scholarly journals Cost-effectiveness analysis of different watershed management scenarios developed by simulation–optimization model

2017 ◽  
Vol 17 (5) ◽  
pp. 1316-1324 ◽  
Author(s):  
Hamzeh Noor ◽  
Somayeh Fazli ◽  
Mohammad Rostami ◽  
Ali Bagherian Kalat
Keyword(s):  
Cost Effectiveness ◽  
Watershed Management ◽  
Best Management Practices ◽  
Sediment Yield ◽  
Optimization Model ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Sediment Reduction ◽  
The Cost

The effort to control sediment yield at watershed scale is an ongoing challenge that needs to take into account trade-offs between two conflicting objective functions, i.e. economic and hydrologic criteria. Therefore, researchers have coupled hydrologic and multi-objective optimization models to find Pareto-optimal solutions. However, very limited studies have been conducted to analyse the cost-effectiveness (C/E) of scenarios obtained in the Pareto-front optimal. This could provide new information leading to effective watershed management. Therefore, in the present study, the Soil and Water Assessment Tool (SWAT) was used to simulate sediment yield under different combinations of best management practices (BMPs) and was coupled with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The model attends to providing the Pareto-optimal solutions by minimizing the costs of BMPs and maximizing sediment reduction. The results of the application of the cost-effective optimization model in Mehran watershed, Iran, showed that the solutions in the Pareto-optimal front reduce sediment yield between 2% and 40.5% from baseline at costs of between $6,500 and $72,100, respectively. Finally, comparison of four sediment reduction solutions (i.e. 10%, 20%, 30%, and 40%) showed that the total cost and C/E ratio of solutions increased as the sediment reduction criteria increased.

Pareto optimal solutions providing optimal routes between every two different nodes in the cost-time trade-off routing network without the objectives being prioritized

OPSEARCH ◽  
2016 ◽  
Vol 53 (4) ◽  
pp. 778-807 ◽  
Author(s):  
Satya Prakash ◽  
Anuj Gupta ◽  
Richa Garg ◽  
Bhuvnesh Tanwar ◽  
Deepika Kaushik ◽  
...  
Keyword(s):  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Trade Off ◽  
The Cost

scholarly journals Heuristic techniques for modelling machine spinning processes

2020 ◽  
Author(s):  
Roman Stryczek ◽  
Kamil Wyrobek
Keyword(s):  
Process Model ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Decision Space ◽  
Practical Applications ◽  
Spinning Process ◽  
Response Surface Designs ◽  
The Cost ◽  
Short Time

Abstract In spite of many efforts made a complete model of machine spinning processes, due to its complexity, multidimensionality of the decision space and the present state of knowledge, is unachievable. The paper addresses the issues of constructing a local process model to enable the search for a locally optimal course of the process, within a short time and with the cost as low as possible. Comparison was made between the theoretically well-grounded response surface designs method with a few approaches to the model construction based on intuitively understood heuristic bases justified by their successful practical applications. In order to determine a set of Pareto-optimal solutions for a discrete decision space, the durations of process execution were generated through a virtual simulation. In order to outline and justify the adopted solutions a comprehensive example of the practical construction of the machine spinning process model was presented, including its various versions. The results obtained were validated and evaluated. The main utilitarian conclusion is the indication whereby basing on a partial experiment plan it is possible, thanks to simple heuristic methods, to obtain Pareto-optimal solutions which are close to those obtained when the full experiment plan is carried out.

Technical and economic optimization of natural gas transmission network operation to balance node delivery flow rate and operation cost

2020 ◽  
pp. 1-22
Author(s):  
Jun Zhou ◽  
Jinghong Peng ◽  
Guangchuan Liang ◽  
Chuan Chen ◽  
Xuan Zhou ◽  
...  
Keyword(s):  
Natural Gas ◽  
Flow Rate ◽  
Optimization Model ◽  
Pareto Optimal ◽  
Transmission Network ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Decision Variables ◽  
Natural Gas Transmission ◽  
Continuous And Discrete Variables

Natural gas transmission network is the major facility connecting the upstream gas sources and downstream consumers. In this paper, a multi-objective optimization model is built to find the optimum operation scheme of the natural gas transmission network. This model aims to balance two conflicting optimization objective named maximum a specified node delivery flow rate and minimum compressor station power consumption cost. The decision variables involve continuous and discrete variables, including node delivery flow rate, number of running compressors and their rotational speed. Besides, a series of equality and inequality constraints for nodes, pipelines and compressor stations are introduced to control the optimization results. Then, the developed optimization model is applied to a practical large tree-topology gas transmission network, which is 2,229 km in length with 7 compressor stations, 2 gas injection nodes and 20 gas delivery nodes. The ɛ-constraint method and GAMS/DICOPT solver are adopted to solve the bi-objective optimization model. The optimization result obtained is a set of Pareto optimal solutions. To verify the validity of the proposed method, the optimization results are compared with the actual operation scheme. Through the comparison of different Pareto optimal solutions, the variation law of objective functions and decision variables between different optimal solutions are clarified. Finally, sensitivity analyses are also performed to determine the influence of operating parameter changes on the optimization results.

scholarly journals Optimization Of Multi-Module CrN/CrCN Coatings

2015 ◽  
Vol 60 (2) ◽  
pp. 1037-1043
Author(s):  
Ł. Szparaga ◽  
P. Bartosik ◽  
A. Gilewicz ◽  
J. Ratajski
Keyword(s):  
Optimization Procedure ◽  
Stress And Strain ◽  
Pareto Optimal ◽  
Mutual Distance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Decision Criteria ◽  
Decision Variables ◽  
42Crmo4 Steel ◽  
The Stability

Abstract In the paper was proposed optimization procedure supporting the prototyping of the geometry of multi-module CrN/CrCN coatings, deposited on substrates from 42CrMo4 steel, in respect of mechanical properties. Adopted decision criteria were the functions of the state of internal stress and strain in the coating and substrate, caused by external mechanical loads. Using developed optimization procedure the set of optimal solutions (Pareto-optimal solutions) of coatings geometry parameters, due to the adopted decision criteria was obtained. For the purposes of analysis of obtained Pareto-optimal solutions, their mutual distance in the space of criteria and decision variables were calculated, which allowed to group solutions in the classes. Also analyzed the number of direct neighbors of Pareto-optimal solutions for the purposes of assessing the stability of solutions.

A DECOMPOSITION-COORDINATION METHOD FOR COMPLEX MULTI-OBJECTIVE SYSTEMS

2009 ◽  
Vol 26 (06) ◽  
pp. 735-757 ◽  
Author(s):  
F. MIGUEL ◽  
T. GÓMEZ ◽  
M. LUQUE ◽  
F. RUIZ ◽  
R. CABALLERO
Keyword(s):  
Information Exchange ◽  
Exchange Process ◽  
Efficient Solutions ◽  
Pareto Optimal ◽  
Relative Importance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Conflicting Objectives ◽  
Upper Level ◽  
Problem Instances

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times.

Multiobjective optimization of the flaxseed mucilage extraction process using normal-boundary intersection approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mariana Souza Rocha ◽  
Luiz Célio Souza Rocha ◽  
Marcia Barreto da Silva Feijó ◽  
Paula Luiza Limongi dos Santos Marotta ◽  
Samanta Cardozo Mourão
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Method ◽  
Extraction Process ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Fiber Concentration ◽  
Trade Off ◽  
Content Type ◽  
Normal Boundary

PurposeThe mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential applications in both food and pharmaceutical industries. To increase the yield and quality of linseed oil during its production process, it is necessary to previously extract its polysaccharides. Because of this, flax mucilage production can be made viable as a byproduct of oil extraction process, which is already a product of high commercial value consolidated in the market. Thus, the purpose of this work is to optimize the mucilage extraction process of L. usitatissimum L. using the normal-boundary intersection (NBI) multiobjective optimization method.Design/methodology/approachCurrently, the variables of the process of polysaccharide extraction from different sources are optimized using the response surface methodology. However, when the optimal points of the responses are conflicting it is necessary to study the best conditions to achieve a balance between these conflicting objectives (trade-offs) and to explore the available options it is necessary to formulate an optimization problem with multiple objectives. The multiobjective optimization method used in this work was the NBI developed to find uniformly distributed and continuous Pareto optimal solutions for a nonlinear multiobjective problem.FindingsThe optimum extraction point to obtain the maximum fiber concentration in the extracted material was pH 3.81, temperature of 46°C, time of 13.46 h. The maximum extraction yield of flaxseed was pH 6.45, temperature of 65°C, time of 14.41 h. This result confirms the trade-off relationship between the objectives. NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows to analyze the behavior of the trade-off relationship. Thus, the decision-maker can set extraction conditions to achieve desired characteristics in mucilage.Originality/valueThe novelty of this paper is to confirm the existence of a trade-off relationship between the productivity parameter (yield) and the quality parameter (fiber concentration in the extracted material) during the flaxseed mucilage extraction process. The NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows us to analyze the behavior of the trade-off relationship. This allows the decision-making to the extraction conditions according to the desired characteristics of the final product, thus being able to direct the extraction for the best applicability of the mucilage.

scholarly journals Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 250 ◽  
Author(s):  
Yahui Li ◽  
Yang Li
Keyword(s):  
Decision Analysis ◽  
Evolutionary Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Power ◽  
Knee Point

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers’ different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.

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