Technique For Order Preference By Similarity To Ideal Solution Algorithm Research Articles

Noninvasive Load Identification Based on Combined Weighting-TOPSIS of Multifeature Fusion

The traditional nonintrusive load identification (NILD) algorithms result in high computational costs based on classification models. And they cannot accurately identify loads with similar voltage-current trajectories that are frequently encountered in practical applications. Aiming at these deficiencies, an NILD algorithm is proposed based on combined weighting-the technique for order preference by similarity to ideal solution (TOPSIS) of feature fusion. The feature is fused by one image feature and eight numerical features with the combination weighting method. The weights are calculated by combining the principal component analysis, entropy, and the criteria importance through intercriteria correlation weighting methods to improve the utilization of features. The similarity between these nine features of a load and the corresponding features of other loads is calculated by the TOPSIS algorithm. Similarity analysis is used to determine whether or not a load is a "known load," and to obtain an identification result. If it is an "unknown load", the result can be obtained by dynamically updating the database and identifying it again. The results obtained indicate that this proposed algorithm can significantly improve the accuracy of load identification while reducing the computational costs.

Application of TOPSIS method for flood susceptibility mapping using Excel and GIS

This study elaborately manifests a simplified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multicriteria decision-making (MCDM) approach that goals to determine the disparity among the distances between the positive and negative ideal solutions. MCDM methods evaluate options based on a variety of criteria by using mathematical and analytical methodologies. This promotes a more transparent and objective decision-making process by removing human biases and subjective judgements. By considering the comparative proximity to the optimal situation, TOPSIS considers the distances between the ideal and the negative-ideal alternatives. This study has concentrated on the normalization process, the appropriate determination of the ideal and the anti-ideal solution, and the metric utilized to compute the euclidean distances from the ideal best and the ideal worst.•This study expresses the simplified TOPSIS methodology as stated by Hwang and Yoon (1981). The categorization and weight assignments of the criteria have been executed from the expert's opinion and based on existing literatures.•Integration of the TOPSIS technique with GIS has been properly performed for the production of a flood susceptibility map of a highly vulnerable region and visual interpretation of the TOPSIS algorithm.•This kind of investigation saved time by sufficiently skilled specialized personnel in this field.

Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms

Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.

Spherical Linear Diophantine Fuzzy TOPSIS Algorithm for Green Supply Chain Management System

Enhancing competitive pressure is one of the most significant roles of supply chain management. The competitive environment and customer perception have shifted in favour of an ecological mentality. As a result, green supplier selection (GSS) has emerged as a critical problem. The challenge of green supplier selection striving for agility, durability, ecological sensitivity, leanness, and sustainability is tackled in this paper. In terms of recycling applications, environmental applications, carbon footprint, and water consumption, the environmental parameters evaluated in GSS and traditional supplier selection differ. Because of the form of the problem, a resolution is defined, which comprises an algorithm entrenched in the spherical linear Diophantine fuzzy sets (SLDFSs) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. Before discussing the approach of the SLDF model, some background information on SLDF sets is provided. To assure the uniqueness of this robust extension, different operations on SLDFSs are described, along with some concise interpretations to help the reader comprehend these ideas. A robust TOPSIS approach has been utilized in the issue of GSS by taking into consideration the multicriteria decision-making (MCDM) technique particularly useful in several areas, like analyzing and choosing traditional and environmental conventionalities. Due to linguistic criteria and the inability to assess all criteria, the fuzzy technique must be used with the TOPSIS method to lessen the consequences of instability and ambiguity. The spherical linear Diophantine fuzzy TOPSIS approach is employed, as it simplifies the evaluation of decision-makers and criteria. The hybrid technique resulting from integrating the SLDFS and TOPSIS is extremely successful in selecting which provider is more suited among the alternatives established on the criteria set by the order of significance, and this method may also be incorporated into similar issues.

Research on supply chain planning based on genetic algorithm and long short-term memory

With the integration of intelligent algorithm into the supply chain process, the fficiency of supply chain planning has been further improved through automatic prediction and decision-making. Although intelligent algorithms are developing, their challenges including real-time nature of supply chain planning and the complexity of scenarios hinder their true potential. In this study, we proposed an improved genetic algorithm (GA)-long short-term memory (LSTM) neural network prediction algorithm to solve various optimization planning problems for the supply chain from suppliers to production enterprises. Specifically, to determine stable suppliers, we first constructed the technique for order preference by similarity to ideal solution (TOPSIS) model to quantitatively evaluate each supplier, and the rationality of the index weight of the TOPSIS algorithm can be enhanced by the entropy method. Finally, the GA and LSTM were used to solve the decision-making and planning problem in raw material supply chain. Our results indicate that the algorithm we proposed can not only efficiently solve the decision planning problem in the raw material supply chain, but it also reasonably analyzes the suppliers quantitatively.

Optimization of Fuzzy programming with TOPSIS Algorithm

Abstract A new hybrid algorithm is proposed for multi-criteria models in decision theory under uncertainty, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to the rank based on the decision making. Stochastic programming and fuzzy programming used the applications by the decision theory. TOPSIS was applied to obtain the best ideal solution ranks from alternative and criteria based on decision making. It is allocated to optimize the bestquality or product service, reducing the cost, weightedfuzzy multiple objective linear programming is used. Optimization models used to solve various problems by making use of decision theory. Analytic hierarchy process (AHP) was applied to the issue of weights in the criteria. For problems dealing with uncertainty and ambiguity Fuzzy approach is the most preferred method in providing solutions. TOPSIS approach makes use of decision theory to identify the optimal solution to the respective problem.

Applications of TOPSIS Algorithm on various Manufacturing Processes: A Review

Different Manufacturing Processes Optimized by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Algorithm are presented in this review paper. The main focus of this review paper is on the optimization of the various manufacturing processes that have been optimized by TOPSIS method. The machining processes that have been considered in this work are Milling, Drilling, Turning, Electric Discharge Machining, Abrasive Jet Machining, Micro Machining, and other advanced types of machining process. The review work on manufacturing processes, on such a large scale was not performed earlier by considering different processes at a time on TOPSIS method, and hence, this review work prepare for the future information at one place for the subsequent researchers, in order to determine their way of research on TOPSIS Algorithm.

A Study on Optimal Site Selection for the Artificial Recharge System Installation Using TOPSIS Algorithm

This paper is intended to propose a novel approach to select an optimal site for a small-scaled artificial recharge system installation using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with geospatial data. TOPSIS is a MCDM (Multi-Criteria Decision Making) method to choose the preferred one of derived alternatives by calculating the relative closeness to an ideal solution. For applying TOPSIS, in the first, the topographic shape representing optimal recovery efficiency is defined based on a hydraulic model experiment, and then an appropriate surface slope is determined for the security of a self-purification capability with DEM (Digital Elevation Model). In the second phase, the candidate areas are extracted from an alluvial map through a morphology operation, because local alluvium with a lengthy and narrow shape could be satisfied with a primary condition for the optimal site. Thirdly, a shape file over all candidate areas was generated and criteria and their values were assigned according to hydrogeologic attributes. Finally, TOPSIS algorithm was applied to a shape file to place the order preference of candidate sites.

Interactive TOPSIS algorithms for solving multi-level non-linear multi-objective decision-making problems

This paper extended the concept of the technique for order preference by similarity to ideal solution (TOPSIS) to develop a methodology for solving multi-level non-linear multi-objective decision-making (MLN-MODM) problems of maximization-type. Also, two new interactive algorithms are presented for the proposed TOPSIS approach for solving these types of mathematical programming problems. The first proposed interactive TOPSIS algorithm includes the membership functions of the decision variables for each level except the lower level of the multi-level problem. These satisfactory decisions are evaluated separately by solving the corresponding single-level MODM problems. The second proposed interactive TOPSIS algorithm lexicographically solves the MODM problems of the MLN-MOLP problem by taking into consideration the decisions of the MODM problems for the upper levels. To demonstrate the proposed algorithms, a numerical example is solved and compared the solutions of proposed algorithms with the solution of the interactive algorithm of Osman et al. (2003) [4]. Also, an example of an application is presented to clarify the applicability of the proposed TOPSIS algorithms in solving real world multi-level multi-objective decision-making problems.

Interactive TOPSIS algorithms for solving multi-level non-linear multi-objective decision-making problems

Abstract This paper extended the concept of the technique for order preference by similarity to ideal solution (TOPSIS) to develop a methodology for solving multi-level non-linear multi-objective decision-making (MLN-MODM) problems of maximization-type. Also, two new interactive algorithms are presented for the proposed TOPSIS approach for solving these types of mathematical programming problems. The first proposed interactive TOPSIS algorithm includes the membership functions of the decision variables for each level except the lower level of the multi-level problem. These satisfactory decisions are evaluated separately by solving the corresponding single-level MODM problems. The second proposed interactive TOPSIS algorithm lexicographically solves the MODM problems of the MLN-MOLP problem by taking into consideration the decisions of the MODM problems for the upper levels. To demonstrate the proposed algorithms, a numerical example is solved and compared the solutions of proposed algorithms with the solution of the interactive algorithm of Osman et al. (2003) [4] . Also, an example of an application is presented to clarify the applicability of the proposed TOPSIS algorithms in solving real world multi-level multi-objective decision-making problems.

TOPSIS for bi-level MODM problems

TOPSIS (technique for order preference by similarity to ideal solution) is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. This paper proposes a fuzzy TOPSIS algorithm to solve bi-level multi-objective decision-making (BL-MODM) problems, and in which the objective function at each level are non-linear functions which are to be maximized. The proposed model for getting the satisfactory solution of the BL-MODM problems includes the membership functions for the upper level decision variables vector with possible tolerances, the membership function of the distance function from the positive ideal solution (PIS) and the membership function of the distance function from the negative ideal solution (NIS). A numerical illustrative example is given to clarify the proposed TOPSIS approach of this paper.

AN INTEGRATED FUZZY TOPSIS METHOD FOR RANKING ALTERNATIVES AND ITS APPLICATION

The main purpose of this paper is to develop an integrated fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method to improve the quality of decisionmaking for ranking alternatives. The proposed fuzzy TOPSIS method mainly accounts for the classification of criteria, the integrated weights of criteria and sub-criteria, and the performance values of decision matrix. In this model, the criteria are classified into subjective criteria and objective ones. The fuzzy analytic hierarchy process approach and the entropy weighting method are used to solve the subjective weights and objective ones. In addition, the adjusted integration weights are measured by combining these two methods. The performance values of subjective criteria and of objective ones will be obtained by linguistic expressions and objective evaluation values, respectively. Furthermore, the graded mean integration representation method and the modified distance method are employed to the integrated fuzzy TOPSIS method. Finally, a hypothetical example of partner selection of a shipping company is designed to demonstrate the computational process of this fuzzy TOPSIS algorithm.

Selection of maintenance policy for textile industry using hybrid multi‐criteria decision making approach

PurposeThe purpose of this paper is to focus on the use of analytic hierarchy process (AHP) under fuzzy environment and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry.Design/methodology/approachFirst by using improved AHP with fuzzy set theory, the weight of each criterion is calculated to overcome the criticism of unbalanced scale of judgments, uncertainty, and imprecision in the pair‐wise comparison process. Then this paper introduces a model that integrates improved fuzzy AHP with TOPSIS algorithm to support maintenance strategy selection decisions.FindingsAn efficient pair‐wise comparison process and ranking of alternatives can be achieved for maintenance strategy selection through the integration of AHP with fuzzy set theory and TOPSIS.Originality/valueThe paper points out a new insight of multi‐criteria decision making techniques to select optimum maintenance policy for a process industry with the use of a case study.