Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling

2021 ◽  
pp. 215-241
Author(s):  
Ramón Quiza ◽  
Iván La Fé-Perdomo ◽  
Marcelino Rivas ◽  
Veena Ramtahalsing
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide Emissions ◽  
Hybrid Approach ◽  
Triple Bottom Line ◽  
Search Space ◽  
The Other ◽  
Wide Sense ◽  
Bottom Line ◽  
Conflicting Objectives ◽  
The Cost

This chapter proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95% confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Three conflicting objectives, which represent the three pillars of the sustainability as defined in the triple bottom line, are simultaneously considered: the carbon dioxide emissions, the cost, and the cutting time. The outcome of the optimization process is a set of non-nominate solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsening the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.

Hybrid Modelling and Optimization of the Oblique Cutting of AISI 1045 Steel

2018 ◽  
Vol 8 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Iván La Fé Perdomo ◽  
Ramón Quiza ◽  
Marcelino Rivas ◽  
Veena Ramtahalsing
Keyword(s):  
Hybrid Approach ◽  
Search Space ◽  
Wide Sense ◽  
Machining Processes ◽  
Oblique Cutting ◽  
Conflicting Objectives ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
Competitive Products ◽  
1045 Steel

Optimization is a very important issue in mechanical industry, especially in machining processes, where different aspects must be considered. Thus, selecting the most proper cutting conditions plays a key role for obtaining efficient and competitive products. This article proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95%-confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Two important and conflicting objectives are simultaneously considered: unit cutting time and tool wear rate, which describe the productivity and tool waste, respectively. The outcome of the optimization process is a set of non-dominated solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsen the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.

scholarly journals Triple Bottom Line and Sustainability: A Literature Review

2015 ◽  
Vol 1 (2) ◽  
pp. 6 ◽  
Author(s):  
Hanan Alhaddi
Keyword(s):  
Literature Review ◽  
Relevant Literature ◽  
Business Management ◽  
Triple Bottom Line ◽  
The Other ◽  
Bottom Line ◽  
Comprehensive Review ◽  
Other Hand ◽  
The Social ◽  
Social Environmental

Triple bottom line (TBL) and sustainability are two related constructs that are used interchangeably in the literature.  A comprehensive review of the relevant literature was conducted and revealed an inconsistent use of the term sustainability.  On the other hand, consistency in terms of referring to the three lines simultaneously is built into the structure of TBL as the construct is explicitly based on the integration of the social, environmental, and economic lines.  The purpose of this paper is not to support an argument that favors the use of one term over the other, but to provide an overview of the presence of both terms in the literature. In light of that, researchers in the business, management, and sustainability fields are encouraged to pay particular attention to how they use these terms in their studies.

A Method for Inferring the Optimization Cost Function of Experimentally Observed Motor Strategies

2005 ◽  
Author(s):  
Carlo L. Bottasso ◽  
Alessandro Croce ◽  
Stefano Sartirana ◽  
Boris I. Prilutsky
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Cost Function ◽  
Motor Task ◽  
Computational Procedure ◽  
Search Space ◽  
Optimality Criteria ◽  
Cost Functions ◽  
The Cost ◽  
Motor Strategies

We propose a computational procedure for inferring the cost functions that, according to the Principle of Optimality, underlie experimentally observed motor strategies. This work tries to overcome the need to hypothesize the cost functions, extracting this non-directly observable information from experimental data. Optimality criteria of observed motor tasks are here indirectly derived using: a) a mathematical model of the bio-system; and b) a parametric mathematical model of the possible cost functions, i.e. a search space constructed in such a way as to presumably contain the unknown function that was used by the bio-system in the given motor task of interest. The cost function that best matches the experimental data is identified within the search space by solving a nested optimization problem. This problem can be recast as a non-linear programming problem and therefore solved using standard techniques. The proposed methodology is tested on representative examples.

scholarly journals Multistage Bounded Evolutionary Algorithm to Optimize the Design of Sustainable Photovoltaic (PV) Pumping Irrigation Systems with Storage

2020 ◽  
Vol 12 (3) ◽  
pp. 1026 ◽  
Author(s):  
Julián Ignacio Monís ◽  
Rafael López-Luque ◽  
Juan Reca ◽  
Juan Martínez
Keyword(s):  
Optimal Design ◽  
Irrigation System ◽  
Hybrid Approach ◽  
Heuristic Method ◽  
Cost Effective ◽  
Search Space ◽  
Irrigation Systems ◽  
Space Reduction ◽  
The Cost

Small off-grid photovoltaic (PV) pumping irrigation systems with storage tanks are an environmentally friendly, cost effective and efficient way of taking advantage of solar energy to irrigate crops, and they are increasingly being used today. However, finding the optimal design of this type of system is cumbersome since there are many possible designs. In this work, a new heuristic method based on the hybrid approach, which uses search space reduction, is developed and adapted to the optimal design for this type of PV irrigation system. At different stages, the proposed approach iteratively combines a bounding strategy based on the application of engineering rules with the aim of reducing the search space with a genetic algorithm to find the optimal design within this search space. The proposed methodology was applied to minimize the cost of a benchmark case study consisting of a real farm placed in the province of Almería (Spain). The proposed methodology was able to provide a faster and an accurate convergence due to the reduction of the search space. This fact led to a reduced total cost of the system. This study proved that the most sensitive variables were the number of modules and the type of pump, whereas the diameter of the pipe and volume of the storage tank remained more stable.

scholarly journals Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

2020 ◽  
Vol 12 (7) ◽  
pp. 2767 ◽  
Author(s):  
Víctor Yepes ◽  
José V. Martí ◽  
José García
Keyword(s):  
Black Hole ◽  
Sustainable Design ◽  
Retaining Walls ◽  
The Other ◽  
Trade Off ◽  
Construction Company ◽  
Geometric Variables ◽  
The Stability ◽  
The Cost ◽  
Black Hole Algorithm

The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

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