Genetic algorithms for task scheduling problem
    Omara, Fatma A.
    Arafa, Mona M.
    [J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2010, 70 (01) : 13 - 22
  • [10] Algorithms of distributed task allocation for cooperative agents
    Kraus, S
    Plotkin, T
    [J]. THEORETICAL COMPUTER SCIENCE, 2000, 242 (1-2) : 1 - 27