Operations Research, A Decision Support System for Management Sciences: Evidence through Extensive Literature

Abstract

In this research review, applications of operations research and mathematical programming has been reviewed, this review is based on literature from research publications in the field of management sciences. This article will help management sciences scholars and management professionals to get awareness of wide variety of applications of operations research in management sciences. The role of decision support system of operations research for the fields of finance, human resource management, marketing and project management has been explored in this literature review. This article will facilitate management research scholars in identifying research topics relevant to their field of interest, as operations research has a critical role of optimization, creating reliability and selection of right resources within given constraints. Operations research can help management scholars in reducing and quantifying uncertainties of business environment which have been a strong concern for them.

Full text

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Operations Research, A Decision Support System for Management
Sciences: Evidence through Extensive Literature
Mubashar Hassan Zia, Shoaib Shafique
Faculty of Management Sciences, Riphah International University, Islamabad, Pakistan
ABSTRACT
In this research review, applications of operations research and mathematical programming has been
reviewed, this review is based on literature from research publications in the field of management
sciences. This article will help management sciences scholars and management professionals to get
awareness of wide variety of applications of operations research in management sciences. The role of
decision support system of operations research for the fields of finance, human resource management,
marketing and project management has been explored in this literature review. This article will
facilitate management research scholars in identifying research topics relevant to their field of interest,
as operations research has a critical role of optimization, creating reliability and selection of right
resources within given constraints. Operations research can help management scholars in reducing
and quantifying uncertainties of business environment which have been a strong concern for them.
Key Words: Operations Research, Mathematical Programming, Decision Making, Optimization,
Management Research, Quantitative Management Sciences
Introduction
Mathematics have been the driving force for the modern management sciences, it has provided
strong basis and its attribute of quantification has made life of managers easy. In general understanding
mathematics has been treated separately from management sciences despite its contribution in the
foundations of management sciences. But in the literature of Operations Management the contribution
of mathematics is being appreciated. All of the modern day businesses require operations to run and
those operations are based on decision making. Mathematical programming or Operations Research
provide role of decision support system for managerial decision making mathematical programming
and operations research help business managers to improve the optimization in their processes. No
matter what process is being performed whether it is Finance, Human Resource Management,
Marketing and Supply Chain Management and Project Management operations research has its
implications in these processes. Operations research is helping all these processes to be achieved with
their true spirit and desired outcomes with optimal decision making. Operations research explains what
to do and how much to do in order to get maximum output. That output can be in form of production of
finished goods, labor productivity, risk minimization, identification and removal of unhealthy business
processes. Operations research is all about management and decision making; it provides the best
achievable outputs to the processes under the constraints of resources. In addition to this it explains
what outputs are more profitable and what value they are going to contribute in maximization of profits,

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7
if produced more. It provides clear insights about what to do, where to expand, where to shrink and
what to procure and what can be possible outputs under different market dynamics.
After realization of the importance operations research has for management sciences, this study
will discuss the important contributions by the research scholars in operations research with
implications in the field of management sciences specifically, Finance, HRM, Marketing and Project
Management. This study will help research scholars of management sciences, in finding new ways to
explore their areas. This study will try to remove a wrong general perception that operations research
has nothing to do with management sciences.
Research scholars have done a tremendous amount of work based on mathematical
programming in pursuit of optimal results for their research. In the field of Finance operations research
has contributed in the field of portfolio management and optimizations, stock market evaluation,
securities evaluation, financial process optimization, mutual funds industries, decision making, risk
assessment, validation of economic variables, equity portfolio construction and selection, optimization
of prices, mathematical finance, filling of duality gaps in mathematical finance, capital budgeting,
estimation of uncertainty in financial markets, project financing and arbitration, optional pricing,
removal of anomalies from stock market, profit maximization, cost effectiveness, cash management,
revenue management, multiple objective achievement, financial strategy development, empirical
estimation, detection of earnings manipulation and efficiency analysis of financial institutions.
In the field of HRM operations research has contributed in decision making process support,
managing human resource constraints, allocation of duties to the right person, job management,
scheduling, managing multiple skills for synergetic effects, decision making for employees,
performance management, performance measurement in terms of goal programming and a lot of more
dimensions of HRM.
In the field of Marketing operations research has contributed in decision support systems,
market segmentation among best available options, evaluation and selection of suppliers, enhancing
customer satisfaction, selection of methods to market, selection of media, selection of product features
to highlight, product differentiation strategy selection, prioritization among marketing functions,
product development and attribution of new products, cost reduction for marketing efforts, time
selection to market, optimal marketing mix, product assessment and evaluation, removal of
imperfections from marketing programs, supply chain optimization, marketing budgeting, inventory
management, process optimization, enhancement and estimation of return on marketing investments
and best selection of strategies.
In the field of project management operations research has contributed in project selection,
supply chain management and supplier selection, technological and product innovation management,
project scheduling, time to cost tradeoffs, resources management, precedence selection, PERT, CPM,
selection of resources on the basis of their utility contribution, production planning, identification of
variants in project management, problems reduction in supply chain management, incidents
management on the basis of multi criteria decision making, member selection for cross functional teams
for collaborative performance, disaster and recovery management, vehicle routing, hierarchy
development, distance reduction for logistics management, irrigation planning, safety and performance
enhancement, working with in limited resources, project management in transitional economies and
managing project problems with resource development and selection of right resources.

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Literature Review
Operations Research in the field of Finance
Financial decisions are one of the great concerns that a modern business organization faces,
Operations research has made life of finance managers and financial decision makers easy, by providing
them a platform where they can get optimal solutions and a right mix and match of techniques which
can provide them the desired outcomes.
The numerical DP algorithm by(Cai and Judd, 2012), gives a strong implication in finance to solve
those problems which are nontrivial concave DP. Mathematical programming is facilitating research in
finance from last 60 years(Pang, 2010). It has contributed in multiple objectives of finance through
multi-objective mathematical programming for issues like equity portfolio construction and selection
(Xidonas, Mavrotas and Psarras, 2010). When it comes to compute fair price keeping in view the
limitations of transaction costs operations research has a solution for this(Pınar and Camcı, 2012). For
portfolio optimization using bi-criteria and keeping in view the systematic risk mathematical
programming has the solution through linear programming to get the things done(SAWIK, 2012).There
are many things which have been contributed by operations research and mathematical programming
for the field of finance examples to them include: For time series calibration in mathematical finance
operations research provides linear and non linear filters (Date and Ponomareva, 2011). Using weight
assignment goal programming multiple objectives of finance are achieved as desired(Toksarı, 2010).
Pricing of stock options can be done through semi infinite linear programming with upper bounds of
infinity(Christensen, 2012). For arbitraging linear programming with absence of duality gaps can help
arbitragers to invest safely this is another implication for finance by mathematical programming
(Pennanen and Perkkiö, 2012). For optimization of capital budgets and capital assets operations
research facilitates the process through its goal programming (Lam and Cheung, 2012). In the field of
mathematical programming researchers have established Existence and Uniqueness Theorem for
uncertain differential equations which has implications for Finance in order to reduce uncertainty for,
Project Management and scheduling (Chen and Liu, 2010). Another removal of uncertainty to facilitate
investors, in case of financial markets and financial institutions can be achieved through Maximum
Entropy Principle of Operations Research (Chen and Dai, 2011). Uncertainty in financial markets can
be quantified with the help of mathematical programming thus, they can be better under stood and
actions against uncertainty can be devised (Chen, 2011). For development of multi-criteria arbitration
in project financing operations research facilitates the process through linear programming (Ballestero,
2000). Reducing the impact of capitalism with quantitative approach has been made possible through
mathematical programming, as algorithmic trading in finance can keep you on the right track of
optimization and avoidance of capitalistic frame of mind, in short mathematical programming keeps the
spirits of realism alive (Galloway, 2013). As has been discussed earlier that operations research helps
in portfolio management, it also provides information about feasibility of investment options in
financial decision making with the help of linear programming(Daum and Werner, 2011). The fields of
operations research and finance have been categorized as neighbors in the body of knowledge and both
of them have a synergy, the complexities of finance can be handled through quantitative and analytical
skills being provided by operations research, operations research can help reduce anomalies helping in
reducing risks by developing new instruments to tackle the advanced and complex financial
problems(Mulvey, 1994). Operations research has helped in reducing ambiguities and promoted
efficiency in situations where the things are not clear or vague, as it has ways to quantify even the
uncertainties through Dynamic Programming (Iyengar, 2005). If estimation of optimal profits is
required mathematical programming has the solution for you by even having an add-on of mitigating

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risk and ensuring a certain level of profitability according to the market trends(Novais, 2011).
Operations research can help in estimation and optimization along with a comprehensive analysis of
outsourcing cost effectiveness with linear multiple goal programming (Wang and Chen, 2010). Cash
management and many other complex problems of finance like value at risk in portfolio optimization
can be handled with the help of mathematical formulation and operations research (Sawik,
2011).Revenue management under the customer choice is the requirement of modern financial markets
where competition is at its peak, but it is a difficult task to achieve, it is difficult but not impossible as
mathematical programming helps in preference modeling with optimality for diverse customers with
different needs (Chen and Homem-de-Mello, 2010). Handling multiple ratios can facilitate finance and
economics body of knowledge by use of fractional linear programming(Charles, Udhayakumar and
Uthariaraj, 2010). Portfolio estimation and selection has been the point where operations research and
finance have been found to be hand in hand together (Fabozzi, Huang and Zhou, 2010). Linear
programming can help in forecasting of gains-loss pricing for arbitragers in the international market,
who want to invest simultaneously in different markets (Pınar, Salih and Camcı, 2010). Operations
research has been supporting the evaluation of alternatives for times where they should be accessed on
the basis of prioritization (Koivu and Pennanen, 2010). Variations and inequalities can be quantified
and controlled with the help of linear programming (Chidume, Chidume and Ali, 2011). Portfolio
selection has been made easy through fuzzy goal programming (Ahari et al., 2011). Estimation for
financial and economic problems are largely solved through mathematical programming (Kanková,
2012).In case of imperfect markets, the earnings manipulation can be traced through three phase cutting
plane algorithm(Dikmen and Küçükkocaoğlu, 2010). Micro financial institutions can measure their
efficiency through mathematical programming and results can facilitate them in improving processes
and increasing growth (Ahmad, 2011). Multistage optimization can be done through linear weighted
goal programming by achieving all the goals which are desired, secondly the desire is directed towards
reality based on quantifiable evidences(Shapiro, 2011). Symmetry of portfolios can also be easily
understood with the help of mathematical programming (Li, Qin and Kar, 2010). Mathematical
programming is being used by many financial institutions for estimation of Forex trends in the
international market (Bastin, Cirillo and Toint, 2010).
“Financial Decision making and financial research can be performed in a better way by use of operations
research.”
Operations Research in the Field of Human Resource Management
Normally organizations are facing problems related to human resources in the form of
constraints, as they have to rely on minimal human resource, as the human resource has some limitations
of expertise and is expensive for an organization. Every organization in the modern business world is
looking for optimal usage of human resource, so they can remain best at the cost and expertise side.
Operations management helps human resource experts to have an optimal combination keeping in the
scenario the valid constraints of holding and using the human recourse. Employee time table allocation,
task assignment with the balance level of expertise on the floor, in the presence of limitations on the
resources to perform the job can be optimized using Assignment model of operations research with
integrated linear programming approach of goal programming in order to get the desired level of
productivity from the human resource allocated on job (Guyon et al., 2012). Similarly task assignment
in the operation theatre or some project room can benefit from operations research models of assignment
model, PERT, CPM and linear programming (Guerriero and Guido, 2011). In the field of software
development multiple human resources are working in multiple locations and they have to be managed
on the basis of their competency, the critical nature of their module and their working efficiency, but
keeping in mind their individual level constraints which are diverse in their nature from individual to
individual, this all can be done in an optimal way with the help of operations research models of linear

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programming and assignment model(Kang, Jung and Bae, 2011). Human resource management has
difficulties in managing diverse people with diverse competencies, every individual has a specific skill
set, and organizations have a certain skill target to be utilized in order to get desired organizational
outcomes, this all is made easy through operations research linear programming models of
maximization with a right combination of resources which are limited (Heimerl and Kolisch, 2010).
Even while hiring employees or assigning them tasks, human resource managers need to come over
many biases and heuristics, which can guide them to a wrong decision, operations research helps those
HR managers to make right decisions of selecting right personnel for the right jobs (Kelemenis and
Askounis, 2010). Organizations which have been using Operations research for their decision support
system have been found to be more productive in comparison to those organizations which have been
avoiding operations research applications in the field of human resource management (de Menezes,
Wood and Gelade, 2010). Research by Sinha & Nabendu Sen, (2011)have explored the benefit of
operations research goal programming in the tea industry, that how much effectively their operations
have been working using goal programming technique. In short operations research has been
contributing in the field of Human Resource Management as decision support system provide for
optimal and desired organizational outcomes (Dodangeh et al., 2010).
“HRM Decision making and HRM research can be performed in a better way by use of operations
research.”
Operations Research in the Field of Marketing
Considering the field of marketing and its growth in the current times, it is very clear that the
marketing industry has been working on very much optimal and realistic combination of their activities
for their huge success. They are the ones who can make and break a product in the mind of the customer,
so they have a huge responsibility on their shoulders. They are working in an environment where
competition is at its peak and they have zero tolerance towards errors and miscommunications. Having
all these constraints how marketing people are performing so well, organizations have been using
operations research a quantitative approach towards management sciences which has been helping
marketing people to work with precision and accuracy. Operations research helps marketing people in
selection of target markets, and segmentation (Masmoudi et al., 2010). When it comes to selection of
suppliers in the supply chain management operations research is there to help as a decision support
system (Amindoust, Ahmed and Ketabi, 2010). Decisions relative to product attributes and product mix
are also aided by operations research (PANDIAN, NAGARAJAN and YAACOB, 2007). Even when it
comes to optimality of marketing tools the operations research linear programming has a lot to offer
(Ishiguro and Amasaka, 2012). Prioritizing the supply chain constructs like logistics are dependent on
operations research and mathematical programming tools like shortest route model, network models
and simple linear programming for reduction of marketing cost and optimality of outputs (Strang, 2012).
When integration of marketing is required with engineering departments in development of a new
product according to the needs and wants of the customer quantification of marketing inputs is done
with the help of linear programming and goal programming (Kwong, Chen and Chan, 2011). For
estimations of results and combinations of different techniques of viral marketing on social media the
decisions are made on the basis of outputs provided by operations research (Dinh, Nguyen and Thai,
2012). Transportation model, transshipment model and assignment model of operations research helps
in determining optimal outflows and inflows of products and raw materials in supply chain
management(Bayati, Rasti Barzoki and Hejazi, 2011). Supply chain management optimization is done
and completely dependent on operations research (Gheidar-Kheljani, Ghodsypour and Ghomi, 2010).
In agro based economies the agricultural marketing decisions are purely based on mathematical
programming (Mohamad and Said, 2011). Marketing budgets is a concern which lies between two
departments namely, Marketing and Finance department integration among these departments and

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decisions relative to allocation of budgets to specific products for the sake of marketing are also based
on mathematical linear programming(Albadvi and Koosha, 2011). In short marketing decisions at large
are dependent on operations research implications, in order to remain optimal and target oriented as
operations research is acting as a decision support system for whole of the management sciences at a
broader level (Dodangeh et al., 2010).
“Marketing Decision making and Marketing research can be performed in a better way by use of
operations research.”
Operations Research in the Field of Project Management
When projects are to be executed by the businesses for growth and expansion, there is a lot of
stake associated with these projects. Firstly they are most of the times new projects with high risk
associated and even if they are replication of some old project activity the environmental variables are
always different which project the risk association. Secondly project managers need to be certain about
their decision making, so they are always searching for a reliable decision support system, which can
provide them with optimal, reliable estimations, so that they can start with confidence and lesser risks
and even those risks are quantifiable due to help of operations research. When it comes to project
selection operations research facilitates project managers through its linear programming and goal
programming for selection of appropriate projects (Amiri, 2010). From technological inventions to
product innovations all projects rely heavily on operations research for their success, optimal allocation
of resources is required, diversity and uncertainty needs to be controlled and measured, this all is
possible through mathematical programming (Ahn, Zwikael and Bednarek, 2010). In project
management scheduling and allocation of resources the decisions are taken on the basis of stochastic
modeling with cross entropy, which are integral part of operations research (Bendavid and Golany,
2011). Time cost trade off decisions are facilitated by goal programming and linear programming for
better management of projects (Ke et al., 2010). Resource selection and resource allocation in project
management is done with the help of boundary interval programming of operations research (Li, Huang
and Nie, 2010). Precedence of processes and tasks in project management are performed through PERT,
CPM and genetic algorithms of operations research (Yun and Moon, 2009). Scheduling of operations
is critical in achieving success in the project management, all activities are to be schedule in such a way
that activities finish on time and quality is not compromised; all the interlinked processes are scheduled
in such a way that they complement each other’s efficiency through multi period and multi product
model (Feylizadeh and Bagherpour, 2011).Resource constrained project scheduling is a difficult task
made easy by use of algorithm RCPSP which can keep a project going on with diverse nature of resource
problems (Hartmann and Briskorn, 2008). A green supply chain in compliance with WEEE and RoHS
and EU directives can result in increased cost and financial losses but the efficiency of supply chain for
project facilitation is done through goal programming models, which keep and ensure the right balance
of activities in supply chain management (Che, 2010). MCDM multi criteria decision making model,
removes uncertainties in new projects management as it quantifies uncertainties and controls them and
provides information of what amount of counter measures are required to overcome uncertainties (Peng
et al., 2011). How to improve collaborative performance can be realized through genetic algorithm of
linear programming and facilitates work performance of projects (Feng et al., 2010). In a broader
context decision support systems relative to project management decision making are made optimal,
feasible and reliable with the help of operations research (Rolland et al., 2010). There are many
algorithms in operations research which cover minute activities like packing and loading and shortest
route selection all these algorithms of operations research provide optimal appropriate solution for the
decision makers (Leung et al., 2011). Signed distance model in integration with linear programming

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and goal programming are helpful for project management with multiple objectives achieved with
optimality (Wang and Yang, 2011). Multi goal programming helps in planning of projects with
complexities, where multiple outputs are required and desired by the project managers (Regulwar and
Gurav, 2010). Creating benchmarks of safety performance are another dimension that has facilitated
project management from the basis from operations research (El-Mashaleh, Rababeh and Hyari, 2010).
“Project Management Decision making and Project Management research can be performed in a better
way by use of operations research.”
Discussion & Conclusion
Management sciences is a broad field, and business are working with the help of management
professionals, management professionals are facing a lot of problems in their decision making due to
very high competition and ever changing market dynamics. Management professionals are humans and
they have a dimension of experience which can drive them away from realization of changing dynamics
of the business environment. In order to improve and sustain working of management professionals
they require decision support systems and mathematical problem formulation is an answer to the
requirement of management professionals. Operations research has been helping management
professionals in their endeavors acting as a decision support system. Development of mathematical
programs seems difficult but can be easily done, if theoretical concepts behind management problems
are well understood. These programs have to be formulated only once, and then they perform difficult
tasks for managers in a very quick time. Operations research has been established as a quantitative
approach towards solving management problems.
Operations research has been solving problems of management professionals from long; it has many
applications in the field of management sciences. Specifically decision making with respect to finance,
human resource management, marketing and project management is facilitated by operations research
and mathematical programming. In this research review the literature has been reviewed to highlight
those areas in which operations research has been carried out in order to facilitate managerial decision
making in the field of management sciences. All the applications of operations research discussed in
this review have provided conformance of optimality and effectiveness of practices which are based on
it. Optimal decision making helps in selecting the right mix of activities with right weight in order to
get the things done in a better way in this changing business world. Operations research has been
concern of research for management and mathematical scholars with emphasis on applied and practical
side of the business world. Mathematical Programming or Operations Research is done in order to get
optimal solutions, and future investments guidelines based on their sensitivity analysis.
There is a misconception among management scholars that operations research has no application in
the field of management sciences. This review will possibly reduce their point of view and will provide
them with a research dimension which is needed by management scholars to be explored. This review
has endorsed the applicability of operations research in the field of management sciences.
Summing it up operations research is necessary for management professionals in order to make reliable
and optimal decisions in uncertain and dynamic situations where competition is all time high and the
business are fighting for their survival and growth.
References

Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 5-16
13
Ahari, S. et al. (2011) ‘A portfolio selection using fuzzy analytic hierarchy process: A case study of
Iranian pharmaceutical industry’, Journal of Industrial, 2, pp. 225–236. doi:
10.5267/j.ijiec.2010.03.001.
Ahmad, U. (2011) ‘Efficiecy Analysis of Micro-finance Institutions in Pakistan’, Research
Economist, (34215).
Ahn, M. J., Zwikael, O. and Bednarek, R. (2010) ‘Technological invention to product innovation: A
project management approach’, International Journal of Project Management. Elsevier Ltd and IPMA,
28(6), pp. 559–568. doi: 10.1016/j.ijproman.2009.11.001.
Albadvi, A. and Koosha, H. (2011) ‘A robust optimization approach to allocation of marketing
budgets’, Management Decision, 49(4), pp. 601–621. doi: 10.1108/00251741111126512.
Amindoust, A., Ahmed, S. and Ketabi, S. (2010) ‘Evaluation and Selection of Supplier in Supply
Chain Network Based on DEA’, The 14th Asia Pacific Regional Meeting of International Foundation
for Production Research, 7(December), pp. 7–10.
Amiri, M. P. (2010) ‘Project selection for oil-fields development by using the AHP and fuzzy
TOPSIS methods’, Expert Systems with Applications. Elsevier Ltd, 37(9), pp. 6218–6224. doi:
10.1016/j.eswa.2010.02.103.
Ballestero, E. (2000) ‘Project finance: A multi-criteria approach to arbitration’, Journal of the
Operational Research Society, 51(2), pp. 183–197. Available at:
http://www.jstor.org/stable/10.2307/254259 (Accessed: 18 May 2013).
Bastin, F., Cirillo, C. and Toint, P. (2010) ‘Formulation and solution strategies for nonparametric
nonlinear stochastic programmes with an application in finance’, Optimization. Available at:
http://www.tandfonline.com/doi/abs/10.1080/02331931003700707 (Accessed: 18 May 2013).
Bayati, M. F., Rasti Barzoki, M. and Hejazi, S. R. (2011) ‘A joint lot-sizing and marketing model
with reworks, scraps and imperfect products’, International Journal of Industrial Engineering
Computations, 2(2), pp. 395–408. doi: 10.5267/j.ijiec.2010.07.005.
Bendavid, I. and Golany, B. (2011) ‘Setting gates for activities in the stochastic project scheduling
problem through the cross entropy methodology’, Annals of Operations Research, 189(1), pp. 25–42.
doi: 10.1007/s10479-011-0940-1.
Cai, Y. and Judd, K. (2012) ‘Dynamic programming with shape-preserving rational spline Hermite
interpolation’, Economics Letters. Elsevier B.V. doi: 10.1016/j.econlet.2012.05.004.
Charles, V., Udhayakumar, A. and Uthariaraj, V. R. (2010) ‘An approach to find redundant objective
function (s) and redundant constraint (s) in multi-objective nonlinear stochastic fractional
programming problems’, European Journal of …. Elsevier B.V., 201(2), pp. 390–398. doi:
10.1016/j.ejor.2009.03.020.
Che, Z. H. (2010) ‘Using fuzzy analytic hierarchy process and particle swarm optimisation for
balanced and defective supply chain problems considering WEEE/RoHS directives’, International
Journal of Production Research, 48(11), pp. 3355–3381. doi: 10.1080/00207540802702080.
Chen, L. and Homem-de-Mello, T. (2010) ‘Mathematical programming models for revenue
management under customer choice’, European Journal of Operational Research. Available at:
http://www.sciencedirect.com/science/article/pii/S037722170900530X (Accessed: 18 May 2013).

Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 5-16
14
Chen, X. (2011) ‘American option pricing formula for uncertain financial market’, International
Journal of Operations Research, 8(2), pp. 27–32. Available at: http://orsc.edu.cn/online/110425.pdf
(Accessed: 18 May 2013).
Chen, X. and Dai, W. (2011) ‘Maximum entropy principle for uncertain variables’, International
Journal of Fuzzy Systems, 13(3), pp. 232–236. Available at:
http://www.orsc.edu.cn/online/090618.pdf (Accessed: 18 May 2013).
Chen, X. and Liu, B. (2010) ‘Existence and uniqueness theorem for uncertain differential equations’,
Fuzzy Optimization and Decision Making, pp. 69–81. doi: 10.1007/s10700-010-9073-2.
Chidume, C., Chidume, C. and Ali, B. (2011) ‘Convergence of hybrid steepest descent method for
variational inequalities in Banach spaces’, Applied Mathematics and Computation, (July), pp. 1–11.
Available at: http://www.sciencedirect.com/science/article/pii/S0096300311003225 (Accessed: 18
May 2013).
Christensen, S. (2012) ‘A METHOD FOR PRICING AMERICAN OPTIONS USING SEMI‐
INFINITE LINEAR PROGRAMMING’, Mathematical Finance, pp. 1–17. Available at:
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9965.2012.00523.x/full (Accessed: 18 May 2013).
Date, P. and Ponomareva, K. (2011) ‘Linear and non-linear filtering in mathematical finance: a
review’, IMA Journal of Management Mathematics. doi: 10.1093/imaman/dpq008.
Daum, S. and Werner, R. (2011) ‘A novel feasible discretization method for linear semi-infinite
programming applied to basket option pricing’, Optimization, (May 2013), pp. 37–41. Available at:
http://www.tandfonline.com/doi/abs/10.1080/02331934.2011.619263 (Accessed: 18 May 2013).
Dikmen, B. and Küçükkocaoğlu, G. (2010) ‘The detection of earnings manipulation: the three‐phase
cutting plane algorithm using mathematical programming’, Journal of Forecasting, pp. 1–28.
Available at: http://onlinelibrary.wiley.com/doi/10.1002/for.1138/abstract (Accessed: 18 May 2013).
Dinh, T. N., Nguyen, D. T. and Thai, M. T. (2012) ‘Cheap , Easy , and Massively Effective Viral
Marketing in Social Networks : Truth or Fiction ? Categories and Subject Descriptors’, ACM.
Dodangeh, J. et al. (2010) ‘Using Topsis Method with Goal Programming for Best selection of
Strategic Plans in BSC Model’, Journal of American Science, 6(3).
El-Mashaleh, M. S., Rababeh, S. M. and Hyari, K. H. (2010) ‘Utilizing data envelopment analysis to
benchmark safety performance of construction contractors’, International Journal of Project
Management. Elsevier Ltd and IPMA, 28(1), pp. 61–67. doi: 10.1016/j.ijproman.2009.04.002.
Fabozzi, F., Huang, D. and Zhou, G. (2010) ‘Robust portfolios: contributions from operations
research and finance’, Annals of Operations Research, pp. 191–220. doi: 10.1007/s10479-009-0515-6.
Feng, B. et al. (2010) ‘A method for member selection of cross-functional teams using the individual
and collaborative performances’, European Journal of Operational Research. Elsevier B.V., 203(3),
pp. 652–661. doi: 10.1016/j.ejor.2009.08.017.
Feylizadeh, M. R. and Bagherpour, M. (2011) ‘Application of Optimization Techniques in Production
Planning Context: A Review and Extension’, International Journal of Manufacturing Systems, 1(1).
Galloway, A. (2013) ‘The Poverty of Philosophy: Realism and Post-Fordism’, Critical Inquiry, 39(2),
pp. 347–366. Available at: http://www.jstor.org/stable/10.1086/668529 (Accessed: 18 May 2013).
Gheidar-Kheljani, J., Ghodsypour, S. H. and Ghomi, S. M. T. F. (2010) ‘Supply chain optimization
policy for a supplier selection problem : a mathematical programming approach’, Iranian Journal of
Operations Research, 2(1), pp. 17–31.

Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 5-16
15
Guerriero, F. and Guido, R. (2011) ‘Operational research in the management of the operating theatre:
a survey.’, Health care management science, 14(1), pp. 89–114. doi: 10.1007/s10729-010-9143-6.
Guyon, O. et al. (2012) ‘Solving an integrated job-shop problem with human resource constraints’, in
Annals of Operations Research. doi: 10.1007/s10479-012-1132-3.
Hartmann, S. and Briskorn, D. (2008) A survey of variants and extensions of the resource-
constrained project scheduling problem, European Journal of Operational Research.
Heimerl, C. and Kolisch, R. (2010) ‘Work assignment to and qualification of multi-skilled human
resources under knowledge depreciation and company skill level targets’, International Journal of
Production Research, 48(13), pp. 3759–3781. doi: 10.1080/00207540902852785.
Ishiguro, H. and Amasaka, K. (2012) ‘Proposal And Effectiveness Of A Highly Compelling Direct
Mail Method - Establishment And Deployment Of PMOS-DM’, International Journal of Management
& Information Systems, 16(1), pp. 1–10.
Iyengar, G. (2005) ‘Robust dynamic programming’, Mathematics of Operations Research, 30(2), pp.
257–280. doi: 10.1287/moor.1040.0129.
Kang, D., Jung, J. and Bae, D. (2011) ‘Constraint-based human resource allocation in software
projects’, SOFTWARE – PRACTICE AND EXPERIENCE, 41, pp. 551–577. doi: 10.1002/spe.
Kanková, V. (2012) ‘Empirical Estimates in Economic and Financial Problems via Heavy Tails’,
Proceedings of the 30-th International Conference …, pp. 396–401. Available at:
http://library.utia.cas.cz/separaty/2012/E/Kankova-empirical estimates in economic and financial
problems via heavy tails.pdf (Accessed: 18 May 2013).
Ke, H. et al. (2010) ‘New fuzzy models for time-cost trade-off problem’, Fuzzy Optimization and
Decision Making, 9(2), pp. 219–231. doi: 10.1007/s10700-010-9076-z.
Kelemenis, A. and Askounis, D. (2010) ‘A new TOPSIS-based multi-criteria approach to personnel
selection’, Expert Systems with Applications. Elsevier Ltd, 37(7), pp. 4999–5008. doi:
10.1016/j.eswa.2009.12.013.
Koivu, M. and Pennanen, T. (2010) ‘Galerkin methods in dynamic stochastic programming’,
Optimization. doi: 10.1080/0233193YYxxxxxxxx.
Kwong, C. K., Chen, Y. and Chan, K. Y. (2011) ‘A methodology of integrating marketing with
engineering for defining design specifications of new products’, Journal of Engineering Design, 22(3),
pp. 201–213. doi: 10.1080/09544820903173180.
Lam, K. and Cheung, S. (2012) ‘Capital budgeting evaluation practices of building contractors in
Hong Kong’, … Economics and Building, 2(1), pp. 81–93. Available at:
http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/2889 (Accessed: 18 May 2013).
Leung, S. C. H. et al. (2011) ‘Extended guided tabu search and a new packing algorithm for the two-
dimensional loading vehicle routing problem’, Computers & Operations Research. Elsevier, 38(1), pp.
205–215. doi: 10.1016/j.cor.2010.04.013.
Li, X., Qin, Z. and Kar, S. (2010) ‘Mean-variance-skewness model for portfolio selection with fuzzy
returns’, European Journal of Operational Research. Elsevier B.V., 202(1), pp. 239–247. doi:
10.1016/j.ejor.2009.05.003.
Li, Y. P., Huang, G. H. and Nie, S. L. (2010) ‘Planning water resources management systems using a
fuzzy-boundary interval-stochastic programming method’, Advances in Water Resources, 33(9), pp.
1105–1117. doi: 10.1016/j.advwatres.2010.06.015.

Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 5-16
16
Masmoudi, Y. et al. (2010) ‘A Mathematical Programming Based Procedure for Breast Cancer
Classification’, Journal of Mathematical Modelling and Algorithms, 9(3), pp. 247–255. doi:
10.1007/s10852-010-9138-9.
de Menezes, L. M., Wood, S. and Gelade, G. (2010) ‘The integration of human resource and
operation management practices and its link with performance: A longitudinal latent class study’,
Journal of Operations Management. Elsevier B.V., 28(6), pp. 455–471. doi:
10.1016/j.jom.2010.01.002.
Mohamad, N. H. and Said, F. (2011) ‘A mathematical programming approach to crop mix problem’,
African Journal of Agricultural Research, 6(1), pp. 191–197. doi: 10.5897/AJAR10.028.
Mulvey, J. (1994) ‘Introduction to the special issue on finance’, Interfaces, 24(3), pp. 1–2. Available
at: http://interfaces.journal.informs.org/content/24/3/1.short (Accessed: 18 May 2013).
Novais, A. (2011) ‘Profit, rent, patrimony, and risk on the large landed estates in Southern Portugal
toward the end of the nineteenth century*’, Análise social, 46(198), pp. 5–39. Available at:
http://www.scielo.oces.mctes.pt/pdf/aso/n198/n198a01.pdf (Accessed: 18 May 2013).
PANDIAN, M. V., NAGARAJAN, R. and YAACOB, S. (2007) ‘Fuzzy linear programming: a
modern tool for decision making’, Jurnal Teknologi, 37(D), pp. 31–43.
Pang, J. (2010) ‘Three modeling paradigms in mathematical programming’, Mathematical
programming, pp. 297–323. doi: 10.1007/s10107-010-0395-1.
Peng, Y. et al. (2011) ‘An incident information management framework based on data integration,
data mining, and multi-criteria decision making’, Decision Support Systems. Elsevier B.V., 51(2), pp.
316–327. doi: 10.1016/j.dss.2010.11.025.
Pennanen, T. and Perkkiö, A. (2012) ‘Stochastic programs without duality gaps’, Mathematical
programming, pp. 1–20. Available at: http://link.springer.com/article/10.1007/s10107-012-0552-9
(Accessed: 18 May 2013).
Pınar, M. and Camcı, A. (2012) ‘An Integer Programming Model for Pricing American Contingent
Claims under Transaction Costs’, Computational Economics, pp. 1–12. doi: 10.1007/s10614-010-
9209-z.
Pınar, M., Salih, A. and Camcı, A. (2010) ‘Expected gain–loss pricing and hedging of contingent
claims in incomplete markets by linear programming’, European Journal of Operational Research, pp.
1–26. Available at: http://www.sciencedirect.com/science/article/pii/S0377221709001283 (Accessed:
18 May 2013).
Regulwar, D. G. and Gurav, J. B. (2010) ‘Fuzzy Approach Based Management Model for Irrigation
Planning’, Journal of Water Resource and Protection, 02(06), pp. 545–554. doi:
10.4236/jwarp.2010.26062.
Rolland, E. et al. (2010) ‘Decision support for disaster management’, Operations Management
Research, 3(1–2), pp. 68–79. doi: 10.1007/s12063-010-0028-0.
Sawik, B. (2011) ‘Conditional value-at-risk and value-at-risk for portfolio optimization model with
weighting approach’, Automatyka, AGH, pp. 429–434. Available at:
http://journals.bg.agh.edu.pl/AUTOMATYKA/2011-02/Auto42.pdf (Accessed: 18 May 2013).
SAWIK, B. (2012) ‘Bi-Criteria Portfolio Optimization Models with Percentile and Symmetric Risk
Measures by Mathematical Programming’, Przegląd Elektrotechniczny (Electrical Review), (10), pp.
176–180. Available at: http://pe.org.pl/articles/2012/10b/47.pdf (Accessed: 18 May 2013).

Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 5-16
17
Shapiro, A. (2011) ‘A dynamic programming approach to adjustable robust optimization’, Operations
Research Letters. Elsevier B.V., 39(2), pp. 83–87. doi: 10.1016/j.orl.2011.01.001.
Sinha, B. and Nabendu Sen (2011) ‘Goal Programming Approach to Tea Industry of Barak Valley of
Assam’, Applied Mathematical Sciences, 5(29), pp. 1409–1419.
Strang, K. D. (2012) ‘Prioritization and Supply Chain Logistics as a Marketing Function in a Mining
Company’, Journal of Marketing Channels, 19(2), pp. 141–155. doi:
10.1080/1046669X.2012.667763.
Toksarı, M. (2010) ‘Taylor series approach for bi-level linear fractional programming problem’,
Selcuk Journal of Applied Mathematics, 11(1), pp. 63–69. Available at:
http://dergi.selcuk.edu.tr/index.php/sjam/article/view/253 (Accessed: 18 May 2013).
Wang, E. and Chen, Y. (2010) ‘Analysis of outsourcing cost-effectiveness using a linear
programming model with fuzzy multiple goals’, International Journal of …, (May 2013), pp. 37–41.
doi: 10.1080/00207540903175046.
Wang, M.-J. and Yang, M.-F. (2011) ‘APPLICATION OF SIGNED DISTANCES METHOD TO
INTEGRATE LINEAR PROGRAMMING FOR FUZZY MULTI-OBJECTIVE’, International
Journal of Innovative Computing, Information and Control, 7(1), pp. 237–252.
Xidonas, P., Mavrotas, G. and Psarras, J. (2010) ‘Equity portfolio construction and selection using
multiobjective mathematical programming’, Journal of Global Optimization, pp. 185–209. doi:
10.1007/s10898-009-9465-4.
Yun, Y. and Moon, C. (2009) ‘Genetic algorithm approach for precedence-constrained sequencing
problems’, Journal of Intelligent Manufacturing, 22(3), pp. 379–388. doi: 10.1007/s10845-009-0296-
4.