International Journal of Computer Trends and Technology ... · PDF fileVidya Sagar Ponnam, ... One possible explanation for the problem is poor software ... Brute force exhaustive

International Journal of Computer Trends and Technology (IJCTT) – Volume 18 Number 6 – Dec 2014

ISSN: 2231-2803 http://www.ijcttjournal.org Page280

Event Based Project Scheduling Using Optimized Ant Colony Algorithm Vidya Sagar Ponnam #1, Dr.N.Geethanjali#2

1 Research Scholar, Dept. of Computer Science & Technology,Sri Krishnadevaraya University, Ananthapuram

2 Associate Professor & Head Dept. of Computer Science & Technology,Sri Krishnadevaraya University,Ananthapuram.

ABSTRACT

The need for computer-aided tools for Task Scheduling and Resource share/allocation in Software Industry is increasing. The traditional method uses both Event-Based Scheduler (EBS) and Ant Colony Optimization (ACO) to tackle both the problems in Project Planning. Due to this significance and challengesof project planning, there is a fast growing need for improvingvaluablecomputer years. The existing approaches typically consider human resource allocation and task scheduling as two distinct activities. The existent models also have the prediction that each person can simply be allotted to a single task at only once due to this project are not completed on time and lacks proper planning and scheduling in the project. The problem of task pre-emption exists in the previous models. The existing system also suffers from the problem of allocating the same task for different group of employees in different periods. ACO solve the problem of project scheduling but it does not consider the employee allocation matrix. The ACO is not a satisfactory model to solve the problem of project scheduling.

INTRODUCTION: Project management is undoubtedly an utilization of knowledge, skills, tools and technique to solve project scheduling problem. Research into developing effective computer aided techniques for planning software projects is valuable and challenging for software engineering. Not the same as projects in other fields, software project development is naturally a human centric activity.[1] Software development organizations often fight t deliver projects in time, within budget keeping the required quality. One possible explanation for the problem is poor software project management and, especially, inadequate project scheduling and ineffective team staffing. [2] Staffing a tool project is tedious activity. Manager is intended to choose between the team of employees , there can be chance many combinations. [1] Objectives Motivated by real-world situations, an array of objectives for project scheduling have already been studied inside the literature. Now we will just bring that up within these individuals, some objectives can be regarding time, simply because they concern temporary usage of renewable and doubly constrained resources, whereas others â€“ to cost, because contend with intake nonrenewable and doubly constrained resources. Both kinds usually represent conflicting objectives, since shortening the processing time outcomes in increasing the resource consumption, and vice versa decreasing the execution cost (in regards to the resources consumed) lengthens this project duration. To formulate a treatment project, this project manager has got to estimate this task workload and price and determine the construction project schedule and resource allocation. Software project tasks require employees with different

skills, and skill proficiency of employees significantly influences the efficiency of project execution as showin in Fig 1.

I

Fig 1. Basic Flow chart of Job Scheduling Allocating employees to the best fitted activities is demanding for software planmanagers, and hr allocation has become an essential part in software project planning according to the experiences and skills of the employees. Project management approaches usually consider hr allocation and task scheduling as two distinct activities and leave the job of hr allocation to be performed by project managers individually, leading to insufficient poor management and resource allocation efficiency. Main assetsin software development are humans in contrast with big resources,machinee in software projects can normally be allocated within a flexible way than those in manufacturing projects or construction. Problem Definition Software projects are individuals intensive action and require employees with varyingskills. Assigning employees towards the best-fitted tasks and recruiting allocation has come to be an essential part in software project organizing.. Because of the importance and difficulty of software project organizing, there exists a growing necessity of developing effective computer aided tools for software project organizing in recent times. The present approaches usually regard task schedule and hr allocation as two separated actions. The most present models may also have the idea that each employee earns the person the right to be granted to just one task simultaneously resulting from this project aren't completed in time and lacks proper organizing and schedule among the project. This assumption reduces the modifiablilty of resource allocation in software project organizing. RCPSP- Resource Constrained Project Schedule Challenge In the most general form, RCPSP asks some fundamental: Handed a multitude of actions, a group of resources, along with a measurement of performance, so which is the best method to assign the resources onto the actions in a way that the performance is maximized? which is the best strategy to assign the resources into the actions at specific times so that

Identify Activity

Identify Activity

Dependencies

Estimate Resources

Create Project Charts

Allocate People To Activities



each and every part of the restrictions are satisfied and of course the best objective measures are generated? RCPSP can easily be thought as follows: particular actions that needs to be executeda multitude of resources with which to carry out theVactivities,specific restrictionswhich will have to be satisfied a group of objectives with which ought to be achieved [1] SOFTWARE PROJECT SCHEDULING PROBLEM (SPSP) SPSP is typically a challenge of discovering a particular schedule for getting a software project to ensure the precedence and resource restrictions are satisfied as well as having the final project cost being inclusive of personal wagesand project duration is minimized. And additionally to for the wages and skills of employees, SPSP also takes workload and required skills of each and every task into mind, so SPSP is perfect and capable to describe the important software project schedule. Although SPSP is closest to RCPSP, usually there are some differences between SPSP and RCPSP. First, there's an extra objective to be optimized in SPSP beyond the project duration minimization objective in RCPSP. Second, employees lots of possible skills are classified as the major resource in SPSP while there are a number of sorts of resources in RCPSP.[2][6] SPSP associated with the resource-constrained project schedule challenge (RCPSP) which aims to locate a specific schedule that meets the precedence and resource requirements while reducing the project duration. A genetic algorithm is among the stochastic search techniques and it's been successfully applied in many search, optimization, and machine learning issues. Optimized schedule issues might be solved using GAs.[7] In accordance to simplifications of natural evolutionary processes, genetic algorithms run on a population of solutions instead of one solution and employ heuristics for instance selection, crossover, and mutation develop better solutions. Colorni, Dorigo and Maiezzo [1] developed ACO approach in 1991 based on the fact that real ants are able to find the shortest path between their nest and the source of food. This is done using pheromone trails, which ants deposit whenever they travel, as a form of indirect communication. Colorni, Dorigo and Maiezzo [1] designed artificial ants, which represents solutions and the collective intelligence of ants are transformed into useful optimization techniques that find application. Dorigo and Blum [2] reviewed the convergence requirements of ACO, connections between ACO algorithms and cross entropy methods. The influence of search bias on the working of ACO algorithms is discussed in Dorigo and Blum [2]. Mohamed [3] developed ACO algorithms with single ant, and also with five ants. The model is used for scheduling resource constrained projects. In the five ants ACO algorithm, each ant finds a solution in all iterations and uses the best-found solution so far developed for the pheromone update. Project scheduling problem (RCPSP) model are some of the project management techniques, that are applied in software project planning[base]. 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 [8]. The human resource allocation is a complex task. Assigning employee to the most suitable task is challenging. Techniques like PERT and CPM lack the consideration of resource allocation and scheduling models like the RCPSP do not consider the allocation of employees with various skills [14]. In Wei-Neng Chen’s work [14] he considers the employee allocation problem. The paper introduces a new method EBS for representing allocation of human resources. ACO is used for planning problem. Event based scheduler is a representational scheme. It is the combination of task list [3] and employee allocation matrix [4]. The task list defines the priorities of tasks to consume resources, and the planned employee allocation matrix specifies the originally planned workload assignments [14]. In this way, the representation takes both the issues of task scheduling and resource allocation into account. The EBS regards the beginning time of the project, the time when resources are released from any finished task, and the time when employees join or leave the project as events. To generate an actual timetable, the EBS adjusts the workload assignments of employees at events and resource conflict is solved according to the priority defined by the task list. In Chang’s work, ‘Genetic Algorithm for Project Management’ [4], it considers a genetic algorithm for project planning. The scheduling of tasks and the allocation of resource in projects is an extremely hard problem. Even though we have an optimal solution the changing conditions will affect it. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. In existing project management (PM) techniques, commercial PM tools, and research prototypes in not efficient in 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 and must build the project database to record such decisions and produce reports in various formats such as Gantt or Pert charts. Marco Dorigo proposed a new optimization technique called Ant Colony Optimization (ACO). In this paper the authors introduced a new computational paradigm called the Ant System, a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. The algorithms they developed are models derived from the study of real ant colonies and are called Ant algorithms. Liu and Wang [6] developed a flexible model for handling the optimization of scheduling problems in linear construction projects involving different objectives and resource argument tasks. The model is suggested for the scheduling of construction activities of high-rise building or bridge projects. Jianxing and Cang [7] demonstrated the use of ant colony optimization algorithm to solve the dynamic



problem of resource scheduling in group project management. 1. ACO has good graph-based search problem solving capability by splitting task and distributing employee dedication to that tasks. Construction graph is generated based on that and naturally SPP Problem is converted into graph-based search problem.

2. ACO supports to use heuristic information to increase the search ability of ants. There are total six heuristics are used in SPPP-ACS, including total dedication of employee, allocation dedication and importance of tasks.ACO gives satisfactory solution as compare to other. The ACO will develop an optimized plan, in the form of matrix, from all the iterations. And from that plan the EBS will develop schedule based on events. When an uncertain event occurs the remaining resource will be calculated, also the remaining tasks to complete. And again a new schedule will be developed according to it. It can also consider uncertainty at the starting phase. 1. ACO model: To solve NP-hard problem of project management ACO is first used. It is used to manage the tasks of project related to precedence and resource constraints. To make a schedule it need to find order of tasks which satisfy task precedence constraint and generate task list. ACO solve the problem of project scheduling but it does not consider the employee allocation matrix. The ACO is not a satisfactory model to solve the problem of project scheduling. 2. Employee allocation model: In this model the problem of how to assign employees to different tasks is find out. The main objective is to minimize the number of constraint violations or to minimize project duration and cost. In this the software project planning have to assume that the task can be done or conducted by an unlimited number of employees and an employee can be assigned to an unlimited number of tasks at a time, which is usually not possible. 3. Multi-skill scheduling model: The model is same as the ACO for task scheduling and regards different combinations of employees as different alternative modes for the implementation of a task. This model solve problem of employee allocation matrix as well as task scheduling this model only consider the allocation of employee to task at only one time, but they does not consider the pre-emption of task . This model reduces the allocation flexibility of the human resources. In this model if one of the employee is busy in other task that time the whole team has to wait till the employee is released. This is the drawback of this model and it reduces the efficiency of the project. 4. The time line based model: The new technique that combines both the human resource allocation and task scheduling is a time line based model. This model generates the time line axis to solution representation and makes a possible plan. This model has two drawbacks. First, assign workload period by period

instead of task by task and second, the plans produced by this model may assign two completely different groups of employees to the same task in different periods. Project planning is part of project management, which is to use the schedules to plan and subsequently report progress within the project environment. The purpose of project planning is to identify the scope of the project, estimate the work involved, and create project schedule. Project planning begins with requirements that define the software to be developed. The project plan describes the tasks that will lead to completion of the project. In the proposed method a practical and effective approach for the task scheduling and human resource allocation problem in software project planning with an ant colony optimization (ACO) algorithm is developed. The underlying idea of ACO is the ants deposit a special chemical called pheromone on the path they travel through while they search for food. The pheromone is the communication medium between the ants and by sensing the concentration of pheromone, others ants follow the path to find the food. An ACO algorithm works by dispatching a group of artificial ants to build solutions to the problem iteratively. ACO algorithm is the repeated execution of three main procedures, Solution construction, Pheromone Management and Daemon actions. Ant Colony Optimization The suggested strategy is defined by two most important features. First, a description scheme made from activity list along with a planned employee allotment matrix together with a novel event based scheduler takes shape. It allows the modeling of resource conflict and activity preemption . Second, an ACO strategy is suggested since it shows successful application to varied combinatorial maximization issues. ACO builds solutions inside a step-by-step manner that will actually make d ants to plan the critical tasks far back as possible and then to assign the construction project tasks to appropriate employees with required skills. The suggested method efficiently manages employees using an employee database and it also identifies tasks utilizing a Activity Precedence Graph which defines than a activity are only able to start when all of that direct predecessor tasks have finished. Hence the planning objective among the suggested strategy is promising. The suggested system will reduce overall project cost ,resources are resourcefully employed in this task as well as a new technique for resolving the software program project planning problem It certainly will decrease the two basic issues in software project management that might be activity scheduling and employee allotment. They provide the clear idea for time scheduling and resource allotment and is going to lessen the manual effort. The suggested system utilize the resources efficiently and allowing the employers to finalize anyone activity among the given time. It furnishes one of the best way to solve this activity scheduling and employee allotment issues in software project management process. Reckoning on the worker expertise, allocating employee to certain activity resource permit the construction project to become completed promptly. By evaluating employees work time that is undoubtedly /hour salary for normal effort and overtime the expense of this project can possibly be minimized. The suggested system helps Project manager in



allocating projects to Team leaders and in its place for Team leaders for allotment of activity to team members. It aids allocate employee to perform in overtimes to deal with their tasks as well as having the suggested algorithm is able to yield better plans with lower costs, stronger workload assignments decreases the dimensions of the request space in contrast to other existent approaches. AOC ALGORITHM

1. Initialize all parameters i.e. Q0, Ngen, Nant which are used in ACO. These parameters are used to evaluate the importance of Heuristic information and history, which also adjust the pheromone updating, balance the behavior of ants. Nant is number of ants and Ngen is number of generations. 2. Initialize all pheromone value as 0. 3. Each ant selects her own path for finding solution. Each ant select next node as per selection scheme and fill the matrix. When travelling of ants is completed Solution matrix is constructed.

4. By using fitness function evaluates the quality of solution, also calculate the cost, duration of project and overwork for that project.

5. Compare the solutions and select the best one, update the pheromone value.

6. Repeat the procedure till condition is satisfied. Generally termination condition is determined by fix number of generation.

7. Select and display the best solution whose cost and duration is less. Event-Based Scheduling A schedule is a listing of a project's milestones, activities, and deliverables, usually with intended start and finish dates. The proposed work combines the task list representation and the employee allocation matrix representation so that both the problems of task scheduling and human resource allocation are addressed. Step 1: Initialize the number of available human resources Step 2: Find the task Step 3: If the planned working hours is not greater than the remaining working hours of the i-th employee, assign planned working hours of the project to the number of working hours of the i-th employee for the task j Step 4: Else, the number of working hours of the i-th employee for the task j is set to the remaining working hours of the i-th employee at t. Step 5: Evaluate the completion situation of the task at time t Step 6: If any task is finished at time t, set t+1 as event Step 7: Increment t

Data Format: ************************************************************************ file with basedata : mf7_.bas initial value random generator: 1431890842 ************************************************************************ projects : 1 jobs (incl. supersource/sink ): 32 horizon : 241 RESOURCES - renewable : 2 R - nonrenewable : 2 N - doubly constrained : 0 D ************************************************************************ PROJECT INFORMATION: pronr. #jobs rel.date duedate tardcost MPM-Time 1 30 0 33 0 33 ************************************************************************ PRECEDENCE RELATIONS: jobnr. #modes #successors successors 1 1 3 2 3 4 2 3 3 5 7 9 3 3 2 6 16 4 3 3 15 20 22 5 3 1 13 6 3 3 12 14 15 7 3 1 8 8 3 3 10 16 22 9 3 1 28 10 3 3 11 13 17 11 3 1 21 12 3 2 17 26 13 3 3 27 28 31 14 3 2 19 30 15 3 2 24 25 16 3 3 21 24 26 17 3 2 18 30 18 3 3 20 25 28 19 3 3 20 21 22 20 3 2 29 31 21 3 1 27 22 3 1 23 23 3 2 24 25 24 3 1 31 25 3 1 27 26 3 1 30 27 3 1 29 28 3 1 29 29 3 1 32 30 3 1 32 31 3 1 32 32 1 0 *************************************************



*********************** REQUESTS/DURATIONS: jobnr. mode duration R 1 R 2 N 1 N 2 ------------------------------------------------------------------------ 1 1 0 0 0 0 0 2 1 1 8 2 0 3 2 6 8 2 0 2 3 8 6 2 0 2 3 1 2 3 7 3 0 2 4 2 6 3 0 3 5 2 6 0 9 4 1 4 9 8 0 10 2 7 7 5 0 6 3 9 3 4 0 4 5 1 4 7 6 0 3 2 5 7 5 0 3 3 8 5 5 0 1 6 1 1 9 8 7 0 2 4 9 8 5 0 3 9 9 6 3 0 7 1 4 9 5 0 4 2 6 9 4 7 0 3 7 9 4 6 0 8 1 1 8 9 6 0 2 5 7 8 5 0 3 9 5 8 4 0 9 1 5 6 6 0 7 2 7 2 4 0 5 3 7 2 2 0 6 10 1 7 8 9 0 10 2 9 7 7 0 9 3 10 6 6 1 0 11 1 2 9 8 6 0 2 6 7 6 0 1 3 9 3 3 4 0 12 1 1 2 3 6 0 2 3 1 3 0 1 3 10 1 2 5 0 13 1 1 5 10 0 6 2 1 4 10 0 8 3 3 2 9 0 6 14 1 3 7 4 0 3 2 5 6 4 4 0 3 10 5 3 0 2 15 1 2 7 3 0 6 2 6 6 3 6 0 3 10 5 2 3 0 16 1 4 10 10 5 0 2 6 9 9 0 5 3 8 7 9 3 0 17 1 3 8 4 8 0 2 5 7 4 7 0 3 9 6 4 5 0 18 1 4 5 7 5 0 2 5 5 7 4 0 3 8 3 6 3 0 19 1 4 4 7 0 3 2 4 4 6 7 0 3 7 3 6 5 0 20 1 1 9 5 0 10 2 3 8 4 0 10 3 10 7 4 0 10

21 1 6 8 7 0 8 2 8 7 6 7 0 3 10 7 3 3 0 22 1 2 8 2 4 0 2 3 7 2 1 0 3 9 7 2 0 4 23 1 1 6 9 10 0 2 7 3 3 0 7 3 7 2 6 2 0 24 1 1 4 6 0 5 2 5 4 5 8 0 3 6 3 2 8 0 25 1 8 7 7 5 0 2 9 5 6 4 0 3 10 2 6 0 3 26 1 2 8 6 0 2 2 5 4 3 0 1 3 5 6 1 0 2 27 1 1 6 4 8 0 2 2 6 2 6 0 3 4 3 1 6 0 28 1 2 5 1 0 8 2 5 3 1 0 7 3 10 3 1 0 4 29 1 4 5 7 0 9 2 8 1 3 0 3 3 8 4 4 7 0 30 1 2 7 8 0 3 2 8 5 4 0 3 3 9 3 4 5 0 31 1 5 8 3 0 4 2 7 8 2 3 0 3 7 7 3 0 3 32 1 0 0 0 0 0 ************************************************************************ RESOURCEAVAILABILITIES: R 1 R 2 N 1 N 2 32 27 48 59 ************************************************************************ The problem of employee allocation is to assign employees to suitable tasks so that the tasks can be done efficiently1]. Suppose m employees are involved in the project, for the ith employee (i=1,2,3 . .;m) the following attributes are considered.

sib =Basic salary for the employee per user defined time period.



his = Salary of the employee per hour normal work.

otis =The salary for the employee’s per-hour overtime work. nw =Legal normal working hours per month. max i =Maximum possible working hours per month of the employee for the project.

{ , }i it tjoin leave =The time window when the employee is

available for the project. 1 2 5{ , ,.... }i i isk sk sk =The skill list for the employee with 5

types of skills and isk is the Proficiency score of the ith skill. Let us suppose that the ith employee devotes ( )t

ihrs s

hours to the project at the t’th month ( ( ) maxti ihrs s ).

If ( )tihrs s is larger than the legal normal working hours nw,

it implies that the employee works overtime for the project. The salary t

isal for the i’th employee at the t’th month is calculated by 1) t

isal = ( )* ( ) nwt tsi i hi ib hrs s s hrs s

2) tisal =

* ( ( ) )* , nh< ( ) maxt tsi hi i oti i ib nh s hrs s nh s hrs s

3) tisal = , ( ) maxt

i ihrs s Suppose t

ijwh is the number of working hours of the ith employee for tj at the tth month Where 1,2,3,4,5id

i is sk

TRADITIONAL EXPERIMENTAL RESULTS

Fig 1. Home Page of ACO based Event Scheduling

Begin

Initialization

i=1

i-th ant constructs a solution by first building a task list and

allocation matrix

Local parameter updation

Local refinement,EBS and objective function

evaluation

i=i-1

i>POPSIZE

Check best solution and local mutation

Global updation Finish

Yes

No



Fig 3. Task related Data Loading

Fig 4. Initial Job Scheduling Before ACO approach

Fig 5. Task Vs Days To complete Job Successor List :[[Job-159]] Mean Value :146023 t-Test Value :-27.0 Job Info :[Job-158] Job ID :158 Job Type :STANDARD

Job Label :Project 4 Job Successor List :[[Job-159]] Mean Value :383015 t-Test Value :-6.0 Job Info :[Job-159] Job ID :159 Job Type :SINK Job Label :Project 4 Job Successor List :[] Mean Value :273991 t-Test Value :-7.0 Job Info :[Job-45] 2014-12-23 11:30:00,950 [SwingWorker-pool-3-thread-1] DEBUG LS step (505), time spent (12913), score (0/-5247/-1427), best score (0/-3769/-1199), accepted/selected move count (4/7), picked move ([Allocation-17] => [ExecutionMode-49]). Job ID :45 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-53]] Mean Value :443562 t-Test Value :-52.0 Job Info :[Job-46] Job ID :46 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-48], [Job-52], [Job-60]] Mean Value :470019 t-Test Value :-71.0 Job Info :[Job-47] 2014-12-23 11:30:00,950 [SwingWorker-pool-3-thread-1] DEBUG LS step (506), time spent (12913), score (0/-5208/-1427), best score (0/-3769/-1199), accepted/selected move count (4/6), picked move ([Allocation-110] => 273). Job ID :47 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-48], [Job-49], [Job-60]] Mean Value :265098 t-Test Value :-30.0 Job Info :[Job-48] Job ID :48 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-55]] Mean Value :203489 t-Test Value :-10.0 Job Info :[Job-49] Job ID :49 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-51], [Job-52], [Job-62]] Mean Value :333782 t-Test Value :-62.0 Job Info :[Job-50] Job ID :50 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-55], [Job-56]] Mean Value :478423 t-Test Value :-76.0



Job Info :[Job-51] Job ID :51 Job Type :STANDARD Job Label :Project 1 Job Successor List :[[Job-53]] Mean Value :267179 t-Test Value :-57.0

JobName Mean Project1 176043Project2 112043Project3 186096Project4 166023Project5 196023Project6 123123

Mean Access Time Vs Projects

050000

100000150000200000250000

Projec

t1

Projec

t2

Projec

t3

Projec

t4

Projec

t5

Projec

t6

Project Names

Mea

n A

cces

s Ti

me

Mean

JobName Statistic-t Project1 -23Project2 -45Project3 0Project4 24Project5 -45Project6 29

Statistic-t Vs Statistical Parameter

-60

-40

-20

0

20

40

Projec

t1

Projec

t2

Projec

t3

Projec

t4

Projec

t5

Projec

t6

Project Names

Stat

istic

-t

Statistic-t

CONCLUSION The main objective of the this paper is first, the method takes advantage of ACO to solve the complicated planning problem, the second one method introduces an event-based scheduler. Both methods have limitation during the project planning and allocation. Experimental results show that the representation scheme with the EBS is effective in small target tasks, and the ACO algorithm manages to yield better plans with high statistic-t and mean access time and more stable workload assignments compared with other existing approaches. A new method for solving the software project planning problem has been proposed in future work. The problem of task pre-emption exists in the previous models. The existing system also suffers from the problem of allocating the same task for different group of employees in different periods. ACO solve the problem of project scheduling but it does not consider the employee allocation matrix. The ACO is not a satisfactory model to solve the problem of project scheduling.

REFERENCES

1. “Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler”, Wei-Neng Chen, Member, IEEE, and Jun Zhang, Senior Member, IEEE, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 39, NO. 1, JANUARY 2013. 2. C. Wang, S. Liu, “Resource constrained construction project scheduling model for profit maximization considering cash flow”. J. of Auto. in Con. 17 (2008) 966-974. 3. L. Drezet, J. Billaut, “A project scheduling problem with labor constraints and time-dependent activities requirements.” Int. J. of Pro. Eco. 112 (2008) 217-225. 4. H. Abdalla, Hassan, “Using Ant colony optimization algorithm for solving project management problems.” J. of Exp. Sys. with App. 36 (2009)10004-10015. 4. S. Christodoulou, G. Ellinas, P.Aslani, “Entropy-based scheduling of resourceconstrained construction projects.” J. of Auto. In Con,.18 (2009) 919-928. 5. V. Thiagarasu, T. Devi, “Multi agent coordination in project scheduling: Priority rules based resource allocation. “Int. J. of Rec. Tr. in Eng. 1 (2009) 314-326. 6. Vahid Khodakarami, Norman Fenton, Martin Neil, “ “Project Scheduling:Imposed Approach to Incorporate Uncertainty using Bayesian Networks”(2007)”. 7. Carl K.Chang,Hsin-Yi Jiang,Yu Di,Dan Zhu,Yujia Ge, “Time-line based model for software project scheduling with genetic lgorithms”(2008) 8. Andrey Glaschenko, Anton Ivaschenko George Rzevski,Petr Skobelev, “Multi-Agent Real Time Scheduling System for Taxi Companies ”(2009) 9. M. Dorigo, V. Maniezzo, A. Colorni,,”Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems Man, and Cybernetics- part B: Cybernetics, vol.26, pp.29-41, 1996 10 R.-G. Ding and X.-H. Jing, “Five principles of project management in software companies,” Project Management Technology (in Chinese), vol.1, 2003.



11 L.C. Liu and E. horowitz, “A formal model for software project management,” IEEE Transactions on software Engineering, vol.15, no.10, pp. 1280-1293, 2001. 12 D. Merkle, M. Middendorf, H. Schmeck, “Ant colony optimization for resourceconstrained project scheduling,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 333-346, 2002.

P.vidya Sagar obtained his M.C.A from

Visveswaraiah Technological University,Belgaum.Then he obtained his M.Tech in Computer Science And Engineering from Acharya Nagarjuna University,Guntur and pursuing PhD in Computer Science and Technology from Sri Krishnadevaraya University,Anantapuram. He is a Professional Member of ISCA . His specializations include software engineering and software reliability, web services and networking.

Dr.N.Geethanjali received her PhD Degree from Sri Krishanadevaraya University. Andhra Pradesh, India she is working as Head, Department of Computer Science & Technology, Sri Krishanadevaraya University. Andhra Pradesh, India. She is a Professional Member ACM. Her research interest includes Computer Networks, Cloud Computing, Software Engineering, Programming languages and Data Mining.

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International Journal of Computer Trends and Technology ... · PDF fileVidya Sagar Ponnam, ... One possible explanation for the problem is poor software ... Brute force exhaustive