Attribute based specification, comparison and selection of a robot

Bhangale, Prasadkumar P.; Agrawal, V. P.; Saha, Subir Kumar

doi:10.1016/j.mechmachtheory.2004.05.020

Cited by 123 publications

(90 citation statements)

References 8 publications

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“…Bhangale et al [1] developed a robot selection methodology using the technique for order performance by similarity to ideal solution (TOPSIS) and graphical methods, and compared the relative rankings of the alternative robots as obtained using these two methods. A coding system is employed for expressing various robot selection attributes and a merit value is used to rank the robots in the order of their suitability for a given industrial application.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This example [1] deals with the selection of the most suitable robot for some pick-n-place operations where it has to avoid certain obstacles. Performance of an industrial robot is often specified using different attributes.…”

Section: Illustrative Examplementioning

confidence: 99%

“…Hence, making this assumption introduces a risk of selecting a robot that may fail to provide the required performance. In this example [1], five different robot selection attributes are considered as load capacity (LC), maximum tip speed (MTS), repeatability (R), memory capacity (MC) and manipulator reach (MR), among which load capacity, maximum tip speed, memory capacity and manipulator reach are the beneficial attributes, whereas, repeatability is a nonbeneficial attribute. Thus, the robot selection problem consists of five criteria and seven alternative robots, as given in Table 1.…”

Section: Illustrative Examplementioning

confidence: 99%

“…Selection of the best suited robot for a given industrial application from a large number of available alternatives is a typical multi-criteria decision-making (MCDM) problem. Several approaches for robot selection have already been proposed by the past researchers [1][2][3][4][5][6][7][8][9][10][11], which include the applications of MCDM methods, production system performance optimization models, computer-assisted models and statistical models. In this paper, a ranking of all the considered alternative robots is obtained taking into account different robot selection attributes and it is observed that the ranking obtained using the VIKOR method matches quite well with that as derived by the past researchers, which proves the applicability of this MCDM method to solve such type of complex industrial problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selection of industrial robots using compromise ranking method

Athawale¹,

Chatterjee

Chakraborty

2012

IJISE

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Selection of an industrial robot for a specific engineering application is one of the most challenging problems in real time manufacturing environment. This has become more and more complicated due to increasing complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. The decision maker needs to select the most suitable industrial robot in order to achieve the desired output with minimum cost and specific application ability. This paper mainly focuses on solving the robot selection problem using VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) method, which has already become a quite popular multi-criteria decision-making (MCDM) tool. One real time example is cited in order to demonstrate and validate the effectiveness and applicability of VIKOR method.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Illustrative Examplementioning

confidence: 99%

Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selection of industrial robots using compromise ranking method

Athawale¹,

Chatterjee

Chakraborty

2012

IJISE

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show abstract

“…There are over 75 attributes, which can be considered while selecting a robot (Bhangale et al, 2004). These attributes can be either objective or subjective.…”

Section: Literature Reviewmentioning

confidence: 99%

Selection of industrial robots using the Polygons area method

Azimi¹,

Taghizadeh²,

Farahmand³

et al. 2014

10.5267/j.ijiec

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Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decisionmaking criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman's rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem.

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Using multi criteria decision making in analysis of alternatives for selection of enabling technology

Georgiadis

Mazzuchi

Sarkani

2012

Systems Engineering

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In September 2009 the U.S. Government Accountability Office (GAO) reported, “Defense Acquisitions: Many Analyses of Alternatives Have Not Provided a Robust Assessment of Weapon System Options” [U.S. Government Accountability Office, Pub. No. GAO‐09‐665, 2009, p. 1]. In their focused review of 32 Acquisition Category I programs, it was found that 10 did not conduct an Analysis of Alternatives (AoA), but rather focused on an already selected weapon system solution. Prior to Milestone A, the Department of Defense (DoD) requires that service sponsors conduct an Analysis of Alternatives (AoA). The AoA is an analytical comparison of multiple alternatives to be completed prior to committing and investing costly resources to one project or decision. Typically, however, sponsors will circumvent the process in an effort to save money or schedule, and capability requirements are proposed that are so specific that they effectively eliminate all but the preferred concepts, practically ignoring other alternatives. Decision making is one of the most challenging parts of Systems Engineering. How one feeds the decision making process is key to eliminating long term waste. “About three‐quarters of a program”s total life cycle cost is influenced by decisions made before it is approved to start development“[U.S. Government Accountability Office, Pub. No. GAO‐09‐665, 2009, pp. “2]. This study evaluates the positive benefits of defining the problem domain prior to expeditiously turning to the solution domain. The goal in any decision making process is to provide the decision maker with the ability to look into the future, and to make the best possible decision based on past and present information and future predictions. There is a need for approaches that combine available quantitative data with the more subjective knowledge of experts. Decision theory techniques have been successfully used for contrasting expert judgments and making educated choices for many years. For a successful analysis, one must focus on selection of specific criteria that are key performance drivers that can lead to an informed selection of the enabling technology. Understanding the requirements of the end state goal is key to a successful analysis and should also assist in the selection of key performance parameters. A case study example is presented to demonstrate a third tier AoA identifying an enabling technology using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) while successfully accounting for tacit knowledge of expert practitioners. © 2012 Wiley Periodicals, Inc. Syst Eng 16

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Attribute based specification, comparison and selection of a robot

Cited by 123 publications

References 8 publications

Selection of industrial robots using compromise ranking method

Selection of industrial robots using compromise ranking method

Selection of industrial robots using the Polygons area method

Using multi criteria decision making in analysis of alternatives for selection of enabling technology

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