Prasenjit Chatterjee

Selection of Industrial Robots using Compromise Ranking Method

Many advanced manufacturers are now extensively using robots to perform repetitious, difficult and hazardous tasks with precision. Selection of industrial robots to suit a particular application and production environment from among a large number of alternatives available in the market is a difficult task in real-time manufacturing environment. This has become more and more complicated due to increase complexity, advanced features and facilities that are continuously being incorporated into the robots by different vendors. The decision maker needs to select the most appropriate industrial robot to achieve the desired performance with minimum cost and specific application ability. This paper mainly focuses on solving the industrial robot selection problems using VIse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, which has already become a quite popular multi-criteria decision-making (MCDM) tool. It evaluates and ranks the alternative candidate robots, while proposing a compromise solution to the robot selection problem. Two real-time examples are illustrated to demonstrate and validate the effectiveness and applicability of VIKOR method, which also prove the computational simplicity of this method.

A NOVEL HYBRID METHOD FOR NON-TRADITIONAL MACHINING PROCESS SELECTION USING FACTOR RELATIONSHIP AND MULTI-ATTRIBUTIVE BORDER APPROXIMATION METHOD

Selection of the most appropriate non-traditional machining process (NTMP) for a definite machining requirement can be observed as a multi-criteria decision-making (MCDM) problem with conflicting criteria. This paper proposes a novel hybrid method encompassing factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) methods for selection and evaluation of NTMPs. The application of FARE method is pioneered in NTMP assessment domain to estimate criteria weights. It significantly condenses the problem of pairwise comparisons for estimating criteria weights in MCDM environment. In order to analyze and rank different NTMPs in accordance with their performance and technical properties, MABAC method is applied. Computational procedure of FARE-MABAC hybrid model is demonstrated while solving an NTMP selection problem for drilling cylindrical through holes on non-conductive ceramic materials. The results achieved by FARE-MABAC method exactly corroborate with those obtained by the past researchers which validate the usefulness of this method while solving complex NTMP selection problems.

Performance evaluation of Indian Railway zones using DEMATEL and VIKOR methods

Purpose – The purpose of this paper is to propose the application of a decision-making tool for performance evaluation of Indian Railway zones. It basically seeks to analyze the effects of various evaluation criteria on the performance of Indian Railways using a combined multi-criteria decision-making approach which employs decision-making trial and evaluation laboratory (DEMATEL) and “VIse Kriterijumska Optimizacija kompromisno Resenje” (VIKOR) methods. Design/methodology/approach – The performance of 16 Indian Railway zones is first evaluated using DEMATEL method which addresses the inter-relationships between different criteria with the aid of a relationship structure. The VIKOR method which is a compromise ranking approach is then adopted to rank those candidate railway zones. Pareto analysis is also carried out to identify the benchmark railway zones for the under/poor performers so as to improve their operational excellence. Findings – A numerical example from Indian Railways is illustrated and solv...

Decision making for facility location selection using PROMETHEE II method

Facility location selection is an important problem in all kinds of businesses, including service and manufacturing organisations. Selecting a location for a new organisation or expansion of an existing facility is a crucial component in the eventual success or failure of the organisation. Proper location selection can be a strategic tool that can significantly improve market share growth and increase customer profitability. The cost associated with acquiring the land and facility construction makes the facility location selection a long-term investment decision. The best facility location is that which results in higher economic benefits through increased productivity and good distribution network. Selecting the proper facility location from a given set of candidate alternatives is a difficult task, as many potential qualitative and quantitative criteria need to be considered. This paper solves two real time facility location selection problems using preference ranking organisation method for enrichment evaluation (PROMETHEE II) method which is observed to an effective multi-criteria decision-making (MCDM) tool often applied to deal with complex problems in the manufacturing environment. The ranking performance of PROMETHEE II method is compared with the other MCDM methods and it is observed that the top-ranked alternatives exactly match with those as obtained by the past researchers.

Materials selection using COPRAS and COPRAS-G methods

Material selection plays an important role for cost-effective manufacturing of products leading to quality production and better utilisation of resources. Improper material selection may cause premature product failure. From a wide range of materials available in market, selection of the best material for a particular application is not an easy task. While selecting material, the designers need to take into account a large number of material selection criteria. Because of these reasons, material selection has got considerable attention by the academicians and researchers. This paper attempts to solve the material selection problems using complex proportional assessment (COPRAS) and complex proportional assessment with grey number (COPRAS-G) methods while considering different material selection criteria and their relative importance. The rankings obtained using these two methods almost corroborate with those derived by the past researchers. Two real time examples are illustrated to demonstrate the potentiality and applicability of the adopted methods.

Advanced manufacturing systems selection using ORESTE method

Selection of advanced manufacturing systems (AMSs) is a multi-criteria decision-making (MCDM) problem, based on assessing a large number of conflicting quantitative and qualitative criteria. Decision making for AMS selection has become more complicated due to availability of a wide range of feasible alternatives. In this paper, an almost unexplored MCDM method, Organization, Rangement Et Synthese De Donnes Relationnelles (ORESTE) is applied to solve five AMS selection problems, e.g., a) an industrial robot; b) a flexible manufacturing system; c) a rapid prototyping process; d) the most suitable machine for a flexible manufacturing cell; e) a non-traditional machining process. This method does not require any precise information about criteria weights or formulation of any type of preference function. In all these cases, the rankings of the alternatives obtained using ORESTE method almost corroborate with those derived by the past researchers, proving the expediency of this method as an effective MCDM technique.

Development of a meta-model for the determination of technological value of cotton fiber using design of experiments and the TOPSIS method

ABSTRACT To meet the requirements of the cotton spinning industry and achieve the best quality of ring-spun yarn, it becomes an imperative task to determine the technological values of cotton fibers. The availability of High Volume Instrument (HIV) data now makes it possible to evaluate the quality of cotton fiber with respect to some of its major physical properties. The fiber quality index (FQI), the spinning consistency index (SCI), and the multiplicative analytic hierarchy process (MAHP) are some of the popular approaches adopted by the spinning industry personnel to determine the quality values of cotton fibers. In this paper, while integrating the design of experiments (DoE) and the technique for order preference by similarity to ideal solution (TOPSIS), a regression meta-model is developed for determining the technological value of cotton fiber with respect to the TOPSIS score. This model identifies the statistically significant fiber properties and their interactions affecting the estimated TOPSIS score while fitting a polynomial to the experimental data in multiple linear regression analysis. It is observed that the uniformity index has no importance in quality value evaluation of the cotton fiber, although its interactions with other properties are statistically significant. A validation analysis shows an excellent degree of congruence of this meta-model with the existing models for cotton fiber quality determination.

Intelligent Decision Making Tools in Manufacturing Technology Selection

The importance of technology in modern companies is literally growing. Technology protects the natural environment and acts as catalyst toward a more productive economy. Technology development has been the most demanding activity in industrial sectors over years and technology selection and implementation is one of the acknowledged projects in many companies. There are many factors influencing the problem of evaluating and choosing a new technology. Therefore, manufacturing operation managers are involved in a decision-making system with conflicting elements in their selection process. In this condition, application of multi-attribute decision-making (MADM) tools is highly recommended. This study examines the utilization of analytic hierarchy process and an adopted MADM method named CoCoSo to simultaneously determine the importance of decision factors and obtain the optimal ranking. At the final stage, we configure a sensitivity analysis to check and examine the accuracy of the results and performance of the present decision system. The study corresponds to a case study of choosing best packaging technology for a dairy company.

A new Fuzzy methodology-based structured framework for RAM and risk analysis

Abstract The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.