A Development and Integration Framework for Optimisation-based

Enterprise Solutions

Rodrigo Lankaites Pinheiro and Dario Landa-Silva

School of Computer Science, The University of Nottingham, Wollaton Road, Nottingham, NG8 1BB, U.K.

Keywords:

Optimisation Development Framework, Decision Support Systems, Academia-industry Collaboration,

Workforce Scheduling, Routing.

Abstract:

The operations research literature includes some papers describing collaborative work between researchers and

industry. However, not much literature exists that outlines methodologies to guide the development of a de-

cision support module and its integration into an existing information management system. Here we describe

a framework to aid the collaborative development of an optimisation solution by researchers and information

system developers. The proposed framework also helps in the effective integration of the information manage-

ment system and the decision support module. The framework is divided into three main components: a data

model, a data extractor and validator, and a solution visualisation and auxiliary platform. We also describe

our experience and positive results from applying the proposed development and integration framework to a

project involving the development on an optimisation-based solution for workforce scheduling and optimisa-

tion problems. We hope that this contribution would be particularly useful for less experienced researchers and

practitioners who embark on a collaborative development of a decision support module based on optimisation

techniques.

1 INTRODUCTION

Decision support systems are pieces of software in-

tended to aid humans in making decisions regard-

ing speciﬁc problems in given domains. Over the

past few decades, research and development of such

systems have increased considerably (Power et al.,

2011). Nonetheless, due to their complexity, decision

support systems are mostly developed jointly by col-

laborative teams. Academics often enter into R&D

projects in partnerships with practitioners to develop

decision support modules – which may consist of one

or more optimisation algorithms, simulation modules,

inference engines, etc. – for solving a given problem

(Fontana et al., 2006).

As with every software development environment,

collaborative development of decision support sys-

tems presents its difﬁculties. The software engineer-

ing literature describes many methodologies for soft-

ware development such as applying basic software

paradigms (Pressman, 2010), or adhering to the Ra-

tional Uniﬁed Process (RUP) (Kruchten, 2004), Ag-

ile methodology (Martin, 2003) or Concurrent Engi-

neering (CE) (Prasad, 1996). However, the general

scope of these methodologies, often fails to address

the unique challenges of R&D development.

The literature provides few works that propose

general methodologies to aid the development of

R&D projects (Barnes et al., 2002; Guarnaschelli

et al., 2013). Some commercialtools exist to aid R&D

projects development, such as the IBM ILOG ODM

Enterprise (IBM, 2013). However, adhering to such

tools implies adopting black-box algorithms (which

may not be in the interest of researchers), limitations

in technology (as compatibility issues arise) and often

a considerable cost.

Here we present a data-centric development

framework to assist in the collaborative develop-

ment of optimisation-based enterprise solutions. The

framework is a tool to facilitate the communication

between development teams and to foster progress on

the research of the optimisation problem being tack-

led. Moreover, it provides a layer between the in-

formation management system and the decision sup-

port module being developed. Hence, the practitioner

does not need to force the use of speciﬁc technologies

and methods and the researchers focus on the devel-

opment of algorithms. We stress that the proposed

framework is not intended to substitute software en-

gineering methodologies and frameworks. Instead, it

233

Lankaites Pinheiro R. and Landa-Silva D..

A Development and Integration Framework for Optimisation-based Enterprise Solutions.

DOI: 10.5220/0004833802330240

In Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems (ICORES-2014), pages 233-240

ISBN: 978-989-758-017-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

is designed to provide both the researchers and prac-

titioners with enough independence while still aid-

ing them in the collaborative development. We also

describe the application of the proposed framework

to a real scenario involving the development of an

optimisation-based enterprise solution.

The rest of this paper is structured as follows. Sec-

tion 2 outlines the Workforce Scheduling and Routing

Problems Project which is used to illustrate the ap-

plication of the proposed framework. Section 2 also

reviews previous related works. Section 3 presents

the framework while section 4 presents the obtained

results. Section 5 provides some insights and the con-

clusion of this work.

2 THE WSRP PROJECT AND

RELATED WORKS

Part of the focus in our currentresearch is the develop-

ment of algorithmic solutions for workforce schedul-

ing and routing problems such as home healthcare ser-

vices. Workforce scheduling and routing problems

(WSRP) are a class of problems in which workers

(nurses, doctors, technicians, etc.) have to be allo-

cated to tasks scattered in a geographical area, hence

workers need to travel between locations. Work-

ers have their own skills, availability, preferences,

etc. while tasks have required skills, associated client

preferences, etc. This class of problems combines

features from scheduling problems and routing prob-

lems. For an overview of WSRP please refer to

Castillo-Salazar et al. (2012).

We are engaged in a R&D project in collabora-

tion with an industrial partner in order to develop the

optimisation engine for tackling large WSRP scenar-

ios. The existing information system collects all the

problem-related data and provides an interface to as-

sist human decision makers in the process of assign-

ing workers to visits. We are in charge of developing

the decision support module that couples well with

the information management system being developed

and maintained by the industrial partner.

The use of methodologies, frameworks and guide-

lines aid the process of elaborate software develop-

ment and can help to reduce risks and problems. In

Section 4) we discuss in detail some of the risks and

problems that the proposed framework has helped

us to address in our project. The literature de-

scribes several methodologies and guidelines to help

on the development of both decision support systems

(Connors, 1992; Bui and Lee, 1999; Elgarah et al.,

2002) and information management systems (Prasad,

1996; Martin, 2003; Kruchten, 2004). However, only

few works refer speciﬁcally to R&D projects and

in our opinion, even fewer could be applicable to

optimisation-based problems. We highlight the fol-

lowing ones:

• Several works focus on the management aspects

of R&D projects. They discuss the advantages

of a collaborative project between researchers and

practitioners, how to assess such partnerships and

how to evaluate the beneﬁts. Among them, we can

list the works of Balachandra and Friar (1997),

Huchzermeier and Loch (2001), Santiago and Bi-

fano (2005) and Fontana et al. (2006).

• There are some case studies of R&D projects that

aim to identify characteristics of the software de-

velopment process in such R&D projects to then

propose a few general guidelines. Among these

works we can highlight those by Pinto and Covin

(1989), and Pillai et al. (2002).

• Barnes et al. (2002) presents a general good prac-

tice model for R&D projects, but not a methodol-

ogy or framework. Hence, we recommend to ad-

here to their model while applying our framework,

as both methodologies can be fully integrated.

Their partner evaluation factors and university-

industry issues practises can be addressed prior to

the application of our framework. Our data mod-

elling contributes to fulﬁll several of their project

management topics, such as the clearly deﬁned

objectives and responsibilities, realistic aims, col-

laborative agreement, progress monitoring and ef-

fective communication. Besides,our data model

helps in managing the cultural gap issue and our

proposed solution visualisation and auxiliary plat-

form aids with the outcome factors they deﬁned.

• Guarnaschelli et al. (2013) proposed a general

methodology for integrating the development of

decision models with model driven software de-

velopment. They divide the methodology in four

parts, namely the business modelling, the decision

problem deﬁnition, the service modelling and the

reference modelling. The framework proposed in

this paper can be integrated with their methodol-

ogy since the elements we propose match the el-

ements proposed in their suggested timeline. Our

proposed data modelling starts after their sug-

gested business modelling and progresses through

the decision model identiﬁcation and speciﬁca-

tion. Our data extractor and validator contributes

to their decision problem realisation and the do-

main elements reference modelling. Finally our

solution visualisation and auxiliary platform aids

with the implementation step.

ICORES2014-InternationalConferenceonOperationsResearchandEnterpriseSystems

234

Figure 1: Overview of the development and integration framework.

3 THE PROPOSED

FRAMEWORK

The framework consists of three components to help

with the communication of teams, the understanding

of the problem being tackled and the integration of the

algorithmic solution. The ﬁrst component is a data

model understandable to both researchers and practi-

tioners. The second component is a data extraction

and validation mechanism. The third component is a

solution visualisation and auxiliary platform. Figure

1 presents an overall diagram of the framework. Alto-

gether,these elements create a layer between the prac-

titioners and the researchers to assist them in achiev-

ing a common understanding of the problem and ob-

taining a higher communication level.

3.1 First Component: Data Model

The data model component applies data-centric de-

velopment concepts (Bhattacharya et al., 2009; Cohn

and Hull, 2009) to optimisation scenarios. The data

model is a data abstraction in a ﬁle format capable

of representing an instance of the optimisation prob-

lem. It serves as a layer between details of the prob-

lem domain and details of the algorithmic solution so

that those building the model approach the problem

from their own perspective. The purpose of the data

model building process is that both practitioners and

researchers work in collaboration to create the ﬁle for-

mat and improve mutual understanding.

3.1.1 Modelling the Data

The data can be modelled into a set of ﬁles or a single

ﬁle, depending on the characteristics of the optimisa-

tion problem. Ideally, the set of ﬁles contains all the

information needed to run the solving algorithms. We

recommend to have in each dataset all the informa-

tion required to solve one problem instance instead of

having data common to several instances in one ﬁle.

This is because the time required for executing the op-

timisation algorithms is usually higher than the time

required for data processing. More importantly, hav-

ing all the data for a problem instance available in this

way helps researchers to better understand the prob-

lem instance.

The WSRP scenario used to illustrate the frame-

work in this paper contains a large dataset, hence we

decided to split an instance into six ﬁles, each one

containing a list of some sort. Figure 2 presents a

simpliﬁed version of the dataset. Each ﬁle contains a

list of the element that deﬁnes the ﬁle. Also, each ﬁle

is linked to the mathematical model in order to show

the correlation between the optimisation problem and

the data ﬁles.

The use of a data model at the centre of the de-

velopment process helps to improve the communi-

cation between researchers and practitioners (Cohn

ADevelopmentandIntegrationFrameworkforOptimisation-basedEnterpriseSolutions

235

Figure 2: The ﬁles set for the WSRP project. Each ﬁle contains a list of the named elements and each element contains several

ﬁelds related to the optimisation problem.

and Hull, 2009). The data ﬁles should preferably be

human-readable and as self-explanatory as possible.

This also allows researchers to easily alter the data in

order to generate different test cases.

When developing the framework we opted to

use eXtensible Markup Language (XML) (Harold

and Means, 2009) as the ﬁle format for the model.

We have chosen XML because it is easily readable,

XML parsers are widely available, and XML offers a

schema model. Such schema allows the creation of

XML ﬁles that model other XML ﬁles and ﬁles at-

tached to XML schemas are easily validated. Also, in

the case of our WSRP project, both researchers and

practitioners were already familiar with XML. How-

ever, an alternative is to use YAML (2013), which

is clearer and more human-readable than XML. Be-

sides, several YAML libraries are available for the

most common programming languages.

Like in any other software development process,

the development of a decision support system may

be subject to many changes during the development.

This must be considered when modelling the ﬁles, for

example, by using required and optional ﬁelds. The

required ﬁelds should be used for very basic data that

must exist in order to validate the data for a problem

instance. The optional ﬁelds are used to capture sec-

ondary constraints and objectives such as preferences.

In the modelling stage, the format used to rep-

resent a solution should help researchers to verify

that the solution is valid, i.e. meets the problem

constraints and additional requirements. This also

helps practitioners and researchers to understand each

other early in the process, on what constitutes a good-

quality solution.

The ﬁnal data model obtained is a layer between

researchers and practitioners. This means that practi-

tioners can work on the information management sys-

tem using the data model to interact with the decision

support module (even though it may not have been de-

veloped yet). Similarly, researchers can work on the

algorithmic techniques without any speciﬁc knowl-

edge of the information management system. Fur-

thermore, this layer helps to ensure that any solution

methodology developed by the academic team can

be incorporated into the company’s information sys-

tem by only handling the corresponding model ﬁles.

Therefore, the data model plays a crucial role when

integrating the company’s information system and the

developed optimisation algorithms.

3.2 Second Component: Data

Extraction and Validation

The sooner the academic team have access to data in

the model format, the sooner research on the solv-

ing algorithms can begin. Then, sample datasets or a

mechanism to efﬁciently assist in extracting the data

would be useful. Figure 3 shows the schematic for

ICORES2014-InternationalConferenceonOperationsResearchandEnterpriseSystems

236

the data extractor and validator developed as part of

the framework. The ﬁgure also shows the interaction

of this component with the database in order to gen-

erate the ﬁle set in the model format.

3.2.1 Data Extractor

The data extractor is the tool that retrieves the data

from the information system and converts it to a set

of ﬁles according to the data model. This data extrac-

tor plays an important role in the development of the

decision support module. This is because a typical in-

formation system is likely to contain a large amount

of data that is not needed for the optimisation prob-

lem and that should be ﬁltered. Clearly the model

itself helps to eliminate some of such data, but as ex-

pressed above, it is highly desirable that each dataset

only contains the data required for a single problem

instance. In our WSRP project, we only extract the

visits/jobs for a given planning period. This helps to

eliminate the processing of unnecessary data and im-

prove the overall system’s performance. Filtering the

data at the retrieval stage is usually easier, more reli-

able and more efﬁcient, specially in a SQL server en-

vironment. Figure 3 outlines this process of extracting

and collecting raw data, sending it to the data valida-

tor, retrieving the validated data and ﬁnally generating

the corresponding ﬁle set in the model format.

Figure 3: The data extractor and validator.

3.2.2 Data Validator

After the data extraction, data should be validated

to account for missing and inaccurate entries in the

database plus other considerations. In the database

for our WSRP project, this means for example, ﬁl-

tering out staff members that are not available in the

planning period considered, or those whose skills are

not appropriate for the given scenario, or those only

available to work on different geographical locations.

We also ﬁlter out data about locations and skills not

used in the given scenario. Hence, the ﬁrst step of

the data validator is to trim data as much as possible

and ensure that data references broken as result of this

data trimming are also ﬁxed.

Moreover the validator also checks for inconsis-

tent data, such as mistyped postcodes, which if not

detected can negatively affect the performance of the

solving algorithm. For example, the data validator

for the WSRP project checks for wrong postcodes by

comparing them to the postcodes of other locations

in the same geographical area. It also checks for any

numerical value that deviates too much from the ex-

pected range of values. Figure 3 presents the proposed

ﬂow diagram for the data validator. The extractor and

validator provides researchers with problem instances

to assist the algorithm development, while also pro-

vides practitioners with a guiding mechanism to con-

tinue the data gathering and preparation.

3.3 Third Component: Solution

Visualisation and Auxiliary

Platform

The third component of the framework is a solution

visualisation and auxiliary platform, a feature that is

almost entirely designed to help researchers in the

development. There are many information system

development methodologies well consolidated in the

software engineering literature. However, these are

usally not fully applicable to the development of de-

cision support systems, for which well established

methodologies and frameworks are not easily identi-

ﬁed in the literature.

This third component aims to be a communica-

tion layer between researchers and later between re-

searchers and practitioners. Figure 4 presents a di-

agram to illustrate how the visualisation and auxil-

iary platform interacts with the solver and the actors

in the scenario, the company and the academic team.

The solutions generated by the algorithmic solver can

be stored into the repository, can be displayed in the

visualisation platform and can be compared to other

stored solutions. Through this visualisation platform,

ADevelopmentandIntegrationFrameworkforOptimisation-basedEnterpriseSolutions

237

Figure 4: The visualisation and auxiliary platform.

researchers and practitioners can have feedback from

the solver and can evaluate the progress in the devel-

opment of the solving algorithm.

3.3.1 Solution Visualiser

In an algorithm development team where each mem-

ber works in a semi-independent manner with its own

knowledge, tools and techniques, having a common

solution visualiser can help to conduct a fair com-

parison between the different solution methods devel-

oped. Moreover, a mean to visualise solutions is a

useful tool for algorithm developers that can help to

save development time. Furthermore, having a com-

mon solution visualiser allows to present results ob-

tained by different optimisation algorithms in a stan-

dardised format for practitioners to evaluate. There-

fore, the visualiser is also a communication asset for

the company and a tool by which researchers can

present progress in their development.

Ideally the solution visualiser should be platform-

free (able to run on any operating system),

programming-free (not dependant of a speciﬁc pro-

gramming language) and based just on the modelled

format (not require extra ﬁles, conﬁguration or pa-

rameters). To achieve these goals we can resort to

some options which are a standalone application, a

network-based application or a web-application. It is

clear that a web-based environmentpresents many ad-

vantages like easy-access, good presentation and ﬂex-

ibility. The decision usually depends on the nature of

the problem and preference of the development team.

Furthermore, web-based and network applications are

preferred since they make it easy to integrate with the

auxiliary platform. In our scenario, we opted to de-

velop a web-based visualisation platform.

3.3.2 Auxiliary Platform

The auxiliary platform is another tool specially con-

ceived to aid on the research of the optimisation tech-

niques. It consists of a repository of problem in-

stances and solutions, plus an automated comparator

of solutions. To compare solutions is fairly easy for

single-objective problems in which the optimisation

objective is a single scalar value. However, research

on multi-objective approaches has expanded consid-

erably and it is now common to apply such approach

to real world problems, thus making the comparison

between solutions more difﬁcult. The speciﬁcs of the

comparison are subject to the problem nature and to

what the decision makers aim to achieve in a solu-

tion. The auxiliary platform should preferably pro-

vide charts showing strong and weak points of the

solution as well as a detailed table containing useful

information. In our WSRP project, the solution com-

parator is able to handle a pair of solutions or two sets

of solutions. For the WSRP we display the distance

travelled by the workers, the distance time, the travel

cost, the payroll cost, and preferences. Also, an inte-

gration between the visualiser, the comparator and the

repository is highly desirable since the usability of the

tools escalates.

4 RESULTS

In this section we present the results obtained by ap-

plying the proposed framework to the WSRP project.

We highlight the problems found during the develop-

ment process and how applying the framework helped

us to tackle these problems.

4.1 Better Collaboration between

Researchers and Practitioners

Communication Issues. Our experience showed that

communication between the actors of the scenario

can potentially be problematic. Information, require-

ments and expectations can be different from the

perspective of researchers and practitioners, leading

ICORES2014-InternationalConferenceonOperationsResearchandEnterpriseSystems

238

to misunderstandings and misconceptions. The data

model building process played a major role in the re-

searchers’ understanding of the optimisation problem

details. During the process we were able to identify

gaps in our conceptualisation and ﬁll them during a

phase where changes caused minimal drawbacks. We

were able to identify more constraints for the optimi-

sation problem as well as alter some features to better

suit the needs of the practitioner.

Knowledge Background Issues. There is obvi-

ously difference between the background knowledge

of the company developers and that of the academic

team. Moreover, researchers in the team may have

different expertise including maths, management sci-

ence, computer science, statistics, etc. This variety

could lead to some difﬁculties such as different in-

terpretation of problem data, unfamiliarity with cer-

tain technology and different assessment of results.

With the data model we created a structured set of

ﬁles in which researchers were able to easily browse

the problem data without knowing any speciﬁc tech-

nology employed on the information system. Be-

sides, the visualisation platform proved to be a valu-

able asset to give feedback to the company. Thus,

the framework aided the academics on minimising the

knowledge-gap difﬁculties.

Integration Issues. Integrating a decision support

module to an information management system can be

difﬁcult and tricky. The ﬁrst issue is the problem data

retrieval. The second issue is applying the developed

algorithms to an existing information management

system. Integrating the solver can be difﬁcult, spe-

cially if it uses a speciﬁc tool, such as mathematical

solvers or speciﬁc software libraries. The data model

provided a layer between our developed algorithms

and the information system and the researchers found

themselves in a comfortable environment where they

employed different technologies from the ones used

by the company.

4.2 Better Research and Development

Process

Teamwork. In our example project, the academic

group was formed by several PhD students, each one

using the WSRP as a backgroundfor its PhD research.

The application of the frameworkhelped considerably

to improve communication among academics. The

optimisation problem was discussed and a common

understanding achieved. Besides, building the tools

in the proposed framework provided a good opportu-

nity for teamwork and knowledge sharing.

Rework. When developing algorithmic solutions

for a given optimisation problem, researchers tend to

create their own versions of tools such as ﬁle models,

data retrieval API’s, solution viewers and representa-

tions, etc. It is rarely the case that researchers reuse

these types of tools developed by other researcher. In-

stead, they tend to reuse algorithms in their quest to

develop improved versions of those. In our WSRP

project, the data extractor and the solution visualisa-

tion and auxiliary platform were employed by all aca-

demics. Although the deploymentof these framework

tools required some time, reusing these tools saved

considerable time and effort overall by eliminating re-

work. Along with the data model we also developed a

Java API to handle the XML ﬁles and access the data.

This API further extended the usefulness of the model

by providing the users with an easy way to access the

problem data.

Benchmarking. In real world problems, a good

benchmark dataset to evaluate the solutions achieved

during the research stage, is often difﬁcult to ob-

tain. Sometimes it is possible to estimate some close

enough targets, but as the complexity of the prob-

lem grows, such approaches become less practical.

Hence, it is necessary to have a fair and clear way to

benchmark solutions to the real world scenarios, ob-

tained with different algorithmic techniques. In our

WSRP project, the auxiliary platform provided the

team with a valuable asset to compare solutions more

fairly and improve the overall benchmarkingprocess.

5 CONCLUSIONS

Research and development projects involving univer-

sities and companies are common in decision support

systems development scenarios. Development teams

around the world usually ﬁnd their own solutions to

such problems, based on experience, trial and error.

However, despite the frequency of this situation, to

the best of our knowledge few speciﬁc methodolo-

gies can be found in the literature to help researchers

and company alike to efﬁciently develop decision

support systems in such circumstances. In particu-

lar, no previous publications seem to exist proposing

a methodological approach to develop optimisation-

based decision-support systems.

In this paper we have presented a methodologi-

cal framework to aid on the development and integra-

tion of optimisation-based decision support modules

and information management systems. The proposed

framework combines a collection of good practices

along with a data-centric development. The proposed

development and integration framework helps to: 1)

promote communication between the research team

and the company, 2) provide an interface for the inte-

ADevelopmentandIntegrationFrameworkforOptimisation-basedEnterpriseSolutions

239

gration of the solving algorithms and the information

management system and, 3) provide tools to develop

and evaluate different algorithmic solutions. More-

over, the framework aims to bestow the academic

team full independence to use whatever methods and

technologies they choose and an easy manner to inte-

grate the designed algorithms to the management in-

formation system.

We also described our experience on applying the

framework to an ongoing project for an optimisation-

based decision support system involving workforce

scheduling and routing problems (WSRP). By adher-

ing to the proposed framework, we were able to iden-

tify several features related to the optimisation prob-

lem prior to the implementation of algorithmic tech-

niques. We also were able to save development time

by avoiding work being redone since the extractor,

validator and visualiser tools were available for the

development team. Furthermore, we were able to im-

prove communication and collaboration between re-

searchers and practitioners and among researchers.

REFERENCES

Balachandra, R. and Friar, J. (1997). Factors for success in r

amp;d projects and new product innovation: a contextual

framework. Engineering Management, IEEE Transac-

tions on, 44(3):276 – 287.

Barnes, T., Pashby, I., and Gibbons, A. (2002). Effective

university industry interaction:: A multi-case evaluation

of collaborative r&d projects. European Manage-

ment Journal, 20:272 – 285.

Bhattacharya, K., Hull, R., and Su, J. (2009). A data-centric

design methodology for business processes. In Hand-

book of Research on Business Process Modeling, chapter

23, pages 503 – 531.

Bui, T. and Lee, J. (1999). An agent-based framework

for building decision support systems. Decision Support

Systems, 25(3):225 – 237.

Castillo-Salazar, J. A., Landa-Silva, D., and Qu, R. (2012).

A survey on workforce scheduling and routing problems.

Practice and Theory of Automated Timetabling, pages 29

– 31.

Cohn, D. and Hull, R. (2009). Business artifacts: A data-

centric approach to modelling business operations and

processes. In IEEE Data Engineering Bulletin 32.

Connors, D. T. (1992). Software development method-

ologies and traditional and modern information systems.

SIGSOFT Softw. Eng. Notes, 17(2):43 – 49.

Elgarah, W., Haynes, J., and Courtney, J. (2002). A dialecti-

cal methodology for decision support systems design. In

System Sciences, 2002. HICSS. Proceedings of the 35th

Annual Hawaii International Conference on.

Fontana, R., Geuna, A., and Matt, M. (2006). Factors af-

fecting universityindustry r&d projects: The impor-

tance of searching, screening and signalling. Research

Policy, 35(2):309 – 323.

Guarnaschelli, A., Chiotti, O., and Salomone, H. E. (2013).

Decision models as software artifacts - bridging the

business-software gap for decision support systems. In

ICORES 2013 - Proceedings of the 2nd International

Conference on Operations Research and Enterprise Sys-

tems, Barcelona, Spain, 16-18 February, pages 86 – 95.

Harold, E. and Means, W. (2009). XML in a Nutshell. In a

nutshell. O’Reilly Media.

Huchzermeier, A. and Loch, C. H. (2001). Project man-

agement under risk: Using the real options approach to

evaluate ﬂexibility in r&d. Manage. Sci., 47:85–101.

IBM (2013). IBM ILOG ODM Enterprise. http://www-

03.ibm.com/software/products/en/ibmilogodmente/.

Kruchten, P. (2004). The Rational Uniﬁed Process: An In-

troduction. The Addison-Wesley object technology se-

ries. Addison-Wesley.

Martin, R. (2003). Agile Software Development: Princi-

ples, Patterns, and Practices. Alan Apt Series. Prentice

Hall/Pearson Education.

Pillai, A., Joshi, A., and Rao, K. (2002). Performance mea-

surement of r&d projects in a multi-project, concur-

rent engineering environment. International Journal of

Project Management, 20(2):165 – 177.

Pinto, J. K. and Covin, J. G. (1989). Critical factors in

project implementation: a comparison of construction

and r&d projects. Technovation, 9:49 – 62.

Power, D., Burstein, F., and Sharda, R. (2011). Reﬂections

on the past and future of decision support systems: Per-

spective of eleven pioneers. In Schuff, D., Paradice, D.,

Burstein, F., Power, D. J., and Sharda, R., editors, Deci-

sion Support, volume 14 of Annals of Information Sys-

tems, pages 25–48. Springer New York.

Prasad, B. (1996). Concurrent Engineering Fundamentals:

Integrated product and process organization. Prentice-

Hall international series in industrial and systems engi-

neering. Prentice Hall PTR.

Pressman, R. (2010). Software Engineering: A Practi-

tioner’s Approach. McGraw-Hill series in computer sci-

ence. McGraw-Hill Higher Education.

Santiago, L. and Bifano, T. (2005). Management of r

d projects under uncertainty: a multidimensional ap-

proach to managerial ﬂexibility. Engineering Manage-

ment, IEEE Transactions on, 52(2):269–280.

YAML (2013). YAML Aint Markup Language.

http://www.yaml.org.

ICORES2014-InternationalConferenceonOperationsResearchandEnterpriseSystems

240