Survey on Project Management System using Event based Scheduler and Ant Colony Optimization

Question

Task: Assessment Details:
The purpose of this assessment is to write a clear and objective summary of the original text. To write the assignment, you need to select One recent article published in peer reviewed journals relevant to the topic of Project Development and Optimisation. Student need to provide a link to the article summary that will be inserted in their article summary page and a full reference. Guidelines for writing a summary of an article:

• State the main ideas of the article.
• Identify the most important details that support the main ideas.
• Write in your own words and avoid copying phrases and sentences from the article unless they are direct quotation.
• Express the underlying meaning of the article, not just the superficial details.
• Your summary should be max 500 words.
• provide a link to the article summary that will be inserted in their article summary page and a full reference

Answer

1. Introduction:
Software Development Projects necessitates the coordinated efforts of a team, comprising of different professionals with diversified skills and competencies. Human resource (HR) allocation and task assignment is very crucial for software project development. The major existing issues with software development are to delegate and assign the right job to the right person with the right skills and expertise at the right time and at the right cost. Different software projects require workers with diversified skills, which contribute to and improve the overall efficiency of the project. So, an optimized based approach is required to solve software project associated task scheduling and most importantly task allocation issues.

2. Main ideas of the article:
The given article majorly emphasises on the importance of ACO (Ant Colony Optimization) swarm intelligent method/ algorithm in the process of development and optimization of software project. It also describes its advantage over the existing algorithms like GA (Genetic algorithms), PERT (Program Evaluation and Review techniques) and CPM (Critical path method) that are inefficient in dwelling with the task allocation and delegation or Human Resource issues of a software development project.

3. Identification of the most important details that support the main ideas:

The article describes the unique ant behavioural (swarm) strategy in solving the major loophole of task allocation, which is the major issue with the existing algorithms that only takes task scheduling into account for solving software development computational problems. Hence, it’s an adequate model for project optimization and development which constitutes the very idea of the article.

4. Key Findings:
ACO (Ant Colony optimization) is a metaheuristical algorithm and swarm intelligent method (Swarm refers to the use of artificial ants for the purpose) for solving software related allocation and task scheduling computational problem by searching for optimal paths through usage of graphs making use of various set of combinations of artificial ants, mimicking the pheromone based communication and behavioural pattern of biological ants.

5. Critical analysis of the findings:
The Ant Colony optimization (ACO) algorithm solves complicated planning problems taking into consideration both the issues of task scheduling and task allocation. ACO works in step by step manner using artificial ants (swarm) to plan critical tasks of task scheduling and then delegate and assign the appropriate project tasks to the most proficient employees possessing the appropriate skills and expertise. This algorithm holds a significant advantage over all the existing algorithms and models and is indeed very promising, as its swarming strategy using artificial ants reduces manual efforts and the overall project cost, time etc making best use of the project resources.

6. The underlying meaning of the article
ACO is an adequate model for solving software computational problems& is efficient in dealing with both the issues of project task scheduling and allocation, making use of the swarm artificial ant behavioral. It works by searching for optimal paths through usage of graphs making use of various set of combinations of artificial ants. It is roughly based on the GA (genetic algorithm)& works in step by step manner using artificial ants (swarm) to plan critical tasks of task scheduling and then delegate and assign the appropriate project tasks to the most proficient employees possessing the appropriate skills and expertise.

7. Summary of the Article:
NP hard (Non deterministic polynomial time) hard issues necessities the use of software tools which can perform such allocation and is able to resolve relating issues of a software development project scheduling plan(SDPSP). ACO (Ant Colony optimization) is an algorithm which can beused for solving computational problems. ThisACO algorithm is rapidly evolving and has already been applied for resolving softwarescheduling problems in the past. ACO-SDPSP metholodologies allows thesoftware projectmanagers and schedulers to delegateand assign work tothe most skilful and effective employees which can contribute by minimising the cost and time of the software project. Techniques like PERT (program evaluation and review techniques) and CPM (critical path method) have be used inthe past that dealsonly with theproblem oftask scheduling. RCPSP (ResourceConstrained project scheduling problem)algorithm doesn’t take into consideration ofallocation of employees with diversified skills. It is only a scheduling model. The end results of RCPSP, PERT and CPM is poormanagement performance andinefficient projectexecution. Thus,these modelsare inadequateto deal with the complexproblem of task allocation. An adequate modelwould be the one which would dealwith both the issues ofproject task schedulingand allocation. ACO is a metaheuristical algorithm and swarm intelligent method (Swarm refers to use of artificial ants for the purpose) for solving software related allocation andtask scheduling Computational problem by searching for optimal paths through usage of graphs making use of various set ofcombinations of artificial ants. It is based on the GA(genetic algorithm) proposed by Chang et al(ref), but holds an advantage over the Changalgorithm as it has the ability to run continuously and has great adaptability ( able to adapt changes in real time when the graph changes dynamically) unlike GAwhich doesn’t support continuityof allocation .ACO works in step by step manner using artificial ants (swarm) to plan critical tasks of task scheduling and then delegate and assign the appropriate project tasks to the mostproficient employees possessing the appropriate skills and expertise. It conductsemployeemanagement using theirinformation based on a database. It also utilizes a precedence graph which only commences when the entire given predecessortasksfinishesusing theswarming strategy. There isno doubting thefact that this algorithm holds a significant advantageover all theexisting algorithms and models and is indeed very promising asits swarming strategy using artificial ants reduces manual efforts and the overallproject cost,time etc making best use of the project resources. Conclusion: To conclude one can say that the Ant Colony optimization (ACO) algorithm solves complicated planning problems taking into consideration both the issues of task scheduling and task allocation. It comprises of a proper event scheduling plan making using of artificial ant behaviour and has better yielding coefficient and thus is advantageous in holding an upper hand over the the existing techniques like Genetic Algorithms (GAs), RCSPC, CPM and PERT etcetera.

8. Bibliography
Vairagade, R. S. et al., 2016. Survey on Project Management System using Event based Scheduler and Ant Colony Optimization. International Journal of Computer Applications, 133(17), pp. 32-35.