Fuzzy Technique For Order Performance Research Articles

Prioritization in Strategic Environmental Assessment Using Fuzzy TOPSIS Method with Random Generation for Absent Information in South Korea

This study evaluated a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) as a multicriteria decision making system that compensates for missing information with undefined weight factor criteria. The suggested Fuzzy TOPSIS was applied to ten potential dam sites in three river basins (the Han River, the Geum River, and the Nakdong River basins) in South Korea. To assess potential dam sites, the strategic environment assessment (SEA) monitored four categories: national preservation, endangered species, water quality, and toxic environment. To consider missing information, this study applied the Monte Carlo Simulation method with uniform and normal distributions. The results show that effects of missing information generation with one fuzzy set in GB1 site of the Geum River basin are not great in fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) estimations. However, the combination of two fuzzy sets considering missing information in Gohyun stream (NG) and Hoenggye stream (NH) sites of the Nakdong River basin has a great effect on estimating FPIS, FNIS, and priority ranking in Fuzzy TOPSIS applications. The sites with the highest priority ranking in the Han River, Geum River, and Nakdong River basins based on Fuzzy TOPSIS are the Dal stream 1 (HD1), Bocheong stream 2 (GB2) and NG sites. Among the sites in all river basins, the GB2 site had the highest priority ranking. Consequently, the results coincided with findings of previous studies based on multicriteria decision making with missing information and show the applicability of Fuzzy TOPSIS when evaluating priority rankings in cases with missing information.

An integrated methodology to overcome barriers to climate change mitigation strategies: a case of the cement industry in India.

Cement is a basic requirement of today's society and is the only thing that humans consume more volume than water, but cement manufacturing is the most energy- and emission-intensive process. Hence, the cement industry is currently under pressure to reduce greenhouse gases (GHGs) emissions. Climate change mitigation strategies implemented in the industry leads to GHGs reduction, climate risks, pollutants, and another adverse impact on the environment. In order to implement climate change mitigation strategies in the cement industry, a careful analysis of barriers that hinder the emission reduction must be taken. However, most existing research on the barriers to mitigation measures is focused on developed countries. Among the most important emerging economies, India, the second-largest producer and consumer of cement, faces challenges to implement emission reduction measures. To bridge this gap, this paper identifies and evaluates the barriers and solutions to overcome these barriers in the context of India. This study employs a three-phase methodology based on fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to identify barriers and solutions to overcome these barriers to climate change mitigation strategies adoption in Indian cement industry. Fuzzy AHP is employed to prioritize these barriers, and to rank solutions of these barriers, Fuzzy TOPSIS is employed. Ten Indian cement manufacturing industry is taken to illustrate the proposed three-phase methodology. Finally, the result of the analysis offers an effective decision support tool to the Indian cement industry to eliminate and overcome barriers to mitigation strategies adoption and build their green image in the market.

Prognosis and Multiobjective Optimization of the Sampling Plan for Cylindricity Evaluation

The actualization of the befitting sampling strategy and the application of an appropriate evaluation algorithm have been elementary issues in the coordinate metrology. The decisions regarding their choice for a given geometrical feature customarily rely upon the user’s instinct or experience. As a consequence, the measurement results have to be accommodated between the accuracy and the inspection time. Certainly, a reliable and efficient sampling plan is imperative to accomplish a dependable inspection in minimal time, effort, and cost. This paper deals with the determination of an optimal sampling plan that minimizes the inspection cost, while still promising a measurement quality. A cylindrical-shaped component has been utilized in this work to achieve the desired objective. The inspection quality of the cylinder using a coordinate measuring machine (CMM) can be enhanced by controlling the three main parameters, which are used as input variables in the data file, namely, point distribution schemes, total number of points, and form evaluation algorithms. These factors affect the inspection output, in terms of cylindricity and measurement time, which are considered as target variables. The dataset, which comprises input and intended parameters, has been acquired through experimentation on the CMM machine. This work has utilized state-of-the-art machine learning algorithms to establish predictive models, which can predict the inspection output. The different algorithms have been examined and compared to seek for the most relevant machine learning regression method. The best performance has been observed using the support vector regression for cylindricity, with a mean absolute error of 0.000508 mm and a root-mean-squared error of 0.000885 mm. Likewise, the best prediction performance for measuring time has been demonstrated by the decision trees. Finally, the optimal parameters are estimated by employing the grey relational analysis (GRA) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). It has been approved that the values obtained from GRA are comparable with those of the FTOPSIS. Moreover, the quality of the optimal results is bettered by incorporating the measurement uncertainty in the outcome.

Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind.

A Hybrid Multi-Criteria Approach for Evaluation and Selection of Sustainable Suppliers in the Avionics Industry of Pakistan

Reliability and quality are the two ultimate objectives in the avionics industry. The risk of counterfeit electronics and the unavailability of screening facilities for 100% components are the most concerning areas in the supply chain of the avionics industry. Unlike most public procurement, the cost is not the only significant criterion. Unbiased decision-making criteria to accommodate all the important factors without compromising on quality, reliability, and maintainability are essential for the evaluation and selection of sustainable suppliers. Therefore, this study proposes an unbiased decision methodology based on the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). In the first phase, six main and twenty-one sub-criteria are selected from the literature and empirically validated by experts of the avionics industry. FAHP is used to evaluate the weight of the main criteria and sub-criteria. FTOPSIS is used to prioritize eight alternatives (suppliers) concerning their effectiveness and superiority in finding the best alternatives. The results of the FAHP reveal that traceability (T) is the most important criterion, followed by quality (Q), and cost (C), which rank as the second and third most significant criteria. The results of the FTOPSIS rate supplier 8, supplier 2, and supplier 1 as the first, second, and third most effective suppliers, respectively.

Selecting technological alternatives for regulatory compliance towards emissions reduction from shipping: An integrated fuzzy multi-criteria decision-making approach under vague environment

Due to the increasing pressure from stricter environmental regulations to reduce emissions in shipping, the maritime industry has been striving for finding more effective measures. Existing measures are often not enough to comply with new regulations. Among various alternative measures, it is not easy for decision-makers (shipowners and operators) to choose the most suitable alternative measure as it involves with multi-criteria decision-making where the prioritization of a number of alternatives vis-à-vis multiple criteria evaluation is undertaken. Further challenges on such analysis are the lack of information as well as its subjectivity and/or the inconsistency. This study proposes an integrative fuzzy multi-criteria decision-making method that combines fuzzy analytic hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) for the selection of technological alternatives for regulatory compliance under vague environment. Nine criteria within three sustainability aspects (social, economic and environmental aspects) were analysed and evaluated as regards four possible alternatives. The weights of these aspects and criteria were determined by the fuzzy AHP; meanwhile, alternatives were prioritized by the fuzzy TOPSIS. According to the outputs of the proposed decision-making framework, the study revealed that low-sulphur fuels are the best suitable alternative for regulatory compliance. The following alternatives are methanol, scrubbers and liquefied natural gas in order. Sensitivity analysis was conducted to tell us that the proposed framework is robust. This proposed method will be potentially applicable to other fields where decisions are required to make under vague information conditions.

Creating a road map for industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology

Industry 4.0 can be defined as a creative manufacturing concept which is the integration of up-to-date technologies such as wireless systems, cyberphysical systems, Internet of things, cloud computing, big data concept to increase flexibility, and speed in production systems. The concept also aims to transform the manufacturing industry into the next generation. Selection among appropriate strategies for transition to industry 4.0 is crucial and should be considered in a multidimensional perspective since the decision process involves many strategies with respect to multicriteria based on the judgments of multiexperts. In this paper, this critical decision has been considered as a multicriteria decision-making (MCDM) problem under the uncertainty and vagueness environments. To increase the applicability of the uncertain data for the proposed methodology, intuitionistic fuzzy sets have been adopted. In other words, an integrated fuzzy MCDM methodology consists of interval-valued intuitionistic fuzzy analytic hierarchy process and interval-valued intuitionistic fuzzy technique for order performance by similarity to ideal solution has been suggested to prioritize of transition strategies for industry 4.0. According to proposed approach, “Training and continuing professional development” is determined as the most important strategy during the transition process, while “Technology” and “Equipment and Tools” are specified as the most crucial main and sub-criterion, respectively. For the validation process, we also applied two distance-based methods on the same decision matrices as a comparative analysis. Both analyses’ results yield that the proposed methodology is applicable and effective for the decision-making process. Besides, the results of the one-at-a-time sensitivity analyses based on the changes of main criteria weights confirm the sensitivity and flexibility of the proposed methodology. Finally, a road map for transition to industry 4.0 has been determined with respect to priorities of strategies based on the constructed context.

An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness

Abstract Supplier selection and order allocation constitute vital strategic decisions that must be made by managers within supply chain management environments. In this paper, we propose a multi-objective fuzzy model for supplier selection and order allocation in a two-level supply chain with multi-period, multi-source, and multi-product characteristics. The supplier evaluation objectives considered in this model include cost, delay, and electronic-waste (e-waste) minimization, as well as coverage and weight maximization. A signal function is used to model the price discount offered by the suppliers. Triangular fuzzy numbers are used to deal with the uncertainty of delay and e-waste parameters while the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain the weights of the suppliers. The resulting NP-hard problem, a Pareto-based meta-heuristic algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA), is developed. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to validate the applicability of the CENSGA algorithm and the Taguchi technique to tune the parameters of the algorithms. The results are analysed using graphical and statistical comparisons illustrating how the proposed CENSGA dominates NSGA-II and MOPSO in terms of mean ideal solution distance (MID) and spacing metrics.

Sustainable hybrid electric vehicle selection in the context of a developing country

The ever increasing global warming is affecting both the environment and quality of life. The dependency on the usage of fossil fuels for transportation and power generation sector is harming the environment in terms of greenhouse gas (GHG) emissions. To limit the use of fossil fuel, the world has to move towards a renewable, clean, and economical form of energy. In the transportation sector, the paradigm shift towards electric mobility is a step towards the same goal. For a developing country like Pakistan, due to the lacking charging infrastructure, load shedding of electricity, and high cost of non-renewable electrical energy, a country like Pakistan cannot go dependent on fully electric vehicles (EVs). The country has to shift from normal internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs). This paper aims to select the most sustainable HEV in the context of a developing country, Pakistan. Using the multi-criteria decision making (MCDM) technique, i.e., fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), based on ten criteria and seven alternatives, it has been found that Toyota Aqua outperforms among all the other alternatives in terms of economic, social, and environmental perspective. Furthermore, to move towards hybrid technology, the government has to give relaxation in terms of customs duty and should encourage auto manufacturers to set up local industries of such vehicles in the country.

Structural equation modelling – FTOPSIS approach for modelling barriers to product development in medical device manufacturing industries

PurposeThe purpose of this paper model and prioritizes barriers to product development in medical device manufacturing industries using an integrated “structural equation modelling” (SEM) and “fuzzy technique for order performance by similarity to ideal solution” (FTOPSIS) framework.Design/methodology/approachBarriers to medical device development (MDD) are adopted from literature. The initial structural model is proposed, exploratory factor analysis and confirmatory factor analysis are used to determine factor loading and model fit, respectively. Further, FTOPSIS is used to rank the barriers and sensitivity analysis is carried to check the robustness of results. The results are discussed in detail and the recommendations to overcome the barriers are presented.FindingsBarriers analysed and prioritized in this research significantly hinder the MDD. The expert survey is used to develop an initial structural equation model of barriers to MDD, find the reliability and validity of the model. Based on the opinion of the experts, the barriers are divided into three categories – internal, policy and induced barriers. FTOPSIS is applied to rank and prioritize the barriers based on views from these three classes of experts. More reliance on imported devices leading to increased imports (B11) and lack of uniform regulatory standards (B6) are found to have the highest rank together, indicating these to be the most important barriers from the perspective considered here. Sensitivity analysis indicates that the factors are less sensitive to the weights of criteria further confirming the reliability of the initial solution.Research limitations/implicationsThe prioritization of barriers may vary based upon experts. Policymakers, existing and new device developers need to give utmost importance to these barriers, which will help to accelerate the indigenous development of medical devices to overcome the present dependence on imports.Practical implicationsThis paper demonstrates an integrated structural based modelling and prioritization technique for statistical modelling and prioritization of barriers to MDD. The results and recommendations will help policymakers and manufacturers to increase the indigenous share of medical devices. The integrated methodology can be effectively applied where the need for the combined quantitative and qualitative approach is there.Originality/valueThis paper demonstrates an effective structural based modelling and prioritization technique. It can be effectively applied in various fields, it will help policymakers and manufacturers to increase the indigenous share of medical devices.

Shipyard location selection based on fuzzy AHP and TOPSIS

The shipyard facility location selection (FLS) decision is a critical process that involves conflicting, qualitative, and quantitative criteria. Multi-Attribute Decision Making (MADM) methods are used as a powerful tool to overcome this complex problem. Today, using these methods in an integrated way, more accurate, efficient, and systematic results are obtained in solving complex issues such as FLS, which contains an uncertain structure. This paper proposes a framework for the weighting of criteria and ranking potential feasible locations (alternatives) using the combination of fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) methods. While fuzzy AHP determines the importance values of the criteria by pairwise comparisons, fuzzy TOPSIS prioritizes the alternatives using the relative weights obtained with Fuzzy AHP. The integration of these two techniques provides a robust approach considering the results obtained for the shipyard FLS decision. The applicability of the proposed method is expressed in Turkey by a case study of the shipyard FLS decision.

Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach

Abstract This study proposes an integrated methodology based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Hierarchy Process (AHP), and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) to evaluate energy strategies for sustainable energy planning. The SWOT analysis is employed to determine the factors and sub-factors essential for sustainable energy planning. Subsequently, AHP, a Multi-Criteria Decision Making (MCDM) method is used to determine the weights of each factor and sub-factor. Finally, F-TOPSIS method is used to rank the 13 energy strategies under the case study for Pakistan's to achieve sustainable energy planning. The results of the study reveal that providing low-cost and sustainable electricity to residential, commercial, and industrial sectors (WO5) is highly prioritized energy strategy. While the utilization of coal resources and transformations of same into electricity (WO2) is prioritized as least favored energy strategy. The sensitivity analysis of the results validates the robustness of the study. This study, as such, proposes a systematic approach to develop and evaluate energy strategies for the sustainable energy planning and policy endeavor of Pakistan.

Prioritizing the performance outcomes due to adoption of critical success factors of supply chain remanufacturing

The objective of this study is to identify and prioritize the performance outcomes (POs) due to adoption of SC remanufacturing critical success factors (CSFs). CSFs and the POs realized due do SC remanufacturing adoption, are identified based on past relevant literature analysis and subsequent discussions with the expert decision panel. This research work propose a hybrid solution methodology namely fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), to prioritize the POs realized due to adoption of SC remanufacturing CSFs. Fuzzy AHP technique is used to obtain the relative weights of the CSFs by performing pair wise comparison amongst the criteria, while fuzzy TOPSIS is used to prioritize the POs due to adoption of SC remanufacturing CSFs. Further, the proposed methodological framework is applied to an Indian manufacturing organization to demonstrate its applicability, and prioritize the POs realized due to adoption of SC remanufacturing CSFs. The study presents more accurate, structured and systematic approach to the organization for improving its POs step-by-step through adoption of SC remanufacturing CSFs, thus increasing its sustenance capability. The impact of vagueness in the criterion weights on the prioritization is also investigated.

A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains

The selection of suitable supply partners is a strategic issue for managers working in humanitarian operations and has received little attention in the literature. In humanitarian operations, complexity characterizes the continuous-aid procurement operations, and the selection criteria can differ from those used in commercial supply chain settings. This paper advances knowledge by introducing a supply partner selection framework for continuous-aid procurement. A proposed multi-criteria decision-making model uses selection criteria attributes verified by the extant literature and by field experts. A fuzzy Analytic Hierarchy Process is then used to compute criterion weights, and a fuzzy Technique for Order Performance by Similarity to Ideal Solution is used to rank supply partner alternatives. Even with elevated levels of subjectivity, these techniques enable humanitarian operation stakeholders to select the best supply partner effectively. An actual case illustrates how the proposed framework efficiently identifies the most suitable continuous-aid supply partner for the prevailing situation.

Interval type-2 fuzzy TOPSIS method for calibration supplier selection problem: a case study in an automotive company

Product standardization has gained importance with the increase of customer demands and developments of technology. Products must be tested with measurement equipment to ensure quality standards. In quality management, the variability between products is targeted to be minimum. Products are produced at a target value with specific tolerance levels. Measurement equipment are used to confirm that the dimensions be in accordance with the required tolerance levels. The calibration of measurement equipment is crucial for acquiring certain measurements. The calibration process aims to minimize the measurement error by providing the accuracy of measurement equipment. The companies can carry out their calibration processes in the company structure for certain equipment. However, companies may need outsourcing for the calibration of complex equipment. This study includes a model to select the most appropriate calibration supplier by using the interval type-2 fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The proposed approach is presented with a case study for torque-limiting wrench calibration in an automotive company. In the case study, ten different evaluation criteria were determined from the decision-makers and the literature for the calibration suppliers’ evaluation and selection. Within the scope of these criteria, three alternative suppliers (A, B, and C) were evaluated and supplier C was selected as the most suitable calibration supplier.

A multi-objective decision-making approach for sustainable energy investment planning

Abstract Rapidly growing energy needs require sustainable and efficient utilization of natural resources. Thus, meticulous development of long-term energy strategies and the proper planning of energy resources are critical. In this study, the energy-planning problem is investigated by formulating a multi-objective decision-making model under several realistic constraints. As an illustrative example, an energy company's renewable energy planning model is formulated. Specifically, a multi-objective mathematical model under several constraints (some of which are fuzzy) is developed to attain multiple goals simultaneously. Methodologically, (i) the energy company's plans to invest in renewable energy power plants in specific regions are formulated using a 0–1 goal programming approach, (ii) the social acceptance factor included in the model is obtained using the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method, and (iii) the goals are weighted by designing and implementing an Analytic Hierarchy Process (AHP) model. According to the results of the developed model, certain renewable energy generation alternatives are superior where the necessary renewable energy resources are efficiently assessed. It is also demonstrated that the proposed integrated analytics approach is capable of providing invaluable insights when the underlying model is systematically evaluated through sensitivity/scenario analyses.

Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers

Fuzzy AHP and fuzzy TOPSIS method are proposed.The case of Thailands electronics industry is used in the proposed method.Technological barriers are the highest weightage barrier for RL practices.Infrastructural barriers are the lowest weightage barrier for RL practices.Top management awareness and support is the highest ranking value of solutions. Due to an increasing demand for green products and also pressures from customers and other players along the supply chain, which now pay more attention to environmental awareness and sustainable management, many companies especially in the electronics industry have begun to realize the importance of applying green supply chain management concepts into their activities; reverse logistics (RL) practice is one of the important strategies to provide efficient resource utilization and minimize waste from end of life (EOL) products by following legislation and green concepts. But recently reverse logistics practices are faced with some barriers which make the implementation of reverse logistics difficult and unsuccessful. To increase efficiency in reverse logistics adaptation of the electronics industry, companies need to understand and consider the priorities of both barriers and solutions for developing policies and strategies to overcome these barriers. Therefore, this study focused on the classification of reverse logistics barriers and ranking of both barriers and solutions of reverse logistics implementation in the electronics industry. This paper proposes a methodology based on fuzzy analytical hierarchy process (Fuzzy AHP) and fuzzy technique for order performance by similarity to ideal solution (Fuzzy TOPSIS) in which fuzzy AHP is applied to get the weights of each barrier by using pairwise comparison, and fuzzy TOPSIS is applied for the final ranking of the solutions of reverse logistics implementation. The case of Thailands electronics industry is used in the proposed method. To illustrate the robustness of the method, sensitivity analysis is used in this study.

Assessment of CSR based supply chain performance system using an integrated fuzzy AHP-TOPSIS approach

ABSTRACTThe objective of this research was to identify Corporate Social Responsibility (CSR) criteria as well as propose and prioritise the alternatives to improve the supply chain performance system of an organisation. To meet the objective, firstly, CSR-based criteria, sub-criteria and alternatives were identified based on the literature review and discussion with the field experts, taken from automobile industries located at the Delhi region of India. Then, kappa statistics has been applied to check the internal consistency between identified criteria and sub-criteria. After finding the evidence of internal consistency, a hierarchical-type model has been developed and analysed using a multi-criteria decision-making as an integrated fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order performance by similarity to the ideal solution (TOPSIS) approach. To make the results more robust and feasible, a sensitivity analysis has also been performed.

Intelligent evaluation of suppliers using extent fuzzy TOPSIS method

PurposeIn a developing economy like India, the contribution of small- and medium-sized enterprises (SMEs) to the national gross domestic product is significant. This sector creates immense employment opportunities and produces economic products and services. To survive in the globalized marked condition, it is essential for SMEs to be competitive on several fronts such as quality, cost, delivery, lead time, flexibility, etc. Hence, it is imperative for them to have a sound supplier base. Therefore, supplier selection problem (SSP) has a vital role to play in supply chain management of SMEs. The paper aims to discuss these issues.Design/methodology/approachHowever, SSP has now become a significant challenge to address due to the complexity, vagueness and various criteria involved in it. Recently, fuzzy Technique for Order Performance by Similarity to Ideal Solution method has been widely used to tackle such problems.FindingsThe present paper aims at developing an intelligent system for SSP, which can consider the multiple criteria and the uncertainty aspects in the decision process. A case study of a small-scale manufacturing company has been presented.Practical implicationsThis study provides a guideline for SME sector to implement intelligent systems for supplier selection decision-making problems. Case application concludes that this model improves firm’s decision making and suppliers’ performance.Originality/valueThe proposed intelligent model can provide the guidelines and directions for the decision makers to effectively choose suppliers in the current competitive environment. And it also provides an opportunity for supplier improvement.

Evaluating and selecting partners in sustainable supply chain network: a comparative analysis of combined fuzzy multi-criteria approaches

Partner selection is a crucial problem in supply chain management in which it is essential today to integrate sustainability criteria due to regulation, stakeholder pressers and economic interests. Thus, a sustainability-focused evaluation model for partner selection is required in order to improve the overall performance of the supply chain. This paper develops a new hybrid approach to evaluate and select the partners (suppliers and 3PRL providers) in sustainable supply chain network by combining Fuzzy Analytic Hierarchy Process (F-AHP) with Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS). A set of sustainability criteria for both supplier and 3PRL provider selection is proposed based on extensive literature review and experts’ opinions. F-AHP is used to calculate the priority weight of each criterion. Then, F-PROMETHEE and F-TOPSIS are both used to rank the partners comparatively. The validity and efficacy of the proposed approach is demonstrated through an application for selecting partners in the case of light bulbs recycling which has been strengthened by sensitivity analysis.