scholarly journals Multiobjective TOU Pricing Optimization Based on NSGA2

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Huilan Jiang ◽  
Bingqi Liu ◽  
Yawei Wang ◽  
Shuangqi Zheng
Keyword(s):  
Peak Load ◽  
Optimal Solution ◽  
Multiple Attribute Decision Making ◽  
Pareto Optimal Solution ◽  
Solution Set ◽  
Pareto Optimal ◽  
Good Convergence ◽  
Multiple Attribute ◽  
Optimal Solution Set ◽  
Pricing Optimization

Fast and elitist nondominated sorting generic algorithm (NSGA2) is an improved multiobjective genetic algorithm with good convergence and robustness. The Pareto optimal solution set using NSGA2 has the character of uniform distribution. This paper builds a time-of-use (TOU) pricing mathematical model considering actual constraint conditions and puts forward a new method which realizes multiobjective TOU pricing optimization using NSGA2. A variety of objective TOU pricing schemes can be provided for decision makers compared with traditional method. Furthermore, the multiple attribute decision making theory is applied in processing the Pareto optimal solution set to calculate the optimal compromise price scheme. The simulation results have shown that the TOU pricing scheme determined by the method proposed above can achieve a better effect of clipping the peak load to fill the valley load. Consequently, the study in this paper is innovative and is a successful exploration of coordinating the relation of various objective functions concerned in TOU pricing optimization problem.

scholarly journals Selection model of work technology based on multi–criteria evaluations

2020 ◽  
Vol 164 ◽  
pp. 08030
Author(s):  
Sergey Barkalov ◽  
Pavel Kurochka ◽  
Anton Khodunov ◽  
Natalia Kalinina
Keyword(s):  
Cost Function ◽  
Optimal Solution ◽  
Budget Constraint ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Customer Preferences ◽  
Optimal Solution Set ◽  
The Cost ◽  
Comprehensive Indicator ◽  
Selection Of

A model for the selection of options for the production of work in a construction project is considered, when each option is characterized by a set of criteria. The number of analyzed options is being reduced based on the construction of the Pareto-optimal solution set. The remaining options are used to solve the problem based on the network model,\ in which the solution will be a subcritical path that meets budgetary constraints. At the same time, the proposed comprehensive indicator characterizing the preferences of the customer makes it possible to determine alternative options for performing work in the energy project in such a way that the amount of costs allocated to implement the set of work under consideration is minimal. Another statement of the problem is also considered when it is necessary to determine a strategy for the implementation of an energy project that, given a planned budget constraint, maximizes the growth of a comprehensive indicator that characterizes customer preferences in this project. The solution of the tasks is given under the assumption of the convexity of the cost function.

Searching for a Pareto Optimal Solution Set of EDM Responses Applying Multi-Objective Simulated Annealing on RSM Model

2012 ◽  
Vol 622-623 ◽  
pp. 51-55
Author(s):  
Ushasta Aich ◽  
Amit Kumar Pal ◽  
Dipak Laha ◽  
Simul Banerjee
Keyword(s):  
Surface Roughness ◽  
Simulated Annealing ◽  
Material Removal ◽  
Removal Rate ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Average Surface Roughness ◽  
Average Surface ◽  
Optimal Solution Set

Simultaneous optimization of conflicting type responses like material removal rate (MRR) and average surface roughness (Ra) in stochastic type electrical discharge machining (EDM) process is a matter of concern to the process engineers. In this paper, EDM is first modeled by response surface methodology (RSM). Current setting, pulse on time and pulse off time were taken as the input parameters while material removal rate and average surface roughness as the responses. Multi-objective simulated annealing (MOSA) is then applied on these models. Pareto optimal solution set is thus developed. It would assist a process engineer to take decision regarding the optimal setting of the process parameters for a specific need-based requirement.

Multi-Objective Optimization of Product Configuration

2008 ◽  
Author(s):  
Sisi Xuanyuan ◽  
Zhaoliang Jiang ◽  
Lalit Patil ◽  
Yan Li ◽  
Zhaoqian Li
Keyword(s):  
Optimal Solution ◽  
Solution Space ◽  
Combinatorial Problem ◽  
Solution Set ◽  
Product Configuration ◽  
Pareto Optimal ◽  
Large Solution ◽  
Multi Objective ◽  
Optimal Solution Set ◽  
Feasible Solutions

In the context of globalization and mass customization, selecting the appropriate product configuration requires a simultaneous consideration of multiple criteria or objectives, which are in conflict with each other. The large solution space implies that analyzing each feasible solution is a combinatorial problem. Furthermore, no single optimal solution exists; on the contrary, there is a set of valid optimal solutions, i.e., the solution set is Pareto-optimal. We present the configuration problem from the perspective of using two types of attributes: static, i.e., the attributes that have pre-defined and constant values throughout the configuration process, and dynamic, i.e., attributes whose values vary according to decisions that are being made during the configuration process. We pose the product configuration as a multiobjective optimization problem requiring that multiple objective functions cannot be combined into a single objective function. We demonstrate the applicability of using Multi-Objective Genetic Algorithms (MOGA) to solve the problem and converge to a Pareto-optimal solution set from the large number of feasible solutions.

A Pareto Optimal Solution Visualization Method Using an Improved Growing Hierarchical Self-Organizing Maps Based on the Batch Learning

2016 ◽  
Vol 20 (5) ◽  
pp. 691-704
Author(s):  
Naoto Suzuki ◽  
◽  
Takashi Okamoto ◽  
Seiichi Koakutsu
Keyword(s):  
Learning Algorithm ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Visualization Method ◽  
Self Organizing Maps ◽  
Optimal Solution Set ◽  
Batch Learning ◽  
Solution Representation ◽  
Self Organizing

In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal solutions is an important issue. This paper focuses on the Pareto optimal solution visualization method using the growing hierarchical self-organizing maps (GHSOM) which is one of promising visualization methods. This method has a superior Pareto optimal solution representation capability, compared to the visualization method using the self-organizing maps. However, this method has some shortcomings. This paper proposes a new Pareto optimal solution visualization method using an improved GHSOM based on the batch learning. In the proposed method, the batch learning algorithm is introduced to the GHSOM to obtain a consistent visualization maps for a Pareto optimal solution set. Then, the symmetric transformation of maps is introduced in the growing process in the batch learning GHSOM algorithm to improve readability of the maps. Furthermore, the learning parameter optimization is introduced. The effectiveness of the proposed method is confirmed through numerical experiments with comparing the proposed method to the conventional methods on the Pareto optimal solution representation capability and the readability of the visualization maps.

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