Abstract: Due to the economic downturn and the deterioration of the living environment, the development of residential buildings as high energy consuming building is gradually changing from “extensive” to green building in China. So, the evaluation system of green building is continuously improved, but the current evaluation work has the following problems: (1) There are differences in the cost of the actual investment and the purchasing power of residents, also construction target of green residential building is single and lacks multi-objective performance development. (2) Green building evaluation lacks regional characteristics and cannot reflect the different regional residents demand. (3) In the process of determining the criteria weight, the experts’ judgment matrix is difficult to meet the requirement of consistency. Therefore, to solve those problems, questionnaires which are about the green residential building for Ningxia area are distributed, and the results of questionnaires can feedback the purchasing power of residents and the acceptance of the green building cost. Secondly, combined with the geographical features of Ningxia minority areas, the evaluation criteria system of green residential building is constructed. Finally, using the improved group AHP method and the grey clustering method, the criteria weight is determined, and a real case is evaluated, which is located in Xing Qing district, Ningxia. A conclusion can be obtained that the professional evaluation for this project and good social recognition is basically the same.
Abstract: Aiming at optimizing the weight and deflection of cantilever beam subjected to maximum stress and maximum deflection, Multi-objective Particle Swarm Optimization (MOPSO) with Utopia Point based local search is implemented. Utopia point is used to govern the search towards the Pareto Optimal set. The elite candidates obtained during the iterations are stored in an archive according to non-dominated sorting and also the archive is truncated based on least crowding distance. Local search is also performed on elite candidates and the most diverse particle is selected as the global best. This method is implemented on standard test functions and it is observed that the improved algorithm gives better convergence and diversity as compared to NSGA-II in fewer iterations. Implementation on practical structural problem shows that in 5 to 6 iterations, the improved algorithm converges with better diversity as evident by the improvement of cantilever beam on an average of 0.78% and 9.28% in the weight and deflection respectively compared to NSGA-II.
Abstract: We address the problem of interference over all the channels in multiuser MIMO-OFDM systems. This paper contributes three beamforming strategies designed for multiuser multiple-input and multiple-output by way of orthogonal frequency division multiplexing, in which the transmit and receive beamformers are acquired repetitious by secure-form stages. In the principal case, the transmit (TX) beamformers remain fixed then the receive (RX) beamformers are computed. This eradicates one interference span for every user by means of extruding the transmit beamformers into a null space of relevant channels. Formerly, by gratifying the orthogonality condition to exclude the residual interferences in RX beamformer for every user is done by maximizing the signal-to-noise ratio (SNR). The second case comprises mutually optimizing the TX and RX beamformers from controlled SNR maximization. The outcomes of first case is used here. The third case also includes combined optimization of TX-RX beamformers; however, uses the both controlled SNR and signal-to-interference-plus-noise ratio maximization (SINR). By the standardized channel model for IEEE 802.11n, the proposed simulation experiments offer rapid beamforming and enhanced error performance.
Abstract: In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.
Abstract: In this paper, a detailed simulation model of a solar-powered triple-effect LiBr–H2O absorption chiller is developed to supply both cooling and heating demand of a large-scale building, aiming to reduce the fossil fuel consumption and greenhouse gas emissions in building sector. TRNSYS 17 is used to simulate the performance of the system over a typical year. A combined energetic-economic-environmental analysis is conducted to determine the system annual primary energy consumption and the total cost, which are considered as two conflicting objectives. A multi-objective optimization of the system is performed using a genetic algorithm to minimize these objectives simultaneously. The optimization results show that the final optimal design of the proposed plant has a solar fraction of 72% and leads to an annual primary energy saving of 0.69 GWh and annual CO2 emissions reduction of ~166 tonnes, as compared to a conventional HVAC system. The economics of this design, however, is not appealing without public funding, which is often the case for many renewable energy systems. The results show that a good funding policy is required in order for these technologies to achieve satisfactory payback periods within the lifetime of the plant.
Abstract: This paper presents the evaluation of performance (BSFC and BTE), combustion (Pmax) and emission (CO, NOx, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results.
Abstract: In this paper, a method is proposed for solving Fuzzy Multi-Objective Linear Programming problems (FMOLPP) with fuzzy right hand side and fuzzy decision variables. To illustrate the proposed method, it is applied to the problem of selecting suppliers for an automotive parts producer company in Iran in order to find the number of optimal orders allocated to each supplier considering the conflicting objectives. Finally, the obtained results are discussed.
Abstract: The main objective of the current work is to introduce sustainability factors in optimizing the supply chain model for process industries. The supply chain models are normally based on purely economic considerations related to costs and profits. To account for sustainability, two additional factors have been introduced; environment and risk. A supply chain for an entire petroleum organization has been considered for implementing and testing the proposed optimization models. The environmental and risk factors were introduced as indicators reflecting the anticipated impact of the optimal production scenarios on sustainability. The aggregation method used in extending the single objective function to multi-objective function is proven to be quite effective in balancing the contribution of each objective term. The results indicate that introducing sustainability factor would slightly reduce the economic benefit while improving the environmental and risk reduction performances of the process industries.
Abstract: In Pakistan, major demands of fuel wood and timber wood are fulfilled by agroforestry. However, the information regarding economic significance of agroforestry and its productivity in Pakistan is still insufficient and unreliable. Survey of field conditions to examine the agroforestry status at local level helps us to know the future trends and to formulate the policies for sustainable wood supply. The objectives of this research were to examine the actual status and potential of agroforestry and to point out the barriers that are faced by farmers in the adoption of agroforestry. Research was carried out in Chiniot district, Pakistan because it is the famous city for furniture industry that is largely dependent on farm trees. A detailed survey of district Chiniot was carried out from 150 randomly selected farmer respondents using multi-objective oriented and pre-tested questionnaire. It was found that linear tree planting method was more adopted (45%) as compared to linear + interplanting (42%) and/or compact planting (12.6%). Chi-square values at P-value
Abstract: Abstract—Attribute or feature selection is one of the basic
strategies to improve the performances of data classification tasks,
and, at the same time, to reduce the complexity of classifiers,
and it is a particularly fundamental one when the number
of attributes is relatively high. Its application to unsupervised
classification is restricted to a limited number of experiments in
the literature. Evolutionary computation has already proven itself
to be a very effective choice to consistently reduce the number
of attributes towards a better classification rate and a simpler
semantic interpretation of the inferred classifiers. We present a feature
selection wrapper model composed by a multi-objective evolutionary
algorithm, the clustering method Expectation-Maximization (EM),
and the classifier C4.5 for the unsupervised classification of data
extracted from a psychological test named BASC-II (Behavior
Assessment System for Children - II ed.) with two objectives:
Maximizing the likelihood of the clustering model and maximizing
the accuracy of the obtained classifier. We present a methodology
to integrate feature selection for unsupervised classification, model
evaluation, decision making (to choose the most satisfactory model
according to a a posteriori process in a multi-objective context), and
testing. We compare the performance of the classifier obtained by the
multi-objective evolutionary algorithms ENORA and NSGA-II, and
the best solution is then validated by the psychologists that collected
the data.
Abstract: Electric discharge machining (EDM) is one of the most widely used non-conventional manufacturing process to shape difficult-to-cut materials. The process yield, in terms of material removal rate, surface roughness and tool wear rate, of EDM may considerably be improved by selecting the optimal combination(s) of process parameters. This paper employs Multi-response signal-to-noise (MRSN) ratio technique to find the optimal combination(s) of the process parameters during EDM of Inconel 718. Three cases v.i.z. high cutting efficiency, high surface finish, and normal machining have been taken and the optimal combinations of input parameters have been obtained for each case. Analysis of variance (ANOVA) has been employed to find the dominant parameter(s) in all three cases. The experimental verification of the obtained results has also been made. MRSN ratio technique found to be a simple and effective multi-objective optimization technique.
Abstract: In this paper, we presented a Multi-Objective Random
Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based
on RDPSO and crowding distance sorting to improve the convergence
and distribution with less computation cost. MORDPSO-CD makes
the most of RDPSO to approach the true Pareto optimal solutions
fast. We adopt the crowding distance sorting technique to update and
maintain the archived optimal solutions. Introducing the crowding
distance technique into MORDPSO can make the leader particles
find the true Pareto solution ultimately. The simulation results reveal
that the proposed algorithm has better convergence and distribution.
Abstract: This paper presented a technique to solve one of the transportation problems that faces us in real life which is the Bus Scheduling Problem. Most of the countries using buses in schools, companies and traveling offices as an example to transfer multiple passengers from many places to specific place and vice versa. This transferring process can cost time and money, so we build a decision support system that can solve this problem. In this paper, a genetic algorithm with the shortest path technique is used to generate a competitive solution to other well-known techniques. It also presents a comparison between our solution and other solutions for this problem.
Abstract: Energy is a fundamental component in sustainability, the access and use of this resource is related with economic growth, social improvements, and environmental impacts. In this sense, energy efficiency has been studied as a factor that enhances the positive impacts of energy in communities; however, the implementation of efficiency requires strong policy and strategies that usually rely on individual measures focused in independent dimensions. In this paper, the problem of energy efficiency as a multi-objective problem is studied, using scientometric analysis to discover trends and patterns that allow to identify the main variables and study approximations related with a further development of models to integrate energy efficiency and MCA into policy making for small communities.
Abstract: This work proposed a multi-objective mathematical programming approach to select the appropriate supply network elements. The multi-item multi-objective production-distribution inventory model is formulated with possible constraints under fuzzy environment. The unit cost has taken under fuzzy environment. The inventory model and warehouse location model has combined to formulate the production-distribution inventory model. Warehouse location is important in supply chain network. Particularly, if a company maintains more selling stores it cannot maintain individual secondary warehouse near to each selling store. Hence, maintaining the optimum number of secondary warehouses is important. Hence, the combined mathematical model is formulated to reduce the total expenditure of the organization by arranging the network of minimum number of secondary warehouses. Numerical example has been taken to illustrate the proposed model.
Abstract: Considering the challenges of short product life cycles
and growing variant diversity, cost minimization and manufacturing
flexibility increasingly gain importance to maintain a competitive
edge in today’s global and dynamic markets. In this context, an
aerodynamic part feeding system for high-speed industrial assembly
applications has been developed at the Institute of Production
Systems and Logistics (IFA), Leibniz Universitaet Hannover. The
aerodynamic part feeding system outperforms conventional systems
with respect to its process safety, reliability, and operating speed. In
this paper, a multi-objective optimisation of the aerodynamic feeding
system regarding the orientation rate, the feeding velocity, and the
required nozzle pressure is presented.
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
Abstract: In this study, a multi objective optimization for end
milling of Al 6061 alloy has been presented to provide better
surface quality and higher Material Removal Rate (MRR). The input
parameters considered for the analysis are spindle speed, depth of cut
and feed. The experiments were planned as per Taguchis design of
experiment, with L27 orthogonal array. The Grey Relational Analysis
(GRA) has been used for transforming multiple quality responses
into a single response and the weights of the each performance
characteristics are determined by employing the Principal Component
Analysis (PCA), so that their relative importance can be properly and
objectively described. The results reveal that Taguchi based G-PCA
can effectively acquire the optimal combination of cutting parameters.
Abstract: In this paper a new methodology for vendor selection
and supply quotas determination (VSSQD) is proposed. The problem
of VSSQD is solved by the model that combines revised weighting
method for determining the objective function coefficients, and a
multiple objective linear programming (MOLP) method based on the
cooperative game theory for VSSQD. The criteria used for VSSQD
are: (1) purchase costs and (2) product quality supplied by individual
vendors. The proposed methodology has been tested on the example
of flour purchase for a bakery with two decision makers.
Abstract: Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.