Value-Based Multiple Software Projects Scheduling with Genetic Algorithm

Xiao, Junchao; Wang, Qing; Li, Mingshu; Yang, Qiusong; Xie, Liangbo; Liu, Dapeng

doi:10.1007/978-3-642-01680-6_7

Cited by 9 publications

(15 citation statements)

References 14 publications

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“…Xiao et al . 37 provided an approach to optimize multiple projects. It scores each project based on the cost and schedule and obtains the optimized allocation for all projects using genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Constraint‐based human resource allocation in software projects

2011

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Resource allocation in a software project is crucial for successful software development. Among various types of resources, human resource is the most important as software development is a human‐intensive activity. Human resource allocation is very complex owing to the human characteristics of developers. The human characteristics affecting allocation can be grouped into individual‐level characteristics and team‐level characteristics. At the individual level, familiarity with tasks needs to be taken into account as it affects the performance of developers. In addition, developers have different levels of productivity, depending on their capability and experience; the productivity of developers also varies according to tasks. At the team level, characteristics such as team cohesion, communication overhead, and collaboration and management also affect human resource allocation. As these characteristics affect the efficiency of project execution, we treat them as constraints of human resource allocation in our approach. We identify individual‐level constraints and team‐level constraints based on the literature and interviews with experts in the industry. With these constraints, our approach optimizes the scheduling of human resource allocations, resulting in more realistic and efficient allocations. We also provide a guideline supporting various factors, with respect to roles and module characteristics, to estimate the productivity of developers based on COCOMO II. As productivity data are hard to obtain and manage, our guideline can provide a useful direction for human resource allocation in case of software projects. To validate our proposed approach, we document a case study using real project data. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Constraint‐based human resource allocation in software projects

2011

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“…From the review, it can be inferred that in all cases the representations of solutions (GA chromosome) and encoding strategies are very project-specific, for example, project costs, project duration and project overtime [43]; task start date [11], [35]; a specific mode for each activity and activity lists [7], [23], [24], [33], [38], [41]; human resources [11], [23], [33] and their priorities [12]. The most common types of chromosome encoding in the reviewed studies are binary code [11], [12], [18], [23], [24], [35], [36] and integer number strings [7], [33], [41]. Other types also used in some studies are chromosome encoding types, such as the list of strings [8], graph nodes [38] and encoding method proposed by the authors [43].…”

Section: Genetic Algorithms In Software Development Project Manamentioning

confidence: 99%

Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review

Ancveire

Poļaka

2019

ITMS

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In software development projects, managers still have to face a variety of organisational and technical limitations despite the development of technology and approaches to improve the project management process. Projects, Human Resources and Costs are planned for a specific period of time. However, in the progression of project execution, there is a need to make various decisions and to dynamically adjust the work plan during the project in order to conform to its evolution. Thus, there is a need for a method that employs the latest technology to support the project management decision-making process.The aim and the expected result of the article are to identify and collect available information in the scientific literature to answer the following questions: (1) Which challenges of project management have been addressed using genetic algorithms? (2) What are the opportunities and limitations of genetic algorithms in the project management decision-making process? (3) What are the potential solutions to the identified genetic algorithm problems?

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“…Full details of the steps used in executing GA are omitted here due to space limitations. These details can be found, however, in [28].…”

Section: Running Gamentioning

confidence: 99%

“…Thus a key problem in software project management is appropriate human resource scheduling. Effective resource scheduling should ensure that assigned resources have the capability and capacity to execute their assigned tasks, that resource contention is minimized, project efficiency is maximized, and that organizational value and customer satisfaction are increased [3,28,29].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Resource Scheduling in Disruption-Prone Software Development Environments

Xiao

Osterweil

Wang

et al. 2010

Fundamental Approaches to Software Engineering

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Abstract. Good resource scheduling plays a pivotal role in successful software development projects. However, effective resource scheduling is complicated by such disruptions as requirements changes, urgent bug fixing, incorrect or unexpected process execution, and staff turnover. Such disruptions demand immediate attention, but can also impact the stability of other ongoing projects. Dynamic resource rescheduling can help suggest strategies for addressing such potentially disruptive events by suggesting how to balance the need for rapid response and the need for organizational stability. This paper proposes a multiobjective rescheduling method to address the need for software project resource management that is able to suggest strategies for addressing such disruptions. A genetic algorithm is used to support rescheduling computations. Examples used to evaluate this approach suggest that it can support more effective resource management in disruption-prone software development environments.

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Value-Based Multiple Software Projects Scheduling with Genetic Algorithm

Cited by 9 publications

References 14 publications

Constraint‐based human resource allocation in software projects

Constraint‐based human resource allocation in software projects

Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review

Dynamic Resource Scheduling in Disruption-Prone Software Development Environments

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