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Third‐party logistics (3PL) provider selection has gained great attention in logistics management literature. The purpose of this paper is to provide a good insight into the use of a‐two‐phase analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) approach that is a multi‐criteria decision‐making methodology in the evaluation of 3PL providers.

In this paper, after the selection criteria of 3PL providers are determined by modified Delphi method, the weights of criteria have been calculated by applying the AHP method. The TOPSIS method is then employed to achieve the final ranking results. And an actual case example is presented to clarify the methodology. Sensitivity analysis is also given to demonstrate how sensitive the proposed model is to changes in the weights of different main criteria.

This model provides decision makers with a simple, flexible, and easy‐to‐use approach to evaluate potential 3PL providers efficiently. Findings demonstrate that the proposed benchmarking framework, with minor modifications, can be useful to all firms in their 3PL provider selection decisions.

A two‐phase AHP and TOPSIS methodology is very flexible and suitable for various decision situations. However, selection of the appropriate 3PL provider requires consideration of multiple alternative solutions and evaluation criteria because of complex methodology, and hence it increases the effort.

This is probably the first time that an attempt has been made to apply the modified Delphi method, AHP and TOPSIS methodology in the decision of 3PL provider selection in a Turkish automotive supplier company. This is the most powerful motivation to consider this problem. In addition, the paper is especially of interest to managers as they make decisions on which criteria should be considered in the evaluation process and how a decision model should be structured by using a two‐phase AHP and TOPSIS methodology.

This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.

The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.

The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.

In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.

The study presents the case of an automotive component, which illustrates practical relevance.

In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

In this paper, two popular multiple-criteria decision-making (MCDM) methods with hesitant fuzzy logic approach; hesitant fuzzy analytic hierarchy process (hesitant F-AHP) and hesitant fuzzy the technique for order preference by similarity to ideal solution (HF-TOPSIS) are integrated as HF-AHP-TOPSIS to evaluating a set of enterprise resource planning (ERP) alternatives and rank them by weight to reach to the ultimate one that satisfies the needs and expectations of a company.

Selecting the best ERP software package among the rising number of the options in market has been a critical problem for most companies for a long time because of the reason that an improper ERP software package might lead to many issues (i.e. time loss, increased costs and a loss of market share). On the other hand, finding the best ERP alternative is a comprehensive MCDM problem in the presence of a set of alternatives and several potentially competing quantitative and qualitative criteria.

In this integrated approach, the hesitant F-AHP is used to determine the criteria weights, as the hesitant F-TOPSIS is utilized to rank ERP package alternatives. The proposed approach was also validated in a numerical example that has five ERP package alternatives and 12 criteria by three decision-makers in order to show its applicability to potential readers and practitioners.

If the number of the alternatives and criteria are dramatically increased beyond reasonable numbers, the reaching to final solution will be so difficult because of the great deal of fuzzy based calculations. Therefore, the number of criteria and alternatives should be at reasonable numbers.

The proposed approach was also validated in a illustrated example with the five ERP package options and 12 criteria by the three decision-makers in order to show its applicability to potential readers and practitioners.

Furthermore, in literature, to the best of our knowledge, the authors did not come cross any work that integrates the HF-AHP with the HF-TOPSIS for ERP software package selection problem.

This research focuses on suggesting an optimized model for selecting best employees using advanced multi-criteria decision making method to a supply chain firm, who is planning to start a new cold chain business vertical.

Study has been conducted in a supply chain firm in North India, who wants to expand its business with the help of efficient team members. In total 38 applicants were considered for the study, as selected by the firm after initial screening from pool of talent. AHP-LP and TOPSIS-LP integrated approach were applied separately for evaluation and implementation of personnel selection model. Further, both the approaches were compared to find the best fit and optimized model.

As per the findings, both AHP and TOPSIS can be used to select the best candidate among the alternatives available. TOPSIS was found easier to implement as it involves ranking of applicants with respect to each skills required for respective job profile only once, whereas AHP involves pair-wise comparison among candidates with respect to each skills required for respective job profile and normalization of each comparison, resulting in the formation of number of comparison matrices. However, AHP is more reliable as it considers consistency check for each level of pair-wise comparison. Hence, there is a chance to avoid or revise the human judgment error. Integrated ranking and optimization approach minimizes the cost by suggesting the relevant positions to be filed to make an efficient team.

Group of interviewers are involved in the decision-making process, hence there are chances of biasness in ranking method which can influence the group decision. Research is limited to a particular geography of North India therefore needs to be tested for other regions also in order to generalize. The research will help the third party logistics (3PL) and other related firms in efficient team selection.

The researcher focuses on formalizing a method for potential candidate selection by considering the constraints of the organization. It has been observed that limited researches have been done on the application of AHP-LP or TOPSIS-LP integrated approach for selection process. Hence, this research proposes two integrated ranking-optimization method and suggests the best fit by comparing both the approaches.

Many developed countries have been using environmental taxes in their economic systems for many years. These taxes have a great impact on reducing the environmental damages of companies and individuals in society. But many developing countries have not used this tool effectively yet, and some countries face barriers to the effective implementation of environmental taxes that make it difficult and unsuccessful. To increase the effectiveness of the implementation of environmental taxes, governments must prioritize barriers and solutions to overcome its barriers. The identified knowledge gap of the pre-literature review is that an overview of the identification which completely considers all barriers and solutions of environmental taxes implementation does not exist. In response to this knowledge gap, this study aims to identify and prioritize the barriers and solutions of environmental taxes implementation.

Ranking the barriers and solutions is a complicated multi-criteria decision making (MCDM) problem that requires consideration of multiple feasible alternatives and conflicting tangible and intangible criteria. This study addresses the prioritization of solutions of Environmental Taxes implementation by proposing hybrid MCDM methods based on the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the Fuzzy Technique for order preference by similarity to an ideal solution (Fuzzy-TOPSIS) under fuzzy environment. Fuzzy AHP is used to determine the weight of each barrier using a pairwise comparison, and fuzzy TOPSIS is used to finalize the ranking of solutions for more effective implementation of environmental taxes.

The results showed that environmental tax reform (ETR) (S3) has the highest value among the solutions for more effective implementation of environmental taxes. The result of the proposed model is validated by performing sensitivity analysis.

This study could foster research on the discussion of these barriers and precise ways of implementing solutions to pay more attention to environmental taxes.

Ratings of solutions can be a guide and help governments to improve the implementation of environmental taxes or even develop this policy by being aware of the ranking of barriers and solutions.

This paper creates a new perspective on the effective implementation of environmental taxes, which is closely related to improving environmental performance and increasing social welfare through improving the tax system.

For the first time, this study comprehensively identifies barriers and solutions for more effective implementation of environmental taxes and ranks them using two MCDM techniques.

The purpose of this study is to provide a decision support tool to deal with the problem of seting priorites among patients competing for limited health care resources. Limited resources and unlimited demands prevent health-care services to be provided to all those in need. This became publicity evident with the current Covid-19 pandemic. Although controversial, health care rationing has always existed and is now inevitable. Setting priorities becomes then inevitable. How to define those priorities is a complex and yet irresolvable issue mainly because it involves several and conflicting criteria, translated into efficiency and equity considerations. This is why multi-criteria decision analysis (MCDA) was introduced to health care as an appropriate decision-support framework for solving complex problems.

This paper proposes the application of two combined approaches – analytic hierarchy process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and AHP-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), as decision support tools to rank patients with competing needs in a more effective and equitable way. A rationing scenario involving four patients, differentiated by personal characteristics and health conditions, is used to illustrate, test and compare the applicability of both approaches. After extraction of the relative weights of the prioritization criteria involved in the hypothetical scenario from paired wise comparison methods, TOPSIS and VIKOR priority setting methods were designed.

Results suggest that patients ranking from both combination approaches are similar and in accordance with the order made directly by health-care professionals. Therefore, the relative weights computed by AHP in combination with TOPSIS and/or VIKOR methods could be used with suitable applicability by health-care decision-makers.

This study is the first attempt to apply a combination of MCDA methods to patients’ prioritization context and the first to cross previous studies to deepen and consolidate the research.

The purpose of this paper is to provide an insight into the use of an integrated approach of fuzzy analytical hierarchy process (fuzzy AHP) and TOPSIS in evaluating the performance of global third party logistics service providers for effective supply chain management.

In this study, the integration of fuzzy AHP with TOPSIS is proposed in determining the relative importance (weight) of criteria and then ranking of 3PLs.

Findings show that the logistics cost and service quality are two most important criteria for performance rating of 3PLs. Deciding the relative importance of various criteria for 3PLs evaluation is a complex task. The superiority of one criterion over the other varies from person to person and firm to firm. Therefore, to capture the variability in decision fuzzy extended AHP is very useful tool. Finally, the preference raking of alternatives are found using TOPSIS.

Fuzzy AHP is a complex methodology and requires more numerical calculations than the traditional AHP and hence it increases the effort. But in this paper single stage fuzzy AHP is used to simplify the process. Fuzzy AHP is integrated with TOPSIS for preference ranking of 3PL, which provides a good methodology to rank 3PLs.

There is a lack of research in the literature to deal directly with the uncertainty of human decisions in evaluating the relative importance of multiple criteria. Therefore, fuzzy AHP is an appropriate methodology to find the relative importance of the criteria to rank the 3PLs using TOPSIS.

The purpose of this paper is to provide a two-phase analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) approach that is a multi-criteria decision-making methodology (MCDM) in the evaluation of third-party logistics (3PL) providers.

After the description of the selection criteria of 3PL providers that are determined by company management, the weights of criteria are calculated by applying the AHP method. The TOPSIS method is then employed to achieve the final ranking results. A sensitivity analysis is developed for a deeper comprehension of results reliability.

The paper shows the complexity of a real 3PL provider change. At the same time, a simple MCDM methodology is provided to assure the success of this activity in which many other companies have failed.

The proposed MCDM methodology represents a very good compromise between a reliable solution and reasonable computational and human effort. 3PL provider selection or change is an extremely complex decision that could be investigated in greater detail with other more complex methodology, with a consequently higher investment, that is not so affordable for all budgets.

It is the first time that a two-phase AHP and TOPSIS methodology is applied to an Italian biscuit company for a 3PL provider change together with the analysis of logistics performance after two years. The paper shows a detailed methodology application that could be very useful for managers for their specific application.

In this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.

The demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.

The incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.

The designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.

The case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.

Inclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.

The objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.