Hybrid Multi-criteria Decision-making Technique Research Articles

Spatial multi-criteria approaches for estimating geogenic radon hazard index

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed to integrate the GRHI-based criteria for identifying the high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI map of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones in Apulia, the hybrid AHP-TOPSIS method is preferable in identifying areas with elevated radon potential.

Cobot selection using hybrid AHP-TOPSIS based multi-criteria decision making technique for fuel filter assembly process

The choice of a suitable collaborative robot (cobot) for a real-time industrial process is one of the obstacles to effective robot implementation in terms of energy and cost. The cobot selection process for an application have become more complex due to increasing sophisticated features and capabilities in cobots offered by the manufacturers. The paper presents a hybrid Multi-Criteria Decision-Making (MCDM) technique based on Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches for selecting cobots for fuel filter assembly operation. The product design methodology, manufacturing method, and associated cost are directly influencing the decision on cobot selection. The most appropriate robot to accomplish the desired task at the lowest possible cost and capability can be selected by AHP with prospective criterion weight for subsequent processing. The TOPSIS approach orders alternatives based on the prominence of criteria. A diesel fuel filter assembly process case was considered for validating the proposed technique of cobot selection process. An expert knowledge base was generated for 12 advocated cobots and the best cobot was selected using the proposed hybrid MCDM technique.

Prioritizing big data applications in E-commerce considering sustainable development indicators

During the Covid-19 pandemic, when strict restrictions were imposed to protect public health, e-commerce played a significant role in providing products on time. E-commerce technology and big data analytics enable companies to gain competitive advantages and respond to customers more efficiently. To make e-commerce more sustainable, the three dimensions of sustainability must be met, otherwise it can have negative consequences that lead to ecosystem destruction. Thus, e-commerce must learn how to effectively manage certain aspects of sustainability and adapt its operations to achieve balance. E-commerce's impact on sustainability can be measured in three pillars: economic, social and environmental and achieving a balance among these is the ultimate goal of sustainable development. Although the sustainability issue and big data analytics have gained increasing popularity in recent years, there is still a gap in evaluating applications of big data based on sustainable development indicators. In this study, we used a hybrid multi-criteria decision-making technique combining fuzzy TOPSIS and BWM to assess big data applications in e-commerce considering sustainable development indicators. The results showed environmental sustainability and energy consumption efficiency received the highest weight for the main pillars and sub criteria of sustainability indicators. Coordinating and monitoring supply chain processes, innovating product, process and business models, and creating new products and services are the top three applications of big data in e-commerce considering sustainable development indicators. E-commerce managers and experts can make better decisions about sustainable approaches by prioritizing big data applications based on sustainable development indicators. In addition, the proposed approach can also be used to evaluate big data analytics in other industries that consider sustainable development indicators.

Intuitionistic fuzzy EM-SWARA-TOPSIS approach based on new distance measure to assess the medical waste treatment techniques

Due to the potential harm to public health as well as the environment, medical waste treatment have emerged as a critical concern, especially in developing nations. The selection of a suitable medical waste treatment technique (MWTT) is now characterized as a critical multi-criteria decision-making (MCDM) problem because of the incorporation of several competing criteria. This study provides a hybrid MCDM technique for assessing MWTTs concerning social, environmental, economic, and technical criteria in an intuitionistic fuzzy environment. The intuitionistic fuzzy set (IFS) is a potent tool for expressing ambiguous and vague information efficiently and has proven effective for MCDM problems. In IFS environment a new distance measure and an entropy measure (EM) are introduced by employing the generalized Csiszar f-divergence measure, which are further used to construct the MCDM algorithm. The introduced entropy measure is used to examine the objective weights of criteria, and the distance measure is employed to construct the technique for order performance by similarity to ideal solution (TOPSIS) for the assessment of the alternatives. Furthermore, the significance of the criteria for the proposed MCDM technique is evaluated by integrated weights (combining objective and subjective weights). The Stepwise Weight Assessment Ratio Analysis (SWARA) technique is used to obtain the subjective weights for criteria. Thus, a hybrid MCDM technique, namely IF-EM-SWARA-TOPSIS, is developed. To exhibit the effectiveness and applicability of the suggested framework, a MWTT selection problem in Indian context is depicted. The comparative study and sensitivity assessment are conducted to show the sustainability and stability of the proposed IF-EM-SWARA-TOPSIS technique for ranking the preference order of the MWTTs. Moreover, the practical implications of the proposed method are discussed.

Analytical evaluation of big data applications in E-commerce: A mixed method approach

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

Applying a hybrid MCDM technique in warehouse management

The main goal of this study is to apply Multi-Criteria Decision Making (MCDM) in managing a warehouse. One of the elements that could impact organization performance is warehouse management. Surplus inventory imposes some ad- ditional costs on the organization, and inadequate inventory stops the operation of an organization. For managing and controlling warehouse inventories, the MCDM method is recommended in this study. The inventories are categorized ba- sed on multi-criteria instead of a single criterion in ABC. To specify the criteria’s weight, Best-Worst Method is used, and to reach the final score of spare parts, the Analytical Hierarchy Process, and Technique for Order of Preference by Similarity to Ideal Solution is applied. Some strategies for managing and controlling organizations’ warehouse is recommended.

A framework for implementing sustainable lean manufacturing in the electrical and electronics component manufacturing industry: An emerging economies country perspective

The survival of the manufacturing industry has been faced with multiple challenges due to changing stakeholder requirements, global competition and pressure to integrate sustainability practices. Lean manufacturing has proven positive relevance in enhancing the sustainability and operational economic performance of manufacturing industries by eliminating the non-value added operations. The growth of the Even with the rapidly growth Electrical and Electronics Component Manufacturing (EECM) is experiencing, its sustainability performance has remained an unexplored avenue. In this context, the present study aims to examine the ‘critical success factors (CSFs)’ for implementing sustainable lean manufacturing (SLM) in the Indian EECM organization through a hybrid multi-criteria decision-making technique. A total of 40 CSFs representing Management, Workforce, Operational and Knowledge factors were collected through extant literature and expert opinion. The collected factors were shortlisted using ‘Complex Proportional Assessment (COPRAS)’ method based on their ease of collecting data. The findings provided 20 CSFs under management, workforce, operational, and knowledge criteria, which will facilitate the EECM organization for implementing SLM. The shortlisted CSFs were prioritized using Best-Worst Method (BWM) to identify the most crucial CSF. “Top-management commitment”, “internal expertise” and “employee involvement” were identified to be the top three CSFs. Involvement of top-management was found to have direct impact on working culture and competitive advantage of the organization over the competitors.

International market selection: an application of hybrid multi-criteria decision-making technique in the textile sector

Purpose The selection of an international market (IMS) is a prime factor in the success and growth of a company. Therefore, the purpose of this study is to consolidate and apply a systematic methodology that contributes toward the evaluation of international markets and promotes entry into the export market of Antioquia’s textile companies. Design/methodology/approach Through a systematic literature review, the criteria and sub-criteria involved in the IMS process are identified and a total of 5 general criteria and 23 sub-criteria are selected. A hybrid approach is used to address the gap. In total, a multiple case study of 11 companies from different range of export values are selected. Data analysis is conducted using two multiple criteria decision-making (MCDM) models, namely, the analytic hierarchy process for weighting the factors and the technique for order of preference by similarity to the ideal solution for the country selection ranking. Findings The results demonstrate the applicability of the hybrid MCDM technique to improve IMS decision-making in the textile sector and other sectors. It is found that Canada, Belgium and the UK are the best destinations for textile exports with a selection score of 0.7716, 0.7488 and 0.7337, respectively. The sub-criteria belonging to the dimensions of trade barriers, economic factors and costs are the main factors affecting the export of a textile-clothing product. Research limitations/implications The possibility of achieving a generalized result through this case study is not possible, but the methodological application carried out is a novel for the selection of markets in the Colombian case and within the literature available in the domain. Practical implications From the managerial point of view, firms associated with trade have a broader vision when looking for new markets. Emerging entrepreneurs can equip themselves to enter the international market. Practitioners and policymakers can also use this methodology, which will allow them to evaluate new markets to outline promotional strategies for positioning products abroad. Social implications To facilitate the selection of international markets for enterprises. Originality/value The contribution of the study is twofold. First, the combination of techniques will allow wider support for the selection of markets and act as a decision support system. On the other hand, this is the first time that such a methodology is used for IMS in the exporting sector not only in Colombia but also in Latin America. Finally, the detailed methodological process described in the study allows both academicians and decision-makers to replicate the study in other contexts and scenarios.

DEVELOPMENT OF A NEW HYBRID MULTI CRITERIA DECISION-MAKING METHOD FOR A CAR SELECTION SCENARIO

Increasing competition in the automobile industry has led to a vast variety of choices when buying a car thus making car selection a tedious task. The objective of this research is to develop a new hybrid multi-criteria decision-making technique, with accuracy greater than that of the already existing methods, in order to help the people in decision-making while buying a car. Hence, considering a broader spectrum, this study aims at easing the process of multi-criteria decision-making problems in different fields. To achieve the objective, seven different alternatives were evaluated with respect to the enlisted evaluation criteria, which were selected after analyzing the secondary data obtained from Pak wheels based on style, fuel economy, price, comfort and performance. These criteria were then analyzed using the proposed Full Consistency Fuzzy TOPSIS method. As the name tells, this method is a unique combination of two techniques. The Full Consistency method is used to calculate the weights of the criteria while the Fuzzy TOPSIS approach is applied to rank the alternatives according to their scores in the selected criteria. The outcomes demonstrate an increase in the consistency ratio of the weight coefficients due to which the ranking of the alternatives by the FCF-TOPSIS is more accurate than the TOPSIS and the Analytical Hierarchy Process. The novelty of the method lies in the fact that this combination has not been used for an alternative selection scenario before. In addition to this, it can be used in various industries where a choice between the available alternatives arises based on a set of evaluation criteria.

A decision support system for the selection of an additive manufacturing process using a new hybrid MCDM technique

Recently, Additive Manufacturing (AM) has been widely used in many applications. For a particular AM component, the choice of available AM processes is critical to the component’s quality, mechanical properties, and other important factors. In that context, this article presents an efficient decision support system for the selection of an appropriate AM process. A novel hybrid Multi-Criteria Decision Making (MCDM) technique has been proposed to select an appropriate AM process from available AM processes. The Best Worst Method (BWM) is used to determine optimal weights of criteria and the Proximity Indexed Value (PIV) method is employed to rank the available AM processes. For benchmarking the abilities of an AM process, a conceptual model of spur gear was fabricated by four available AM processes viz., Vat Photopolymerization (VatPP), Material Extrusion (ME), Powder Bed Fusion (PBF), and Material Jetting (MJ). Additionally, Dimensional accuracy (A), surface roughness (R), tensile strength (S), percentage elongation (%E), heat deflection temperature (HDT), process cost (PC) and build time (BT) has been considered as most significant criteria. Further, sensitivity analysis has been performed to validate the reliability of the results. The results suggested that the Material Jetting (MJ) process produces dimensionally accurate and quality parts among available alternatives AM processes. The ranking obtained using the PIV method is consistent and reliable.

Analysis of Electricity Pricing in Emerging Economies With Hybrid Multi-Criteria Decision-Making Technique Based on Interval-Valued Intuitionistic Hesitant Fuzzy Sets

This study aims to analyze the factors that influence electricity prices. For this purpose, a hybrid multi-criteria decision-making (MCDM) model based on interval-valued intuitionistic hesitant fuzzy (IVIHF) sets is proposed. Firstly, a large literature review is carried out and 10 different factors that can affect electricity prices are determined. After that, IVIHF decision making trial and evaluation laboratory (DEMATEL) methodology is considered to find which factors are more significant in electricity prices. The finding shows that inflation rate and technological improvement are the most important criteria that affect electricity prices. Thus, the countries should consider expected future inflation rates to take necessary precautions to minimize volatility in electricity prices. Moreover, countries should follow current technologies regularly and make effective research and development to provide electricity with lower prices. In the second stage of the analysis, emerging 7 (E7) economies are ranked with respect to the performance related to the management of electricity price volatility. In this context, IVIHF VIšeKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach is considered. It is concluded that Russia and China are the most successful countries in keeping electricity prices away from volatility whereas India and Turkey get the latest order. Furthermore, a comparative evaluation is also implemented by considering IVIHF technique for order preference by similarity to ideal solution (TOPSIS) methodology to check the consistency of the analysis results. The results of both approaches are quite similar which gives information about the consistency of the ranking results. On the other hand, a sensitivity analysis is performed to ten different cases consecutively. It is determined that the ranking results are coherent by considering the changes in the criteria weights.

A Novel Hybrid Fuzzy Grey TOPSIS Method: Supplier Evaluation of a Collaborative Manufacturing Enterprise

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

A novel MCDM model for prioritizing the renewable power plants’ construction

Purpose The purpose of this paper is to propose a new multi-criteria decision making (MCDM) technique to determine the priority of renewable power plants construction conceding technical, economic, social, political and environmental aspects. Design/methodology/approach First, a comprehensive set of 5 main criteria of technical, economic, social, political and environmental are considered for renewable power plants construction, each including 5 sub-criteria (a total of 25 sub-criteria). Then, the analytic hierarchy process method is used to determine the weight of the criteria. Finally, a new MCDM technique proposed to prioritize the construction of renewable power plants, named TOPKOR. To demonstrate the capability of the proposed method, a case study is conducted in which six types of renewable power plants are evaluated. Findings Comparison results of the main criteria weights show that the “economic” [0.403], “environmental” [0.296] and “technical” [0.17] aspects have the highest importance, respectively. The results also show that solar, hydroelectric and wave and tidal power plants have the highest priority for construction, respectively. Research limitations/implications The result of this research could be useful for related decision makers in construction of the renewable power plants to have a comprehensive set of criteria in technical, economic, social, political and environmental aspects in their decision process. Originality/value This research provides a comprehensive set of criteria and sub-criteria for prioritizing the renewable power plants. Moreover, a new hybrid MCDM technique is introduced for prioritizing the construction of power plants.

Third-party logistics service providers selection using AHP-DEAHP approach

Third-party logistics (3PLs) service providers have become an extremely important part of any organisation's supply chain. They are required in almost all business operations right from supplying the raw material to the manufacturing plants to delivering finished products to the end customers and hence appropriate selection of these service providers will not only make the organisation competitive but also contribute significantly to profitability. Present research focuses on first identifying criteria or enablers, which can be used to evaluate 3PLs. These enablers are further utilised by a hybrid multi-criteria decision-making technique of data envelopment analytic hierarchy process (DEAHP) to select the most appropriate 3PL service provider amongst choices available. With the help of exhaustive literature review, 14 major selection criteria were identified. Uniqueness of the research lies in the application of this hybrid technique to the selection of 3PLs for a manufacturing company.

The best fuel selection with hybrid multiple-criteria decision making approaches in a CI engine fueled with their blends and pure biodiesels produced from different sources

In engine tests where the feasibility of alternative fuels is being investigated, the results of the engine performance, combustion characteristics, and exhaust emissions should be considered as a whole. It is difficult to determine the optimal parameters due to a large number of results obtained in multi-variable experiments. Multi-criteria decision making (MCDM) methods are preferred in solving such problems to energy management and energy efficiency. This paper deals with an application of a novel hybrid MCDM technique is suggested to select the optimum fuel for the compression ignition (CI). Five academicians who are an expert in the field of CI engines are selected to set criteria in the MCDM. Engine tests carried out at the constant engine speed and the resistive load of 10.8 kW in the generator-engine set. The results were used in the MCDM process. In this study, the hybrid models which are SWARA-MOORA, and ANP-MOORA has been preferred as the MCDM methods. The best fuel choice was made from fuels such as animal fat biodiesel (AFB), vegetable oil biodiesel (VOB), diesel fuel and the blend fuels. According to results, it was determined that the best fuel is VOB20 in both hybrid methods according to the determined criteria.

Prediction of the main caving span in longwall mining using fuzzy MCDM technique and statistical method

Immediate roof caving in longwall mining is a complex dynamic process, and it is the core of numerous issues and challenges in this method. Hence, a reliable prediction of the strata behavior and its caving potential is imperative in the planning stage of a longwall project. The span of the main caving is the quantitative criterion that represents cavability. In this paper, two approaches are proposed in order to predict the span of the main caving in longwall projects. Cavability index (CI) is introduced based on the hybrid multi-criteria decision-making technique, combining the fuzzy analytical network processes (ANP) and the fuzzy decision-making trial and evaluation laboratory (DEAMTEL). Subsequently, the relationship between the new index and the caving span is determined. In addition, statistical relationships are developed, incorporating the multivariate regression method. The real data for nine panels is used to develop the new models. Accordingly, two models based on CI including the Gaussian and cubic models as well as the linear and non-linear regression models are proposed. The performance of the proposed models is evaluated in various actual cases. The results obtained indicate that the CI-Gaussian model possesses a higher performance in the prediction of the main caving span in actual cases when compared to the other models. These results confirm that it is not possible to consider all the effective parameters in an empirical relationship due to a higher error in the prediction.

A Hybrid MCDM Technique for Risk Management in Construction Projects

Multi-stakeholder based construction projects are subject to potential risk factors due to dynamic business environment and stakeholders’ lack of knowledge. When solving project management tasks, it is necessary to quantify the main risk indicators of the projects. Managing these requires suitable risk mitigation strategies to evaluate and analyse their severity. The existence of information asymmetry also causes difficulties with achieving Pareto efficiency. Hence, to ensure balanced satisfaction of all participants, risk evaluation of these projects can be considered as an important part of the multi-criteria decision-making (MCDM) process. In real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D numbers domain to handle three types of ambiguous information’s, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D numbers based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster–Shafer (D–S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D numbers extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D numbers is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches in D numbers domain. Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed to verify the robustness of the proposed method.

A genetic algorithm-based artificial neural network model with TOPSIS approach to optimize the engine performance

ABSTRACTThis paper describes the application of an artificial neural network, genetic algorithm and analytic network process-technique for order preference by similarity to ideal solution for the selection of the optimum fuel blend. A single-cylinder, constant-speed direct-injection diesel engine with a rated output of 4.4 kW was used for exploratory analysis at different load conditions. Ten objectives – brake thermal efficiency, maximum rate of pressure rise, NOx, CO2, CO, HC, smoke, exhaust gas temperature, ignition delay and combustion delay were considered. The proposed ANN model is integrated with GA and the hybrid multi-criteria decision-making technique of ANP-TOPSIS to evaluate the optimum blend. First the ANN model was developed to predict the performance, combustion and emission parameters of the engine. A multi-layer perception network was used for non-linear mapping between input and output parameters. The performance of the ANN model is determined and shows the efficiency of the model to predict the performance, emission and combustion parameters, with a determination coefficient of 0.9627. Second, GA was used to determine the optimum load and blend based on the predicted ANN output parameter. Third, an approach based on the TOPSIS method was used for finding the best blend from the observed optimum parameters of GA.

Ship System Maintenance Strategy Selection Based on DELPHI-AHP-TOPSIS Methodology

Maintenance decision problems generally involve multiple criteria which apparently are best addressed using Multi-Criteria Decision Making (MCDM) tools. This paper describes the use of a hybrid MCDM technique in prioritizing maintenance strategy for ship systems. The Hybrid MCDM technique combines Delphi method, AHP and TOPSIS methods. While the Delphi method and AHP are applied in screening of decision criteria and decision criteria weights determination respectively, the TOPSIS method is used in the ranking of alternative maintenance strategies. Five alternative maintenance strategies which include corrected maintenance, scheduled overhaul, scheduled replacement, continuous on-condition task and scheduled on-condition task are considered and the optimum maintenance strategy is selected based on twelve critical maintenance decision criteria. To demonstrate the suitability of the approach a case study of sea water pump of the central cooling system of a marine diesel engine is used.

Lean Six Sigma project selection using hybrid approach based on fuzzy DEMATEL–ANP–TOPSIS

Purpose – The purpose of this paper is to select the optimal Lean Six Sigma (LSS) project using hybrid fuzzy-based Multi-Criteria Decision-Making (MCDM) approach for an automotive component manufacturing organization. Design/methodology/approach – The LSS project selection has been formulated as the MCDM problem. Hybrid MCDM method based on Decision-Making Trial and Evaluation Laboratory Model (DEMATEL), Analytical Network Process (ANP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been used to select the optimal LSS project. The methodology enabled the practitioners to systematically prioritize LSS projects. Findings – The finding of this study is that, out of five LSS projects, project P3 is the best LSS project. P3 is the optimal LSS project with reduced failure risk, and efforts are being taken to implement the selected project. Research limitations/implications – The problem formulation and methodology has been tested for a single study. In future, more number of studies could be conducted using the hybrid approach. This method is presently applied for an automotive component manufacturing organization; in future, the approach could be applied in different industrial sectors for improving its effectiveness. Practical implications – The case study has been conducted in a real-time industrial problem. The practitioners expressed the usefulness of the methodology for prioritizing LSS projects Hence, the inferences derived are found to possess practical relevance. Originality/value – The original contribution of the study is the selection of optimal LSS project using hybrid MCDM technique.