Computational Management Science (Comput Manag Sci)

Publications in this journal

• Article: Kernel logistic regression using truncated Newton method

  Maher Maalouf, Theodore B. Trafalis, Indra Adrianto
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  ABSTRACT: Kernel logistic regression (KLR) is a powerful nonlinear classifier. The combination of KLR and the truncated-regularized iteratively re-weighted least-squares (TR-IRLS) algorithm, has led to a powerful classification method using small-to-medium size data sets. This method (algorithm), is called truncated-regularized kernel logistic regression (TR-KLR). Compared to support vector machines (SVM) and TR-IRLS on twelve benchmark publicly available data sets, the proposed TR-KLR algorithm is as accurate as, and much faster than, SVM and more accurate than TR-IRLS. The TR-KLR algorithm also has the advantage of providing direct prediction probabilities. KeywordsClassification–Logistic regression–Kernel methods–Truncated Newton method
  Computational Management Science 05/2012; 8(4):415-428.
• Article: Introduction to the special issue on computational optimization under uncertainty

  Ronald Hochreiter, Georg Ch. Pflug
  Computational Management Science 05/2012; 6(2):115-116.
• Article: Gain–loss based convex risk limits in discrete-time trading

  Mustafa Ç. Pınar
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  ABSTRACT: We present an approach for pricing and hedging in incomplete markets, which encompasses other recently introduced approaches for the same purpose. In a discrete time, finite space probability framework conducive to numerical computation we introduce a gain–loss ratio based restriction controlled by a loss aversion parameter, and characterize portfolio values which can be traded in discrete time to acceptability. The new risk measure specializes to a well-known risk measure (the Carr–Geman–Madan risk measure) for a specific choice of the risk aversion parameter, and to a robust version of the gain–loss measure (the Bernardo–Ledoit proposal) for a specific choice of thresholds. The result implies potentially tighter price bounds for contingent claims than the no-arbitrage price bounds. We illustrate the price bounds through numerical examples from option pricing. KeywordsIncomplete markets–Acceptability–Martingale measure–Contingent claim–Pricing
  Computational Management Science 05/2012; 8(3):299-321.
• Article: Participating life insurance policies: an accurate and efficient parallel software for COTS clusters

  S. Corsaro, P. L. De Angelis, Z. Marino, F. Perla
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  ABSTRACT: In this paper we discuss the development of a parallel software for the numerical simulation of Participating Life Insurance Policies in distributed environments. The main computational kernels in the mathematical models for the solution of the problem are multidimensional integrals and stochastic differential equations. The former is solved by means of Monte Carlo method combined with the Antithetic Variates variance reduction technique, while differential equations are approximated via a fully implicit, positivity-preserving, Euler method. The parallelization strategy we adopted relies on the parallelization of Monte Carlo algorithm. We implemented and tested the software on a PC Linux cluster. KeywordsLife insurance policies–Monte Carlo method–Parallel computing
  Computational Management Science 05/2012; 8(3):219-236.
• Article: Solving a large scale semi-definite logit model

  Hiroshi Konno, Sadanori Kameda, Naoya Kawadai
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  ABSTRACT: This paper is concerned with an algorithm for solving a large scale semi-definite logit model which cannot be solved by an outer approximation (cutting plane) algorithm proposed earlier by one of the authors. Outer approximation algorithm can solve a problem with up to 10 financial attributes and 7,800 companies which is less than satisfactory from the viewpoint of failure discriminant analysis. The new algorithm can generate an approximately optimal solution for problems with over 14 attributes and 8,000 companies, by which the quality of failure discriminant analysis would be substantially improved. KeywordsFailure probability-Failure discriminant analysis-Quadratic logit model-Semi-definite condition Mathematics Subject Classification90-08
  Computational Management Science 05/2012; 7(2):111-120.
• Article: Linear classification tikhonov regularization knowledge-based support vector machine for tornado forecasting

  Theodore B. Trafalis, Olutayo O. Oladunni, Michael B. Richman
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  ABSTRACT: A knowledge-based linear Tihkonov regularization classification model for tornado discrimination is presented. Twenty-three attributes, based on the National Severe Storms Laboratory’s Mesoscale Detection Algorithm, are used as prior knowledge. Threshold values for these attributes are employed to discriminate the data into two classes (tornado, non-tornado). The Weather Surveillance Radar 1998 Doppler is used as a source of data streaming every 6min. The combination of data and prior knowledge is used in the development of a least squares problem that can be solved using matrix or iterative methods. Advantages of this formulation include explicit expressions for the classification weights of the classifier and its ability to incorporate and handle prior knowledge directly to the classifiers. Comparison of the present approach to that of Fung etal. [in Proceedings neural information processing systems (NIPS 2002), Vancouver, BC, December 10–12, 2002], over a suite of forecast evaluation indices, demonstrates that the Tikhonov regularization model is superior for discriminating tornadic from non-tornadic storms. KeywordsKnowledge-based model–Linear classification–Knowledge set–Prior knowledge–Tikhonov regularization–Support vector machines
  Computational Management Science 04/2012; 8(3):281-297.
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  Available from: SSRN

  Article: Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems

  Jean-Paul Watson, David L. Woodruff
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  ABSTRACT: Numerous planning problems can be formulated as multi-stage stochastic programs and many possess key discrete (integer) decision variables in one or more of the stages. Progressive hedging (PH) is a scenario-based decomposition technique that can be leveraged to solve such problems. Originally devised for problems possessing only continuous variables, PH has been successfully applied as a heuristic to solve multi-stage stochastic programs with integer variables. However, a variety of critical issues arise in practice when implementing PH for the discrete case, especially in the context of very difficult or large-scale mixed-integer problems. Failure to address these issues properly results in either non-convergence of the heuristic or unacceptably long run-times. We investigate these issues and describe algorithmic innovations in the context of a broad class of scenario-based resource allocation problem in which decision variables represent resources available at a cost and constraints enforce the need for sufficient combinations of resources. The necessity and efficacy of our techniques is empirically assessed on a two-stage stochastic network flow problem with integer variables in both stages.
  Computational Management Science 04/2012; 8(4):355-370.
• Article: Multiobjective evolutionary algorithms for complex portfolio optimization problems

  Konstantinos P. Anagnostopoulos, Georgios Mamanis
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  ABSTRACT: This paper investigates the ability of Multiobjective Evolutionary Algorithms (MOEAs), namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), for solving complex portfolio optimization problems. The portfolio optimization problem is a typical bi-objective optimization problem with objectives the reward that should be maximized and the risk that should be minimized. While reward is commonly measured by the portfolio’s expected return, various risk measures have been proposed that try to better reflect a portfolio’s riskiness or to simplify the problem to be solved with exact optimization techniques efficiently. However, some risk measures generate additional complexities, since they are non-convex, non-differentiable functions. In addition, constraints imposed by the practitioners introduce further difficulties since they transform the search space into a non-convex region. The results show that MOEAs, in general, are efficient and reliable strategies for this kind of problems, and their performance is independent of the risk function used. KeywordsMultiobjective optimization–NSGA-II–PESA–Portfolio selection–SPEA2
  Computational Management Science 04/2012; 8(3):259-279.
• Article: Real options analysis of investment in carbon capture and sequestration technology

  Somayeh Heydari, Nick Ovenden, Afzal Siddiqui
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  ABSTRACT: Among a comprehensive scope of mitigation measures for climate change, CO2 capture and sequestration (CCS) plays a potentially significant role in industrialised countries. In this paper, we develop an analytical real options model that values the choice between two emissions-reduction technologies available to a coal-fired power plant. Specifically, the plant owner may decide to invest in either full CCS (FCCS) or partial CCS (PCCS) retrofits given uncertain electricity, CO2, and coal prices. We first assess the opportunity to upgrade to each technology independently by determining the option value of installing a CCS unit as a function of CO2 and fuel prices. Next, we value the option of investing in either FCCS or PCCS technology. If the volatilities of the prices are low enough, then the investment region is dichotomous, which implies that for a given fuel price, retrofitting to the FCCS (PCCS) technology is optimal if the CO2 price increases (decreases) sufficiently. The numerical examples provided in this paper using current market data suggest that neither retrofit is optimal immediately. Finally, we observe that the optimal stopping boundaries are highly sensitive to CO2 price volatility. KeywordsReal options analysis–CCS–Geometric Brownian motion–Mutually exclusive options
  Computational Management Science 04/2012; 9(1):109-138.
• Article: Collective adjustment of pension rights in ALM models

  Willem K. Klein Haneveld, Matthijs H. Streutker, Maarten H. van der Vlerk
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  ABSTRACT: Collective adjustment of pension rights is a way to keep defined benefit systems tenable. In asset liability management (ALM) models presented in the literature these decisions are modeled both at the aggregate level of the liabilities as a whole and at a more detailed level. In this paper we compare the approximate aggregate approach to the accurate detailed approach for the average earnings scheme with conditional indexation. We prove that the aggregate approach leads to one-sided errors. Moreover, we show that for semi-realistic data these biases are considerable. KeywordsAsset liability management–Pension funds–Indexation
  Computational Management Science 04/2012; 8(1):137-156.
• Article: Portfolio selection under downside risk measures and cardinality constraints based on DC programming and DCA

  Hoai An Le Thi, Mahdi Moeini, Tao Pham Dinh
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  ABSTRACT: In this paper, we consider the case of downside risk measures with cardinality and bounding constraints in portfolio selection. These constraints limit the amount of capital to be invested in each asset as well as the number of assets composing the portfolio. While the standard Markowitz’s model is a convex quadratic program, this new model is a NP-hard mixed integer quadratic program. Realizing the computational intractability for this class of problems, especially large-scale problems, we first reformulate it as a DC program with the help of exact penalty techniques in Difference of Convex functions (DC) programming and then solve it by DC Algorithms (DCA). To check globality of computed solutions, a global method combining the local algorithm DCA with a Branch-and-Bound algorithm is investigated. Numerical simulations show that DCA is an efficient and promising approach for the considered problem.
  Computational Management Science 04/2012; 6(4):459-475.
• Article: Bottom-up design of strategic options as finite automata

  Fernando S. Oliveira
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  ABSTRACT: In this paper we look at the problem of strategic decision making. We start by presenting a new formalisation of strategic options as finite automata. Then, we show that these finite automata can be used to develop complex models of interacting options, such as option combinations and product options. Finally, we analyse real option games, presenting an algorithm to generate option games (based on automata). KeywordsAutomata-Game-Real options-Strategy
  Computational Management Science 04/2012; 7(4):355-375.
• Article: Comparative studies on dynamic programming and integer programming approaches for concave cost production/inventory control problems

  Hiroshi Konno, Takaaki Egawa, Rei Yamamoto
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  ABSTRACT: This paper is concerned with classical concave cost multi-echelon production/inventory control problems studied by W. Zangwill and others. It is well known that the problem with m production steps and n time periods can be solved by a dynamic programming algorithm in O(n 4 m) steps, which is considered as the fastest algorithm for solving this class of problems. In this paper, we will show that an alternative 0–1 integer programming approach can solve the same problem much faster particularly when n is large and the number of 0–1 integer variables is relatively few. This class of problems include, among others problem with set-up cost function and piecewise linear cost function with fewer linear pieces. The new approach can solve problems with mixed concave/convex cost functions, which cannot be solved by dynamic programming algorithms.
  Computational Management Science 04/2012; 6(4):447-457.
• Article: Computational study of the GDPO dual phase-1 algorithm

  István Maros
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  ABSTRACT: The GDPO algorithm for phase-1 of the dual simplex method developed by Maros possesses some interesting theoretical features that have potentially huge computational advantages. This paper gives account of a computational analysis of GDPO that has investigated how these features work in practice by exploring the internal operation of the algorithm. Experience of a systematic study involving 48 problems gives an insight how the predicted performance advantages materialize that ultimately make GDPO an indispensable tool for dual phase-1. KeywordsLinear programming-Dual simplex-Phase-1-Piecewise linear
  Computational Management Science 04/2012; 7(2):207-223.
• Article: An approximate solution approach for a scenario-based capital budgeting model

  Anabela Costa, José M. P. Paixão
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  ABSTRACT: Real options techniques such as contingent claims analysis and dynamic programming can be used for project evaluation when the project develops stochastically over time and the decision to invest into this project can be postponed. Following that perspective, Meier etal. (Oper Res 49(2):196–2 06, 2001) presented a scenario based model that captures risk uncertainty and managerial flexibility, maximizing the time-varying of a portfolio of investment options. However, the corresponding linear integer program turns out to be quite intractable even for a small number of projects and time periods. In this paper, we propose a heuristic approach based on an alternative scenario based model involving a much less number of variables. The new approach allows the determination of reasonable quality approximate solutions with huge reductions on the computational times required for solving large size instances. KeywordsReal options-Capital budgeting-Scenario-based optimization-0-1 Integer programming
  Computational Management Science 04/2012; 7(3):337-353.
• Article: A new path-based cutting plane approach for the discrete time-cost tradeoff problem

  Eleni Hadjiconstantinou, Evelina Klerides
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  ABSTRACT: Determining discrete time-cost tradeoffs in project networks allows for the control of the processing time of an activity via the amount of non-renewable resources allocated to it. Larger resource allocations with associated higher costs reduce activities’ durations. Given a set of execution modes (time-cost pairs) for each activity, the discrete time-cost tradeoff problem (DTCTP) involves selecting a mode for each activity so that either: (i) the project completion time is minimized, given a budget, or (ii) the total project cost is minimized, given a deadline, or (iii) the complete and efficient project cost curve is constructed over all feasible project durations. The DTCTP is a problem with great applicability prospects but at the same time a strongly NP{\mathcal N}\,P-hard optimization problem; solving it exactly has been a real challenge. Known optimal solution methodologies are limited to networks with no more than 50 activities and only lower bounds can be computed for larger, realistically sized, project instances. In this paper, we study a path-based approach to the DTCTP, in which a new path-based formulation in activity-on-node project networks is presented. This formulation is subsequently solved using an exact cutting plane algorithm enhanced with speed-up techniques. Extensive computational results reported for almost 5,000 benchmark test problems demonstrate the effectiveness of the proposed algorithm in solving to optimality for the first time some of the hardest and largest instances in the literature. The promising results suggest that the algorithms may be embedded into project management software and, hence, become a useful tool for practitioners in the future. KeywordsDiscrete time-cost tradeoff problem-Project scheduling-Cutting plane
  Computational Management Science 04/2012; 7(3):313-336.
• Article: DC programming and DCA for globally solving the value-at-risk

  Tao Pham Dinh, Nguyen Canh Nam, Hoai An Le Thi
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  ABSTRACT: The value-at-risk is an important risk measure that has been used extensively in recent years in portfolio selection and in risk analysis. This problem, with its known bilevel linear program, is reformulated as a polyhedral DC program with the help of exact penalty techniques in DC programming and solved by DCA. To check globality of computed solutions, a global method combining the local algorithm DCA with a well adapted branch-and-bound algorithm is investigated. An illustrative example and numerical simulations are reported, which show the robustness, the globality and the efficiency of DCA.
  Computational Management Science 04/2012; 6(4):477-501.
• Article: Mean-variance versus expected utility in dynamic investment analysis

  Leonard C. MacLean, Yonggan Zhao, William T. Ziemba
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  ABSTRACT: Given the existence of a Markovian state price density process, this paper extends Merton’s continuous time (instantaneous) mean-variance analysis and the mutual fund separation theory in which the risky fund can be chosen to be the growth optimal portfolio. The CAPM obtains without the assumption of log-normality for prices. The optimal investment policies for the case of a hyperbolic absolute risk aversion (HARA) utility function are derived analytically. It is proved that only the quadratic utility exhibits the global mean-variance efficiency among the family of HARA utility functions. A numerical comparison is made between the growth optimal portfolio and the mean-variance analysis for the case of log-normal prices. The optimal choice of target return which maximizes the probability that the mean-variance analysis outperforms the expected utility portfolio is discussed. Mean variance analysis is better near the mean and the expected utility maximization is better in the tails. KeywordsMarkovian state price density–Expected utility–Mean variance analysis–Growth optimal portfolio–The capital asset pricing model
  Computational Management Science 04/2012; 8(1):3-22.
• Article: Multiobjective optimization using differential evolution for real-world portfolio optimization

  Thiemo Krink, Sandra Paterlini
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  ABSTRACT: Portfolio optimization is an important aspect of decision-support in investment management. Realistic portfolio optimization, in contrast to simplistic mean-variance optimization, is a challenging problem, because it requires to determine a set of optimal solutions with respect to multiple objectives, where the objective functions are often multimodal and non-smooth. Moreover, the objectives are subject to various constraints of which many are typically non-linear and discontinuous. Conventional optimization methods, such as quadratic programming, cannot cope with these realistic problem properties. A valuable alternative are stochastic search heuristics, such as simulated annealing or evolutionary algorithms. We propose a new multiobjective evolutionary algorithm for portfolio optimization, which we call DEMPO—Differential Evolution for Multiobjective Portfolio Optimization. In our experimentation, we compare DEMPO with quadratic programming and another well-known evolutionary algorithm for multiobjective optimization called NSGA-II. The main advantage of DEMPO is its ability to tackle a portfolio optimization task without simplifications, while obtaining very satisfying results in reasonable runtime. KeywordsPortfolio optimization–Multiobjective–Real-world constraints–Value-at-Risk–Differential evolution
  Computational Management Science 04/2012; 8(1):157-179.