International Journal of Emerging Technology & Research An Implementation Scheme For Software Project Management With Event-Based Scheduler Using Ant Colony Optimization Roshni Jain 1, Monali Kankariya 2, Ashwini Jadhav 3, Dhanvantri Kharade Patil 4 1, 2, 3, 4 Department of Information Technology, BVCOEW, Pune, Maharashtra, India Abstract Ant colony optimization (ACO) is a population-based heuristics that can be used as solutions for difficult optimization problems. In ACO, a set of software agents, called artificial ants, searches for good solution to a given optimization problem research into developing effective computer aided techniques for planning software projects which is important and challenging for software engineering. Different from projects in other fields, software projects are people-intensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at non-events. With this strategy, the proposed method enables the modeling of resource conflict, task preemption and preserves the exhibition in human resource allocation. To solve the planning problem, an ACO algorithm is further is used. Keywords: Ant colony optimization (ACO), event scheduler, resource allocation, software project planning, project scheduling, workload assignment. 1. Introduction With the rapid development of the software industry, software companies are now facing a highly competitive market. To succeed, companies have to make efficient project plans to reduce the cost of software construction. However, in medium to large-scale projects, the problem of project planning is very complex and challenging. To plan a software project, the project manager needs to estimate the project workload along with its cost and decide the project schedule and resource allocation. For scheduling and staffing management, similarly to other projects (e.g., construction projects), management is usually conducted by project management tools and techniques. For example, traditional project management techniques like the program evaluation and review technique (PERT), the critical path method (CPM), and the resource-constrained project scheduling problem (RCPSP) model have been applied in software project planning. Although these methods are important and helpful, they are increasingly considered to be inadequate for modelling the unique characteristics of today s software projects. The main reason is that, differently from other projects, a software project is a people intensive activity and its related resources are mainly human resources. Different software project tasks require employees with different skills, and skill proficiency of employees significantly influences the efficiency of project execution. As such, assigning employees to the best-fitted tasks is challenging for software project managers, and human resource allocation has become a crucial part in software project planning. The main purpose is that the method takes advantage of ACO to solve the complicated planning problem and also introduces an event-based scheduler. The proposed algorithm manages to yield better plans with lower costs and more stable work-load assignments compared to the other existing approaches. A new method for solving the software project planning problem has been developed. 2. Related Work Done In paper [1] software engineering field, developing software tools is challenging and important. In software Copyright reserved by IJETR (Impact Factor: 0.997) 60
project humans are important. Human resources are mainly needed. In software project, planning is important. Since software project is much related to human resource, the human resource allocation is the important problem. A software project planning tool must consider the project planning as well as human resource allocation problem. Also the uncertainty factors can occur. In current approach it develops an event based scheduler and an ant colony optimization. The given system represents a plan by task list and employee allocation matrix. In the EBS, the beginning time of the project, the time when resources are released from accomplished tasks, and the time when employees join or leave the project are regarded as events. For planning and employee allocation ACO is used. In real world projects the uncertain events can occur. Previous models did not consider much about the uncertainty. The uncertainty can be considered as an event in event based scheduler. The existing event based scheduler is modified in order to include the uncertain events such as unexpected absence of employee, termination of employee. Such uncertainty can be handled in the current system. In [2] given that research into developing effective computer aided techniques for planning software projects is important and challenging for software engineering. Different from projects in other fields, software projects are people intensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. But as both of these two problems are difficult, existing models either suffer from a very large search space or have to restrict the flexibility of human resource allocation to simplify the model. To develop a flexible and effective model for software project planning, this paper proposes a novel approach with an ant colony optimization (ACO) algorithm. The given approach represents a plan by a task list and a planned employee allocation matrix. In this way, both the issues of task scheduling and employee allocation can be taken into account. In [3] given that Ant colony optimization (ACO) is a population-based met heuristic that can be used to find approximate solutions for difficult optimization problems. In ACO, a set of software agents called artificial ants searches for good solutions for a given optimization problem research into developing effective computer aided techniques for planning software projects is important and challenging for software engineering. Different from projects in other fields, software projects are peopleintensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. But as both of these two problems are difficult, the basic idea of the EBS is introduced to adjust the allocation of employees at events and keep the allocation unchanged at non-events. With this strategy, the proposed method enables the modeling of resource conflict, task pre-emption and preserves the flexibility in human resource allocation. To solve the planning problem, an ACO algorithm is further is used. 3. Proposed System Architecture Fig.1 System Architecture As shown in fig.1 we are going to implement the following algorithm in our system. We are taking ontology files from database process it at server using ACO-EBS and return the result of the server process to the browser. 3.1 Event Based Scheduler (EBS) The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at nonevents. With this strategy, the proposed method enables the modeling of resource conflict and task preemption and preserves the flexibility in human resource allocation. Following explanation show the studied pseudo code for EBS implementation: Copyright reserved by IJETR (Impact Factor: 0.997) 61
1. Initialize number of available employees & time t=1 2. Set beginning time & joining and leaving times of employees as events 3. While the project is not over: If t is an event: o Make a queue of tasks to be performed at t according to priorities in task list o While the queue is not empty: Select t j as the first task and remove it from the queue For every employee (i=1 to m): If planned working hours of i th employee for j th task is not larger than remaining working hours of i th employee: Set working hours spent by i th employee on j th task to planned working hours of i th employee for j th task. Else: Set working hours spent by i th employee on j th task to remaining working hours of i th employee. Else: o Workloads are same as those at t-1 4. If some tasks are complete at t: Set t+1 as an event, for eliminating redundant working hours and reset the queue. 5. t=t+1 End 3.2 Ant Colony Optimization (ACO) An ACO algorithm works by dispatching a group of artificial ants to build solutions to the problem iteratively. In general, an ACO algorithm can be viewed as the interplay and the repeated execution of the following three main procedures: 1] Solution construction: During each iteration of the algorithm, a group of the ants set out to build solutions to the problem. 2] Pheromone management: Along with the solution construction procedure, pheromone values are updated according to the performance of the solutions built by ants. 3] Daemon actions: Daemon actions mean the centralized operations that cannot be done by single ants. 1] Input module: The following data pertaining to the problem are given as input: Number of Tasks (n), number of machines in the shop (m), number of operations Ji of each task i. 2] Initialization module: The number of ants is defined, and the pheromone trails used by them for constructing solutions are initialized. This problem uses two pheromone trails: pheromone trail intensity for route selection gives information about the desirability of choosing route r for operation Oij at iteration tn and pheromone trail intensity, which indicates the desirability of choosing operation Oij directly after the operation Oi j is loaded on machine k, is used for task conflict resolution while generating feasible schedule. 3] Solution construction and Evaluation module: Each ant constructs a solution in two stages. In the I stage, an ant, at each construction step, allocates an operation of a particular task to one of its available resources. The ants use a probabilistic choice rule which is a function of the pheromone trail and heuristic information based on processing time. In the II stage, on allocation of all operations to the machines, each ant generates a schedule based on algorithm. 4] Sorting module: The best solution of the current iteration and the global best are sorted and stored separately. 5] Termination Check module: A specified number of iterations is estimated to terminate the algorithm depending on the size of the problem. Termination directs to the output module; otherwise, continue to the pheromone updating module. 6] Pheromone updating module: At the end of iteration, the pheromone trails corresponding to only one single ant is updated. This ant may be the one which found the best solution in the current iteration or the one which found the best solution from the beginning of the trial. 7] Output Module: This module prints the best solution of the optimal route choices of all operations and schedule for minimum make span time criterion. Following fig.2 shows the flowchart of ACO Algorithm and diagrammatically the algorithm is explained stepwise. The different modules of the proposed Ant Colony Optimization approach are described below. Copyright reserved by IJETR (Impact Factor: 0.997) 62
In agile methodology client requirement is considered first. All the allocation of resources for project will be dependent on client requirement. In future, during project development, if there are some changes in requirement then whole process of project management will be changed or modified. But in our proposed scheme the allocation of resources is done very well, by which company gets the perfect analysis of money, resources and required time. 6 4 2 0 Ant colony Methodology Ant colony Methodology Fig.2 Flowchart of ACO Algorithm Fig.4 Graph showing system efficiency 4. Advantages 1. Reduces the size of the search space and thus accelerates the search process. 2. Lower costs and more stable workload assignments 3. It is used to reduce the flexibility of the resource allocation Above graph shows the efficiency of project resource management. Our methodology will give overall efficiency for better management. According to the time line or budget of the project how many people will work in that project as well as how much salary will be given to those people for completing project, all such issues will be resolved easily by our scheme. 5. Result Analysis Following are some graphical analysis of our methodology with existing methodology. Fig.3 Graph showing comparative study 6. Conclusion ACO is a recently proposed heuristic approach for solving hard combinatorial optimization problems. Artificial ants implement a randomized construction heuristic which makes probabilistic decisions. The accumulated search experience is taken into account by the adaptation of the pheromone trail. ACO Shows great performance with the structured problems like network routing. In ACO Local search is extremely important to obtain good results. The proposed algorithm will manage to yield better plans with lower costs and more stable workload assignments compared with other existing approaches. In addition, since the model proposed in this paper provides a flexible and effective way for managing human resources, it is Copyright reserved by IJETR (Impact Factor: 0.997) 63
promising to apply the proposed approach to other complex human-centric projects like consulting projects. References [1] Lowe and A. R. Webb, Optimized feature extraction and Bayes decision in feed-forward classifier networks, IEEE Trans. Pattern Anal.Machine Intell, vol. 13, pp. 355364, 1991. [2] P. H. Winston, Artificial Intelligence, 3rd ed. Reading, MA: Addison-Wesley, 1992. [3] Software Project Planning and Resource Allocation Using Ant Colony Optimization with Uncertainty Handling, International Journal of Innovative Research in Science, Engineering and Technology An ISO 3297: 2007 Certified Organization Volume 3, Special Issue 5, July 2014 International Conference On Innovations & Advances In Science, Engineering And Technology [IC - IASET 2014]. [4] Ants and reinforcement learning: A case study in routing in dynamic networks (1997) by Devika Subramanian, Peter Druschel, Johnny Chen Proceedings of the Fifteenth International Joint Conf. on Arti Intelligence. [5] Review of Solving Software Project Scheduling Problem with Ant Colony Optimization, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 4, April 2013. [6] Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems.Oxford. University code,1. [7] M. Dorigo, M. Birattari, and T. Stutzle, Ant Colony Optimization-Arti_cial Ants as Computational Intelligence Technique, IEEE Comput. Intel. Mag., vol. 1, no. 4, pp. 28-39, 2006. [8]http://www.sciencedirect.com/science?ob=ArticleURLudi = B6V CT- 4S5FJCY [9] O. Bellenguez and E. Ne ron, A Branch-and-Bound Method for Solving Multi-Skill Project Scheduling Problem, RAIRO- Operations Research, 2007. [10] L.Ozdamar, A Genetic Algorithm Approach to a General Category Project Scheduling Problem, IEEE Trans. Systems, Man, and Cybernetics-Part C: Applications and Rev., Feb 1999. [11] W.N Chen and J Zhang, Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler, IEEE Trans. Software Engineering, Jan 2013. Copyright reserved by IJETR (Impact Factor: 0.997) 64