Multi-criteria Function Research Articles

FORMALIZING THE PROBLEM OF A PROJECT TEAM BULDING BASED ON THE UTILITY THEORY

The subject matter of the article is mathematical models for solving particular problems of sorting out candidates for a project team. The aim is to create a region of compromises for a variety of particular criteria for selecting candidates and to develop an integral optimization function as a utility function for selecting preferred alternatives. The tasks are: developing the approximate area of compromises for solving single-criterion optimization problems; selecting the utility function of partial criteria; using general principles of utility theory application for solving personnel problems; sorting out the best option on the ground of multi-criteria evaluation and optimization methods; defining the degree of particular criteria importance; analyzing the peculiarities of multi-criteria assessment of problems in human resource management. The methods used are: multi-criteria assessment and optimization methods, utility function. The following results are obtained. The problem of estimating the relative importance of particular criteria in the utility function is considered. The procedure of determining the approximate region of compromises is described with account of a number of personal and psychological characteristics by successive solving single-criterion optimization problems for each particular criterion. The problem of developing the integral multi-criteria function for selecting preferred alternatives is considered. The peculiarities of using general principles of utility theory for personnel problems solving are analyzed. The function of multi-criteria optimization as a utility model for partial criteria is formulated. The general task of assessing and producing labour force with account of professional, personal and psychological properties of a candidate is formulated. Conclusions. The task of building a project team is considered in the article. The stages of team building are considered. The procedure for determining the approximate region of compromises is described with account of a number of personal and psychological characteristics by successive solving single-criterion optimization problems for each particular criterion. The function of multi-criteria optimization is formulated as a utility model for partial criteria. The method for selecting and evaluating personnel for planned projects is suggested on the basis of the procedure for sorting out candidates for a new project team from the workers who were previously involved in similar projects.

Dynamic Utility-Based Buffer Management Strategy for Delay-tolerant Networks

Delay tolerant network (DTN) is a new networking concept characterised by intermittent connections between nodes communicating through the store-carry-and-forward mechanism. Indeed, the nodes may keep a message in their buffer for long periods of time until a communication opportunity occurs and then transmit it to other relays or to its final destination. Accordingly, the use of many messages can induce buffer congestion of some nodes due to the limited buffer capacity. To manage properly the use of network resources, particularly bandwidth and buffer space of nodes, we propose, in this paper, a new buffer management strategy that consists of messages scheduling and dropping policies on the basis of a multi-criteria utility function. The simulations results under the ONE simulator have shown that the proposed buffer management strategy achieves a higher delivery ratio, reduces the average delivery delay and generates minimal overhead when compared to the Random, MOFO and FIFO usual strategies.

On the measure based formulation of multi-criteria decision functions

The basic features and properties of a fuzzy measure are first introduced. We next discuss the Choquet integral and describe its usage in finding the aggregation of a collection of values guided by a fuzzy measure. We then show how the Choquet integral and an associated fuzzy measure can be applied to the construction of multi-criteria decision functions and describe how the associated fuzzy measure is used to model the desired multi-criteria decision function. We then look at various fuzzy measures and investigate the types of decision functions they allow us to formulate. Notable among the measures investigated here are those that allow us to model a prioritized aggregation of the criteria and a rule based aggregation of multiple criteria. Finally we show how to model linguistically specified decision functions using fuzzy measures.

Efficient Heterogeneous Multi-relational Classification Using Multi-criteria Ranking Approach Based on Characteristics of Multiple Relations

Traditional data mining algorithms will not work efficiently for most of the real world applications where the data is stored in relational format. Even well-known traditional classification technique such as J48, Nai ve Bayes often suffers from poor scalability and unsatisfactory predictive performance when it comes to working with relational data. Moreover the performance of existing relational classification is also limited as the existing algorithms are not able to use different classifiers based on characteristics of different relations. Proposed approach in this paper is to select appropriate classifiers based on characteristics of dataset and give ranking based on multi criteria function using Ratio of Success Rate and Time (RST). In RST we combine success rate as a measure of benefit and running time as a measure of cost. The goal of the proposed relational classification is to use most appropriate and efficient classifier for the relation to achieve better efficiency as compared to the common classifiers. The experimental results show that the performance of proposed relational classification is better in terms of accuracy and efficiency when compared to all other existing algorithms available in the literature.

Multi-criteria optimisation of the Muskingum flood model: a new approach

Existing Muskingum hydrological routing models adopt a single criterion in the calibration process. Some models minimise the sum of the squared deviations between estimated and observed outflows (outflow criterion), while others minimise the sum of the squared deviations between the estimated and observed storages (storage criterion). However, models that adopt the outflow (storage) criterion result in a poor fit to the observed storages (outflows). This paper presents a new approach that incorporates both criteria in the calibration process and aids trade-off analysis. The multi-criteria function is expressed as a weighted function of normalised outflow and storage criteria, representing the deviations from ideal outflow and storage values. The routing procedure is based on the author's four-parameter Muskingum model with constant parameters and the fourth-order Runge–Kutta method. The proposed model was applied to three examples. A criterion weight of 0·4–0·6 was found to produce an excellent trade-off between outflow and storage criteria. The results show that the model substantially improves on both criteria compared with single-criterion models. The proposed model, which properly captures the entire flood propagating characteristics in calibration, should be of interest to hydrological engineers and practitioners.

Multi-criteria optimisation of the Muskingum flood model: a new approach

Existing Muskingum hydrological routing models adopt a single criterion in the calibration process. Some models minimise the sum of the squared deviations between estimated and observed outflows (outflow criterion), while others minimise the sum of the squared deviations between the estimated and observed storages (storage criterion). However, models that adopt the outflow (storage) criterion result in a poor fit to the observed storages (outflows). This paper presents a new approach that incorporates both criteria in the calibration process and aids trade-off analysis. The multi-criteria function is expressed as a weighted function of normalised outflow and storage criteria, representing the deviations from ideal outflow and storage values. The routing procedure is based on the author's four-parameter Muskingum model with constant parameters and the fourth-order Runge–Kutta method. The proposed model was applied to three examples. A criterion weight of 0·4–0·6 was found to produce an excellent trade-off between outflow and storage criteria. The results show that the model substantially improves on both criteria compared with single-criterion models. The proposed model, which properly captures the entire flood propagating characteristics in calibration, should be of interest to hydrological engineers and practitioners.

Fuzzy Measures in Multi-Criteria Decision Making

Abstract We are interested in the formulation of multi-criteria decision functions based on the use of a measure over the space of criteria. Specifically the relationship between the criteria is expressed using a fuzzy measure. We then use the Choquet integral to construct decision functions based on the measure. We look at a number of different decision functions generated from specific classes of measures

MULTICRITERIA HYBRID FLOW SHOP SCHEDULING PROBLEM: LITERATURE REVIEW, ANALYSIS, AND FUTURE RESEARCH

This research focuses on the Hybrid Flow Shop production scheduling problem, which is one of the most difficult problems to solve. The literature points to several studies that focus the Hybrid Flow Shop scheduling problem with monocriteria functions. Despite of the fact that, many real world problems involve several objective functions, they can often compete and conflict, leading researchers to concentrate direct their efforts on the development of methods that take consider this variant into consideration. The goal of the study is to review and analyze the methods in order to solve the Hybrid Flow Shop production scheduling problem with multicriteria functions in the literature. The analyses were performed using several papers that have been published over the years, also the parallel machines types, the approach used to develop solution methods, the type of method develop, the objective function, the performance criterion adopted, and the additional constraints considered. The results of the reviewing and analysis of 46 papers showed opportunities for future researchon this topic, including the following: (i) use uniform and dedicated parallel machines, (ii) use exact and metaheuristics approaches, (iv) develop lower and uppers bounds, relations of dominance and different search strategiesto improve the computational time of the exact methods, (v) develop other types of metaheuristic, (vi) work with anticipatory setups, and (vii) add constraints faced by the production systems itself.

A new outlook on map use in social sciences and humanities research

Since their first appearance, maps increasingly secured their irreplaceable role in our civilization. As the human society develops, so is cartography to become more useful than ever. Transforming from their designated role as a tool of positioning or navigating, maps have become a second language to visualize real world, and a useful tool in scientific research. Particularly, recent decades have experienced the creative use of map in social sciences and humanities researches that yield interesting results. However, in Viet Nam this is still a relatively new approach that has not yet received the attention it deserves. This paper systematically illustrates how maps can be applied in social sciences and humanities researches, as such suggesting how researchers in social sciences and humanities can employ maps in a greater variations and more effective, creative ways. Researches done in social sciences and humanities (in which maps had been used) accompanied with indepth-interviews are closely observed, studied and analyzed to identify how maps are being used in different stages of research. To enhance ease and flexibility of map usage in research, we have presented all the situations according to map functionalities. It is concluded that maps can be used for pre-arrival to site analysis, studysite selection (multi-criteria analysis function), field trip planning (navigating function). Maps could also be used as primary data collection tool in associated with questionnaires, indepth interviews and focus groups, in order to explore spatial aspect of collected data (multi-criteria and multi-time analysis functions). Last but not least, map use is a great way to illustrate spatial research information in a more creative and visualized way.

The self regulation problem as an inexact steepest descent method for multicriteria optimization

In this paper we study an inexact steepest descent method for multicriteria optimization whose step-size comes with Armijo’s rule. We show that this method is well-defined. Moreover, by assuming the quasi-convexity of the multicriteria function, we prove full convergence of any generated sequence to a Pareto critical point. As an application, we offer a model for the Psychology’s self regulation problem, using a recent variational rationality approach.

An Inexact Steepest Descent Method for Multicriteria Optimization on Riemannian Manifolds

In this paper, we present an inexact version of the steepest descent method with Armijo’s rule for multicriteria optimization in the Riemannian context given in Bento et al. (J. Optim. Theory Appl., 154: 88–107, 2012). Under mild assumptions on the multicriteria function, we prove that each accumulation point (if any) satisfies first-order necessary conditions for Pareto optimality. Moreover, assuming that the multicriteria function is quasi-convex and the Riemannian manifold has nonnegative curvature, we show full convergence of any sequence generated by the method to a Pareto critical point.

A Subgradient Method for Multiobjective Optimization on Riemannian Manifolds

In this paper, a subgradient-type method for solving nonsmooth multiobjective optimization problems on Riemannian manifolds is proposed and analyzed. This method extends, to the multicriteria case, the classical subgradient method for real-valued minimization proposed by Ferreira and Oliveira (J. Optim. Theory Appl. 97:93–104, 1998). The sequence generated by the method converges to a Pareto optimal point of the problem, provided that the sectional curvature of the manifold is nonnegative and the multicriteria function is convex.

Secure and robust clustering for quantized target tracking in wireless sensor networks

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

Manganese waste mud immobilization in cement – natural zeolite – lime blend: Process optimization using artificial neural networks and multi‐criteria functions

AbstractIn this study, stabilization/solidification process of manganese contaminated mud using portland cement was optimized. For that purpose, immobilization process was modeled applying artificial neural networks with radial basis activation function. The optimal model presented satisfactory prediction characteristics (R2 value for manganese leaching was 0.9615 while and for concrete flexural strength 0.8748). Therefore, it was used in combination with seven in‐house developed multi‐criteria optimization functions, separately, in order to optimize concrete formulation. The used approach proved itself as efficient and cost effective alternative in ecological material formulation process. The best properties (i. e. high flexural strength and lowest manganese leaching) manifested stabilization/solidification matrix consisted of 350 g of portland cement, 20 g of lime, 70 g of natural zeolite, 10 g of manganese waste mud and 180 g of water.

Settings, Experimentation and Evaluation of the Simulation Models

This paper deals with research in computer simulation of manufacturing processes. The paper summarizes the procedures associated with developing the model, experimenting with and evaluating the model results. The key area is of experimentation with the simulation model and evaluation using indicators or multi-criteria functions. With regards to the experiment the crucial variables are the simulation model. The key ideas are to set the number of variables, depending on what a given simulation will be. For example, when introducing new technology into production, modify the type of warehouse, saving workers, thus economizing. The simulation models for the operational management uses simplified models, if possible, a minimum number of variables to obtain the result in shortest possible time. These models are more user friendly and the course will be conducted mostly in the background. An example of a criteria function is the number of parts produced or production time. Multi-criteria function has given us the opportunity to make better quality decisions. It is based on the composition of several parameters, including their weight to one end point. The type of evaluation functions, whether it is an indicator or criteria function is selected and based on customer requirements. In most cases it is recommended to use the multi-dimensional function. It gives us a more comprehensive view of the results from the model and facilitates decision-making. The result of this paper is a display of setting parameters for the experimentation on a sample model. Furthermore, the comparisons of results with a multi-criteria objective function and one-criterion indicator.

Multi-Criteria Function for Optimizing the Number of Workers in an E-Maintenance

This article deals with the procedure during designing the multi-criteria function. The multi-criteria function optimizes the number of workers in an e-maintenance. The basis for defining the multi-criteria function is to gain multi-dimensional assessment knowledge of procedures, and to integrate the procedures into e-maintenance systems. These systems also include an e-maintenance system for staffing optimization. Staffing optimization is necessary as there is direct indirection between service staff members and the customers. This sort of system is intended primarily for service organizations. Service organizations on a larger scale involve more than just machinery and equipment-they involve the manufacturer and the supplier. The key is to design a multi-criterion optimization function for staff members who use special tools or specialists in their field. The ultimate goal is to limit losses and ensure costs are kept to a minimum, or even transfer onto the customer. The verification of the proposed multi-criteria function as a suitable tool for computer simulation in the manufacturing process is done in the simulation program named Witness. The simulation software is linked to MS Excel.

Scheduling in flow shop with sequence-dependent setup times: literature review and analysis

This research focuses on the flow shop production scheduling problem with sequence-dependent setup times. The study reviews and analyses the methods used in the literature to solve this problem. The analyses were performed using the number of papers published during the study period, the approach used to develop solution methods, the type of objective function and performance criterion adopted, and the additional constraints considered. It were identified and analysed 83 papers that address the flow shop scheduling problem with setup times separated from processing times. The results of the review and analysis showed several opportunities for future research on this topic, including 1 working with setups dependent on batch sequences 2 sing bi- and multi-criteria functions related to the flow time objective 3 adding practical constraints faced by production systems.

MeSO: simulation optimization using a multicriteria process capability index and evolutionary algorithms

A successful implementation of a simulation-optimization (SO) methodology is presented. Based on evolutionary algorithms with a multicriteria fitness function, the new SO is used to developed weekly schedules at a ship building factory that manufactures around 60 jobs per week. Simulation modeling is used to account for randomness on the input data, as well as to correctly abstract the complex operations carried out in the real system. A variant of genetic algorithms is used to search for the appropriate schedule. Its fitness function is a multicriteria process capability index that aggregates three individual criteria, namely, makespan, line blockage and idleness of resources. The index is based on the satisfaction of thresholds for each and every criterion, thresholds that are tightened as improved schedules are found. The thresholds are also used to reject non-promising alternatives without having to perform the same number of runs as for the candidates that stand out for implementation. The name of the methodology is meSO: multicriteria evolutionary SO.

Multi-criteria selection of optimum WWTP control setpoints based on microbiology-related failures, effluent quality and operating costs

This work aims at optimising the performance of WWTP with simultaneous biological C, N and P removal by improving its automatic control system. The success of control strategies is highly dependent on the selected operational setpoints of the controlled variables. These setpoints should be optimised to achieve the best effluent quality with the lower operating costs and, at the same time, ensuring an operation with low probability to develop biomass settling problems. Two different objective functions were used to optimise the setpoints of the tested control strategies: a cost function based on the operational costs by converting the effluent quality into monetary units and a multi-criteria function based on the effluent quality, the operational costs and settling problems of microbiological origin. Three different control strategies were evaluated: (i) open loop; (ii) optimised fixed setpoints for the ammonium and nitrate control loops and (iii) daily optimised setpoints. The multi-criteria optimisation resulted in a set of optimal setpoints with a Pareto distribution. This multi-criteria function was developed so that any of the criteria (effluent quality operating costs or microbiological risks) was conditional to another and thus, optimal scenarios with a high risk of failure due to microbiological issues could be rejected. Moreover, it was concluded that the optimisation process could be enhanced by using both objective functions in a complementary way. While the multi-criteria function enabled a more extensive evaluation of the different alternatives, once the weights are selected the operational costs function optimisation could be used to define an optimum set of setpoints to adapt the system to influent variations.

Unconstrained Steepest Descent Method for Multicriteria Optimization on Riemannian Manifolds

In this paper, we present a steepest descent method with Armijo’s rule for multicriteria optimization in the Riemannian context. The sequence generated by the method is guaranteed to be well defined. Under mild assumptions on the multicriteria function, we prove that each accumulation point (if any) satisfies first-order necessary conditions for Pareto optimality. Moreover, assuming quasiconvexity of the multicriteria function and nonnegative curvature of the Riemannian manifold, we prove full convergence of the sequence to a critical Pareto point.