Multi-objective Layout Optimization Research Articles

Multiobjective layout optimization for low impact development considering its ecosystem services

The augmented proportion of impervious surfaces and heavy rainfall have resulted in waterlogging, nonpoint source pollution, and environmental degradation. Low impact development (LID) is an effective storm management practice. Based on six different LID paving scenarios, a four objective simulation-optimization framework coupled with stormwater management model (SWMM) and NSGA-Ⅱ that incorporates the ecosystem service value (ESV) was proposed for the optimal layout of LID facilities. A case study in Beijing, China, was taken as an example. The results showed that the rain garden paving scenario is remarkable compared to the other scenarios in realizing ESV, with the dominant ESV of net carbon sinks. The optimization revealed that the change in pollution control rate was more sensitive than the runoff reduction rate. Simultaneously, the optimization of these two indicators will be accompanied by more cost and lower ESV. The optimal solution achieved 32.48 % of runoff reduction rate, 82.22 % of water pollution control rate, 0.73×105 CNY of ESV, and 2.29×106 CNY of cost. The framework provides reference and technical support to achieve the synergistic objectives among cost, water quality and quantity control, and ecosystem services.

Research on Multi-objective Layout Optimization Model of Rural Industry based on Improved Ant Colony Algorithm Under the Background of Digital Economy

Research on Multi-objective Layout Optimization Model of Rural Industry based on Improved Ant Colony Algorithm Under the Background of Digital Economy

Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model

Wind farm layout optimization (WFLO) can reduce the turbulence intensity on the wind turbines while maximizing power generation. To accurately compute turbulence intensity distribution in the wind farm, we utilize the innovative three-dimensional cosine-shaped turbulence intensity (3D-COTI) model. By integrating the three-dimensional cosine-shaped linear entrainment (3DCLE) wake model previously developed by the authors, we introduce a new multiple wind turbine wake model (3DCLE-M). This model considers the wake superposition effect, enabling precise calculation of power generation. Building upon the 3D-COTI and 3DCLE-M models, we propose a multi-objective WFLO approach to maximize power generation and minimize streamwise turbulence intensity in the wind-turbine-swept area. The results indicate that under maximized power generation, this approach significantly reduces turbulence intensity and its non-uniform distribution in the wind-turbine-swept area. In two ideal cases, the maximum turbulence intensity in all wind-turbine-swept areas of the optimized layout is 25 % and 3.8 % lower than that of the selected benchmark layout. By optimizing the layout of the Horns Rev offshore wind farm, a new layout can be obtained with increased total power output by 1.58 % and the reduced total duration of high-fatigue-load wind turbines under high turbulence intensity by 25 % compared to the benchmark.

MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

The proposal of the wind farm layout optimization (WFLO) problem aims to better utilize wind energy. A multi-objective reptile search algorithm (MORSA) based on elite non-dominated sorting and grid indexing mechanism was proposed to solve the multi-objective optimization problem of wind farm layout under the Jansen wake model to maximize power generation while minimizing costs. Firstly, the elite non-dominated sorting method was used to sort the population, and then the crowding distance method was used to maintain the diversity between the optimal sets. By calculating the crowding distance, the same level of non-dominated populations are sorted. Then, the grid index mechanism is added to the archive for selection and deletion. Leaders can use the roulette wheel to select and delete some solutions in high-density grids. By obtaining the optimal Pareto solution set while maintaining the diversity of the population, the effectiveness of solving multi-objective optimization problems is improved. The improved algorithm solves the WFLO problem based on the Jansen wake model by using two different initial self-flow winds for performance testing while considering the effects of single wake and multiple wake with or without partial wake occlusion. The performance of the WFLO (output power and cost) was compared without considering wake obstruction, MORSA can obtain the minimum turbine cost of 26.3601 and the maximum power generation of 2.3078E-08 with a single wake flow of 16 m/s. The experimental results show that the proposed MORSA has better convergence and applicability, and has shown good results in multi-objective layout optimization of wind farms.

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

The development and popularization of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimize the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviors of electric taxis (ETs). In the case study of Shenzhen, China, geographical positioning system (GPS) trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a geographic information system (GIS) context of an urban road network with traveling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviors of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximizing the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimization technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.

Collaborative Workplace Design: A Knowledge-Based Approach to Promote Human–Robot Collaboration and Multi-Objective Layout Optimization

The innovation-driven Industry 5.0 leads us to consider humanity in a prominent position as the center of the manufacturing field even more than Industry 4.0. This pushes us towards the hybridization of manufacturing plants promoting a full collaboration between humans and robots. However, there are currently very few workplaces where effective Human–Robot Collaboration takes place. Layout designing plays a key role in assuring safe and efficient Human–Robot Collaboration. The layout design, especially in the context of collaborative robotics, is a complex problem to face, since it is related to safety, ergonomics, and productivity aspects. In the current work, a Knowledge-Based Approach (KBA) is adopted to face the complexity of the layout design problem. The framework resulting from the KBA allows for developing a modeling paradigm that enables us to define a streamlined approach for the layout design. The proposed approach allows for placing resource within the workplace according to a defined optimization criterion, and also ensures compliance with various standards. This approach is applied to an industrial case study in order to prove its feasibility. A what-if analysis is performed by applying the proposed approach. Changing three control factors (i.e., minimum distance, robot speed, logistic space configuration) on three levels, in a Design of Experiments, 27 layout configurations of the same workplace are generated. Consequently, the inputs that most affect the layout design are identified by means of an Analysis of Variance (ANOVA). The results show that only one layout is eligible to be the best configuration, and only two out of three control factors are very significant for the designing of the HRC workplace layout. Hence, the proposed approach enables the designing of standard compliant and optimized HRC workplace layouts. Therefore, several alternatives of the layout for the same workplace can be easily generated and investigated in a systematic manner.

Non-dominated sorted genetic algorithm-II algorithm-based multi-objective layout optimization of solid wood panels

Common solid wood panel defects affect the appearance of timber products and reduce their value for use. It is necessary to remove defects from solid wood panels to achieve a panel layout. A whole wooden beam column is cut into solid wood panels of different sizes, according to the requirements. Aiming to overcome problems of weak convergence ability, single-objective optimization, and the poor optimization effect of solid wood panel layout optimization based on a traditional genetic algorithm, an improved multi-objective solid wood panel layout optimization based on NSGA-II (Non-dominated sorted genetic algorithm-II) algorithm was proposed. Reverse learning was used to generate a reverse population to increase the search capability of the algorithm and to solve the problem of insufficient population diversity in the genetic algorithms. A combination of directional variation and uniform variation was used to improve the optimization effect and solve the problem of small individual differences in the evolution of the algorithm. The improved multi-objective optimization algorithm showed better optimization and stability than the NSGA-II algorithm. The number of convergence iterations was reduced and simultaneous optimization of multiple objectives can be realized.

Multi-objective layout optimization for sponge city by annealing algorithm and its environmental benefits analysis

With the development of urbanization, cities are facing crises such as waterlogging, water pollution, and floods. In order to actually alleviate urban floods and other disasters, China has proposed the "sponge city" construction concept. A multi-objective optimization method of the minimization of rainfall runoff, pollutant discharge and construction and operation costs and the maximization of environmental benefit is proposed by Simulated Annealing (SA) algorithm in order to fulfill the planning and construction of sponge cities. Some Low-Impact Developments (LID) are optimized for the study area and the optimal underlying surface layout of sponge city is obtained. Results indicates that the optimal underlying surface layout of the study area satisfies Tianjin sponge city construction pilot implementation plan. For the study area, the annual runoff coefficient is 0.169 and the annual discharged pollutant mass of TSS, COD, TN and TP is reduced by 65.11 %, 62.06 %, 64.67 % and 80.94 %, respectively, which meets the requirements of annual runoff control coefficient (less than 0.22) and annual pollutant reduction rate (about 65 %) of sponge city construction pilot implementation plan. The optimal cost of the study area is about RMB 9.77*107 and the environmental benefits of the study area quantified by CO2 emission reduction is 1167 t.

Modelling and layout design for an automated fibre placement mechanism

The fibre transmission path in an automated fibre placement (AFP) mechanism greatly affects the final embedded defects and mechanical properties when producing composite components. In this study, the technical requirements, logical movements and complex coupled relationships within multiple functional modules during fibre transmission are analysed. Using the functional classification method, we decompose AFP mechanism into four subsystems: the creel assembly, the tension measurement unit, the CCR unit (clamping cutting and restarting) and the compacting and heating mechanisms. To express the orientation and position of each subsystem as design variables, coordinate transformation is introduced. On the basis of the multidisciplinary optimization principle and the complex coupled relationship within the AFP mechanism, a multi-objective layout optimization with nested analysis and design approach is proposed for each functional component to address the complex mechanism layout problem. Finally, a 4-strip and 6-strip AFP mechanisms are then assessed to verify the proposed methodology. The results demonstrate that this approach can effectively realize the layout design for the AFP.

Multi-objective optimization considering cost-benefit ratio for the placement of gas detectors in oil refinery installations

It is of great importance for an oil refinery installation to have an effectively designed gas detection system. The majority of current research on the optimization of gas detector placement focuses on a single-objective stochastic programming (SP) approach. However, it is actually a multi-objective optimization problem, trying to obtain optimal solutions with conflicting criteria. This paper proposes a multi-objective optimization method for detector placement, achieving a balance between the detection efficiency improvement and the required investment. Based on the leakage scenario and the consequence modelling, the cumulative death probability (CDP) in the whole unit is quantitatively represented. The reduced fatality can also be evaluated according to the detection time. Then, a cost-benefit ratio (CBR) model of the gas detector investment and benefit from fatality reduction can be established. With the objective of minimizing both the CDP and CBR, a multi-objective layout optimization model considering the cost-benefit (MLOM-CB) is proposed. A non-dominated sorting genetic algorithm-II (NSGA-II) is used to obtain acceptable results. The optimization of hydrogen sulfide gas detector placement is taken as a case study. Results demonstrate that this approach can improve the detection efficiency of gas detector networks and the flexibility of decision-making for gas detector investment.

Multi-objective layout optimization for an orbital propellant depot

The overall layout optimization design of an orbital propellant depot involves the optimization of shape, size, and positions of propellant tanks in functional module and the optimization of positions of equipment in service module, with the aim of making the carrying capacity of propellant, dry/wet ratio, and mass properties meet the allowable values. To alleviate the difficulty in dealing with the overall optimization problems involving two modules of the orbital propellant depot, a step-by-step modeling and solving strategy is presented. Two multi-objective optimization mathematical models for the tanks in functional module (model I) and the equipment in service module (model II) are constructed separately, which are solved one after another. In the solution process of the two models, model I is solved firstly and the obtained optimization solution is transmitted to model II as a known condition. We mainly focus on the layout optimization of equipment in the service module and give a batch component assignment and layout integration optimization method. In the proposed method, all the components are grouped firstly according to the functional subsystem, and then the obtained component groups are sorted in descending order of their feature values. Finally, the sorted component groups are added into the service module one by one for both assignment optimization and layout optimization. The computational results of the case study show that the obtained Pareto solutions meet the given allowable values of carrying capacity of propellant, dry/wet ratio, and mass properties of the orbital propellant depot.

Multi-objective layout optimization of a generic hybrid-cooled data centre blade server

The rapid global increase in energy consumption by data centres requires new improved cooling solutions and techniques to be developed and implemented. In a typical data centre, approximately a third of the total power consumption is needed for the cooling infrastructure, resulting in high power usage effectiveness (PUE) values. The main culprits of raised PUE are legacy air-cooled data centres, exhausting only low grade waste heat for which capture and re-use is challenging. This study investigates numerically the potential for energy recuperation by a server-level internal layout optimization for a hybrid air/liquid-cooled server. The approach combines multi-objective genetic algorithm (MOGA) and entropy generation minimization (EGM) techniques to incorporate the multiple objectives involved in solving this problem, and examines the cooling performance and waste heat recovery potential. In order to evaluate the potential for waste heat recovery, an extra entropy generation term ṠΔT,extis introduced, representing an air/liquid heat exchanger at the rear of the server. The effect of modifying the internal component layout on pressure drop and the outlet temperature profile are of primary interest, due to their direct impact on fan power usage and energy recuperation potential. The CFD model of the baseline configuration is validated using experimental pressure measurements conducted on a real blade server. The research demonstrates that a basic server layout optimization such as changing the memory module angles and spacing could enhance both the cooling effectiveness but also improve the potential for waste heat recovery from the air stream. The maximum reduction in entropy generation rate due to server layout optimization is 15%, while the outlet temperature uniformity can be improved by up to 42%.

Multi-objective Layout Optimization for Branch Pipe of Aero-engine Based on MOPSO

Multi-objective Layout Optimization for Branch Pipe of Aero-engine Based on MOPSO

A Non-Dominated Sorting Cooperative Co-Evolutionary Differential Evolution Algorithm for Multi-Objective Layout Optimization

Different from traditional multi-objective evolutionary algorithms (MOEAS), multi-objective cooperative co-evolutionary algorithms (MOCCEAs) divide the decision variables into several subproblems to optimize. Solutions of each subproblem are evaluated by complete solutions formed through combining representative solutions from all subproblems. Therefore, the combination of representative solutions is a key issue in MOCCEAs. To improve the capability of MOCCEAs to complex multi-objective optimization problems, we propose a non-dominated sorting cooperative co-evolutionary differential evolution algorithm (NSCCDE). The proposed NSCCDE uses an external archive for storing complete solutions to establish a new collaboration mechanism, which forms a complete solution by combining collaborators from each subpopulation as well as from the external archive. On the one hand, the external archive is updated continuously through non-dominated sorting of complete solutions, which is conducive to speeding up the convergence. On the other hand, the external archive evolves itself through spatial dispersal and mutation operation to increase the diversity. The performance of proposed NSCCDE is then evaluated on a suite of satellite module layout optimization problem. Experimental results demonstrate that the proposed algorithm outperforms NSCCGA and NSGA- II.

Hard uncertainties in multiobjective layout optimization of photovoltaic power plants

This paper describes how to treat hard uncertainties defined by so-called uncertainty maps in multiobjective optimization problems. For the uncertainty map being set-valued, a Taylor formula is shown under appropriate assumptions. The hard uncertainties are modeled using parametric set optimization problems for which a scalarization result is given. The presented new approach for the solution of multiobjective optimization problems with hard uncertainties is then applied to the layout optimization of photovoltaic power plants. Since good weather forecasts are difficult to obtain for future years, weather data are really hard uncertainties arising in the planning process. Numerical results are presented for a real-world problem on the Galapagos island Isabela.

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.

Economic objectives, uncertainties and decision making in the energy sector

The German Energiewende has led to serious changes on the energy market. Technological innovations, growing technical and economical maturity and ongoing optimizations make renewable energies increasingly competitive. After describing appropriate economic objectives in the energy sector, the present paper highlights the difficulties arising in decision making in the area of renewable energies. Since decisions in the field of wind and solar energy depend on good weather forecasts, this paper focuses on the treatment of risks and uncertainties. For a wind power station convex combinations of Weibull distributions are presented as a general model for the description of the distributions of wind speeds. For the investigation of deterministic uncertainties a method is proposed, which has been recently developed for a specific case (Bischoff et al., Hard uncertainties in multiobjective layout optimization of photovoltaic power plants, 2015) and which is now formulated for the standard case of non-dynamic uncertainty sets. Finally, a new approach of error propagation is presented, which describes the propagation of errors in wind speeds to the resulting Weibull probability density functions.

Layout optimization of wireless sensor networks for structural health monitoring

Node layout optimization of structural wireless systems is investigated as a means to prolong the network lifetime without, if possible, compromising information quality of the measurement data. The trade-off between these antagonistic objectives is studied within a multi-objective layout optimization framework. A Genetic Algorithm is adopted to obtain a set of Pareto-optimal solutions from which the end user can select the final layout. The information quality of the measurement data collected from a heterogeneous WSN is quantified from the placement quality indicators of strain and acceleration sensors. The network lifetime or equivalently the network energy consumption is estimated through WSN simulation that provides realistic results by capturing the dynamics of the wireless communication protocols. A layout optimization study of a monitoring system on the Great Belt Bridge is conducted to evaluate the proposed approach. The placement quality of strain gauges and accelerometers is obtained as a ratio of the Modal Clarity Index and Mode Shape Expansion values that are computed from a Finite Element model of the monitored bridge. To estimate the energy consumption of the WSN platform in a realistic scenario, we use a discrete-event simulator with stochastic communication models. Finally, we compare the optimization results with those obtained in a previous work where the network energy consumption is obtained via deterministic communication models.

Multiobjective layout optimization of robotic cellular manufacturing systems

This paper proposes a multiobjective layout optimization method for the conceptual design of robot cellular manufacturing systems. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. In this paper, the design criteria for robot cellular manufacturing system layout designs are clarified, and objective functions are formulated. Next, layout design candidates are represented using a sequence-pair scheme to avoid interference between assembly system components, and the use of dummy components is proposed to represent layout areas where components are sparse. A multiobjective genetic algorithm is then used to obtain Pareto optimal solutions for the layout optimization problems. Finally, several numerical examples are provided to illustrate the effectiveness and usefulness of the proposed method.

212 プラント配置設計の多目的最適化

While engineering a power plant layout, objective functions and constraints of the power plant frequently conflict each other in terms of factors such as safety, economic costs, maintainability, and construction term of works. This paper proposes a multi-objective layout optimization system for a power plant through effective interaction between the designer and the computer; this system is developed by applying an interactive Satisficing Trade-Off Method (STOM) for multiple-criteria decision-making. In order to obtain the layout candidates for decision-making, the authors present a suitable genetic algorithm (GA) approach to arrange the plot plan for the layout of buildings and equipment in a site. In this GA approach, the coding method and evolution operation method used for obtaining a diverse optimal layout solution are employed with a low computational load and simple design information. Finally, the validity and usefulness of this proposed system is demonstrated by solving a layout design problem for an actual power plant.