Robot Selection Problem Research Articles

Developing Fuzzy-AHP-Integrated Hybrid MCDM System of COPRAS-ARAS for Solving an Industrial Robot Selection Problem

Robots are one of the most commonly used automated material handling equipment (MHE) in an industry, installed to perform a variety of hazardous and repetitive tasks, e.g., loading, unloading, pick-and-place operations, etc. The selection of an appropriate industrial robot is influenced by a number of subjective and objective factors that define its characteristics and working accuracy. As a result, robot selection can be regarded as a multi-criteria decision-making problem. In this article, a new hybrid MCDM model combining COPRAS and ARAS is developed to execute an industrial robot selection process based on three alternatives and five criteria. Fuzzy analytic hierarchy process is integrated to compute the parametric weights. It is discovered that Robot 3 and Robot 1 are coming out to be the best and worst alternative robots from this hybrid model. Finally, comparative analysis among eight other MCDM tools and sensitivity analysis are also performed to assess the stability and robustness of the developed hybrid model and other applied MCDM tools.

Decision Models for Selection of Industrial Robots—A Comprehensive Comparison of Multi-Criteria Decision Making

Due to increased demands of production capacity and higher quality requirements, industries are automating at a fast pace. Industrial robots are an important component of the industrial automation ecosystem. However, the selection of appropriate robots is a challenging task due to the sheer number of alternatives present and their varied specifications. The various characteristics or attributes of industrial robots that need due consideration before selection of an optimal robot for a given application are found to be conflicting in nature. Thus, in this paper, several multi-criteria decision-making (MCDM) methods are deployed to select an optimal robot depending on the application. Three different industrial robot selection problems are solved in this paper by using Simple Additive Weighing (SAW), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Linear Programming Technique (LINMAP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Elimination and Choice Translating Priority III (ELECTRE-III), and the Net Flow Method (NFM).

Robot Selection Using An Integrated MAGDM Model Based on ELECTRE Method and Linguistic q-Rung Orthopair Fuzzy Information

With the development of engineering sciences, many companies are using robots in different industries to improve the efficiency and accuracy of their work. However, the need for robots under different requirements is different, which makes the selection of robots complex. In this paper, a linguistic q-rung orthogonal fuzzy multiple attribute group decision-making (MAGDM) method based on ELECTRE is proposed for robot selection. First, the basic Hausdorff distance is extended to the linguistic q-rung orthopair fuzzy environment to measure the deviation between two linguistic q-rung orthopair fuzzy numbers and two linguistic q-rung orthopair fuzzy sets. Then, the properties of the linguistic q-rung orthopair fuzzy distance measure based on Hausdorff distance are investigated. In addition, two maximum deviation models for deriving the weights of decision-makers and attributes are proposed. Moreover, a new MAGDM method is proposed by extending the ELECTRE method to the linguistic q-rung orthopair fuzzy environment. Finally, the practicality as well as the effectiveness of the method is demonstrated through a case study of the robot selection problem. The linguistic q-rung distance measure is used to construct two maximum deviation models to objectively derive the weights of attributes and decision-makers, and the linguistic q-rung orthopair fuzzy ELECTRE method is used to complete the selection of robots for a clean energy company. Furthermore, the sensitivity analysis of the parameter in the proposed method is provided, and the superiority of the new method is illustrated by the comparison with existing MAGDM methods.

New extension of ordinal priority approach for multiple attribute decision-making problems: design and analysis

The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs.

Technique of Accurate Ranking Order (TARO): A novel multi criteria analysis approach in performance evaluation of industrial robots for material handling

Rank reversal in decision making is a common phenomenon resulting in confusion and ambiguity in selection procedure especially while multiple multi-criteria decision making (MCDM) techniques are independently applied. To eradicate this confusion, this paper proposes a novel MCDM methodology namely Technique of Accurate Ranking Order (TARO). The TARO method is an extension of conventional MCDM approaches. The proposed method commences at the end of conventional methodologies with the final selection values. The proposed technique, using an advanced version of entropy weighting method, initially measures weights of the final selection values. Subsequently, based on the final section values and their computed weights, TARO measures accurate selection indices that determine the accurate ranking order of the alternatives. The proposed technique is illustrated by three real life examples on robot selection problems. The results obtained by TARO justify the validity, applicability and requirements of the proposed techniques for proper decision making under the MCDM environment.

Industrial robot selection using a multiple criteria group decision making method with individual preferences.

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection

In order to avoid working in a constrained hazardous environment, manual spray painting operation is gradually being replaced by automated robotic systems in many manufacturing industries. Application of spray painting robots ensures defect-free painting of dissimilar components with higher repeatability, flexibility, productivity, reduced cycle time and minimum wastage of paint. Due to availability of a large number of viable options in the market, selection of a spray painting robot suitable for a given application poses a great problem. Thus, this paper proposes the integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods to identify the most apposite spray painting robot for an automobile industry based on seven evaluation criteria (payload, mass, speed, repeatability, reach, cost and power consumption). The SWARA method identifies cost as the most significant criterion based on a set preference order, whereas, Fanuc P-350iA/45 is selected as the best spray painting robot by CoCoSo method. The derived ranking results are also contrasted with other popular multi-criteria decision making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, PROMETHEE and MOORA) and subjective criteria weighting methods (AHP, PIPRECIA, BWM and FUCOM). High degrees of similarity in the ranking patterns between the adopted approach and other MCDM techniques prove its effectiveness in solving complex industrial robot selection problems. This integrated approach is proved to be quite robust being almost unaffected by the changing values of the corresponding tuning parameter in low-dimensional MCDM problems.

Robot Selection Problem via Fuzzy TOPSIS Method Using Novel Distance and Similarity Measure for Generalized Fuzzy Numbers with Unequal Heights

Background: Mega multinational companies are highly dependent on robots to handle the maximum of their machinery workload, which significantly reduces human labor and saves valuable time as well. However, as vital as the role of robots is, a much more challenging task is its selection. Moreover, the robots need to be evaluated on the grounds of different specifications and their ease of handling, which results in a smooth and work-efficient environment. Objective: The prime objective of this paper is to devise a fruitful decision-making model for a robot selection problem, which utilizes a multi-criteria decision-making method known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS method is based on the newly defined distance measure involving generalized fuzzy numbers with unequal heights (GFNUHs). Methodology/Approach: At first, we define a novel distance measure based on the “expected value” and “variance” of GFNUHs, where both the parameters are evaluated with the help of the [Formula: see text]-cut method. We then also give the expression for the distance-based similarity measure and investigate some of their properties. Both the distance and the similarity measure(s) are then validated for their effectiveness through a hypothetical case study of pattern recognition. Moreover, we consider 10 different bunches of generalized fuzzy numbers (GFNs) and present a comparative study with the already established measures to establish the efficiency and superiority of our proposed measures. Finally, the distance measure is deployed in the TOPSIS method, which facilitates suitable robot selection by an automobile company. Findings/Results: A comparison of results for the proposed distance measure and the similarity measure with the existing ones is presented which proves that the proposed measure(s) are effective and usable. Novelty/Value: The evaluation of expected value and variance of GFNUHs with the help of [Formula: see text]-cut technique is a completely original idea showcased in this paper and its improved version of TOPSIS for GFNUHs as discussed shall add a new direction in the realm of decision-making.

Effect of normalization methods on rank performance in single valued m-polar fuzzy ELECTRE-I algorithm

The m-polar fuzzy set is the extended form of the bipolar fuzzy set. It is helpful in multi-criteria group decision-making (MCGDM). While solving problems with actual values of various criteria, normalization methods are used to make consistent data. Uniform data is input to multi-criteria decision-making (MCDM) methods. The single-valued m-polar fuzzy ELECTRE-I algorithm also uses normalized data as input parameters. There are different methods of normalization, which can be used to make data uniform. In this paper, with the help of the robot selection problem, we study the effect of five different normalization methods on the rank performance of the single-valued m-polar fuzzy ELECTRE-I algorithm. The m-polar fuzzy ELECTRE-I algorithm is beneficial in finding outranking relations between two alternatives. However, normalization methods affect outranking relations of other options and ultimately affect rank performance in the selection process. This study concluded in the recommendation of a suitable normalization method for the single-valued m-polar fuzzy ELECTRE-I method. It is supposed that linear sum (N3) and vector normalization (N4) methods are ideal for the m-polar fuzzy ELECTRE-I process to achieve consistent rank performance.

DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

AbstractAlthough a considerable amount of efforts have been made to overcome the drawbacks of traditional equal aggregation in the Data Envelopment Analysis cross‐efficiency evaluation, the endogenous preference structures in the cross‐efficiency matrix are still largely unexplored. We first untangle both preferential differences denoting the preference degrees among different Decision Making Unit (DMU), and preferential priorities denoting the favorite ranking of the DMUs. A revised cross‐efficiency matrix is formulated. We then apply the stochastic multicriteria acceptability analysis for group decision‐making method to obtain a set of holistic acceptability indices corresponding to different DMUs. Finally, we develop a satisfaction index to evaluate the satisfactory level of the proposed method. Two numerical examples with respect to the Flexible Manufacturing System selection problem and industrial robot selection problem are conducted to validate the proposed methodology. The superiority of our methodology is manifested in terms of an improvement of the overall satisfactory level as 18.69% and 8.48%, respectively.

Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS

Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

Group decision making in data envelopment analysis: A robot selection application

Data envelopment analysis is a technique widely used to measure the relative efficiency of a set of decision making units and for ranking alternatives based on the measured efficiency. When there are data that need to be estimated, a group of experts is consulted. The opinions of the experts can be aggregated at either the data level, based on which the efficiency is calculated, or the efficiency level, where the efficiencies calculated from the data provided by individual experts are aggregated. However, the results may not be consistent, which leads to a puzzling situation as which result to follow. In this paper, a method of determining appropriate weights by which the opinions of the experts can be used to calculate the efficiency of alternatives is proposed. One radial model and one slacks-based measure (SBM) model are constructed based on this idea. It is shown that, for both models the results obtained from aggregating the opinions at the data level and at the efficiency level are the same. The final efficiencies are thus reliable. A case of a robot selection problem for a manufacturing company in Taiwan is used for the purpose of illustration. The results show that the radial efficiency is dependent on the non-Archimedean number and will overstate the performance of the robot. The SBM efficiency, which does not have this drawback, is more reliable than the radial efficiency to be used for ranking. A robot is suitably selected based on the SBM efficiency.

The Robot Selection Problem for Mini-Parallel Kinematic Machines: A Task-Driven Approach to the Selection Attributes Identification.

In the last decades, the Robot Selection Problem (RSP) has been widely investigated, and the importance of properly structuring the decision problem has been stated. Crucial aspect in this process is the correct identification of the robot attributes, which should be limited in number as much as possible, but should be also able to detect at best the peculiar requirements of specific applications. Literature describes several attributes examples, but mainly dedicated to traditional industrial tasks, and applied to the selection of conventional industrial robots. After a synthetic review of the robot attributes depicted in the RSP literature, presented with a custom taxonomy, this paper proposes a set of possible requirements for the selection problem of small scale parallel kinematic machines (PKMs). The RSP is based on a task-driven approach: two mini-manipulators are compared as equivalent linear actuators to be integrated within a more complex system, for the application in both an industrial and a biomedical environment. The set of identified criteria for the two environments is proposed in the results and investigated with respect to working conditions and context in the discussion, emphasizing limits and strength points of this approach; finally, the conclusions synthesizes the main results.

A FBWM-PROMETHEE approach for industrial robot selection

In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively.

A TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem

A TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem

Multi-Criteria Decision Making using Fuzzy Cognitive Maps – Preliminary Results

In this work a new methodology for Multi-Criteria Decision Making (MCDM) with the ability to address dependence and feedback among criteria and their interrelations is proposed. Although a variety of MCDM methods have been developed during the past years, Fuzzy Cognitive Maps (FCMs) consist an alternative, interesting and promising research direction in decision support. The proposed FCM-based method, exploiting the natural FCMs ability to effectively represent concepts interactions, offers a dynamic and flexible way for ranking alternatives on the basis of different scenarios regarding the nature of causal relationships between criteria. The method is validated on the basis of a robot selection problem.

Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making

Most of the existing studies investigate the robot selection problem (RSP) in a multiple criteria decision making (MCDM) manner, from the viewpoint of a single person. This contradicts the reality that the robot selection decision is usually made by a committee or a group of experts with different expertise and concerns. For this reason, this paper proposes a group decision making (GDM) methodology for handling multiple criteria robot selection problem (MCRSP), the working process of which is (i) identifying experts, (ii) implementing the standard MCDM process and (iii) achieving a group consensus. Four objective weight determination methods, namely, Shannon entropy, CRITIC, ideal point and distance-based, are proposed to represent four experts. Experts play the role of think tank in supporting the decision maker who is responsible for MCRSP. In light of that the preference among different experts is uncertain, stochastic multicriteria acceptability analysis is then applied to achieve a holistic evaluation results for identifying good compromise choices. Two illustrative examples are presented to demonstrate the effectiveness and validity of our methodology, and compare the results with those obtained through VIKOR and ELECTRE II.

Robot Evaluation and Selection Using the Hesitant Fuzzy Linguistic MULTIMOORA Method

Abstract With the development of modern technology, industrial robots have been applied extensively in different industries to perform high-risk jobs and produce high-quality products. However, selecting an appropriate robot for a specific manufacturing environment is a difficult task for decision makers because of the increase in complexity, production demands, and the availability of different robot types. Normally, robot selection can be regarded as a complex multicriteria decision-making problem, and decision makers often use uncertain linguistic terms to express their assessments because of time pressure, lack of data, and their limited expertise. In this article, a modified MULTIMOORA (Multiobjective Optimization by Ratio Analysis plus the Full Multiplicative Form) method based on hesitant fuzzy linguistic term sets (named HFL-MULTIMOORA) is proposed for evaluating and selecting the optimal robot for a given industrial application. This method deals with the decision makers’ uncertain assessments with hesitant fuzzy linguistic variables, which can increase the flexibility of representing linguistic information. Finally, an empirical example is presented to demonstrate the proposed method, and the results indicate that the HFL-MULTIMOORA provides a useful and practical tool for solving robot selection problems within a hesitant linguistic information environment.

Fuzzy extended VIKOR-based mobile robot selection model for hospital pharmacy

To stimulate the development of the Internet of Health Things and the construction of a robotic automation system in the healthcare industry, the mobile robot selection problem for a hospital pharmacy is studied. We target the mobile robot selection as a multi-criteria decision-making problem. The VIKOR-based implementation steps integrating fuzzy extended analytic hierarchy process technique are developed. To avoid any loss of information and ensure the veracity of the VIKOR-based calculations, we employ the fuzzy ranking technique based on the degree of possibility to select the best mobile robot alternative by deriving the minimum fuzzy comprehensive utility value. The results of a case study and the accompanying sensitivity analysis show the effectiveness and robustness of the fuzzy extended VIKOR approach for the mobile robot selection problem.

Robot Evaluation and Selection with Entropy-Based Combination Weighting and Cloud TODIM Approach

Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and number of robot systems. In this paper, we explore two key issues of robot evaluation and selection: the representation of decision makers’ diversified assessments and the determination of the ranking of available robots. Specifically, a decision support model which utilizes cloud model and TODIM (an acronym in Portuguese of interactive and multiple criteria decision making) method is developed for the purpose of handling robot selection problems with hesitant linguistic information. Besides, we use an entropy-based combination weighting technique to estimate the weights of evaluation criteria. Finally, we illustrate the proposed cloud TODIM approach with a robot selection example for an automobile manufacturer, and further validate its effectiveness and benefits via a comparative analysis. The results show that the proposed robot selection model has some unique advantages, which is more realistic and flexible for robot selection under a complex and uncertain environment.