Assessment of alternative industrial robots using AHP and TOPSIS
Ateekh Ur Rehman and
Abdulrahman Al-Ahmari
International Journal of Industrial and Systems Engineering, 2013, vol. 15, issue 4, 475-489
Abstract:
An ever increasing trend in present dynamic markets, the industrial organisations should provide the right amount of flexibility at right time in the right direction. Among many manufacturing strategies available, use of advance industrial robot technology is emerging as an attractive alternative. Although, the successful implementation of advance industrial robot offers manufacturing organisations numerous benefits, the assessment and preference of these technologies is a very versatile task due to the multiple parameters involved. The objective here is to help decision makers to ensure that the selected industrial robot comply with the objective of the organisation. Thus, the paper mainly demonstrates and compares the ranking of the industrial robots using analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). In the present study, the model presented takes into consideration the economic and technical criterion.
Keywords: industrial robots; analytical hierarchy process; AHP; TOPSIS; robot selection; organisational objectives; robot ranking; economic criteria; technical criteria. (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.inderscience.com/link.php?id=57481 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:15:y:2013:i:4:p:475-489
Access Statistics for this article
More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().