Analytic hierarchy process for robot selection

doi:10.1016/S0278-6125(97)88467-1

Journal of Manufacturing Systems

Volume 16, Issue 5, 1997, Pages 381-386

https://doi.org/10.1016/S0278-6125(97)88467-1 Get rights and content

Abstract

Investment decisions involving robots are capital intensive and are usually made by a committee of experts from different functional backgrounds within a company. In spite of this knowledge, most models in the literature for robot selection assume that there is only a single decision maker. In this paper, a robot selection model that incorporates the inputs from multiple decision makers is provided. This model is based on the analytic hierarchy process method, and both the subjective and objective criteria for robot selection are used. It does not assume that the decision makers have achieved a consensus; that is, they may not agree on evaluations of the robots with respect to each of the criteria. A numerical example is used to illustrate the model.

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Cited by (64)

An integrated fuzzy MCDM approach based on Bonferroni functions for selection and evaluation of industrial robots for the automobile manufacturing industry
2023, Expert Systems with Applications
Citation Excerpt :
Therefore, according to almost all previous works existing in the literature: the selection of industrial robotics is a highly complex decision-making problem quite affected by uncertainties (Bhattacharya, Sarkar, & Mukherjee, 2005; Fu, Li, Luo, & Huang, 2019), and it is required to use a powerful and applicable tool that can enable to deal with uncertainties for optimally solving these kinds of problems. The most commonly used subjective and objective techniques to solve the industrial robot selection in literature are the AHP (Bhattacharya et al., 2005; Goh, 1997; Seidmann, Arbel, & Shapira, 1984; Tansel Iç, Yurdakul, & Dengiz, 2013), TOPSIS (Agrawal, Kohli, & Gupta, 1991; Bhangale, Agrawal, & Saha, 2004; Ghrayeb, Phojanamongkolkij, Marcellus, & Zhao, 2004; Iç, 2012; Kahraman, Çevik, Ates, & Gülbay, 2007; Parkan & Wu, 1999), ELECTREE (Chatterjee, Athawale, & Chakraborty, 2010), VIKOR (Fu et al., 2019; Garg & Sharma, 2020), PROMETHEE (Sen, Datta, & Mahapatra, 2016), EDAS (Rashid, Ali, & Chu, 2021), DEA (Braglia & Petroni, 1999; Karsak, 1998; Khouja, Rabinowitz, & Mehrez, 1995). As these studies ignored uncertainties, contributions of these previous studies applying objective MCDM techniques, unfortunately, are limited, and their applicability for solving a decision-making problem encountered in real life is also weak.
In recent years, there have been dramatic changes in manufacturing systems in many industries depending on technological developments. Robotics is one of the essential components of these changes. Today, the usage of robotics in manufacturing processes has become widespread in almost all industries. Also, it has become a very strong desire ever-increasing for even small and medium-sized enterprises at present. Almost all the previous studies emphasized that industrial robot selection is a highly complex decision-making problem as there are many conflicting factors and criteria. Besides, different and advanced specifications of these robotics added by robotic manufacturers have caused to increase the complexities much more. Hence, decision-makers encounter more complicated decision-making problems affected by many uncertainties. Because of that, an integrated fuzzy group MCDM framework can help overcome many ambiguities proposed in the current paper. The proposed fuzzy integrated model consists of the fuzzy SWARA (F-SWARA’B) and the fuzzy CoCoSo (F-CoCoSo'B), which are extended with the help of the Bonferroni function. The model selected the appropriate industrial robotics used in the automotive industry by considering 15 criteria and ten alternatives. According to the result of the study, the three most significant criteria have been determined: Working Accuracy, Reaching Distance, and Performance; and the most suitable option is the A8. The obtained results were validated with the help of a comprehensive sensitivity analysis consisting of different 150 scenarios. The results are also compared with some existing techniques. The sensitivity analysis results approve the validity and applicability of the proposed model.
Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making
2019, Robotics and Autonomous Systems
Citation Excerpt :
The criteria values are estimated by analytical models and simulation. Although the existing studies have realized that the robot selection decision is typically made by a committee or a group of experts with diverse expertise [9,10,30], there exist very few papers that seek to achieve a group wisdom. In other words, almost all of the extant methods in the literature assume that the RSP is handled by a single person.
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.
Selecting industrial robots for milling applications using AHP
2017, Procedia Computer Science
Industrial robots are usually used for pick-and-place applications, which require only point-to-point motion control. However, the recent developments, both in robot technology and in Computer Automated Machining (CAM) software, allow the use of these equipment in applications which require continuous path control, such as multi-axis milling processes. However, the producers do not offer robots specifically developed for this kind of application, thus the user has to choose the most appropriate robot for this goal from a wide range of general purpose robot types. This research work proposes a method based upon Analytic Hierarchy Process (AHP) for selecting the industrial robot for milling applications. Several attributes of the robots are proposed as selection criteria, and a simulation study of the milling capabilities of the robotic structure is also taken into consideration.
A de Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device
2015, Computers and Industrial Engineering
Application of multiple conventional approaches to a particular multi-criteria decision making (MCDM) problem often suffers rank reversal giving rise to confusion and ambiguity in appropriate decision making. To eradicate the confusion, this paper proposes a De Novo multi-approaches multi-criteria decision making method namely Technique of Precise Order Preference (TPOP). The TPOP first examines the inconsistency in the ranking order of the alternatives of a MCDM problem by using multiple conventional approaches. If inconsistency/rank reversal in ranking order of the alternatives exists then TPOP, using advanced version of entropy weighting method introduced in this research work, measures weights of the final selection values of conventional approaches. Subsequently, TPOP based on these weights and final selection values computes precise selection indices (PSI) that determines accurate ranking order for the alternatives. The proposed technique is illustrated by two real life examples on material handling device (MHD) ranking and selection problems. The first example is initially solved using five conventional integrated fuzzy multi-criteria decision making techniques (FMCDMs) whereas the second example is taken from previous researchers’ works. The results obtained using TPOP justify the validity, applicability and requirements of the proposed technique. The study shows that the proposed multi-approaches, multi-criteria decision making technique can be a useful and effective model in MCDM.
An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria
2015, Applied Soft Computing Journal
Robots with vastly different capabilities and specifications are available for a wide range of applications. Selection of a robot for a specific application has become more complicated due to increase in the complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. The aim of this paper is to present an integrated approach for the optimal selection of robots by considering both objective and subjective criteria. The approach utilizes Fuzzy Delphi Method (FDM), Fuzzy Analytical Hierarchical Process (FAHP), Fuzzy modified TOPSIS or Fuzzy VIKOR and Brown–Gibson model for robot selection. FDM is used to select the list of important objective and subjective criteria based on the decision makers’ opinion. Fuzzy AHP method is then used to find out the weight of each criterion (both objective and subjective). Fuzzy modified TOPSIS or Fuzzy VIKOR method is then used to rank the alternatives based on objective and subjective factors. The rankings obtained are used to calculate the robot selection index based on Brown–Gibson model. The proposed methodology is illustrated with a case study related to selection of robot for teaching purpose. It is found that the highest ranked alternative based on Fuzzy VIKOR is closest to the ideal solution.
The Construction of an Evaluation Index System for Assistive Teaching Robots Aimed at Sustainable Learning
2023, Sustainability (Switzerland)

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Technical noteAnalytic hierarchy process for robot selection

Abstract

OMEGA, International Journal of Management Science

Computers & Industrial Engineering

International Journal of Production Economics

Robotics and Computer-Integrated Manufacturing

Journal of Mathematical Psychology

European Journal of Operational Research

Journal of Mathematical Psychology

The Industrial Robot Selection Problem: Literature Review and Directions for Future Research

IIE Transactions

Robotics: Procedures Given for Evaluating, Selecting Robots

Industrial Engineering

Robotics: Decision Support Systems Used for Robot Selection

Industrial Engineering

Technical note
Analytic hierarchy process for robot selection