A solution to robot selection problems using data envelopment analysis

Selection of industrial robots for the present day's manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved

A Comprehensive Solution to Automated Inspection Device Selection Problems using ELECTRE Methods

Selection of an automated inspection device for an explicit industrial application is one of the most challenging problems in the current manufacturing environment. It has become more and more complicated due to increasing complexity, advanced features and facilities that are endlessly being integrated into the devices by different manufacturers. Selection of inspection devices plays a significant role in a manufacturing system for cost effectiveness and improved productivity. This paper focuses on the application of a very popular Multi-Criteria Decision-Making (MCDM) tool, i.e. ELimination and Et Choice Translating REality (ELECTRE) for solving an automated inspection device selection problem in a discrete manufacturing environment. Using a sample case study from the published literature, this paper attempts to show how different variants of the ELECTRE method, namely ELECTRE II, IS, III, IV and TRI can be suitably applied in choosing the most efficient alternative that accounts for both the decision maker’s intervention and other technical elements. Using different ELECTRE methods, a list of all the possible choices from the best to the worst suitable devices is obtained while taking into account different selection attributes. The ranking performance of these methods is also compared with that of the past researchers

AUTOMOBILE WHEEL MATERIAL SELECTION USING MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS (MOORA) METHOD

 Material selection is one of the most vital decisions in finest design of any manufacturing process and product. Proper material selection plays an elementary role for a productive manufacturing system with better product and process superiority along with cost optimization. Improper material selection often causes huge cost contribution and drives an organization towards unripe product failure. In this paper, multi-objective optimization on the basis of ratio analysis (MOORA) method is applied to solve magnesium alloy material selection problem to use in automotive wheel applications. A comprehensive list of all the prospective materials from the best to the worst is obtained, taking into account multi-conflicting material selection attributes. The ranking performance of the method is also compared with that of the past researchers. It is observed that the method is very simple to understand, easy to implement and provide almost exact rankings to the automotive wheel material alternative

Automobile Wheel Material Selection Using Multi-objective Optimization on the Basis of Ratio Analysis (Moora) Method

 Material selection is one of the most vital decisions in finest design of any manufacturing process and product. Proper material selection plays an elementary role for a productive manufacturing system with better product and process superiority along with cost optimization. Improper material selection often causes huge cost contribution and drives an organization towards unripe product failure. In this paper, multi-objective optimization on the basis of ratio analysis (MOORA) method is applied to solve magnesium alloy material selection problem to use in automotive wheel applications. A comprehensive list of all the prospective materials from the best to the worst is obtained, taking into account multi-conflicting material selection attributes. The ranking performance of the method is also compared with that of the past researchers. It is observed that the method is very simple to understand, easy to implement and provide almost exact rankings to the automotive wheel material alternative