Selection of 3PL service suppliers using a fuzzy analytic network process

Currently, the analytic network process (ANP) method is widely employed to consider the multiple criteria analysis problems with dependence and feedback effects. However, in order to extend the ANP to resolve the problem of uncertainty or human subjective judgment, the concepts of fuzzy numbers should be incorporated into the ANP to represent the subjective uncertain pairwise judgments. In this paper, therefore, we propose a novel fuzzy analytic network process (FANP) model by solving a mathematical programming problem. Unlike other FANPs, the proposed method does not require the reciprocity assumption of the weight ratios between criteria, and it can derive local and global weights simultaneously in a single model. Two numerical examples of international investment problems are used to demonstrate the proposed method.

In this study, an intelligent approach is presented to help any simulation practitioner to evaluate simulation software alternatives and determine the best satisfying one based on his/her needs. On the other hand, this evaluation process is a typical multiple criteria decision making (MCDM) problem in the presence of evaluation criteria and a set of possible alternatives, and there are many methods in the literature, which have been used to successfully carry out this difficult and time-consuming process. In this paper, one of these methods, the analytic network process (ANP) method integrated with alpha-cut fuzzy logic is used because it can accommodate the variety of interactions, dependencies and feedback between higher and lower level elements, rather than analytic hierarchy process (AHP). In addition, an alpha-cut fuzzy extension of ANP uses uncertain human preferences as input information in the decision-making process. Instead of using the classical eigenvector prioritisation method in AHP, only employed in the prioritisation stage of ANP, an alpha-cut fuzzy logic method providing more accuracy on judgements is applied. The resulting alpha-cut fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgements.

Shopping center plays an important role in distribution system and marketing. These canters provide an appropriate atmosphere for customers; so that, the customers achieve the best service within a short time. However, there is an intense competition among shopping centers to attract more customers for increasing the profit. Therefore, a powerful model assists authorities in identifying the critical competitive aspects and directing their efforts toward performance improvement. However, a number of strategies have been developed to identify the most relevant components. The Delphi technique under intuitionistic fuzzy environment, called intuitionistic fuzzy Delphi method (IFDM) study, is a group-based technique that can simply formulate the uncertainty imposed by decision making circle. On the other hand, multi criteria decision making (MCDM) method such as analytical network process (ANP) is a mathematical tool for taking into account mutual relationships in order to rank a number of criteria. Nonetheless, the ANP is unable to account for the uncertainty involved in the decision-making process. Similarly, the intuitionistic fuzzy set (IFS) can express ambiguity and vagueness by utilizing the given scale. Because the IFS is robust in dealing with complexity and ambiguity, the IFS-GANP (an integrated model of the IFS and ANP methods under group decision) can result in a more specific description of the situation. As a result, the IFS-GANP approach outperforms both conventional ANP and fuzzy ANP. To demonstrate the model’s feasibility, a case study rating the essential aspects impacting the attractiveness of retail centers is shown. The result demonstrates factor C31 (Location) with value of 0.202 plays the greatest role in attracting customers.

The uncertainty of data and weighting of variables are prima facie problems to model the green supply chain (GrSC) performance. The paper modelled GrSC performance of plastic film by fuzzy logic analysis-fuzzy analytic network process approach to overcome these hurdles. Fuzzy logic analysis is used to derive fuzzy score of variables using MATLAB. Further, fuzzy analytic network process is used to calculate global weights of variables. Thus, green score of supply chain performance for plastic film is computed as 28.83. Green supply chain performance analysis of plastic films is done for present status and computed as 48.62. The solutions developed by author's viz. adoption of best practices for blown film extrusion process and 'design for environment' tool for segregating MSW at source are analysed. It is found that, if the tools adopted simultaneously, the GrSC performance score resulted in 27.38. This is well below green score making the plastic film industry environment friendly. Approach is suitable for modelling small scale systems GrSC performance.

Because of the unreliability of human experts judgements, fuzzy systems are widely applied in decision making problems related to adoption, implementation and maintenance of ERP systems. This paper presents a novel application of the fuzzy Analytical Network Process (ANP) for evaluating ERP post-implementation alternatives. The proposed framework aims at ranking different alternatives to implement a given post-implementation business requirement based on experts perception of implementation effort and risk. Regarding decision criteria, the ANP network considers two levels. At the higher level of abstraction we consider technical effort, organisational effort and long-term risk, whereas at the lower level we consider different strategies for implementing given business objects, functions and processes in an existing ERP system. Decision makers preferences are translated into preference weights using triangular fuzzy numbers. An example is presented to show the application of the proposed framework.

Wenchuan earthquake disaster reconstruction planning is a typical government investment project, the performance evaluation of which is conducive to improve the management ability of government of managing disaster reconstruction projects and enriches the finance expenditure decision-making reliance of disaster reconstruction projects. There may be dependence and feedback among factors of disaster reconstruction performance evaluation. In this study, analytic network process (ANP) was adopted to model a multi-criteria disaster reconstruction performance evaluation indices system with dependence and feedback. Due to complexity in evaluating disaster reconstruction performance, it is difficult for decision-makers to provide exact numerical values for the factors. Therefore, a fuzzy ANP method was taken to calculate the weights of each factor of the model, which can solve problems in an uncertain condition effectively.

Analytic Network Process and Fuzzy Analytic Network Process

Landfill site selection using multi criteria decision making: Influential factors for comparing locations

Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company

In this study, we utilize analytic network process (ANP), a more general form of AHP, for justifying stand-alone machine tools out of available alternatives in market due to the fact that AHP cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. However, due to the vagueness and uncertainty on judgments of a decision-maker, the crisp pair wise comparison in the conventional ANP seems to be insufficient and imprecise to capture the right judgments of the decision-maker. That is why, also in this paper, fuzzy number logic is introduced in the pair wise comparison of ANP to make up for this deficiency in the ANP. In short, here, an intelligent approach to machine tool selection (MTS) problem through fuzzy ANP is proposed to improve the imprecise ranking of company's requirements which is based on the conventional ANP. In order to reach to final solution, a preference ratio (PR) analysis is done by using the results of the fuzzy ANP, and investment costs of alternatives. In addition, a numerical example is presented to illustrate the proposed approach.

One of novel manners to achieve sustainable development is concentrating on circular economy in order to manage greenhouse gas emissions, energy consumption, and waste. It also helps to protect the environment and optimize usage of input resources. The main element of circular economy is closed‐loop supply chain which covers both forward and reverse products flows. This study aims to outsource logistics operations in a closed‐loop supply chain. An innovative analytical multi‐step fuzzy decision‐making method is proposed to rank sustainable third‐party logistics service providers (3PLSPs). Fuzzy Decision‐Making Trial and Evaluation Laboratory (DEMATEL) is first used to evaluate the relationships among the main criteria. Then, fuzzy analytic network process (ANP) is applied to weight the determined set of criteria. In order to develop the hybrid fuzzy DEMATEL‐ANP method and to simplify the computation, the expert judgment method is used between the two techniques as a middle step to reduce the number of criteria. As many of the criteria overlap with each other on content and context, expert opinions are used to shortlist and rank the criteria. Finally, the 3PLSPs are ranked through the fuzzy complex proportional assessment (COPRAS) method. To validate the practicability and applicability of the improved decision‐making model, a household appliance case study is applied with data obtained from industry experts. In this case, five service providers are considered and ranked, while the first one gets the best score. A sensitivity analysis is performed by altering the criteria weights to validate the proposed approach, data, and the obtained results. In terms business and the environment, this study provides success critical criteria for decision‐making problems in circular economy for addressing sustainable development.

Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

A fuzzy analytic network process (ANP) model for measurement of the sectoral competititon level (SCL)

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.