Optimization (OPTIMIZATION )

Publisher: Taylor & Francis

Description

Optimization publishes refereed, theoretical and applied papers on the latest developments in fields such as linear, nonlinear, stochastic, parametric, discrete and dynamic programming, control theory and game theory. A special section is devoted to review papers on theory and methods in interesting areas of mathematical programming and optimization techniques. The journal also publishes conference proceedings, book reviews and announcements.

  • Impact factor
    0.71
    Show impact factor history
     
    Impact factor
  • 5-year impact
    0.70
  • Cited half-life
    9.50
  • Immediacy index
    0.11
  • Eigenfactor
    0.00
  • Article influence
    0.47
  • Website
    Optimization website
  • Other titles
    Optimization (Online)
  • ISSN
    0233-1934
  • OCLC
    50446924
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Taylor & Francis

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 12 month embargo for STM, Behavioural Science and Public Health Journals
    • 18 month embargo for SSH journals
  • Conditions
    • Some individual journals may have policies prohibiting pre-print archiving
    • Pre-print on authors own website, Institutional or Subject Repository
    • Post-print on authors own website, Institutional or Subject Repository
    • Publisher's version/PDF cannot be used
    • On a non-profit server
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statements to accompany deposits (see policy)
    • Publisher will deposit to PMC on behalf of NIH authors.
    • STM: Science, Technology and Medicine
    • SSH: Social Science and Humanities
    • 'Taylor & Francis (Psychology Press)' is an imprint of 'Taylor & Francis'
  • Classification
    ​ yellow

Publications in this journal

  • Optimization 06/2015;
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    ABSTRACT: In this article we propose a simple heuristic algorithm for approaching the maximally predictable portfolio, which is constructed so that return model of the resulting portfolio would attain the largest goodness-of-fit. It is obtained by solving a fractional program in which a ratio of two convex quadratic functions is maximized, and the number of variables associated with its nonconcavity has been a bottleneck in spite of continuing endeavour for its global optimization. The proposed algorithm can be implemented by simply solving a series of convex quadratic programs, and computational results show that it yields within a few seconds a (near) Karush–Kuhn–Tucker solution to each of the instances which were solved via a global optimization method in [H. Konno, Y. Takaya and R. Yamamoto, A maximal predictability portfolio using dynamic factor selection strategy, Int. J. Theor. Appl. Fin. 13 (2010) pp. 355–366]. In order to confirm the solution accuracy, we also pose a semidefinite programming relaxation approach, which succeeds in ensuring a near global optimality of the proposed approach. Our findings through computational experiments encourage us not to employ the global optimization approach, but to employ the local search algorithm for solving the fractional program of much larger size.
    Optimization 11/2014; 63(11).
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    ABSTRACT: We consider the problem of finding an arrangement of rectangles with given areas that minimizes the total length of all inner and outer border lines. We present a polynomial time approximation algorithm and derive an upper bound estimation on its approximation ratio. Furthermore, we give a formulation of the problem as mixed-integer nonlinear program and show that it can be approximatively reformulated as linear mixed-integer program. On a test set of problem instances, we compare our approximation algorithm with another one from the literature. Using a standard numerical mixed-integer linear solver, we show that adding the solutions from the approximation algorithm as advanced starter helps to reduce the overall solution time for proven global optimality, or gives better primal and dual bounds if a certain time-limit is reached before.
    Optimization 11/2014; 63(11).
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    ABSTRACT: This work considers the allocation problem for multivariate stratified random sampling as a problem of integer non-linear stochastic multiobjective mathematical programming. With this goal in mind the asymptotic distribution of the vector of sample variances is studied. Two alternative approaches are suggested for solving the allocation problem for multivariate stratified random sampling. An example is presented by applying the different proposed techniques.
    Optimization 11/2014; 63(11).
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    ABSTRACT: Supplier selection problem has been a typical concern of optimization where good supplier selection is essential for firms in the context of supply chain management. If a firm networks with different suppliers, each one having its own advantages over the others, then there is great opportunity to reduce a firm’s risk arising from supply concentration and also to provide it with the opportunity to leverage its comparative strength. In this paper, we propose a new approach referred to as Supplier Portfolio Optimization (SPO) that improves the state of the art of supplier selection by incorporating the benefits of supplier diversification using the trade-offs between the criteria of expected unit price, expected score of quality and expected score of delivery. We present three different optimization models with interval coefficients to model the uncertain SPO problem corresponding to optimistic, pessimistic and combination strategies. Further, using lower and upper bonds for fractions of order quantity, we ensure supplier portfolio diversification and also avoidance of the situation of impractical fraction of quantity ordered. Numerical experiments conducted on a dataset of a multinational firm are provided to demonstrate the applicability and efficiency of the SPO models to real-world applications of supplier selection.
    Optimization 10/2014; 63(10).
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    ABSTRACT: With the globalization of the economy, more and more companies have made and will continue to make project investment abroad. This paper discusses a multinational project adjustment and selection problem in which project parameters are regarded as random variables. Special cash flows and value sources brought from the new foreign projects and the adjustment of the existing foreign projects are introduced. A new risk measurement for budget exceeding is proposed and a new model is developed based on the new risk measurement. In addition, a genetic algorithm is designed for solving the problem and an example is given as an illustration. The results of the example show that the algorithm is effective for solving the proposed problem. In addition, the results show that the budget can be better used and bigger investment return in net present form can be obtained when new foreign project selection and the existing project adjustment are simultaneously considered than when only new projects are selected.
    Optimization 10/2014; 63(10).
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    ABSTRACT: The aim of this paper is to propose a solution method for the minimization of a class of generalized linear functions on a flow polytope. The problems will be solved by means of a network algorithm, based on graph operations, which lies within the class of the so-called ‘optimal level solutions’ parametric methods. The use of the network structure of flow polytopes, allows to obtain good algorithm performances and small numerical errors. Results of a computational test are also provided.
    Optimization 10/2014; 63(10).
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    ABSTRACT: Many software reliability growth models (SRGMs) have developed in the past three decades to estimate software reliability measures such as the number of remaining faults and software reliability. The underlying common assumption of many existing models is that the operating environment and the developing environment are the same. This is often not the case in practice because the operating environments are usually unknown due to the uncertainty of environments in the field. In this paper, we develop a new software reliability model incorporating the uncertainty of system fault-detection rate per unit of time subject to operating environments. Examples are included to illustrate the goodness-of-fit of proposed model and several existing non-homogeneous Poisson process (NHPP) models based on a set of failure data collected from software applications. Three goodness-of-fit criteria, such as mean square error, predictive power and predictive-ratio risk, are used as an example to illustrate model comparisons. The results show that the proposed model fit significantly better than other existing NHPP models based on mean square error value. As we know, different criteria have different impact in measuring the software reliability and that no software reliability model is optimal for all contributing criteria. In this paper, we discuss a new method called, normalized criteria distance, for ranking and selecting the best model from among SRGMs based on a set of criteria taken all together. Example results show the proposed method offers a promising technique for selecting the best model based on a set of contributing criteria.
    Optimization 10/2014; 63(10).
  • Optimization 10/2014; 63(10).
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    ABSTRACT: In this paper, we propose a nonparametric efficiency-based approach for the optimal trading strategy that trades off the execution risk with that of the execution cost. A shortage function is defined that looks for possible decrease in the execution cost as well as decreases in the execution risk. Global optimality is guaranteed for the resulting optimal trading strategy. An empirical section on a small sample of assets serves as an illustration.
    Optimization 10/2014; 63(10).
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    ABSTRACT: In this paper, we propose a multiobjective model of portfolio rebalancing problem considering return, risk and liquidity as key financial criteria. Further, a more realistic situation of financial market is considered where the portfolio, at the end of a typical time period, will be modified by buying and/or selling asset(s) in response to changing conditions. We assume that the transaction costs are paid on the basis of incremental discounts and are adjusted in the net return of the portfolio. A real-coded genetic algorithm (RGGA) is developed to solve the portfolio rebalancing problem and build an optimal portfolio. An empirical study is included to illustrate the behaviour of the proposed model using data of some randomly selected assets listed on the National Stock Exchange (NSE), Mumbai, India.
    Optimization 10/2014; 63(10).
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    ABSTRACT: The bilevel programming (BLP) problem is a leader-follower game in which two players try to maximize their own objective functions over a common feasible region. This paper discusses an integer BLP with bounded variables in which the objective function of the first level is linear fractional, the objective function of the second level is linear and the common constraint region is a polyhedron. Various cuts have been discussed, which successively rank and scan the set of feasible solutions in decreasing order of leader’s objective function. By making use of these ranked solutions, we are able to solve the given BLP. An extension of BLP is also discussed in the form of constrained BLP, where in addition to the existing primary constraints, a set of secondary constraints is also introduced.
    Optimization 10/2014; 63(10).
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    ABSTRACT: In this paper, we cope with a two-stage distribution planning problem of supply chain regarding fixed charges. The focus of the paper is on developing efficient solution methodologies of the selected NP-hard problem. Based on computational limitations, common exact and approximation solution approaches are unable to solve real-world instances of such NP-hard problems in a reasonable time. These approaches involve cumbersome computational steps in real-size cases. In order to solve the mixed integer linear programming model, we develop an artificial immune system and a sheep flock algorithm to achieve better solutions in comparison to earlier approaches. The evaluations are set up based on two phases; first, comparing performances of two proposed algorithms with two previous studies with the same data (two previously proposed genetic algorithm and ant colony optimization methods) and second, evaluating two proposed algorithms in larger instances. Computational studies reveal that the proposed algorithms present acceptable performance by achieving solutions that are more robust in a proper time.
    Optimization 10/2014; 63(10).
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    ABSTRACT: In this paper, we study a multi-objective multi-choice assignment problem considering cost and time objectives subject to some realistic constraints including multi-job assignment. We assume that the decision-maker provides multiple aspiration levels regarding both cost and time objectives using discrete choices as well as interval values. To obtain efficient allocation plans, we use multi-choice goal programming methodology to solve the assignment problem. The proposed methodology is illustrated through numerical examples.
    Optimization 10/2014; 63(10).
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    ABSTRACT: No equipment (system) can be perfectly reliable in spite of the utmost care and best efforts on the part of the designer, decision-maker and manufacturer. The two sides of maintenance are corrective and preventive maintenance. It is generally assumed that a preventive maintenance action is less costly than a repair maintenance action. We examine this proposition in detail on the basis of a failure-time model that relates conformance quality to reliability. Illustratively, we present reliability in the context of contracts with asymmetric information. The model shows how to overcome information rents through price distortions and quantity rationing. The paper ends with a conclusion and an outlook to future studies.
    Optimization 05/2014;
  • Optimization 03/2014;
  • Optimization 01/2014; 63(1).

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