Vector Optimization Techniques Research Articles

Analytical Model for Information Flow Management in Intelligent Transport Systems

The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems’ structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system’s variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature.

Energy efficiency in multicore shared cache by fault tolerance using a genetic algorithm based block reuse predictor

Aggressive voltage scaling to reduce energy consumption in Multicore causes exponential cell failures in SRAM. Last-level-cache (LLC), the major contender of chip area, exhibits highest sensitivity to low voltage parametric failures and limits energy efficiency. To break the energy barrier, exclusive fault protection mechanism is solicited to ensure on-chip data recovery out of the low voltage failures. This work proposes Cache evolution for energy efficiency by protecting cache blocks from SRAM cell failures due to voltage scaling. A set of coherence and reuse aware in-cache selective replication policies are proposed to ensure on-chip data recovery. Reuse likelihood is predicted through a vector optimization technique using Genetic Algorithm (GA). Reuse aware selective invalidation is employed to balance cache load due to replications. A replication aware replacement policy is also developed that victimizes the lowest reusable cache block. The proposed scheme is evaluated in Multi2Sim 5.0 simulation framework with SPEC CPU benchmark programs. Experimental results claim 43.66% and 38.80% reductions in miss rate and 59.10% and 52.73% reductions in vulnerability for integer and floating point benchmarks respectively. Energy reduction of 39.21% is observed for integer and 25.73% is observed for floating point benchmarks. Up to 34.78% and 26.98% power reductions in integer and floating point benchmarks are also observed. The minimum supply voltage of 350 mV is achieved at the cost of 7.05% area overhead and 3% performance drop-off.

Model and transient Control strategy design of an Organic Rankine Cycle Plant for waste heat recovery of an Internal Combustion Engine

The multi-sources hybrid polygeneration energy systems are of great interest and topicality as they are one of the most promising technologies in the European’s Green Deal panorama, with the aim of serving users with electrical and thermal energy using a single plant powered by one or more energy sources. In the waste heat recovery field Organic Rankine Cycle (ORC) power plants are becoming increasingly popular, especially for exploiting medium and low temperature heat sources as a micro-small scale power plant. However, the development and diffusion of this technology is still limited due to the high costs and consequently prototype development and experimental assessment of performance is very poor, especially for non-stationary systems. In this work the modelling and validation of a micro-scale waste heat recovery (WHR) plant coupled with a control system is presented. An ORC plant has been modelled through a map-based model approach for the piston pump and the scroll expander while the pipes and the heat exchangers through a 1D thermo-fluid dynamic approach. A preliminary comparison was made between some numerical quantities of the modelled plant and the same experimental quantities in 61 different operating conditions, showing an average error of 50.1%. The model has been calibrated using a vector optimization technique: two calibration parameters of the heat exchangers were calibrated with a genetic algorithm (MOGA II) by reducing the error of 5 quantities obtained from the model with the respective experimental quantities in 15 different operating conditions. The remaining 46 operating conditions were used to evaluate the calibrated model, showing an average error of 3%. Furthermore, in order to provide for the use of the system coupled to highly variable heat sources, such as the exhaust gases of an internal combustion engine, a control strategy has been designed to perform two tasks: leading the ORC performance where the efficiency is higher, acting on the pump speed through a map-based control, implemented by a look-up table control, and protecting the organic fluid from damage caused by high working temperatures through a bypass control system with a PI control, depending on the proportional and integral gains. In order to verify the control strategy behaviour at different thermal transient inputs, a set of simulations has been run, showing a robust and stable manner preserving the organic fluid properties and limiting the superheated steam at expander inlet.

Modeling of an Organic Rankine Cycle Waste Heat Recovery system for automotive engine applications

The remarkable investments made by manufacturers over the last few decades have contributed to improving the performance of internal combustion engines in every aspect: lower polluting emissions, greater specific power and thermal efficiency. Despite this, on an average, about 40% of the thermal power theoretically available from the combustion of the fuel is still stored in the exhaust gases and therefore dispersed in the environment. In this work the modeling and validation of a waste heat recovery (WHR) plant will be described, combining the engine with a low temperature Organic Rankine Cycle (ORC) system, in order to investigate the feasibility of this system on board of a vehicle, analyzing the quantity of thermal power recovered and made available in the form of electrical power. The ORC plant is modeled using a 0D/1D thermo-fluid dynamic approach. Starting from experimental tests, a map-based model for the piston pump and the scroll expander has been developed. The model has been validated through the use of a vector optimization technique, exploiting a genetic algorithm (MOGA). Subsequently, this system has been coupled to a spark ignition engine for automotive applications, adapting its speed range to comply with the ORC experimental tests. To have an accurate control over the expander inlet temperature, a bypass circuit and two throttles actuated by a PI controller have been implemented. The simulations were performed by considering 18 engine points at maximum load and different rpm. An average thermal efficiency increase of the system of 2.6% was obtained by introducing the recovery plant, and wide improvement chance can be foreseen in the case of ORC full-power use.

Variational Inequalities and Vector Optimization

As very powerful and important tools in the study of nonlinear sciences, variational inequalities and vector optimization have attracted so much attention. Over the last decades, variational inequality and vector optimization techniques have been applied extensively in such diverse fields as biology, chemistry, economics, engineering, game theory, management science, physics, and so on. The thorough study of both theory and methods about variational inequalities and vector optimization contained in the literature will help us to find new variational inequalities and vector optimization techniques for solving the practical problems. The aim of this special issue is to present new approaches and theories for variational inequalities and vector optimization problems arising in mathematics and applied sciences. This special issue includes 24 high-quality peer-reviewed papers that deal with different aspects of variational inequalities and vector optimization problems.These papers contain some new, novel, and innovative techniques and ideas. We hope that all the papers published in this special issue can motivate and foster further scientific works and development of the research in the area of the theory, algorithms, and applications of variational inequalities and vector optimization problems.

Vector optimization technique in the problems of interaction between an enterprise and its counteragents

In the paper we consider the vector optimization problems connected with those of the choice of effective portfolios of the counteragents of an enterprise. In the first section the supply cost and risk of delay, both are to be minimized, are taken as criteria functions. The second section is devoted to optimization of the work of a bank with its customers by construction of an efficient portfolio of the bank instruments. The developing approach is methodologically related to that, which goes back to the paper by H. Markowitz, but considered for the objects different from those of financial market.

On linear vector program and vector matrix game equivalence

We define a vector matrix game, which is a vector version of the usual matrix game and consists of more than two skew symmetric matrices and vector ordering. Using vector optimization techniques, we characterize solutions for the vector matrix game. We establish equivalent relations between a linear vector optimization problem and its corresponding vector matrix game.

Analysis of the eastern Pacific yellowfin tuna fishery based on multiple management objectives

Vector optimization techniques were used to generate arbitrary segments of a policy frontier for a dynamic yellowfin tuna ( Thunnus albacares) fishery model assuming fixed technology and considering four policy objectives: minimizing dolphin mortality, minimizing incidental catch (all species except dolphins), maximizing sustainable yield, and minimizing biological risk for the yellowfin tuna stock. Results show that along the policy frontier: (1) reducing incidental dolphin mortality increases the incidental catch of other species in a nonlinear way; (2) yield increases (subject to a biomass precautionary level) can only be obtained at the expense of higher levels of dolphin mortality and incidental catch; (3) biological risk increases as the level of tunas caught increases, but this increase depends on the type of fishery (longline fishing and three different modes of purse-seining: log-sets, dolphin-sets or school-sets) that dominates the fishing effort; (4) there is an indirect relationship between the dolphin mortality levels and those of biological risk; (5) there is a direct relationship between the incidental catch levels and biological risk. Catch obtained with dolphin-sets dominates the Pareto-optimal solutions with highest dolphin mortality levels but is associated with lower biological risk, whereas catch obtained with log-sets dominates in Pareto-optimal solutions with higher incidental catch and higher biological risk. In general, trade-offs or shadow prices among objectives are not linear, indicating that marginal costs vary along the policy frontier. Results of the trade-off analysis may provide useful information for decision-makers and other policy actors. Complete information about the preferences of the decision-makers regarding the objectives is necessary to recommend a specific management policy.

On utility of inductive learning in multiobjective robust design

Most engineering design problems involve optimizing a number of often conflicting performance measures in the presence of multiple constraints. Traditional vector optimization techniques approach these problems by generating a set of Pareto-optimal solutions, where any specific objective can be further improved only at the cost of degrading one or more other objectives. The solutions obtained in this manner, however, are only single points within the space of all possible Pareto-optimal solutions and generally do not indicate to designers how small deviations from predicted design parameters settings affect the performance of the product or the process under study.In this paper we introduce a new approach to robust design based on the concept of inductive learning with regression trees. Given a set of training examples relating to a multiobjective design problem, we demonstrate how a multivariate regression tree can utilize an information-theoretic measure of covariance complexity to capture optimal, tradeoff design surfaces. The novelty of generating design surfaces as opposed to traditional points in the design space is that now designers are able to easily determine how the responses of a product or process vary as design parameters change. This ability is of paramount importance in situations where design parameter settings need to be modified during the lifetime of a product/process due to various economic or operational constraints. As a result, designers will be able to select optimal ranges for design parameters such that the product's performance indices exhibit minimal or tolerable deviations from their target values. To highlight the advantages of our methodology, we present a multiobjective example that deals with optimum design of an electric discharge machining (EDM) process.

Robust Multi-Objective Feedback Design with Linear Guaranteed-Cost Bounds

This paper considers the problem of designing a state-feedback control law which “minimizes” multiple quadratic performance objectives in the presence of uncertain linear plant dynamics. Vector optimization techniques are used to minimize upper-bounds on each performance objective (guaranteed-cost). By exploiting the linear uncertainty bound introduced by Bernstein (1987), the vector optimization problem is scalarized and Pareto optimal solutions are computed. The theory is applied to a numerical example with two quadratic performance measures.

Robust Multi-Objective Feedback Design with Linear Guaranteed-Cost Bounds.

This paper considers the problem of designing a state-feedback control law which "minimizes" multiple quadratic performance objectives in the presence of uncertain linear plant dynamics. Vector optimization techniques are used to minimize upper-bounds on each performance objective (guaranteed-cost). By exploiting the linear uncertainty bound introduced by (Bernstein, 1987), the vector optimization problem is scalarized and Pareto optimal solutions are computed. The theory is applied to a numerical example with two quadratic performance measures.

Compromise solution for economic-environmental decisions in agriculture

Least-cost production versus the environmental on- and off-site erosion damage of agriculture is evaluated in a policy context for a major Corn Belt watershed. Compromise programming, previously utilized in firm-level multi-criteria decision-making problems, is applied to a regional agricultural production model with environmental policy goal trade-offs. The crop sector model allocates land, water, labor, capital and commodity-program base acres to crop production. Production options include four conservation practices, three tillage methods and several crop rotations. Crop yield and fertilizer levels are dependent upon erosion. Cropping options selected allow for both wind and water erosion. The vector of objectives include three minimization functions: current production cost, future value of productivity loss and sediment damage. Vector optimization technique was used to generate the payoff matrix containing efficient but simultaneously unobtainable solutions. Given the ideal but infeasible solution vector we generated efficient solutions in the compromise subset corresponding to the L1, L2 and L∞ metrics. Trade-off relations were developed using the non-inferior set estimation technique.

Flight control system design for an in-flight simulator

This paper describes the theoretical development and nonlinear digital simulation results of the model-following flight control system for a new in-flight simulator. A detailed description of the manner in which the feedforward and feedback gains are obtained using an interactive computer-aided-design technique is given. Additional filters in the forward loop enable the feedforward gains to be made independent of the model aircraft parameters even when the actuator dynamics are modeled explicitly. The feedback gains are obtained by using a vector optimization technique. Several timeand frequency-domain measures are used to obtain the required performance. Modified robustness measures based on singular-value decomposition are used to constrain the feedback gains. The sensitivity of the model-following system to changes in flight condition is minimized using a multimodel formulation. Nonlinear simulation results are presented to show the quality of the model-following system and to substantiate the theory and design procedure.

Application of multiple objective optimization techniques to finite element model tuning

This report examines tuning a finite element model using vector optimization techniques. Structural models using finite element theory often need to be adjusted so they can accurately simulate the real structure. The goal is to tune the model such that it will reproduce data derived from structural tests. First, the performance indices are extremized using multiple objective optimization theory, producing a set of possible solutions. Next, the solutions are rank ordered according to a decision maker's preferences to select the best answer. The tuning process was applied to a T-38 horizontal stabilizer. Numerous weighted solutions contained a best static deformation model, a best frequency model and three intermediate combinations of these two models. This automated procedure proved to be a versatile method capable of producing solutions for many types of tuning problems.

An Operational Approach to Welfare Considerations in Applied Public-Facility-Location Models

The paper discusses several criteria by which locational alternatives for public facilities should be evaluated, and suggests metrics for quantifying these criteria in terms of the decision variables of the models that are often used in applied public-facility-location problems. Subsequently, multiobjective programming is considered as an important tool in locational analysis, and several applications of vector optimization techniques in that context are demonstrated.