TAILIEUCHUNG - Genetic Algorithms for Project Management

Will your project lead to materials production or a publication? If so, you need to get an estimate from the printers or the organisation producing the materials. Don’t forget the costs of distributing the materials if this is part of your aims. CD-ROMs are often cheaper than print nowadays. Do you have access to the computers and other hardware you need for the project? Some publicly funded project sources will contribute to buying what you need. | g Annals of Software Engineering 11 107-139 2001 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Genetic Algorithms for Project Management CARL K. CHANG chang@ Department of EECS M C154 The University of Illinois at Chicago Chicago IL 60607 USA MARK J. CHRISTENSEN . markchri@ Independent Consultant St. Charles Illinois TAO ZHANG Tao_Zhang-CTZ020@ Motorola-iDEN Engineering Development 1301 E. Algonquin Rd Schaumburg IL 60196 USA Abstract. The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions either optimal or near optimal must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management PM techniques commercial PM tools and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience must build the project database to record such decisions and represent them as project nets then use the tools to track progress perform simple consistency checks analyze the project net for critical paths etc. and produce reports in various formats such as Gantt or Pert charts. Our research has developed a new technique based on genetic algorithms GA that automatically determines using a programmable goal function a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based

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