Multi-criteria Design Research Articles

Optimal thermal operation of liquid-cooled electronic chips

A novel framework is developed to determine the optimal operating conditions of water cooled microprocessor chips through a tradeoff between heat recovery from the coolant and the chip thermal reliability. For illustration, a manifold microchannel heat sink is evaluated experimentally and computationally. First, a single objective optimization is demonstrated by combining the heat recovery and chip reliability into a single parameter. Then, multi-objective optimizations are performed by using Pareto optimality and Multi-Criteria Design Analysis. Using conservative guidelines, these approaches suggest that for an optimal coolant flow rate of 1l/m, optimal coolant inlet temperature lies between 40 and 50°C.

Evaluation of entomopathogenic fungi as potential biological control agents of the dengue mosquito, Aedes aegypti (Diptera: Culicidae)

Dengue is a global health concern. Growing insecticide resistance in the primary mosquito vector, Aedes aegypti, limits the effectiveness of vector control, so alternative tools are urgently needed. One approach is the use of biopesticides comprising entomopathogenic fungi, e.g., Beauveria bassiana and Metarhizium anisopliae. These fungi may decrease disease transmission by reducing mosquito vector longevity and also occur worldwide, although many isolates have not been tested for virulence against mosquitoes. Ninety-three isolates of entomopathogenic fungi representing six species (B. bassiana, M. anisopliae, Isaria fumosorosea, I. farinosa, I. flavovirescens, and Lecanicillium spp.) were screened as potential biological control agents of Aedes aegypti. A hierarchical, multi-criteria experimental design was undertaken to find suitable isolates. Initial screening was performed via in vitro assays measuring radial growth and spore persistence, eliminating isolates with poor growth or viability on nutrient-rich substrate. Subsequent measurements of spore persistence revealed that only nine of 30 strains tested had half-lives exceeding 3 weeks. Ten isolates were chosen for in vivo bioassays against adult Ae. aegypti. From these assays, two Australian isolates of B. bassiana, FI-277 and FI-278, appeared to be most promising. Both isolates were shown to be virulent against Ae. aegypti at 20, 26, and 32°C. Spreading spores manually onto substrate was found to be more efficacious than spraying. Ae. aegypti infected by manually-spread spores on cotton substrate were found to have an LT50 of 3.7±0.3 days. These characteristics suggest that FI-277 has promise as a dengue mosquito biocontrol agent, either alone or combined with conventional chemical insecticides.

Incorporating geological and market uncertainties and operational flexibility into open pit mine design

This work outlines a procedure for integrating uncertainty and operational flexibility into open pit mine design selection. A multi-criteria design ranking system based on advanced uncertainty and financial modeling techniques such as Monte Carlo simulation and real options is proposed. A case study at a copper mine is provided.

Multi-criteria optimization with multiple responses for technological innovation

This study integrated a multi-criteria response factor design and a multi-criteria optimization method to develop a new model and found the technical innovation multiple responses with multi-criteria optimization design of products and manufacturing processes. This research combined the multiple responses with the multi-criteria optimization design of products and manufacturing processes, and utilized the principle component analysis to compute the principle score of five indicators of innovation ability as dependent variables. Utilizing the factor analysis, the variables were retrenched and the factor scores were computed as independent variables. Furthermore, this research established the response surface models by using principle scores as dependent variables and factor scores as independent variables. Finally, this research analyzed the key influence factors on innovation ability by desirability function and sensitivity analysis. This research proposed the most complete innovation measurement indicators and contributed to the present innovation theory and academic. The results of this research indicated the optimal combination of innovation sources and pointed out that firm supporting, external information sources, evaluation on marketing effect, feasibility study and professional innovation information were the main factors that had a positive impact on innovation performance, while innovation uncertainty was the only factor that had a negative influence on innovation performance in a company. The contribution of this study is seen, more obviously, in the electronic industry. This research enhanced the innovation ability of the industry and analyzed the optimized combination of innovation ability. Key words: Multi-criteria optimization, response surface model, technological innovation.

Multicriteria Design of Plastic Recycling Based on Quality Information and Environmental Impacts

Summary In this study, we develop a framework for the multicriteria design of plastic recycling based on quality information and environmental impacts for the purpose of supporting collaborative decision making among consumers, municipalities, and recyclers. The subject of this article is the mechanical recycling of postconsumer polyethylene terephthalate (PET) bottles. We present a “quality conversion matrix,” which links the quality of recycled PET resin to the quality of waste PET bottles and operational conditions, described in terms of the functions of modules constituting the entire recycling process. We estimate the quality of recycled PET resin and simulate the applicability to the intended products as the primary criterion by confirming whether the estimated quality of recycled resin satisfies the quality demands of PET resin users. The amounts of carbon dioxide (CO2) emissions and fossil resource consumption are also estimated as the secondary criteria. An approach to collaborative decision making utilizing mixed-integer linear programming (MILP) and Monte Carlo simulation is proposed on the premise of different objectives of various stakeholders, where all the feasible optimal solutions for achieving the quality demands are obtained. The quality requirements of waste bottles, along with the CO2 emissions and fossil resource consumption estimated for each solution, contribute to the collaborative multicriteria design of plastic recycling.

Thermal–mechanical optimization of a novel nanocomposite-film typed flip chip technology

The paper aims at optimization of the residual thermal–mechanical behaviors of a novel Flip Chip (FC) technology during and after the fabrication process. In this study, we first introduce the novel adhesive-typed FC packaging technology, consisting of a nanocomposite film for anisotropic electrical conduction and a nonconductive paste (NCP) for developing NCP joints. In the optimization work, the material and geometry properties and the bonding process parameters are considered as the design parameters, and the constraints on the residual behaviors and the design parameters are included. To deal with the multi-criteria design optimization problem, an effective metamodeling-based design optimization scheme is applied, which integrates parametric finite element (FE) analysis, a response surface methodology (RSM) and an updating scheme. Moreover, to assess the residual behaviors, a process-dependent simulation methodology that integrates both transient thermal and nonlinear contact FE analyses and a “death–birth” meshing scheme is carried out. The validity of the process-dependent FE simulation methodology is confirmed through experiment. Finally, two design practices are performed, and the calculated optimal designs are compared with each other and with the original design. It turns out that the present optimization methodology can be very effective and robust in seeking the optimal design of the FC technology with a better residual thermal–mechanical performance after the NCP bonding process.

Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain

Despite the availability of weather radar data at high spatial (1 km2) and temporal (5–15 min) resolution, ground‐based rain gauges continue to be necessary for accurate estimation of storm rainfall input to catchments during flash flood events, especially in mountainous catchments. Given economical considerations, a long‐standing problem in catchment hydrology is to establish optimal placement of a small number of rain gauges to acquire data on both rainfall depth and spatiotemporal variability of intensity during extreme storm events. Using weather radar observations and a dense network of 40 tipping bucket rain gauges, this study examines whether it is possible to determine a reliable “best” set of rain gauge locations for the Sabino Canyon catchment near Tucson, Arizona, USA, given its complex topography and dominant storm track pattern. High‐quality rainfall data are used to evaluate all possible configurations of a “practical” network having from one to four rain gauges. A multicriteria design strategy is used to guide rain gauge placement, by simultaneously minimizing the residual percent bias and maximizing the coefficient of correlation between the estimated and true mean areal rainfall and minimizing the normalized spatial mean squared error between the estimated and true spatiotemporal rainfall distribution. The performance of the optimized rain gauge network was then compared against randomly designed network ensembles by evaluating the quality of streamflows predicted using the Kinematic Runoff and Erosion (KINEROS2) event‐based rainfall‐runoff model. Our results indicate that the multicriteria strategy provided a robust design by which a sparse but accurate network of rain gauges could be implemented for semiarid basins such as the one studied.

A practical generative design method

A generative CAD based design exploration method is proposed. It is suitable for complex multi-criteria design problems where important performance criteria are uncomputable. The method is based on building a genotype of the design within a history based parametric CAD system and then, varying its parameters randomly within pre-defined limits to generate a set of distinctive designs. The generated designs are then filtered through various constraint envelopes representing geometric viability, manufacturability, cost and other performance related constraints, thus reducing the vast design space into a smaller viable design space represented by a set of distinctive designs. These designs may then be further developed by the designer. The proposed generative design method makes minimal imposition on the designer’s work process and maintains both flexibility and fluidity that is required for creative design exploration. Its ability to work seamlessly with current CAD based design practices from early conceptual to detailed design is demonstrated. The design philosophy behind this generative method and the key steps involved in its implementation are presented with examples.

Multi-Criteria Optimal Design of Parallel Manipulators Based on Natural Frequency

The Stewart manipulator has characteristics of low natural frequency, high cost and large size which make it difficult to obtain optimum performance with high dynamic response. The lowest natural frequency in the total workspace and average of six frequencies at home configuration of Stewart manipulator are introduced as indices to evaluate dynamic stability. Multi-criteria optimal design based on genetic algorithm (GA) was presented synthetically considering the workspace requirement, lowest natural frequency, average frequency and global dimensionally homogeneous Jacobian matrix condition number. An optimal result was obtained through standard GA using penalty function and the Pareto-optimal set was also obtained through parallel selection method.

Multi-criteria Axiomatic Design Approach to Evaluate Sites for Grid-connected Photovoltaic Power Plants: A Case Study in Turkey

Global warming and climate change are the most serious problems for developing countries as well as the world. Therefore, the usage of renewable energy sources is gaining importance for sustainable energy development and environmental protection. Turkey is one of the developing countries whose demand of electricity is sharply increasing. In order to meet this demand by means of renewable sources, solar power is a suitable source due to the high solar energy potential of Turkey among the renewable sources. In this article, the multi-criteria axiomatic design (AD) approach is proposed for the evaluation of sites for a grid-connected photovoltaic power plant (GCPP) in Turkey. For this aim, four evaluation criteria, which have great influence on determination of a GCPP site are taken into account.

Analysis and multi-criteria design optimization of geometric characteristics of grooved micromixer

Computational fluids dynamics (CFDs) and numerical optimization techniques are applied in an integrated methodology to explore the effects of different geometric characteristics on fluid mixing in a staggered herringbone micromixer (SHM). To quantify the mixing intensity in the mixer a mixing index is defined on the basis of the intensity of segregation of the mass concentration on a cross-section plane in the mixing channel. Four geometric parameters, i.e., aspect ratio of the mixing channel, ratio of groove depth to channel height, ratio of groove width to groove pitch and the asymmetry factor (offset) of groove, are the design variables initially selected for optimization, then two more parameters, i.e., angle of the groove and number of grooves per channel section, are evaluated. The whole optimization is conducted with a multi-objective approach for which the mixing index at the outlet section and the pressure drop in the mixing channel are the performance criteria used as objective functions. The Pareto front of designs with the optimum trade-off, maximum mixing index with minimum pressure drop, is obtained.

Design of two-dimensional recursive digital filters with specified magnitude and group-delay characteristics using Taguchi-based Immune Algorithm

This paper presents one modern heuristic optimisation algorithm, named Taguchi-Based Immune Algorithm (TBIA), to solve the problem of designing 2D recursive digital filters with specified magnitude and group-delay characteristics. The algorithm is detailed for the design of three recursive filters' categories, namely filters with predefined magnitude, delay and magnitude and delay. On the basis of minimising the magnitude and group-delay errors, multi-criterion design combination is employed to obtain optimal recursive filters that satisfy the required specifications. Computational experiments show the ability of the proposed algorithm to obtain more robust stable complex filters compared with previously reported design methods.

Méthode de (re)conception des processus pour une performance multicritère : application à l’Établissement français du sang

The organisations’ performance is not restricted to economic criteria, it integrates other dimensions: social, safety, quality, environmental, ethical, scientific… There are few multicriteria design and management methods for organisations. In this article, we propose an analysis and modelling method of the values creation based on systematic approach. This method enables to integrate all the stakeholders’ points of view and expectations in order to have a global vision of the performance. We have applied this method to one French Blood Establishment process.

Design Criteria for Serial Chain Mechanisms Based on Kinetic Energy Ratios

In this article, a description of the kinetic energy partition values (KEPVs) of serial chain mechanisms, as well as their rates of change, is presented. The KEPVs arise from the partitioning of the mechanism’s kinetic energy and correspond to the critical values of the kinetic energy ratios of the actuators and links and are indicators of the kinetic energy distribution within the system while the rates of change in the KEPVs indicate the sensitivity to change in the location of that kinetic energy. A high value for the KEPV rate of change together with a high operational state implies that the amount of kinetic energy flowing in the system is large, further implying a loss of precision in the system due to large inertial torques being generated. The KEPVs and their rates can be used to identify the actuators whose parameters (mass, inertia, and mechanical gain) greatly affect the overall distribution of kinetic energy within the system, thereby allowing the designer to optimize these actuators to improve the performance of the overall mechanism. Two design criteria, one based on the KEPVs and another based on their rates of change, are developed. A two degree-of-freedom (DOF) planar mechanism is used to demonstrate the concepts and to verify the properties of the KEPVs and their rates. Also, a 6DOF spatial manipulator is used to illustrate the solution of a multicriteria design optimization problem where two conflicting criteria are considered: a KEPV criterion and an effective mass criterion. The goal of the design problem is to demonstrate how the inertial parameters of the actuators and the mechanical gains of the actuator transmissions alter the kinetic energy of the system both in terms of its magnitude, which is “measured” via the effective mass criterion, and its distribution, which is measured via the KEPV criterion. It is demonstrated that the mechanical gains in the actuators significantly influence the magnitude of the kinetic energy as well as its distribution within the system due to the inertial amplification. The KEPVs and their rates of change provide a novel new approach to analyzing the dynamic character of serial chain mechanisms.

Multi-criteria design optimization study of solvent extraction in mixer–settler units

Solvent extraction is considered as a multi-criteria optimization problem, since several chemical species with similar extraction kinetic properties are frequently present in the aqueous phase and the selective extraction is not practicable. This optimization, applied to mixer–settler units, considers the best parameters and operating conditions, as well as the best structure or process flow-sheet. Global process optimization is performed for a specific flow-sheet and a comparison of Pareto curves for different flow-sheets is made. The positive weight sum approach linked to the sequential quadratic programming method is used to obtain the Pareto set. In all investigated structures, recovery increases with hold-up, residence time and agitation speed, while the purity has an opposite behaviour. For the same treatment capacity, counter-current arrangements are shown to promote recovery without significant impairment in purity. Recycling the aqueous phase is shown to be irrelevant, but organic recycling with as many stages as economically feasible clearly improves the design criteria and reduces the most efficient organic flow-rate.

Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm

Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study, an improved particle swarm optimization algorithm is developed to derive these nondominated solutions. Hybrid generation systems under different design scenarios are designed based on the proposed approach. First, a grid-linked hybrid system is designed without incoroprating system uncertainties. Then, adequacy evaluation is conducted based on probabilistic methods by accounting for equipment failures, time-dependent sources of energy, and stochastic generation/load variations. In particular, due to the unpredictability of wind speed and solar insolation as well as the random load variation, time-series models are adopted to reflect their stochastic characteristics. An adequacy evaluation procedure including time-dependent sources, is adopted. Sensitivity studies are also carried out to examine the impacts of different system parameters on the overall design performance.

Bounds on transient phase plastic deformations in optimal design of steel frames subjected to cyclic loads

A minimum volume multicriterion design of elastic perfectly plastic steel frames subjected to a combination of quasi-static fixed and cyclic loads, bounding the transient phase plastic deformations, is proposed. The problem is formulated according to a plastic shakedown criterion, so that incremental and instantaneous collapse are certainly prevented when the frame is subjected to very strongly amplified cyclic loads. The further condition that the structure must also behave elastically in serviceability conditions is imposed. Since the steady-state loading history is known it is possible to directly bound the steady-state plastic deformations. By applying a suitable own bounding theorem, it is also possible to indirectly bound the transient plastic deformations, whichever the unknown transient real loading history is. A numerical application confirms the fundamental role played by the transient phase plastic deformations both on the design as well as on the structural behaviour and the great performance of the proposed formulation.

Cellular genetic algorithm technique for the multicriterion design optimization

There have been increased activities in the study of genetic algorithms (GA) for problems of design optimization. The present paper describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the Cellular Genetic Algorithm approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site, and as in cellular automata computations, takes place on the basis of local interaction with neighboring cells. A focus of the paper is in the adaptation of the cellular genetic algorithm approach in the solution of multicriteria design optimization problems. The proposed paper describes the implementation of this approach and examines its efficiency in the context of representative design optimization problems.

Evolutionary Multicriteria Design Optimization of Integrally Stiffened Airframe Structures

A, B, k, Cf , nf, Kc = Forman coefficients a = crack length am, bm, m = shape coefficients of the mass mapping function au, bu = shape coefficients of the deflection mapping function aSIF, bSIF = shape coefficients of the fatigue-response mapping function Cfeas, Dfeas = coefficients of the failure mapping function, feasible state Climit, Dlimit = coefficients of the failure mapping function, limit state di = design response i E = Young’s modulus GI = energy release rate, mode I GII = energy release rate, mode II g = gravitational acceleration K = stress intensity factor Kth = stress intensity factor, threshold value N = load cycle n = load factor p = pressure R = stress ratio t = thickness u = longitudinal displacements at the crack surface v = normal displacements at the crack surface wi = weight factor i Xi = longitudinal force at the crack tip Yi = normal force at the crack tip

Evolutionary multi criteria design optimization of robot grippers

This paper explores the use of intelligent techniques to obtain optimum geometrical dimensions of a robot gripper. The optimization problem considered is a non-linear, complex, multi-constraint and multicriterion one. Three robot gripper configurations are optimized. The aim is to find Pareto optimal front for a problem that has five objective functions, nine constraints and seven variables. The problem is divided into three cases. Case 1 has first two objective functions, the case 2 considers last three objective functions and case 3 deals all the five objective functions. Intelligent optimization algorithms namely Multi-objective Genetic Algorithm (MOGA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE) are proposed to solve the problem. Normalized weighting objective functions method is used to select the best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure (SSM) and ratio of non-dominated individuals (RNIs)) are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead (OO) and algorithm effort are used to find the computational effort of MOGA, NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.