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Supplier selection in food supply chains (FSCs) is not much explored due to the inherent difficulties, complexities and nature of food industry. Food security and quality are top row topics in today’s world health scenario. During sudden food crisis, it needs extra attention where producers, suppliers, and stakeholders play the most vital roles. This paper puts forward a two-phase sustainable multi-tier supplier selection model for FSC based on an integrated decision analysis under multi-criteria perspectives considering sustainability criteria, suppliers and sub-suppliers. In the first phase, the model estimates supplier selection criteria weights using a combined version of step-wise weight assessment ratio analysis (SWARA) and level based weight assessment (LBWA) in conjunction with D-numbers. In the second phase, Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS)-D method is applied to obtain a ranking pre-order of different tier suppliers. Moreover, several sensitivity analyses are carried out in order to examine model reliability. To check application practicability, the proposed model is implemented in a case study of WineSol Corporation in Spain. The proposed model is expected to serve as a kickoff point for developing advanced decision-making models for effectually address multi-tier supplier selection problems under uncertain environment.

In recent decades, due to the rapid consumption of natural resources and the need for environmental protection, sustainability in supply chain management has emerged as an increasingly important issue. Therefore, in this study, supplier selection has been performed in order to achieve sustainability, taking into account all aspects: economic, social and environmental criteria. For this purpose, combined FUCOM - Rough SAW approach has been used. The evaluation of criteria grouped at two levels has been carried out by decision makers according to the needs of the company whose main activity is lime production. To obtain the criterion weight values, the FUCOM (FUll COnsistency Method) has been used.. In order to avoid uncertainty and imprecision in the supplier evaluation process, combination with the Rough SAW method, which is used for ranking and supplier selection, has been performed. In order to control the stability of the used model, a sensitivity analysis has been performed. The first phase involves changing the weights of the criteria, while the second phase involves a comparative analysis using other MCDM methods.

Purpose Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk. Design/methodology/approach At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics. Findings A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model. Practical implications The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors. Originality/value A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.

Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting method s called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process.

Multi-attribute decision-making (MADM) methods represent reliable ways to solve real-world problems for various applications by providing rational and logical solutions. In reaching such a goal, it is expected that MADM methods would eliminate inconsistencies like rank reversal issues in a given solution. In this paper, an endeavor is taken to put forward a new MADM method, called RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval), which successfully eliminates the rank reversal problem. The developed RAFSI method has three major advantages that recommend it for further use: (i) its simple algorithm helps in solving complex real-world problems, (ii) RAFSI method has a new approach for data normalization, which transfers data from the starting decision-making matrix into any interval, suitable for making rational decisions, (iii) mathematical formulation of RAFSI method eliminates the rank reversal problem, which is one of the most significant shortcomings of existing MADM methods. A real-time case study that shows the advantages of RAFSI method is presented. Additional comprehensive analysis, including a comparison with other three traditional MADM methods that use different ways for data normalization and testing the resistance of RAFSI method and other MADM methods to rank the reversal problem, is also carried out.

Selection of the most appropriate cotton fibre type for yarn manufacturing is often treated as a multi-criteria decision-making (MCDM) problem as the optimal selection decision needs to be taken in presence of several conflicting fibre properties. In this paper, two popular MCDM methods in the form of decision making trial and evaluation laboratory (DEMATEL) and VIse Kriterijumska Optimizacija kompromisno Resenje (VIKOR) are integrated to aid the cotton fibre selection decision. DEMATEL method addresses the interrelationships between various physical properties of cotton fibres while segregating them into cause and effect groups, whereas, VIKOR method helps in ranking all the considered 17 cotton fibres from the best to the worst. The derived ranking of cotton fibre alternatives closely matches with that obtained by the past researchers. This model can assist the spinning industry personnel in the blending process while making accurate fibre selection decision when cotton fibre properties are numerous and interrelated.

The decision-making in energy sector involves finding a set of energy sources and conversion devices to meet the energy demands in an optimal way. Making an energy planning decision involves the balancing of diverse ecological, social, technical and economic aspects across space and time. Usually, technical and environmental aspects are represented in the form of multiple criteria and indicators that are often expressed as conflicting objectives. In order to attain higher efficiency in the implementation of renewable energy (RE) systems, the developers and investors have to deploy multi-criteria decision-making techniques. In this paper, a novel hybrid Decision Making Trial and Evaluation Laboratory and analytic network process (DEMATEL-ANP) model is proposed in order to stress the importance of the evaluation criteria when selecting alternative REs and the causal relationships between the criteria. Finally, complex proportional assessment and weighted aggregated sum product assessment methods are used to assess the performances of the REs with respect to different evaluating criteria. An illustrative example from Costs assessment of sustainable energy systems (CASES) project, financed by European Commission Framework 6 programme (EU FM 6) for EU member states is presented in order to demonstrate the application feasibility of the proposed model for the comparative assessment and ranking of RE technologies. Sensitivity analysis, result validation and critical outcomes are provided as well to offer guidelines for the policy makers in the selection of the best alternative RE with the maximum effectiveness. European Commission Framework 6 programme (EU FM 6) No data JCR 2018 0.636 SJR (2018) Q2, 38/200 Environmental Engineering, 122/350 Management, Monitoring, Policy and Law; Q3, 55/119 Environmental Chemistry No data IDR 2018 UEM

Evolution of supply chain management (SCM) in recent years has transformed it beyond the simple logic of benefit and economic point of views. One of such key strategic elements in establishing a sustainable and socially responsive SCM is supplier selection and performance assessment. This study brings forward a sustainable supplier evaluation structure under multiple criteria and interval valued fuzzy neutrosophic (IVFN) model. The proposed structure uses CRiteria Importance Through Inter -criteria Correlation (CRITIC) and combined compromised solution (CoCoSo) under IVFN environment for evaluation and selection of suppliers for a dairy company in Iran. Alternative supplier 5 (S5) emerges as the best supplier with the highest overall score (1.168). Average Spearman rank correlation coefficient between the proposed model and other well established decision-making models methods is found as 0.9651 which establishes reliability of model outcomes. The estimated zero value of Gini Index indicates that supplier 5 has a constant ranking in all considered methods. This is a practical sustainable supplier selection platform which allows decision makers in procurement and SC industries to select the most suitable supplier in any pre-determined period.

In the logistics world, special attention should be given to warehousing systems, cost rationalization, and improvement of all the factors that affect efficiency and contribute to smooth functioning of logistics subsystems. In real time industrial practice, the issue of evaluating and selecting the most appropriate forklift involves a complex decision-making problem that should be formulated through an efficient analytical model. The forklifts efficiency plays a very important role in the company. The forklifts are being used on a daily basis and no logistical processes could be done without them. Therefore, it has been decided to determine their efficiency, which will contribute to the optimization of the process in this logistics subsystem. This study puts forward an integrated forklift selection model using Data Envelopment Analysis (DEA), Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking According to the Compromise Solution (MARCOS) methods. Five input parameters (regular servicing costs, fuel costs, exceptional servicing costs, total number of all minor accidents and damage caused by forklifts) and one output parameter (number of operating hours) were first identified to assess efficiency of eight forklifts in a warehousing system of the Natron-Hayat company using the DEA model. This step allows sorting of efficient forklifts which are subsequently evaluated and ranked using FUCOM and MARCOS methods. A sensitivity analysis is also performed in order to check reliability and accuracy of the results. The findings of this research clearly show that the proposed decision-making model can significantly contribute to all spheres of business applications.

Selection of cotton fabrics for providing optimal clothing comfort is often considered as a multi-criteria decision making problem consisting of an array of candidate alternatives to be evaluated based of several conflicting properties. In this paper, design of experiments and technique for order preference by similarity to ideal solution (TOPSIS) are integrated so as to develop regression meta-models for identifying the most suitable cotton fabrics with respect to the computed TOPSIS scores. The applicability of the adopted method is demonstrated using two real time examples. These developed models can also identify the statistically significant fabric properties and their interactions affecting the measured TOPSIS scores and final selection decisions. There exists good degree of congruence between the ranking patterns as derived using these meta-models and the existing methods for cotton fabric ranking and subsequent selection.

Sustainability in industrial organizations has become a predominant concept in the 21st century due to environmental regulations, economic importance, and social obligations. In this context, sustainable supplier selection plays an epochal role for taking strategic business decisions. So, a systematic approach is required to deal with the sustainable factors. By integrating the three pillars of sustainability, namely economic, environmental, and social factors, this chapter presents an integrated model for supplier selection from a sustainability perspective. The proposed framework combines analytic hierarchy process (AHP) and quality function deployment (QFD) methods to deal with the sustainable criteria. Finally, suppliers are ranked using VIse Kriterijumska Optimizacija kompromisno Resenja (VIKOR) method. The proposed framework is used to analyze a case study of a dairy company, but it can also be implemented for sustainable supplier selection in any industries. The study brings an artefact for managers to effectively analyze suppliers with the integrated decision-making model.

Biomaterials are artificial or natural materials that substitute the impaired organic parts to fulfill the global medical requirements for improving resilience and quality of human life. Thus, it has become indispensable to select the most appropriate materials for various biomedical applications. In this chapter, a decision-making model integrating analytic hierarchy process (AHP) as well as combinative distance-based assessment (CODAS) techniques based on rough numbers has been developed for determining the performance scores of different biomaterials for a hip joint prosthesis application. Sensitivity analyses and comparative result analysis show an excellent performance of the integrated model against with respect to some well-established decision-making methods in terms of derived ranking patterns.

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 solved for better understanding of the integrated decision-making tool in which the relevant information for the considered railway zones with respect to different evaluation criteria are collected from various websites and Indian Railways annual statistical report. Western and North-Eastern zones, respectively, take the first and the last positions in the derived ranking list. The relevance of selecting different performance indices/evaluation criteria is also discussed. Practical implications – The application of this integrated methodology would serve as a systematic approach for measurement of the aggregate operational performance of Indian Railway zones so as to gain valuable academic and practical insights. It is also expected to provide an insightful guidance to the railway administrators in taking valuable strategic decisions in promoting the service of Indian Railways. Originality/value – The integrated DEMATEL-VIKOR method is conceptually simple and easily comprehensible which can consider numerous attributes simultaneously. This paper enables the readers to gain some valuable inputs from a managerial perspective for Indian Railways to formulate strategies for its zones to foster better performance.

Multi-criteria decision-making (MCDM) methods are very useful tools for daily decision-making in different fields. In addition, determining an acceptable solution with respect to different factors is certainly a very demanding and difficult task. In this paper, a new Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method for a sustainable supplier selection in the healthcare industry (in a polyclinic) in Bosnia and Herzegovina is developed. The advantages of the developed method are: the consideration of an anti-ideal and ideal solution at the very beginning of the formation of an initial matrix, closer determination of utility degree in relation to both solutions, the proposal of a new way to determine utility functions and their aggregation, the possibility to consider a large set of criteria and alternatives while maintaining the stability of the method. Supplier selection is very important for organizations in the medical industry. Sustainability in the supplier selection process in the medical industry is a strategically important issue, and poorly implemented in the private medical sector. Therefore, the example explains how to use the MARCOS method to select sustainable suppliers in the private medical sector. A case study of a sustainable supplier selection for the healthcare industry (a polyclinic) includes ranking of eight alternatives with regard to 21 criteria for all aspects of sustainability. The results and verification of the new method are carried out throughout a comprehensive sensitivity analysis. 21 scenarios with changes in the weight values of criteria were established, the measurement scale from 1 to 9 was changed to 1–5, a comparison with six other MCDM methods was performed, and it was verified in dynamic conditions which implied a change of the elements of the initial decision-making matrix. All phases of the sensitivity analysis showed the validity of MARCOS method. The obtained results and all scenarios in sensitivity analysis show that A2 remains the best alternative.

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.

Tourism development has a unique responsibility in regional policy of almost all countries. It is a well known fact that tourism has a remarkable contribution in sustainable development, economic growth and social benefits for a country, if planned methodically. This is due to its unquestionable advantages and benefits for the local community with respect to economic, social and environmental aspects. Since few decades, it has become a driving area in Indian economic planning strategy to deal with tourism issues for effective utilization of its wide range of destination resources and also optimize the intensity of financial involvement for developing tourist infrastructure in a restraint economic province. This paper applies a simple methodology to quantify the tourism potential of Indian states using an integrated visual decision aid model of preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). Several social and physical attributes are considered to evaluate and rank the Indian states with respect to their performance in tourism. The adoption of this integrated visual decision aid model identifies Jammu and Kashmir, and Jharkhand as the best and the worst performing states respectively. In GAIA plane, the position of Jammu and Kashmir is the farthest from origin, followed by Madhya Pradesh. A close comparison between these two top performing states reveals that Jammu and Kashmir mainly outperforms Madhya Pradesh with respect to budget allocation, population density, pollution index and cost of living index criteria. The performance of Jharkhand is not at all the best with respect to even a single criterion. In GAIA plane, it is also observed that the considered beneficial and non-beneficial criteria form two different clusters, as expected. This performance evaluation problem is identified to be not at all a hard problem to solve.

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.

Since decades, proper material selection plays an important and crucial role in an effective manufacturing system for superior production quality, cost optimization and improved productivity. Systematic selection of materials for a given engineering application begins with the study of different material selection properties and their related costs with specific application capabilities. Wrong selection of materials often leads to huge cost contribution and eventually drives an organization towards early product failure. So, the designers need to identify and select proper materials with specific functionalities in order to get the desired output with minimum cost involvement. But, the selection of proper materials for engineering applications is not an easy task to perform. While choosing the proper material for a specific industrial application, there is not always a single definite selection criterion. Designers need to consider a huge number of material selection-attributes and a large number of alternative materials with complex relationships between various attributes. A systematic, efficient and easy approach for material selection is thus required to help the manufacturing organizations for selecting the best material for a particular application. This paper focuses on the application of a compromise ranking method in the perspective of regret theory as a multiple-criteria decision-making (MCDM) tool for solving a material selection problem in a given manufacturing environment. A complete list of all the prospective materials from the best to the worst is obtained, taking into account different material selection attributes. The ranking performance of the proposed method is also compared with that of the original compromise ranking method.

 Abstract—Material selection is one of the most important decisions in optimal design of any manufacturing process and product. Proper material selection plays an elementary role for a productive manufacturing system with superior product and process excellence along with cost optimization. Improper material selection frequently causes enormous cost contribution and drives an organization towards immature product failure. A proficient methodology for material selection is thus required to help the manufacturing organizations for selecting the best material for a particular application. This paper focuses on the applications of two almost unrevealed multi-criteria approaches, namely complex proportional assessment (COPRAS) and additive ratio assessment (ARAS)-based methods for solving a gear material selection problem in a given manufacturing environment. A complete 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 these two methods is also compared with that of the past researchers. Index Terms—gear material selection, MCDM, COPRAS, ARAS, performance analysis

Article history: Received 1 June 2011 Received in revised form November, 26, 2011 Accepted 14 December 2011 Available online 20 December 2011 Evaluation of proper supplier for manufacturing organizations is one of the most challenging problems in real time manufacturing environment due to a wide variety of customer demands. It has become more and more complicated to meet the challenges of international competitiveness and as the decision makers need to assess a wide range of alternative suppliers based on a set of conflicting criteria. Thus, the main objective of supplier selection is to select highly potential supplier through which all the set goals regarding the purchasing and manufacturing activity can be achieved. Because of these reasons, supplier selection has got considerable attention by the academicians and researchers. This paper presents a combined multi-criteria decision making methodology for supplier evaluation for given industrial applications. The proposed methodology is based on a compromise ranking method combined with Grey Interval Numbers considering different cardinal and ordinal criteria and their relative importance. A ‘supplier selection index’ is also proposed to help evaluation and ranking the alternative suppliers. Two examples are illustrated to demonstrate the potentiality and applicability of the proposed method. © 2012 Growing Science Ltd. All rights reserved

The role of materials in the engineering design process has already been well recognized. Choice of an appropriate material for a particular product is one of the critical tasks for the designers. Designers need to identify materials with specific functionalities in order to find feasible design concepts and fulfill the product’s end requirements. There is a vast array of materials with diverse properties available to the designers to satisfy different design requirements. The large number of available materials together with the complex relationships between various selection criteria, often make the material selection process a difficult and time consuming task. A systematic and efficient approach towards material selection is necessary in order to select the best alternative for a given engineering application. This paper focuses on the application of four preference ranking-based multi-criteria decision-making (MCDM) methods for solving a gear material selection problem. These are extended PROMETHEE II (EXPROM2), complex proportional assessment of alternatives with gray relations (COPRAS-G), ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) and operational competitiveness rating analysis (OCRA) methods. Using these four methods, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The ranking performance of these methods is also compared with that of the past researchers.

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is 'VIsekriterijumsko KOmpromisno Rangiranje' (VIKOR), a compromise ranking method and the other one is 'ELimination and Et Choice Translating REality' (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.

Selection of proper materials for different components is one of the most challenging tasks in the design and development of products for diverse engineering applications. Materials play a crucial and important role during the entire design and manufacturing process. Wrong selection of material often leads to huge cost involvement and ultimately drives towards premature component or product failure. So the designers need to identify and select proper materials with specific functionalities in order to obtain the desired output with minimum cost involvement and specific applicability. This paper attempts to solve the materials selection problem using two most potential multi-criteria decision-making (MCDM) approaches and compares their relative performance for a given material selection application. The first MCDM approach is ‘Vlse Kriterijumska Optimizacija Kompromisno Resenje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two examples are cited in order to demonstrate and validate the effectiveness and flexibility of these two MCDM approaches. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The rankings of the selected materials almost corroborate with those as obtained by the past researchers.

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.

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.