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This paper introduces a modified sequential version method for optimizing a linear function over an integer efficient set, as well as a new exact parallel algorithm. The performance of parallel programming in this context is clear and shown through different instances with different sizes. Each procedure builds a finite monotonous sequence of values for the main criterion to be optimized, in a reasonable amount of CPU execution time. This latter remains much better. For the first time, the Algerian IBNBADIS cluster—CERIST—was used with this type of problem. Significant results are obtained by both proposed techniques, particularly with the parallel one. [ABSTRACT FROM AUTHOR]

Using advantages of interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) for describing the hesitant and intuitionistic decisions of experts and identifying the limitations of previous research works about optimization techniques, this paper introduces a new optimization technique and provides a new computational algorithm, applicable in various real life multiobjective optimization problem (MOOP) of engineering and management sectors, and for this, a new operation between IVIHFSs is first introduced. On the basis of this concept, a stepwise computational algorithm is constructed, and it is an extension of both fuzzy and intuitionistic fuzzy optimization techniques. Finally, the proposed algorithm is illustrated using a production planning problem, and the obtained results are compared with the existing optimization techniques. [ABSTRACT FROM AUTHOR]

Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper proposes a scheduling method that allows the use of continuous precedence relationships and piece-wise linear and nonlinear activity-time-production functions that are described by the use of singularity functions. This work further develops an adaptive multiple objective symbiotic organisms search (AMOSOS) algorithm that modifies benefit factors in the basic SOS to balance exploration and exploitation processes. Two case studies of its application are analyzed to validate the scheduling method, as well as to demonstrate the capabilities of AMOSOS in generating solutions that optimally trade-off minimizing project time with minimizing the cost of non-unit repetitive projects. The results thus obtained indicate that the proposed model is feasible and effective relative to the basic SOS algorithm and other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper proposes a scheduling method that allows the use of continuous precedence relationships and piece-wise linear and nonlinear activity-time-production functions that are described by the use of singularity functions. This work further develops an adaptive multiple objective symbiotic organisms search (AMOSOS) algorithm that modifies benefit factors in the basic SOS to balance exploration and exploitation processes. Two case studies of its application are analyzed to validate the scheduling method, as well as to demonstrate the capabilities of AMOSOS in generating solutions that optimally trade-off minimizing project time with minimizing the cost of non-unit repetitive projects. The results thus obtained indicate that the proposed model is feasible and effective relative to the basic SOS algorithm and other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recent empirical and conceptual work in data envelopment analysis (DEA) have emphasised its potential importance in highlighting the environmental performance of economic entities. Initial work in this emerging research area has focused on the separation of output factors into desirable and undesirable ones. In this paper, we describe recent developments in the modelling undesirable outputs. In particular, the modelling of undesirable outputs in the range adjusted measure (RAM) is investigated. We discuss some of the difficulties of RAM in assessing the environmental efficiency of decision-making units (DMUs) and develop a multiple objective DEA model to overcome these difficulties. The proposed multiple objective model is solved as a linear programming and its applicability as a mechanism for assessing environmental efficiency is demonstrated by evaluating the technical, ecological and process environmental quality efficiency scores of China's provinces. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the admissible region of a biobjective knapsack problem is our main interest. Although the reduction of feasible region has been studied by some authors, yet more investigation has to be done in order to deeply explore the domain before solving the problem. We propose, however, a new technique based on extreme supported efficient solutions combined with the dominance relationship between items' efficiency. An illustration of the algorithm by a didactic example is given and some experiments are presented, showing the efficiency of the procedure compared to the previous techniques found in the literature. [ABSTRACT FROM AUTHOR]

Cloud computing, while becoming more and more popular as a dominant computing platform, introduces new security challenges. When virtual machines are deployed in a cloud environment, virtual machine placement strategies can significantly affect the overall security risks of the entire cloud. In recent years, the attacks are specifically designed to co-locate with target virtual machines in the cloud. The virtual machine placement without considering the security risks may put the users, or even the entire cloud, in danger. In this paper, we present a comprehensive approach to quantify the security risk of cloud environments from network, host and VM. Accordingly, we propose a Security-aware Multi-Objective Optimization based virtual machine Placement scheme (SMOOP) to seek a Pareto-optimal solution that reduces the overall security risks of a cloud, while considering workload balance, resource utilization on CPU, memory, disk, and network traffic. New placement strategies are designed and our evaluation results demonstrate their effectiveness. The security of clouds could be improved with affordable overheads. The latest VM allocation policies are further studied and integrated into our designs to defeat the co-residence attacks. [ABSTRACT FROM AUTHOR]

Identifying the efficient extreme units in a production possibility set is a very important matter in data envelopment analysis, as these observed, real units have the best performances. In this paper, we proposed a multiple objective programming model, in which the feasible region is the production possibility set under the assumption of variable returns to scale and the objective function consists of input and output variables. As we know, by increasing the dimensions of the problem, the set of efficient points would increase as well; thus, using the multiple objective linear programming problem-solving methods in a decision set would lead to computational problems and it would be much easier to work in the outcome set instead of the decision set. In this research, we show that the efficient points in the outcome set of the suggested multiple objective linear programming problems correspond with the efficient extreme points in data envelopment analysis. An outer approximation algorithm is presented for production of all efficient extreme points in the outcome set. This algorithm provides us with the equations for all efficient surfaces. In the outcome set, this algorithm would use few calculations to produce all the extreme points. Finally, we demonstrate the presented approach through numerical examples. [ABSTRACT FROM AUTHOR]

As ocean resources received considerable attention, the autonomous underwater vehicle (AUV) has been more widely applied due to its excellent flexibility and adaptability, and the optimization design of AUV has becoming a more important issue. The heavier-than-water underwater vehicle (HUV) is a new conceptual AUV, and its design is a typical multidisciplinary problem, but heavily dependent on the experience with naval architects at the present engineering design. To realize the optimization design of HUV, the multidisciplinary design optimization (MDO) method is applied to the multiple objective MDO design of an HUV. In this paper, a system synthesis model of the HUV has been set up. To predict the hydrodynamic characteristics accurately, the computational fluid dynamics (CFD) method is used. In the MDO process, the all-in-one (AIO) method has been adopted with non-dominated sorting genetic algorithms (NSGA)-II and a Kriging model for constructing global approximations to reduce the cost of the CFD numerical simulation. According to the multiple objective MDO design, the Pareto-optimal solutions are analyzed and verified by the numerical simulation, and it is found that the optimal design of HUV has better performance than that of prototype. [ABSTRACT FROM AUTHOR]

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The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series-parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi-Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε-constraint method to generate non-dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non-dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper a feasible direction method is presented to find all efficient extreme points for a special class of multiple objective linear fractional programming problems, when all denominators are equal. This method is based on the conjugate gradient projection method, so that we start with a feasible point and then a sequence of feasible directions towards all efficient adjacent extremes of the problem can be generated. Since methods based on vertex information may encounter difficulties as the problem size increases, we expect that this method will be less sensitive to problem size. A simple production example is given to illustrate this method. [ABSTRACT FROM PUBLISHER]

ABSTRACT This paper focuses on the divergence behaviour of the successive geometric mean (SGM) method used to generate pairwise comparison matrices while solving a multiple stage, multiple objective (MSMO) optimization problem. The SGM method can be used in the matrix generation phase of our three-phase methodology to obtain pairwise comparison matrix at each stage of an MSMO optimization problem, which can be subsequently used to obtain the weight vector at the corresponding stage. The weight vectors across the stages can be used to convert an MSMO problem into a multiple stage, single objective (MSSO) problem, which can be solved using dynamic programming-based approaches. To obtain a practical set of non-dominated solutions (also referred to as Pareto optimal solutions) to the MSMO optimization problem, it is important to use a solution approach that has the potential to allow for a better exploration of the Pareto optimal solution space. To accomplish a more exhaustive exploration of the Pareto optimal solution space, the weight vectors that are used to scalarize the MSMO optimization problem into its corresponding MSSO optimization problem should vary across the stages. Distinct weight vectors across the stages are tied directly with distinct pairwise comparison matrices across the stages. A pairwise comparison matrix generation method is said to diverge if it can generate distinct pairwise comparison matrices across the stages of an MSMO optimization problem. In this paper, we demonstrate the SGM method's divergence behaviour when the three-phase methodology is used in conjunction with an augmented high-dimensional, continuous-state stochastic dynamic programming method to solve a large-scale MSMO optimization problem. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multiattribute utility functions are widely used for multiobjective optimization of project selection problems in network-level bridge management, based on their capability of incorporating multiple performance measures and decision-makers' preferences. However, the existing way of using them needs to be improved. First, the addition of individual projects' utility values to obtain a total utility score for a candidate project portfolio may lead to biased solutions because such additivity property is not always valid. In addition, there is an increasing need to express the network-level performance of bridges in terms of raw performance measures, which are most meaningful to decision makers, to reflect the impacts of systemwide investment. To address these issues, the paper presents a method that first evaluates the network performance of each candidate project portfolio using network-level performance measures prior to employing a multiattribute utility function and then identifies the optimal portfolio with the best network performance. This method effectively incorporates decision-makers' preferences into decision making, avoids possible bias from nonassumption of additivity, and interprets investment performance directly in terms of raw performance measures. [ABSTRACT FROM AUTHOR]

Efforts toward systematic conservation planning for freshwaters have progressed less than similar efforts in terrestrial and marine environments. Although there are important differences, many of the tools that have been used in terrestrial and marine systems can also be used for freshwater conservation planning. In this paper, we used return on investment (ROI) analysis to identify optimal riparian land conservation portfolios under different objectives. Our overarching research question is: how do different strategies for evaluating ROI objectives influence the resulting conservation portfolio and the outcome of interest, lake water quality? We examined agricultural riparian land investments to improve water quality in 55 inland lakes in southwestern Michigan. We simulated investments and compared the ROIs for the following strategies: (1) economic; (2) ecological; and (3) environmental policy. Furthermore, as a reference point, we also compared the resulting three conservation portfolios to that derived by simulating riparian land enrollment under the Conservation Reserve Program. We found that (1) investments in freshwater resources through the conservation of riparian land were more effective than the conservation of randomly selected parcels of similar land area in the catchment; (2) the costs and benefits of riparian land conservation varied considerably among lakes; (3) the choice of investment objectives, and therefore ROI strategies, resulted in very different conservation portfolios; (4) the simulated enrollment process of the Conservation Reserve Program fared poorly in terms of improving lake water quality as compared to the three strategies. [ABSTRACT FROM AUTHOR]

The aim of this paper is to deal with the optimal navigation coordinated scheduling (NCS) problem in ship transportation of the Three Gorges Project in China, i.e. the Three Gorges Dam and the Gezhouba Dam. The NCS includes operational scheduling for two five-step locks in Three Gorges Dam and three single-step locks in Gezhouba Dam. A birandom multiple objective decision-making model is first proposed for the NCS problem to cope with hybrid uncertain environment where twofold randomness exists in practice. Then, particle swarm optimization is applied to search for the optimal solution. Based on real execution data, the results generated by a computer validate effectiveness of the proposed model and algorithm in solving large-scale practical problems is presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new exact algorithm, using an augmented weighted Tchebychev norm, for optimizing a linear function on the efficient set of a multiple objective integer linear programming problem. This norm is optimized progressively by improving the value of the linear criteria and going through some efficient solutions. The method produced not only the best efficient solution of the linear objective function but also a subset of nondominated solutions that can help decision makers to select the best decision among a large set of Pareto solutions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to deal with resource-constrained multiple project scheduling problems (rc-mPSP) under a fuzzy random environment by a hybrid genetic algorithm with fuzzy logic controller (flc-hGA), to a large-scale water conservancy and hydropower construction project in the southwest region of China, whose main project is a dam embankment. The objective functions in this paper are to minimize the total project time (that is the sum of the completion time for all projects) and to minimize the total tardiness penalty of multiple projects, which is the sum of penalty costs for all the projects. After describing the problem of the working procedure in the project and presenting the mathematical formulation model of a resource-constrained project scheduling problem under a fuzzy random environment, we give some definitions and discuss some properties of fuzzy random variables. Then, a method of solving solution sets of fuzzy random multiple objective programming problems is proposed. Because traditional optimization techniques could not cope with the rc-mPSP under a fuzzy random environment effectively, we present a new approach based on the hybrid genetic algorithm (hGA). In order to improve its efficiency, the proposed method hybridized with the fuzzy logic controller (flc) concept for auto-tuning the GA parameters is presented. For the practical problems in this paper, flc-hGA is proved the most effective and most appropriate compared with other approaches. The computer generated results validate the effectiveness of the proposed model and algorithm in solving large-scale practical problems. [ABSTRACT FROM AUTHOR]

Global competition has forced companies to consider increasing quality and decreasing all costs, including quality costs. When constructing the quality plan for both the incoming raw materials and the manufacturing process, all quality characteristics (QCs) of products or processes cannot be measured or tested because of preventive costs. Companies should decide which QCs are significant and should be measured or tested. This paper presents a methodology for selecting the significant QCs of a product. A quality function deployment failure matrix is proposed, incorporating failure information from design failure mode and effect analysis, related QCs, vague information on technical difficulties, and imprecise cost and time measurements for QCs. In addition, this method uses a fuzzy multiple-objective decision model that addresses two objectives: maximising the priorities of QCs and minimising the technical difficulties within the constraints of limited cost and time. A two-phase approach is used for solving the fuzzy multiple-objective model. A real application to a backhoe loader wheel is presented as an illustration. [ABSTRACT FROM AUTHOR]

A ubiquitous problem for community-based regional environmental agencies is to set strategic management priorities among a myriad of issues and multiple stakeholder perspectives. Here, we quantify the strategic management priorities for natural capital and ecosystem services using multicriteria decision analysis (MCDA) in a case study of the South Australian Murray-Darling Basin Natural Resources Management Board (the Board) region. A natural capital and ecosystem services framework was tailored to present decision-makers with a range of potential issues for strategic consideration as goal hierarchies in MCDA. Priorities were elicited from the Board and its four regionally based consultative groups using the Analytical Hierarchy Process and swing weights. Centered log ratio transformed weights were analyzed using multiple pairwise ANOVA comparisons (Dunnett’s T3) and hierarchical cluster analysis. Substantial variation in priorities occurred between decision-makers. Nonetheless, analysis of priorities for assets and services robustly demonstrated that water was the highest priority, followed by land, then biota, with atmosphere the lowest priority. Decision-makers also considered that environmental management should not impact negatively on built or social capital. Few significant differences in priorities were found between decision-maker groups. However, clusters of manager types were found which represent distinct alternative management strategies, notably the prioritization of either intermediate or final ecosystem services. The results have implications for regional environmental decision-making and suggest that embracing variation in perspectives may be a better way forward for multistakeholder MCDA. The study operationalizes natural capital and ecosystem services by providing strategic priorities for targeting management and policy within the context of community-based, regional environmental management. [ABSTRACT FROM AUTHOR]

This article presents a way of determining heat transfer calibrations for multi-objective and single-objective optimization by means of genetic algorithms. The need for optimization arises from the necessity for mathematical model validation and is very relevant to practical applications. The SPEA algorithm is used for multi-objective optimization. Scalarization of the fitness function is also addressed in combination with a small population size in order to keep the computational cost of the problem to a minimum. This is because of the time spent for a fitness function evaluation which comes from numerical solution of the nonlinear partial differential equations. The result is that a value of the heat transfer coefficient is determined that produces minimal difference between experimental results and numerical predictions. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective nonlinear programming method is proposed. That is, the problems which have fuzzy multiple objective functions and constraints with GUB (Generalized Upper Bounding) structure are solved by the proposed Hybridized Genetic Algorithms (HGA). This approach enables a flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this Genetic Algorithm (GA), we propose a new chromosome representation that represents the GUB structure simply and effectively at the same time. Also, by introducing the HGAs that combine the proposed heuristic algorithm and makes use of the peculiarity of GUB structure to GA, the proposed approach is more efficient than the previous method in finding a solution. Further, to demonstrate the effectiveness of the proposed method, a large-scale optimal system reliability design problem is introduced. [ABSTRACT FROM AUTHOR]

In this paper we study a multiple-objective dispersion problem from an efficient solution and value function perspective. A general fundamental theorem on non-dominance is given and a polar co-ordinate elimination heuristic is given for the case of [R². Some general results are given for the existence of a linearly weighted objective function whose maximal value will give a most preferred solution, for convex or concave value functions, and a heuristic is developed for the case where these conditions do not hold. [ABSTRACT FROM AUTHOR]

We generalize many of the results on efficient points for linear multiple objective optimization problems to the nonlinear case by focusing on an auxiliary problem. The approach, which relies on duality theory, is a straightforward development that even in the linear case yields simpler proofs. [ABSTRACT FROM AUTHOR]

We investigate two versions of multiple objective minimum spanning tree problems defined on a network with vectorial weights. First, we want to minimize the maximum of Q linear objective functions taken over the set of all spanning trees (max-linear spanning tree problem, ML-ST). Secondly, we look for efficient spanning trees (multi-criteria spanning tree problem, MC-ST). Problem ML-ST is shown to be NP-complete. An exact algorithm which is based on ranking is presented. The procedure can also be used as an approximation scheme. For solving the bicriterion MC-ST, which in the worst case may have an exponential number of efficient trees, a two-phase procedure is presented. Based on the computation of extremal efficient spanning trees we use neighbourhood search to determine a sequence of solutions with the property that the distance between two consecutive solutions is less than a given accuracy. [ABSTRACT FROM AUTHOR]

Most of the multiple objective linear programming (MOLP) methods which have been proposed in the last fifteen years suppose deterministic contexts, but because many real problems imply uncertainty, some methods have been recently developed to deal with MOLP problems in stochastic contexts. In order to help the decision maker (DM) who is placed before such stochastic MOLP problems, we have built a Decision Support System called PROMISE. On the one hand, our DSS enables the DM lo identify many current stochastic contexts: risky situations and also situations of partial uncertainty. On the other hand, according to the nature of the uncertainty, our DSS enables the DM to choose the most appropriate interactive stochastic MOLP method among the available methods, if such a method exists, and to solve his problem via the chosen method. [ABSTRACT FROM AUTHOR]

Production planning is a necessary process that directly affects the efficiency of production systems in most industries. The complexity of the current production planning problem depends on increased options in production, uncertainties in demand and production resources. In this study, a stochastic multi-objective mixed-integer optimization model is developed to ensure production efficiency in uncertainty conditions and satisfy the requirements of sustainable development. The efficiency of the production system is ensured through objective functions that optimize backorder quantity, machine uptime and customer satisfaction. The other three objective functions of the proposed model are related to optimization of profits, emissions, and employment changing. The objective functions respectively represent the three elements of sustainable development: economy, environment, and sociality. The proposed model also assures the production manager's discretion over whether or not to adopt production options such as backorder, overtime, and employment of temporary workers. At the same time, the resource limits of the above options can also be adjusted according to the situation of each production facility via the model's parameters. The solutions that compromise the above objective functions are determined with the Chebyshev goal programming approach together with the weights of the goals. The model is applied to the multinational production system of a Southeast Asian supplier in the textile industry. The goal programming solution of the model shows an improvement in many aspects compared to this supplier's manufacturing practices under the same production conditions. Last but not least, the study develops different scenarios based on different random distributions of uncertainty demand and different weights between the objective functions. The analysis and evaluation of these scenarios provide a reference basis for managers to adjust the production system in different situations. Analysis of uncertain demand with more complex random distributions as well as making predictions about the effectiveness of scenarios through the advantages of machine learning can be considered in future studies. [ABSTRACT FROM AUTHOR]

Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research. [ABSTRACT FROM AUTHOR]

In the literature on the scheduling of construction projects it is difficult to find a justification for adopting the criteria for evaluating work schedules based explicitly on the preferences of those who develop them. This article tries to fill this gap and provide information on the preferences of construction site managers for sustainable scheduling and organization of construction projects. The publication focuses on the views and needs of construction site engineering staff regarding methods of work organization, the need to reduce downtimes in the work of crews, the deadline for project realization and the selection of crews for tasks. This information was obtained on the basis of surveys conducted among managerial staff of various levels (74 people). The authors hope that the information provided in this publication will allow the creation of more reliable tools to support construction managers better adapted to their preferences and needs, and therefore more likely to be applied in practice. [ABSTRACT FROM AUTHOR]

The topic of this article is inspired by the problem faced by many people around the world: investment portfolio selection. Apart from the standardly used methods and approaches, non-traditional multiple objective programming methods can also be significant, providing even more efficient support for making a satisfactory investment decision. A more suitable method for this purpose seems to be a concept working with an interactive procedure through the portfolio that may gradually be adapted to the investor's preferences. Such a method is clearly the Step Method (STEM) or the more suitable improved version KSU-STEM. This method is still burdened by partial algorithmic weaknesses or methodical aspects to think about, but not as much as the other methods. The potentially stronger application power of the KSU-STEM concept motivates its revision. Firstly, an unnecessarily negative principle to determine the basal value of the objectives is revised. Further, the fuzzy goals are specified, which leads to a reformulation of the revealed defuzzified multi-objective model. Finally, the imperfect re-setting of the weights (importance) of unsatisfactory objectives is revealed. Thus, the alternative approaches are proposed. The interventions to the algorithm are empirically verified through a real-life selection of a portfolio of the open unit trusts offered by CONSEQ Investment Management traded on the Czech capital market. This application confirms a significant supporting power of the revised multiple objective programming approach KSU-STEM in a portfolio-making process. [ABSTRACT FROM AUTHOR]