An integrated resource planning for multiple software projects
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An Integrated Resource Planning for Multiple Software Projects. 指導教授:葉榮懋 學生:朱獻翔 學號: M97U0222. C ontents. Abstract Introduction Research Background Workflow of Projects Taguchi ' s Parameter Design Experimental Results Conclusion. Abstract.

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An Integrated Resource Planning for Multiple Software Projects

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An integrated resource planning for multiple software projects

An Integrated Resource Planning for Multiple Software Projects

M97U0222


C ontents

Contents

  • Abstract

  • Introduction

  • Research Background

  • Workflow of Projects

  • Taguchi ' s Parameter Design

  • Experimental Results

  • Conclusion


Abstract

Abstract

  • In a multi-project and multi-resource environment, a resource planning is to decide what needs to be done, by whom, when, and in which project. In this article, an integrated method, which includes critical resource diagram, heuristic methods and Taguchi's parameter design, is proposed to solve resource planning problems for multiple projects with different starting time.

  • Key WordsParameter Design, Heuristic Method, Resource Planning


Introduction

Introduction

  • In a multi-project and multi-resource environment, competitions arise for human resource among software projects since skillful resources are difficult to find. In this matter, some resources are assigned to multiple software projects simultaneously to achieve a cost-effectively resource planning. Those human resources are called as common resources, otherwise are called as non-common resources who are full time involved in a single software project.


An integrated resource planning for multiple software projects

  • Resources planning for multiple software projects considers what tasks should be done, when, by whom, and in which project. In this concern, there are two major problems for resource planning.

  • Task Priority

  • Resource policy

  • For the first problem of task priority, researchers discussed heuristic methods for multiple software projects and multiple resources over decades. Scheduling heuristic methods are used to prioritize competing activities for allocating constrained resources.


An integrated resource planning for multiple software projects

  • Fendley ( 1968 ) recommended the shortest operation first rule inferior to a minimum slack in the situation of a single project . Patterson ( 1973 ) found that a shortest operation first rule performed well in multi-project and multi-resource problems. However, the minimum slack as compared with several heuristic methods by Davis and Patterson ( 1975 ) achieves shortest duration.

  • Later , Kurtulus and Davis ( 1982 ) examined heuristic methods including minimum slack rule, shortest activity from Shortest project(SASP), and maximum total work content. They recommended that the SASP rule and maximum total work content are better than minimum slack rule . Dumond and Mabert (1988) found that the SASP method is more effective in terms of reducing mean completion time in a dynamic environment.


An integrated resource planning for multiple software projects

  • For the second problem of resource policy, methods for achieving optimal resource planning are widely discussed. Davis (1973) classified the most popular solving procedures into two major categories : optimal procedures and heuristic methods . Simulation is also proposed by researchers to solve the resource planning problems ( Van Slyke1963 Schonberger 1981). Design of experiment method is another effective procedure for resource planning problems. antell, Jung , and Warner ( 1992 ) had applied Taguchis parameter design with PERT / CPM to solve a single project management problem .

  • Multiple software projects can be classified into two categories: projects with same starting time and projects with different starting time.


Research background

Research Background

  • Institute of Information Industry is a leading organization in software industry in Taiwan. One of their recent software products is reusable data retrieval kernel software. By using this kernel software, they started to implement many data retrievable application systems for business, which have highly similarity in system structure. The kernel software had been proved as a successful quick tool for developing application systems.


An integrated resource planning for multiple software projects

  • They held many application projects to meet requirements of different customers. However, the existing resources are limited. They have to decide who will involve, in which project with how many working hours. Therefore, the planning problem occurs in a multi-project and multi -resource environment. We then use this case as a background example to develop an proposed integrated method.


An integrated resource planning for multiple software projects

  • The framework of an application system includes the following software components:

  • DBa

  • DBd

  • DBb

  • DBk

  • DBf

  • Ma

  • Md

  • Mb

  • Qk

  • Qf

  • Those software components are developed by C language and each has its own architecture and functions.


Workflow of projects

Workflow of Projects

  • Graphical scheduling tools are often used to describe the relationships among project tasks and human resources, such as PERT / CPM. Badiru (1992 and 1993) proposed a critical resource diagram (CRD) takes a reverse view to activity scheduling, and focuses on resource scheduling so that the workflow among resources can be observed. Since CRD can appropriately demonstrates the position of a resource, in a project, it is adopted as the major scheduling tool to show the workflow of the application project. In Figure 1, each node refers to a task and its corresponding resource unit, a human resource who is in charge of the task.


An integrated resource planning for multiple software projects

  • In this study, two application projects, denoted as Project 1 and 2, are taken as examples for developing proper project planning of multiple-software project with multiple resources. To present the workflow of these two projects, an augmenting CRD for both projects is shown in Figure 2.


Taguchi s parameter design

Taguchi ' s Parameter Design

  • In this section, Taguchi ' s parameter design is applied to solve resource planning problems The general steps of Taguchi ' s parameter design include : ( 1 ) define desired improvement or objective ; ( 2 ) select factors and factor levels ; ( 3 ) lay out design arrays ; ( 4 ) conduct experiments ; and ( 5 ) evaluate performance .


An integrated resource planning for multiple software projects

  • Cost Mode

  • The objective of multiple projects with n project with n projects and m resources is to minimize the total project cost including tardiness cost and resource cost . The cost model is given below


An integrated resource planning for multiple software projects

  • Factors and Factor Levels

  • For the two projects, factors and factor levels are illustrated with two major categories:

  • 1. Resource Factors


An integrated resource planning for multiple software projects

2. Noise Factors


An integrated resource planning for multiple software projects

  • Layout of Parameter Design

  • The layout of this parameter design is set up as follows : an inner array using a two -level factorial design to include six controllable factors with 26 = 64 level combinations , and an outer array using two L81 orthogonal arrays for Project 1 and 2 , each contains ten noise factors of task complexity .


An integrated resource planning for multiple software projects

  • EXPERIMENTS FOR MULTI PROJECTS

  • First In, First serve (FIFS)

  • Based on the FIFS method, the first component for common resource is completed in Project l, the next is in Project 2, then back to Project 1, and will be continue alternatively until all components are finished. Therefore, the back to Project l, and will be until all components are task priorities for common resources A and B will be [DBa DBa- DBd DBd- DBb DBb] and [DBk DBk- DBf DBf], respectively. These task priorities are presented by resource links as shown in Figure4.


An integrated resource planning for multiple software projects

  • Each trial produces a critical duration time and its corresponding project cost by following the computational rule of CRD and the cost model Namely, for each combination of controllable factors, there are 81response data for each project , then its average , standard deviation , and SN ratio are obtained accordingly . An optimal condition will be chosen by minimizing the summation of average costs of projects. An optimal condition is A1B2C1D2E1F2, which means that resources A , C , and E need normal work rate only while resources B , D , and F need overtime . In Table 3, some selected results for other conditions are listed for comparison.


An integrated resource planning for multiple software projects

  • Shortest Activity form Shortest Project (SASP)

  • The method of SASP determines task priority by judging task duration and the remaining critical path time. Since the scale of Project 2 is much smaller that Project l, the critical path time of Project 2 is much shorter than Project 1. In this case, task priorities for common resources will do all the responded tasks first in Project 2, then back to Project 1 to finish the rest of tasks. Therefore , the task priorities for common resources A and B will be [DBa DBa-DBd- DBb- DBd - DBb] and [ DBk DBk- DBf - DBf] respectively These task priorities are presented by resource links as shown in Figure 5.


An integrated resource planning for multiple software projects

  • An optimal condition is A1B2C1D2E1F1, which means that only B and D need overtime . Some selected results of two projects by using SASP are listed in Table 4 for comparison.


Experimental results

Experimental Results

In this section, experimental results of FIFS and SASP heuristic methods for multiple software projects are compared and summarized as follows:

1. The optimal condition is A1B2C1D2E1F2 by using FIFS, while A1B2C1D2E1F1by using SASP. It is clear that overtime is not necessary for resource F.

2. For FIFS method, the resulting total cost is $ 458,560 =343029 + 1115531 for Project 1 and 2; for SASP method, the total cost is $ 441,949=343882 + 98067. Hence SASP can achieve lower cost than FIFS.

3 For Project 1, both, heuristic methods achieve same project duration time of 86 day. For Project 2, SASP obtains shorter duration time (29 Days) than FIFS (43 Days). On the other hand, since the objective in this experiment is total project cost instead of duration, it is clear that duration for the optimal condition it minimized.


Conclusions

Conclusions

  • In this study , an integrated method , which includes critical resource diagram ( CRD ) , heuristic methods such as SASP or FIFSand Taguchi ' s parameter design , was proposed to solve resource planning for multiple projects with different starting time .

  • The main purpose of the integrated method is to determine the work rate level of each resource (normal or overtime) to achieve robust performance of multiple projects against noise factors of task complexity.


An integrated resource planning for multiple software projects

  • In this article, we have discussed resource planning problems for multiple projects having different project starting time, resource planning for multiple projects With same starting time can be further discussed. Moreover, other heuristic methods for task priority can be studied and compared.


An integrated resource planning for multiple software projects

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