An Interactive Multi-Criteria Decision-Making Approach for Autonomous Vehicles and Distributed Resources Based on Logistic Systems: Challenges for a Sustainable Future

Abstract

The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. This research provides useful information for policymakers in the field of logistics. Finally, the results indicate that the velocity of AVs criterion is the most influential criterion in the selection of an intelligent AV.

1. Introduction

Nowadays, the transportation of products and people plays a crucial role in economic and everyday life concerns [1]. As a consequence, environmental repercussions are rising, and the sustainability of the ecosystem is in jeopardy. Government authorities and organizations are searching for novel methods of freight delivery that have high levels of efficiency, flexibility, dependability, and quality [2]. Logistics refers to the complete management process for resource movement, maintenance, and restoration [3]. Logistics management results in cost savings and increased benefits [4]. As a result of the streamlining of processes, businesses are able to handle an increase in sales to endpoints. Logistics management in a company is essential if it is to be successful. Effective logistics management seeks to boost operational performance, customer loyalty, and output. These instructions and methods are necessary to improve logistics and transportation system procedures. Due to air pollution, many academics are now concentrating on the long-term viability of all concerns, including industrial planning, the supply chain, logistics, and transportation [5].

A city is considered to be smart when its transportation system is sustainable and does not have an influence on the environmental sustainability of the surrounding area [6,7]. Transportation and logistics play a vital role in the current corporate environment, particularly with regard to the delivery of services and goods to their final destinations. In order to guarantee the effective management of logistics transportation and the protection of information, resources, energy, and the means of transportation, important management choices need to be made. The logistics environment is fragile and in desperate need of significant adjustments. In addition, the use of modern technology is required as a solution to a variety of social, economic, political, technical, and technological problems [8]. In addition, the majority of accidents that take place in the transportation sector are often caused by a lack of technologically advanced equipment, and these accidents can cause environmental catastrophes.

Client services in the field of logistics are often described as the amount of time that elapses between two separate customer operations or purchase orders [9]. The level of value that a client receives from a product or service will, over time, be directly proportional to the quality of the customer service provided. The pleasure of one’s customers is an essential component of the transportation sector of a logistics business, especially in the logistics sector. In the field of industrial logistics, autonomous vehicles (AVs) are utilized to cut down on the amount of time wasted waiting for transportation. Trucks that operate on their own and never require the assistance of a human driver are known as AVs [10]. They integrate sensors and software for vehicle monitoring, navigation, and operation. The Internet of Things has the potential to link all computer systems to the Internet so that they may share and utilize additional value. The interconnection of AVs enables information to be shared between onboard sensors, cellphones carried by pedestrians and riders, sensors embedded in the road, and parking detectors [11]. The Internet of Things enables businesses to collect data remotely about the areas around their facilities, which makes it simpler to manage and get rid of unwelcome negative replies. The Internet of Things and improvements in mobile technology have a significant impact on the operations of businesses involved in transport and logistics.

There is a significant possibility that AVs will improve both the efficiency of transportation and the overall quality of life [12]. Recent advances in technology have allowed us to design automobiles that are more dependable and fuel-efficient. However, owing to ongoing safety concerns, the widespread use of AVs is still in its infancy [13]. The use of AVs is restricted in a number of nations as a result of legislation and regulations. It may be possible to increase the rate at which AVs are adopted by defining the criteria for assessing AVs and establishing a selection procedure that is complete. The development of full AVs has emerged as a significant topic of study in recent years. There has been an uptick in the number of research projects concentrating on the introduction of AVs. Particularly as a result of recent technological advancements, self-driving vehicles have grown more dependable and convenient. Despite this, there are still a lot of worries, particularly with respect to ethical and safety considerations. Various governments are working on formulating regulations and protocols in an effort to control the proliferation of AVs [13]. The difficulty of the assessment procedure is increased by the fact that the appraisal of AVs is fraught with hesitation and imprecision.

The transportation industry is undergoing profound transformations as a direct result of the implementation of Industry 4.0 and the digitization of the whole value chain [14,15]. The idea of the AV is a relatively new notion in the field of transportation. In a perfect world, an AV or self-driving car would be able to detect its surroundings and function independently of human input. To assume control of the vehicle at any point in time, a human passenger is not required in any way. In the last ten years, the AV industry has seen phenomenal expansion. Advanced computer systems and electric mobility are front runners in this endeavor. Software is executed by a self-driving car’s sensors, actuators, machine learning systems, and complicated and efficient algorithms. A person does not need to intervene in order for the car to travel from one location to another. As a result, the use of environmentally friendly technology may eliminate all forms of environmental harm. AVs are one of the newest solutions that have been developed to deal with the issue of traffic and accidents. These vehicles, which are equipped with electronic systems, have great performance and higher precision than human capabilities. Because of the rapid pace of technological advancement in AVs, the logistics industry is a natural fit for the deployment of AVs.

The deployment of AVs in the logistics industry may provide a beneficially competitive environment. The regulated environmental activities that are provided by logistics, such as distant sites and storerooms, make it a perfect working environment for AVs. Therefore, it is likely that the logistics industry will adopt this technology sooner than passenger transport [16]. The assessment of autonomous vehicles according to the many capabilities they possess may be seen as a form of multi-criteria decision-making (MCDM). When it comes to finding solutions to issues involving transportation decisions, the techniques of MCDM may provide an approach that is both more effective and more rational [17,18,19,20,21].

This paper aims to address the selection of the optimal AV for adoption in the logistics industry. This problem is solved by using mathematical models with a group of decision-makers. Accordingly, the paper aims to present a hybrid approach developed using a large set of conflicting criteria. The proposed approach integrates two decision-making methods, namely, the method based on the removal effects of criteria (MEREC) method, which was introduced by Keshavarz et al. [22], and the combined compromise solution (CoCoSo) method, which was provided by Yazdani et al. [23]. Additionally, the proposed approach is presented in the type-2 neutrosophic number (T2NN) environment, which was developed by Abdel-Basset et al. [24]. Thus, the T2NN–MEREC method is used to determine the weights of the selection criteria influencing the solution to the problem of selecting the most appropriate logistic AV. Afterward, the T2NN–CoCoSo method is applied to evaluate and rank the candidate AVs.

1.1. Contributions of the Paper

The following list summarizes the most important contributions resulting from this research:

➢

Selecting the optimal AV for optimized industrial logistics to enhance transportation and delivery;

➢

Introducing a comprehensive approach by considering the criteria holistically (i.e., price, environmental friendliness, battery capacity of the autonomous vehicle, lane management, velocity of the autonomous vehicle, the park and ride system, the vehicular communication systems, and the capacity of the autonomous vehicle). The suggested approach also addresses various sources of uncertainty to accommodate the opinions of the most realistic decision-makers;

➢

Developing a reliable and resilient MCDM approach combining the MEREC method and the CoCoSo method based on the T2NN for the assessment of logistic AVs;

➢

The suggested T2NN–MEREC–CoCoSo approach improves performance and decreases the cost function and calculation time, according to the numerical findings;

➢

Providing a decision-making approach that is more robust and stable, with the ability to express separate membership functions in a neutrosophic environment, as well as assigning more degrees of freedom to experts and achieving more accurate outcomes;

➢

Finally, this research presents a sensitivity analysis and a comparative analysis to prove the strength, robustness, and stability of the proposed approach and to clarify different opportunities with the empirical results drawn from the research.

1.2. Originality of the Paper

The novelty and authenticity of this research are justified by establishing a hybrid methodology that makes the selection of logistic AVs more successful and resilient in complicated scenarios characterized by a range of factors and points of view. This research introduces a significant approach in the field of autonomous vehicles and logistics. In addition, the suggested framework is the first one to investigate the selection of the optimal logistic AVs using the T2NN–MEREC–CoCoSo approach to give further information on the present status of autonomous vehicles in the field of logistics and delivery. Furthermore, the suggested framework helps policymakers and stakeholders to know the necessary reasons and determine convenient AVs in the early stages of logistics development projects. The suggested framework organizes the development process by identifying the optimal logistic AVs using the extended T2NN–CoCoSo model. The suggested methodology is the first of its type, and it investigates the MEREC and CoCoSo techniques inside a T2NN environment to cope with the imprecision and vagueness that can occur throughout the judgment process.

1.3. Organization of the Paper

The rest of the study is structured as follows. In Section 2, we provide a review of related state-of-the-art papers in addition to the process enhancement benefits of MCDM approaches. In Section 3, we briefly examine the concepts and methods used in the suggested methodology. In Section 4, we investigate the concerned problem and describe the related criteria. In Section 5, we apply the suggested methodology to a case study and discuss the obtained findings. In addition, sensitivity and comparative analyses are presented on the results of the study. Finally, we discuss the conclusions of the paper in Section 6.

2. Literature Review

In this section, a comprehensive review of studies related to the subject of the study is presented. This section is divided into three parts. The first part presents studies related to AVs. The second part introduces the studies that have been conducted on the neutrosophic environment using T2NNs. The third part presents some of the literature related to the MEREC and CoCoSo methods.

2.1. Autonomous Vehicles

In this part, studies related to AVs and some studies related to the logistics industry are presented. The idea of AVs has attracted the attention of researchers, academics, and those interested in the industry since its inception, which has provided an abundance of research related to this field [25]. The studies presented are not significantly related to logistics. Nevertheless, presenting a review of studies on autonomous vehicles highlights their benefits to the logistics industry. Kumar et al. [14] pointed out the obstacles that will be faced in the general adoption of AVs in developing nations. Their findings will assist decision-makers in policy development and AV producers in better comprehending the degree of relevance associated with each obstacle and the interrelationships between them. Raj et al. [26] conducted a study to investigate the different barriers to the adoption of AVs. Their study was presented in five stages to identify ten barriers to the adoption of AVs. Then, an MCDM approach was presented using the Grey-based Decision-Making Trial and Evaluation Laboratory (Grey–DEMATEL) method to search for these barriers and clarify the causal relationships between them. Abosuliman and Almagrabi [4] presented a very important research paper investigating the challenges of managing industrial logistics. They introduced the Internet of Things-assisted smart logistics transportation administrative approach in an industrial setting for establishing an optimum logistics strategy, increasing customer service, and lowering transportation costs. Gokasar et al. [27] investigated the available solutions to manage the congestion problem to improve traffic for the success of the AV driving system on public and open streets. Their approach adopted the Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method under a rough set to determine the most appropriate street traffic administration systems. Rahnamay Bonab et al. [1] presented a research paper to determine the optimal self-driving vehicle to be adopted by a modern transport system and in logistical planning. Their methodology adopted the Choquet integral (CI) method to conduct the study under a spherical fuzzy set (SFS) environment. Abdel-Basset et al. [17] developed a study on the risk assessment of self-driving vehicles using an MCDM approach. Their approach consisted of two methods of decision-making: the Analytic Hierarchy Process (AHP), Multi-Attributive Border Approximation Area Comparison (MABAC) and the Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II). In addition, the study was conducted in a neutrosophic environment.

2.2. T2NN Environment

Undoubtedly, the neutrosophic theory has attracted the attention of academics and researchers in the current period, especially the environment of T2NNs [24]. Simić et al. [28] offered a research paper for selecting the most appropriate route for transporting petroleum using an MCDM approach. Their approach consists of two decision-making methods: the indifference threshold-based attribute ratio analysis (ITARA) method and the evaluation based on distance from average solution (EDAS) method. Their study was conducted in a T2NN environment to address ambiguity and uncertainty. Görçün et al. [29] presented a study extending the version of the weighted aggregated sum product assessment (WASPAS) method under T2NNs based on the Bonferroni function (WASPAS’B) for selecting the suitable Ro–Ro vessel in the second-hand vessel market. Zolfani et al. [30] provided a paper for discussing and assessing the benefits and notability of the extended efficiency analysis technique with input and output satisficing (EATWIOS) technique based on type-2 neutrosophic fuzzy numbers (T2NFNs). Their methodology was applied using a case study in the container industry.

2.3. MEREC and CoCoSo Methods

In this part, some of the literature related to MEREC and CoCoSo decision-making methods are reviewed. Hezam et al. [31] presented a study researching alternative fuel vehicles to reduce fuel costs and reduce emissions. Their study relied on a decision-making approach to select the optimal alternative fuel vehicle for a private home health care provider in Chandigarh, India. Their approach combined the MEREC method, the ranking sum (RS), and the Double Normalization-based Multi-Aggregation (DNMA) based on an intuitionistic fuzzy approach. Rani et al. [32] introduced a paper for selecting the food waste treatment technology. Their approach applied the MEREC method and the additive ratio assessment (ARAS) method based on the Fermatean fuzzy set (FFS). Accordingly, the MEREC method was applied to prioritize criteria. Su et al. [33] pointed out the importance of achieving the sustainability of blockchain systems in the field of manufacturing. They adopted a decision-making approach composed of three methods: the entropy method, the rank sum (RS) method, and the CoCoSo method based on the Pythagorean fuzzy set to investigate the transformation of blockchain technology into a sustainable manufacturing model in the Industry 4.0 era. The CoCoSo method was applied to rank the candidate alternatives. Chen et al. [34] presented a study on occupational health and safety risks in order to increase productivity and competitiveness. Their approach adopted the CoCoSo method based on Fermatean fuzzy linguistic sets (FFLSs) to assess risk.

According to previous studies, the field of AV encourages researchers to conduct more studies. Despite this, there is a paucity of studies evaluating logistics and AVs. Also, the environment of T2NNs is a promising environment for conducting studies and dealing with certainty and the lack of ambiguity. In addition, the MEREC and CoCoSo methods are applied in many fields. Accordingly, a comprehensive decision-making approach, T2NN–MEREC–CoCoSo, was introduced to assess and rank logistic AVs to support the logistics industry.

3. Materials and Methods

This section is considered the charter and the main focus of the study. First, some basic concepts about T2NNs are provided. These concepts are the score function, comparison method, and weighted average. Subsequently, Figure 1 is presented to illustrate the proposed approach used in this study. The approach used is divided into three stages. First, the selection of the committee of decision-makers participating in the study and the definition of criteria and alternatives for logistic AVs are discussed. The details of the three stages are provided as follows. In the first stage, significance weights are calculated for the criteria selected for the study using T2NN–MEREC. In the second stage, the substitutes selected for the study are ranked using T2NN–CoCoSo. In the third and final stage, comparative and sensitivity analyses are performed and the obtained results are interpreted.

3.1. Preliminaries

Theorem 1

([24]). Presume R as the limited universe of discourse and $D\left[0, 1\right],$ as the set of all triangular neutrosophic sets on $D\left[0, 1\right]$. A type-2 neutrosophic number set characterized by $\tilde{U}$ can be well-defined in R as an object having the form:

$$\tilde{U}=\left\{〈r,{\tilde{T}}_{\tilde{U}}\left(r\right),{\tilde{I}}_{\tilde{U}}\left(r\right),{\tilde{F}}_{\tilde{U}}\left(r\right)|r\in R〉\right\},$$

(1)

where, ${\tilde{T}}_{\tilde{U}}\left(r\right):R\to D\left[0,1\right],{\tilde{I}}_{\tilde{U}}\left(r\right):R\to D\left[0,1\right],{\tilde{F}}_{\tilde{U}}\left(r\right):R\to D\left[0,1\right]$. The type-2 neutrosophic number set ${\tilde{T}}_{\tilde{U}}\left(r\right)$ = $\left(T_{T_{\tilde{U}}}\left(r\right),T_{I_{\tilde{U}}}\left(r\right),T_{F_{\tilde{U}}}(r)\right)$, ${\tilde{I}}_{\tilde{U}}\left(r\right)$ = $\left(I_{T_{\tilde{U}}}\left(r\right),I_{I_{\tilde{U}}}\left(r\right),I_{F_{\tilde{U}}}(r)\right)$, ${\tilde{F}}_{\tilde{U}}\left(r\right)$ = $\left(F_{T_{\tilde{U}}}\left(r\right),F_{I_{\tilde{U}}}\left(r\right),F_{F_{\tilde{U}}}(r)\right)$, characterizes the truth, indeterminacy, and falsity memberships of $r$ in $\tilde{U}$, respectively.

The following circumstances are fulfilled by the membership factors:

$$0\le {{\tilde{T}}_{\tilde{U}}\left(r\right)}^3+{{\tilde{I}}_{\tilde{U}}\left(r\right)}^3+{{\tilde{F}}_{\tilde{U}}\left(r\right)}^3\le 3, \forall r\in R.$$

(2)

For ease of simplicity,

$\tilde{U}$=$〈\left(T_{T_{\tilde{U}}}\left(r\right),T_{I_{\tilde{U}}}\left(r\right),T_{F_{\tilde{U}}}(r)\right),\left(I_{T_{\tilde{U}}}\left(r\right),I_{I_{\tilde{U}}}\left(r\right),I_{F_{\tilde{U}}}(r)\right),\left(F_{T_{\tilde{U}}}\left(r\right),F_{I_{\tilde{U}}}\left(r\right),F_{F_{\tilde{U}}}(r)\right)〉$is determined as the T2NN.

Theorem 2

([24]). Presume $\tilde{U}$= $⟨\left(T_{T_{\tilde{U}}}\left(r\right), T_{I_{\tilde{U}}}\left(r\right), T_{F_{\tilde{U}}}(r)\right), \left(I_{T_{\tilde{U}}}\left(r\right), I_{I_{\tilde{U}}}\left(r\right), I_{F_{\tilde{U}}}(r)\right), \left(F_{T_{\tilde{U}}}\left(r\right), F_{I_{\tilde{U}}}\left(r\right), F_{F_{\tilde{U}}}(r)\right)⟩$, ${\tilde{U} }_1$= $⟨\left(T_{T_{{\tilde{U}}_1}}\left(r\right), T_{I_{{\tilde{U}}_1}}\left(r\right), T_{F_{{\tilde{U}}_1}}(r)\right), \left(I_{T_{{\tilde{U}}_1}}\left(r\right), I_{I_{{\tilde{U}}_1}}\left(r\right), I_{F_{{\tilde{U}}_1}}(r)\right), \left(F_{T_{{\tilde{U}}_1}}\left(r\right), F_{I_{{\tilde{U}}_1}}\left(r\right), F_{F_{{\tilde{U}}_1}}(r)\right)⟩$ and ${\tilde{U} }_2$ = $⟨\left(T_{T_{{\tilde{U}}_2}}\left(r\right), T_{I_{{\tilde{U}}_2}}\left(r\right), T_{F_{{\tilde{U}}_2}}(r)\right), \left(I_{T_{{\tilde{U}}_2}}\left(r\right), I_{I_{{\tilde{U}}_2}}\left(r\right), I_{F_{{\tilde{U}}_2}}(r)\right), \left(F_{T_{{\tilde{U}}_2}}\left(r\right), F_{I_{{\tilde{U}}_2}}\left(r\right), F_{F_{{\tilde{U}}_2}}(r)\right)⟩$ be three T2NNs and $\lambda >0$. The following describes how their operations are decided:

a. 

Addition “$\oplus$”

$$\begin{gathered} {\tilde{U}}_1\oplus {\tilde{U}}_2= \\ \langle \begin{array}{c}\left(\begin{array}{c}{T}_{T_{{\tilde{U}}_1}}\left(r\right)+T_{T_{{\tilde{U}}_2}}\left(r\right)-T_{T_{{\tilde{U}}_1}}\left(r\right)\times T_{T_{{\tilde{U}}_2}}\left(r\right), T_{I_{{\tilde{U}}_1}}\left(r\right)+T_{I_{{\tilde{U}}_2}}\left(r\right)-T_{I_{{\tilde{U}}_1}}\left(r\right)\times T_{I_{{\tilde{U}}_2}}\left(r\right), \\ T_{F_{{\tilde{U}}_1}}\left(r\right)+T_{F_{{\tilde{U}}_2}}\left(r\right)-T_{F_{{\tilde{U}}_1}}\left(r\right)\times T_{F_{{\tilde{U}}_2}}\left(r\right)\end{array}\right),\\ \left(I_{T_{{\tilde{U}}_1}}\left(r\right)\times I_{T_{{\tilde{U}}_2}}\left(r\right),{ I}_{I_{{\tilde{U}}_1}}\left(r\right)\times I_{I_{{\tilde{U}}_2}}\left(r\right), I_{F_{{\tilde{U}}_1}}\left(r\right)\times I_{F_{{\tilde{U}}_2}}\left(r\right)\right), \\ \left(F_{T_{{\tilde{U}}_1}}\left(r\right)\times F_{T_{{\tilde{U}}_2}}\left(r\right),{ F}_{I_{{\tilde{U}}_1}}\left(r\right)\times F_{I_{{\tilde{U}}_2}}\left(r\right), F_{F_{{\tilde{U}}_1}}\left(r\right)\times F_{F_{{\tilde{U}}_2}}\left(r\right)\right) \end{array}\rangle \end{gathered}$$

(3)

b. 

Multiplication “$\otimes$”

$$\begin{gathered} {\tilde{U}}_1\otimes {\tilde{U}}_2= \\ \langle \begin{array}{c}\left(T_{T_{{\tilde{U}}_1}}\left(r\right)\times T_{T_{{\tilde{U}}_2}}\left(r\right),{ T}_{I_{{\tilde{U}}_1}}\left(r\right)\times T_{I_{{\tilde{U}}_2}}\left(r\right), T_{F_{{\tilde{U}}_1}}\left(r\right)\times T_{F_{{\tilde{U}}_2}}\left(r\right)\right), \\ \left(\begin{array}{c}{I}_{T_{{\tilde{U}}_1}}\left(r\right)+I_{T_{{\tilde{U}}_2}}\left(r\right)-I_{T_{{\tilde{U}}_1}}\left(r\right)\times I_{T_{{\tilde{U}}_2}}\left(r\right), I_{I_{{\tilde{U}}_1}}\left(r\right)+I_{I_{{\tilde{U}}_2}}\left(r\right)-I_{I_{{\tilde{U}}_1}}\left(r\right)\times I_{I_{{\tilde{U}}_2}}\left(r\right), \\ I_{F_{{\tilde{U}}_1}}\left(r\right)+I_{F_{{\tilde{U}}_2}}\left(r\right)-I_{F_{{\tilde{U}}_1}}\left(r\right)\times I_{F_{{\tilde{U}}_2}}\left(r\right)\end{array}\right),\\ \left(\begin{array}{c}{F}_{T_{{\tilde{U}}_1}}\left(r\right)+F_{T_{{\tilde{U}}_2}}\left(r\right)-F_{T_{{\tilde{U}}_1}}\left(r\right)\times F_{T_{{\tilde{U}}_2}}\left(r\right), F_{I_{{\tilde{U}}_1}}\left(r\right)+F_{I_{{\tilde{U}}_2}}\left(r\right)-F_{I_{{\tilde{U}}_1}}\left(r\right)\times F_{I_{{\tilde{U}}_2}}\left(r\right), \\ F_{F_{{\tilde{U}}_1}}\left(r\right)+F_{F_{{\tilde{U}}_2}}\left(r\right)-F_{F_{{\tilde{U}}_1}}\left(r\right)\times F_{F_{{\tilde{U}}_2}}\left(r\right)\end{array}\right)\end{array}\rangle \end{gathered}$$

(4)

c. 

Scalar Multiplication

$$\xi \tilde{U}=\langle \begin{array}{c}\left({1-(1-T_{T_{\tilde{U}}}\left(r\right))}^{\xi }, {1-(1-{ T}_{I_{\tilde{U}}}\left(r\right))}^{\xi }, {1-(1-T_{F_{\tilde{U}}}\left(r\right))}^{\xi }\right), \\ \left({\left(I_{T_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(I_{I_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(I_{F_{\tilde{U}}}\left(r\right)\right)}^{\xi }\right), \\ \left({\left(F_{T_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(F_{I_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(F_{F_{\tilde{U}}}\left(r\right)\right)}^{\xi }\right) \end{array}\rangle$$

(5)

d. 

Power

$${\tilde{U}}^{\xi }=\langle \begin{array}{c}\left({\left(T_{T_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(T_{I_{\tilde{U}}}\left(r\right)\right)}^{\xi },{\left(T_{F_{\tilde{U}}}\left(r\right)\right)}^{\xi }\right), \\ \left({1-\left(1-I_{T_{\tilde{U}}}\left(r\right)\right)}^{\xi }, {1-\left(1-I_{I_{\tilde{U}}}\left(r\right)\right)}^{\xi }, {1-\left(1-I_{F_{\tilde{U}}}\left(r\right)\right)}^{\xi }\right),\\ \left({1-\left(1-F_{T_{\tilde{U}}}\left(r\right)\right)}^{\xi }, {1-\left(1-F_{I_{\tilde{U}}}\left(r\right)\right)}^{\xi }, {1-\left(1-F_{F_{\tilde{U}}}\left(r\right)\right)}^{\xi }\right) \end{array}\rangle$$

(6)

Theorem 3

([24]). Let $\tilde{U}$ = $⟨\left(T_{T_{\tilde{U}}}\left(r\right), T_{I_{\tilde{U}}}\left(r\right), T_{F_{\tilde{U}}}(r)\right), \left(I_{T_{\tilde{U}}}\left(r\right), I_{I_{\tilde{U}}}\left(r\right), I_{F_{\tilde{U}}}(r)\right), \left(F_{T_{\tilde{U}}}\left(r\right), F_{I_{\tilde{U}}}\left(r\right), F_{F_{\tilde{U}}}(r)\right)⟩$ be a T2NN. The following formula is used to calculate the score function of the T2NN$\tilde{U}$ :

$$\begin{gathered} S\left(\tilde{U}\right)=\frac{1}{12} ⟨8+\left(T_{T_{\tilde{U}}}\left(r\right)+2\left(T_{I_{\tilde{U}}}\left(r\right)\right)+T_{F_{\tilde{U}}}(r)\right)-\left(I_{T_{\tilde{U}}}\left(r\right)+2\left(I_{I_{\tilde{U}}}\left(r\right)\right)+ \\ {I}_{F_{\tilde{U}}}(r)\right)-\left(F_{T_{\tilde{U}}}\left(r\right)+2\left(F_{I_{\tilde{U}}}\left(r\right)\right)+F_{F_{\tilde{U}}}(r)\right)⟩ \end{gathered}$$

(7)

Theorem 4

([24]). Let ${\tilde{U}}_g$ = $⟨\left(T_{T_{{\tilde{U}}_g}}\left(r\right), T_{I_{{\tilde{U}}_g}}\left(r\right), T_{F_{{\tilde{U}}_g}}\left(r\right)\right), \left(I_{T_{{\tilde{U}}_g}}\left(r\right), I_{I_{{\tilde{U}}_g}}\left(r\right), I_{F_{{\tilde{U}}_g}}\left(r\right)\right), \left(F_{T_{{\tilde{U}}_g}}\left(r\right), F_{I_{{\tilde{U}}_g}}\left(r\right), F_{F_{{\tilde{U}}_g}}\left(r\right)\right)⟩ (g=1, 2, \dots , t)$ is a group of T2NNs, and w = ${\left(w_1, \dots ,w_g, \dots ,w_t\right)}^T$ is the weight vector of them with $w_j$ $\in [0, 1]$ and ${\sum }_{g=1}^t{w}_g=1$. A T2NNWA operator is determined as follows:

$${T2NNWA}_w ({\tilde{U}}_1, {\tilde{U}}_g, \dots , {\tilde{U}}_t)=w_1{\tilde{U}}_1\oplus w_g{\tilde{U}}_g\oplus \dots {\oplus w}_t{\tilde{U}}_t=\begin{array}{c}t\\ \oplus \\ g=1\end{array}\left(w_g{\tilde{U}}_g\right)=$$

(8)

$$\langle \genfrac{}{}{0pt}{}{\left(1-{\prod }_{g=1}^t{\left(1-T_{T_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g},1-{\prod }_{g=1}^t{\left(1-T_{I_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g},1-{\prod }_{g=1}^t{\left(1-T_{F_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}\right),}{\begin{array}{c}\left({\prod }_{g=1}^t{\left(I_{T_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}, {\prod }_{g=1}^t{\left(I_{I_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}, {\prod }_{g=1}^t{\left(I_{F_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}\right),\\ \left({\prod }_{g=1}^t{\left(F_{T_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}, {\prod }_{g=1}^t{\left(F_{I_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}, {\prod }_{g=1}^t{\left(F_{F_{{\tilde{U}}_g}}\left(r\right)\right)}^{w_g}\right)\end{array}}\rangle$$

(9)

3.2. The Proposed MCDM Methodology

In this part, the steps followed to conduct the study are provided in full, starting from an examination of the details related to the problem of evaluating logistic AVs to arranging these vehicles with a hybrid MCDM approach.

Step 1: Initially, the problem is studied in detail, the main purpose of conducting it is determined, the aspects and factors affecting the solution of this problem are determined, and the possibilities of benefiting from all the details are provided.

Step 2: The committee participating in the study is determined by the authors. The committee participating in the study is determined by several recommendations and criteria, as presented in

Table 1. Details on the participants of the panel of logistic AVs decision-makers.

Expert	Experience (Years)	Occupation	Profession	Academic Degree	Estimated Weight
${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_1$	15	Industry	Senior-Manager	M.Sc.	0.25
${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_2$	20	Academia	Research Professor	Ph.D.	0.30
${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_3$	10	Industry	Transport planner	M.Sc.	0.15
${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_4$	20	Academia	Research Professor	Ph.D.	0.30

.

Step 3: Aspects of the problem, its details, previous studies, and expert opinions are examined to find out the most important criteria that have a direct impact on evaluating and arranging the best independent logistic AVs. Also, the set of alternatives to be used in the study is determined.

Step 4: A set of semantic terms and their corresponding T2NNs are identified to be used in the evaluation of the specific criteria and the selected alternatives for the study problem, which is the evaluation and arrangement of logistic AVs.

Presume a set of m substitutes is characterized by A = $\left\{A_1,\dots , A_l, \dots A_m\right\}$ and a set of n criteria is symbolized by C = $\left\{C_1,\dots , C_j, \dots , C_n\right\}$. Let decision-makers = $\left\{{DM}_1,\dots ,{DM}_d,\dots ,{DM}_k\right\}$ be a set of decision-makers who expressed their judgment report for each substitute $A_i$ (i = 1, 2… m) against their criteria $C_j$ (j = 1, 2… n). Let w = ${\left({\mathrm{w}}_1,{\mathrm{w}}_2,\dots ,{\mathrm{w}}_{\mathrm{t}}\right)}^T$ be the weight vector for decision-makers ${DM}_d$(d = 1, 2… k) such that ${\sum }_{j=1}^n{\mathrm{w}}_l$ = 1.

Stage 1: Details of T2NN–MEREC for identifying criteria weights.

T2NN MEREC evaluates logistic AVs’ criteria objectively. T2NN–MEREC computes substitute overall and partial performances using a logarithmic function. Based on these two performance measures, criterion removal impacts are calculated. Finally, criterion weights are based on their influence on substitute performance. Criteria that have a large impact on performance are given more weight, and vice versa.

Step 1: Construct a pairwise comparison matrices for criteria by decision-makers to indicate their predilections ${\tilde{Q}}^d$ = ${\left({{\tilde{Q}}_{ij}}^d\right)}_{m\times n}$. The comparison decision matrix is constructed by applying the semantic terms that are provided in

Table 2. T2NN semantic terms for evaluating criteria and appraising alternatives.

Semantic Terms	Abbreviations	Type-2 Neutrosophic Number
Extremely not predilection	ENP	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
Strongly not predilection	SNP	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
Moderately not predilection	MNP	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
Evenly predilection	EDP	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
Moderately predilection	MOP	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
Strongly predilection	SLP	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$
Extremely predilection	EXP	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$

and then by using T2NNs provided in Table 2 according to Equation (10).

$$\begin{gathered} \begin{array}{cccc} \hspace{1em} & C_1& \dots & { C}_n\end{array} \\ {\tilde{Q}}^d=\begin{array}{c}{A}_1\\ ⋮\\ A_m\end{array} \left(\begin{array}{ccc}\langle \genfrac{}{}{0pt}{}{\left(T_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), T_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), T_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right),}{\begin{array}{c}\left(I_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), I_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), I_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right), \\ \left(F_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), F_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), F_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right)\end{array}}\rangle & \dots & \langle \genfrac{}{}{0pt}{}{\left(T_{T_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), T_{I_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), T_{F_{{{\tilde{Q}}_{1n}}^d}}\left(r\right)\right),}{\begin{array}{c}\left(I_{T_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), I_{I_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), I_{F_{{{\tilde{Q}}_{1n}}^d}}\left(r\right)\right), \\ \left(F_{T_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), F_{I_{{{\tilde{Q}}_{1n}}^d}}\left(r\right), F_{F_{{{\tilde{Q}}_{1n}}^d}}\left(r\right)\right)\end{array}}\rangle \\ ⋮& \dots & ⋮\\ \langle \genfrac{}{}{0pt}{}{\left(T_{T_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), T_{I_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), T_{F_{{{\tilde{Q}}_{m1}}^d}}\left(r\right)\right),}{\begin{array}{c}\left(I_{T_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), I_{I_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), I_{F_{{{\tilde{Q}}_{m1}}^d}}\left(r\right)\right), \\ \left(F_{T_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), F_{I_{{{\tilde{Q}}_{m1}}^d}}\left(r\right), F_{F_{{{\tilde{Q}}_{m1}}^d}}\left(r\right)\right)\end{array}}\rangle & \dots & \langle \genfrac{}{}{0pt}{}{\left(T_{T_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), T_{I_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), T_{F_{{{\tilde{Q}}_{mn}}^d}}\left(r\right)\right),}{\begin{array}{c}\left(I_{T_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), I_{I_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), I_{F_{{{\tilde{Q}}_{mn}}^d}}\left(r\right)\right), \\ \left(F_{T_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), F_{I_{{{\tilde{Q}}_{mn}}^d}}\left(r\right), F_{F_{{{\tilde{Q}}_{mn}}^d}}\left(r\right)\right)\end{array}}\rangle \end{array}\right) \end{gathered}$$

(10)

where ${{\tilde{Q}}_{ij}}^d$ =

$$⟨\left(T_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), T_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), T_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right), \left(I_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), I_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), I_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right), \left(F_{T_{{{\tilde{Q}}_{11}}^d}}\left(r\right), F_{I_{{{\tilde{Q}}_{11}}^d}}\left(r\right), F_{F_{{{\tilde{Q}}_{11}}^d}}\left(r\right)\right)⟩$$

(i = 1, 2, …, m; j = 1, 2, …, n; d = 1, 2, …, k) is the T2NN that indicates the semantic estimation of the substitute $A_i$ under the criterion $C_j$ supposed by the decision-makers ${ DM}_d$.

Step 2: Combine the T2NN decision matrices by all decision-makers $\tilde{R}$ = ${\left({\tilde{R}}_{ij}\right)}_{m\times n}$ by utilizing the T2NNWA operator (Theorem 4), as presented in Equation (11).

$$\begin{gathered} {\tilde{R}}_{ij}=\mathrm{T}2\mathrm{NNW}{A}_{\mathrm{w}} ({{\tilde{Q}}_{ij}}^1,\dots ,{{\tilde{Q}}_{ij}}^d,\dots ,{{\tilde{Q}}_{ij}}^k)=\begin{array}{c}k\\ \oplus \\ d=1\end{array}(w_d{{\tilde{Q}}_{ij}}^d)= \\ \left(\genfrac{}{}{0pt}{}{\left(1-{\prod }_{d=1}^k{\left(1-T_{T_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_{d }},1-{\prod }_{d=1}^k{\left(1-T_{I_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{W_{d }},1-{\prod }_{d=1}^k{\left(1-T_{F_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_{d }}\right),}{\begin{array}{c}\left({\prod }_{d=1}^k{\left(I_{T_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d}, {\prod }_{d=1}^k{\left(I_{I_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d},{\prod }_{d=1}^k{\left(I_{F_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d}\right),\\ \left({\prod }_{d=1}^k{\left(F_{T_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d}, {\prod }_{d=1}^k{\left(F_{I_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d}, {\prod }_{d=1}^k{\left(F_{F_{{{\tilde{Q}}_{ij}}^d}}\left(r\right)\right)}^{w_d}\right)\end{array}}\right), i=1,\dots , m; j=1,\dots , n. \end{gathered}$$

(11)

Consequently, the combined T2NN estimations ${\tilde{R}}_{ij}$ =

$$⟨\left(T_{T_{{\tilde{R}}_{ij}}}\left(r\right), T_{I_{{\tilde{R}}_{ij}}}\left(r\right), T_{F_{{\tilde{R}}_{ij}}}\left(r\right)\right), \left(I_{T_{{\tilde{R}}_{ij}}}\left(r\right), I_{I_{{\tilde{R}}_{ij}}}\left(r\right), I_{F_{{\tilde{R}}_{ij}}}\left(r\right)\right), \left(F_{T_{{\tilde{R}}_{ij}}}\left(r\right), F_{I_{{\tilde{R}}_{ij}}}\left(r\right), F_{F_{{\tilde{R}}_{ij}}}\left(r\right)\right)⟩$$

Step 3: Compute the score function for the T2NN combined estimations by applying Equation (12) based on Theorem 3. Based upon this, determine the normalized decision matrix $N$ = ${\left(N_{ij}\right)}_{m\times n}$ by applying Equation (13).

$$\begin{array}{l}Score({\tilde{R}}_{ij})=\frac{1}{12} ⟨8+\left(T_{T_{{\tilde{R}}_{ij}}}\left(r\right)+2\left(T_{I_{{\tilde{R}}_{ij}}}\left(r\right)\right)+T_{F_{{\tilde{R}}_{ij}}}(r)\right)-\left(I_{T_{{\tilde{R}}_{ij}}}\left(r\right)+2\left(I_{I_{{\tilde{R}}_{ij}}}\left(r\right)\right)+I_{F_{{\tilde{R}}_{ij}}}(r)\right)-\\ \left(F_{T_{{\tilde{R}}_{ij}}}\left(r\right)+2\left(F_{I_{{\tilde{R}}_{ij}}}\left(r\right)\right)+F_{F_{{\tilde{R}}_{ij}}}(r)\right)⟩, \mathrm{i}=1,\dots , \mathrm{m}; \mathrm{j}=1,\dots , \mathrm{n}.\end{array}$$

(12)

$$N_{ij}=\left\{\begin{array}{c}\frac{\underset{1\le l\le m}{\min }\left[score\left({\tilde{R}}_{lj}\right)\right]}{score\left({\tilde{R}}_{ij}\right)} |C_j\in C^{+} \\ \frac{score\left({\tilde{R}}_{ij}\right)}{\underset{1\le l\le m}{\max }\left[score\left({\tilde{R}}_{lj}\right)\right]} |C_j\in C^{-}\end{array}\right., \mathrm{i}=1,\dots , \mathrm{m}; \mathrm{j}=1,\dots , \mathrm{n}.$$

(13)

where $C^{+}\subseteq$ C is the set of benefit criteria, $C^{-}\subseteq$ C is the set of cost criteria.

Step 4: Compute the overall performance of each substitute by applying Equation (14), in which higher ratings of overall performances are produced by normalizing combined estimations with lower values.

$$S_i=\ln \left\{1+\left[\frac{1}{n}{\sum }_{j=1}^n\left|\ln \left(N_{ij}\right)\right|\right]\right\}, \mathrm{i}=1,\dots , \mathrm{m}.$$

(14)

Step 5: Compute the partial performance of each substitute by applying Equation (15), in which individual criterion removals are conducted for each substitute, and $O_{ij}$ represents the partial performance of substitute $A_i$ when criterion $C_j$ is uninvolved.

$$O_{ij}=\ln \left\{1+\left[\frac{1}{n}{\sum }_{l\in C|l\ne j}^n\left|\ln \left(N_{ij}\right)\right|\right]\right\}, \mathrm{i}=1,\dots , \mathrm{m}; \mathrm{j}=1,\dots , \mathrm{n}.$$

(15)

Step 6: Determine the removal influence of the criteria by applying Equation (16).

$$q_j={\sum }_{i=1}^m\left|O_{ij}-S_i\right|, \mathrm{j}=1,\dots , \mathrm{n}.$$

(16)

Step 7: Compute the criteria weights by applying Equation (17).

$$w_j=\frac{q_j}{{\sum }_{l=1}^n{q}_l}, \mathrm{j}=1,\dots , \mathrm{n}.$$

(17)

where w = ${(w_1,\dots , w_j,\dots ,w_n)}^T$ is the significance vector of the criteria, and ${\sum }_{j=1}^n{w}_t$ = 1.

Stage 2: Details of T2NN–CoCoSo for evaluating and ranking logistic AVs.

The T2NN–CoCoSo method ranks the logistic AVs substitutes. Based on a reasonable characterization of the connection between ideal and anti-ideal criterion values, the T2NN–CoCoSo method avoids the rank reversal issue in multi-attributes optimization.

Step 1: Compute the normalized extended decision matrix $V$ = ${\left(v_{ij}\right)}_{m\times n}$ by applying the Equation (18).

$$v_{ij}=\left\{\begin{array}{c}\frac{score\left({\tilde{R}}_{ij}\right)-\underset{i}{\min }\left(score\left({\tilde{R}}_{ij}\right)\right)}{\underset{i}{\max }\left(score\left({\tilde{R}}_{ij}\right)\right)-\underset{i}{\min }\left(score\left({\tilde{R}}_{ij}\right)\right)}, C_j\in C^{+} \\ \frac{\underset{i}{\max }\left(score\left({\tilde{R}}_{ij}\right)\right)-score\left({\tilde{R}}_{ij}\right)}{\underset{i}{\max }\left(score\left({\tilde{R}}_{ij}\right)\right)-\underset{i}{\min }\left(score\left({\tilde{R}}_{ij}\right)\right)}, C_j\in C^{-}\end{array}\right., \mathrm{i}=1,\dots , \mathrm{m}; \mathrm{j}=1,\dots , \mathrm{n}.$$

(18)

where $C^{+}\subseteq$ C is the set of benefit criteria, $C^{-}\subseteq$ C is the set of cost criteria.

Step 2: Determine the sum of the weighted comparability matrix by applying Equation (19).

$${\phi }_i={\sum }_{j=1}^m{w}_j. v_{ij}$$

(19)

Step 3: Determine the entire of the power weight of comparison matrix by applying Equation (20).

$${\beta }_i={\sum }_{j=1}^m{\left(v_{ij}\right)}^{w_j}$$

(20)

Step 4: Determine the proportional weight of the substitutes with the support of score strategies, arithmetic average of sums of the score (${\psi }_{ia}$), the total of proportional scores (${\psi }_{ib}$), and the score value of balanced compromise (${\psi }_{ic}$), for each substitute by applying Equations (21)–(23), respectively. The parameter λ expresses the pliability and stability of the suggested T2NN–CoCoSo method. The λ value is determined by the decision-makers.

$${\psi }_{ia}=\frac{{\phi }_i+{\beta }_i}{{\sum }_{i=1}^n{(\phi }_i+{\beta }_i)}$$

(21)

$${\psi }_{ib}=\frac{{\phi }_i}{\underset{i}{\min }{\phi }_i}+\frac{{\beta }_i}{\underset{i}{\min }{\beta }_i}$$

(22)

$${\psi }_{ic}=\frac{\lambda {\phi }_i+(1-\lambda ){\beta }_i}{\lambda \underset{i}{\max }{\phi }_i+(1-\lambda )\underset{i}{\max }{\beta }_i}, \mathrm{where} 0 \le \lambda \le 1.$$

(23)

Step 5: Compute the whole score by applying the Equation (24). The substitutes are ranked in descending order according to ${\psi }_i$ value, where the maximum value refers to the optimal substitute, while the minimum value refers to the least substitute.

$${\psi }_i=\sqrt[3]{{\psi }_{ia}{\psi }_{ib}{\psi }_{ic}}+\frac{{\psi }_{ia}+{\psi }_{ib}+{\psi }_{ic}}{3}$$

(24)

Stage 3: Comparative and sensitivity analyses.

Step 1: Conduct a sensitivity analysis to show the extent of the change in the results of the arrangement of alternatives to confirm the reliability of the presented methodology.

Step 2: Conduct a comparison analysis with other methodologies to show the strength of the presented methodology.

4. Problem Definition

In this section, a general perception of the problem and the importance of autonomous vehicles in transportation and delivery is presented. The multiple levels of autonomous vehicles are also explained. Finally, criteria are set by which vehicles are evaluated and the most appropriate selection is made, which helps in the spread of autonomous car technology to the logistics areas.

4.1. Illustration of Problem

Awareness has increased about the value of time and its importance in societies, which requires more efficiency and speed in every service that is used. Hence, the logistics industry finds itself at the forefront of the climate of urgency and insistence on the use of utility autonomous vehicles in abundance. It is thus making significant progress into the future by introducing advanced technologies to push its capabilities to these new frontiers. The transportation and delivery industry is rapidly developing and undergoing a major transformation after years of fierce competition between traditional competitors where the main battles centered on services and prices. Artificial intelligence, machine learning, and predictive technologies provide solutions to deal with the problem of traffic and accidents involving autonomous vehicles. This will certainly have a positive impact on the logistics industry in the region, and in particular, will greatly benefit the economies of the countries. AVs can be divided into several levels, as presented in Figure 2 [26]. However, making the right decision in the process of selecting the most appropriate autonomous vehicles requires taking a number of criteria into account. Accordingly, a brief description of these criteria is given as follows.

4.2. Description of the Decision-Making Criteria

Some of the criteria in this paper were prioritized by interviewing and questioning the experts and using their answers as inputs in the constructed MCDM approach, as exhibited in Figure 3. Below are the details of the selected criteria that are prioritized in this study.

➢

Price ($C_1$)

AVs need sensors to function. It is essential to find a way to bring down these costs in order to make this system more appealing. In a smart city, these sensors may also provide assistance to AVs as they are attempting to navigate the city [35]. There are studies that can be found in the published research literature that build conceptual frameworks for the purpose of making more effective use of these sensors, particularly in AVs. If the price of the equipment could be brought down, then more people would be interested in purchasing such a system.

➢

Environmentally friendly ($C_2$)

A reduction in carbon dioxide emissions is one of the benefits that will result from the successful introduction of autonomous cars [31]. These solutions have the potential to not only alleviate traffic problems, but to also have a positive impact on the surrounding environment;

➢

Battery capacity of the autonomous vehicles ($C_3$)

One of the most important criteria used to compare autonomous vehicles is the battery capacity of the vehicles. With the recent spread of AVs, the best types of batteries that help AVs to travel the longest distance after charging must be chosen [36]. Even though manufacturers have improved battery efficiency for AVs, the public recognizes charging times and battery capacity as the most significant impediments;

➢

Lane management ($C_4$)

Changing lanes unnecessarily is a major contributor to traffic congestion. By requiring vehicles to stay inside their lanes and not switch lanes in needless situations, the flow of traffic will be enhanced [37];

➢

Velocity of autonomous vehicles ($C_5$)

When there is a significant gap in travel speeds between vehicles on a highway facility, the facility becomes less predictable, leading to an increase in the number of encounters and overtaking maneuvers. As a result, consistent traffic speeds are preferred in order to lower the likelihood of accidents. For instance, it is anticipated that driverless cars will have skills far greater than those of human drivers [38]. Because these AVs are able to follow the speed values in a much more effective way than human drivers, the substantial variations in speed that now exist between vehicles may be decreased, which would make traffic much safer;

➢

Park and ride system ($C_6$)

This refers to the process of parking autonomous vehicles, especially in narrow spaces. The system is intended to protect the safety of the vehicle in restricted environments that require expert vehicle steering to prevent collisions;

➢

Vehicular communication systems ($C_7$)

This refers to the transmission of data to and from moving vehicles, which plays an important role in the development of AVs, in several ways. First, vehicles can use cloud-based resources; for example, AVs can use updated “maps” based in part on sensor data from other vehicles. Similarly, if a vehicle’s sensors fail, the vehicle may be able to partially rely on other vehicle sensors;

➢

Capacity of the autonomous vehicles ($C_8$)

While autonomous vehicles increase overall vehicle travel, they may also support higher rates of production vehicle capacity on existing roads [39]. First, the ability to constantly monitor traffic, respond with brakes, and precisely adjust speed should enable autonomous vehicles to travel safely at faster speeds and allow for shorter vehicle-to-vehicle separation.

5. Experimental Results

In this section, the experimental results of the study are explained. The section was divided into several parts. The first part shows the steps of applying the proposed methodology, the T2NN–MEREC–CoCoSo, to evaluate and rank logistic AVs. The second part discusses the obtained results. The third part presents the sensitivity analysis for the model used. The fourth part presents a comparison of the proposed model with different decision-making methodologies used to solve the problem.

5.1. Application of the Suggested T2NN–MEREC–CoCoSo Approach

Step 1: The problem was studied from several aspects, and the main goal was identified, which was to identify suitable AVs to help in the adoption of this car technology in the field of logistics services, effectively, on a large scale;

Step 2: The committee participating in the study with the authors was determined based on several criteria, as shown in Table 1. Also, a weight was assigned to each decision-maker based on the number of years of experience. Participants with both an academic and an industry background were selected in order to make the assessments more reliable;

Step 3: Eight evaluation criteria were defined based on problem details from previous studies and expert opinions. The selected criteria included price, environmental friendliness, battery capacity of the autonomous vehicles, lane management, the velocity of the autonomous vehicles, the park and ride system, vehicular communication systems, and the capacity of the autonomous vehicles. These criteria were indicated by: $C_1$, $C_2$, $C_3$, $C_4$, $C_5$, $C_6$, $C_7$, and $C_8$, respectively. These criteria were also divided into cost criteria and benefit criteria. All criteria were considered benefit criteria, except for the price criterion, which was considered a cost criterion;

Step 4: Seven semantic terms and their corresponding T2NNs were identified, as presented in Table 2, for use by the study participants. The semantic terminology was applied both in the evaluating criteria and in ranking alternatives.

Step 5: In the first step, for the T2NN–MEREC method, pairwise comparison matrices were constructed for the criteria by decision-makers to indicate their predilections, as exhibited in

Table 3. Appraisal matrix of criteria by the four decision-makers using the semantic terms.

Alternatives	Decision-Makers	Criteria
		${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_1$	EXP	EDP	SLP	SNP	MNP	EDP	EDP	EDP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_2$	EXP	EDP	SLP	MNP	MNP	MOP	MOP	MOP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_3$	EXP	EDP	SLP	SNP	MNP	SLP	SLP	SLP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_4$	EXP	EDP	SLP	MNP	MNP	EXP	EXP	EXP
${\mathrm{A}\mathrm{V}}_2$	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_1$	SLP	MNP	SLP	EDP	SNP	SNP	EDP	SNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_2$	SLP	MNP	SLP	EDP	MNP	MNP	EDP	MNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_3$	SLP	MNP	SLP	EDP	SNP	SNP	EDP	SNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_4$	SLP	MNP	SLP	EDP	MNP	MNP	EDP	MNP
${\mathrm{A}\mathrm{V}}_3$	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_1$	SLP	MNP	EXP	ENP	ENP	MNP	MNP	MNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_2$	SLP	EDP	EXP	ENP	ENP	MNP	MNP	MNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_3$	SLP	MOP	EXP	ENP	ENP	MNP	MNP	MNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_4$	SLP	SLP	EXP	ENP	ENP	MNP	MNP	MNP
${\mathrm{A}\mathrm{V}}_4$	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_1$	SLP	SNP	EXP	MNP	EDP	MOP	ENP	SNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_2$	SLP	MNP	EXP	MNP	EDP	MOP	SNP	SNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_3$	SLP	EDP	EXP	MNP	EDP	MOP	MNP	SNP
	${\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{m}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{r}}_4$	SLP	MOP	EXP	MNP	EDP	MOP	EDP	SNP

. The comparison decision matrices were constructed by applying the semantic terms provided in Table 2, then by using the T2NNs provided in Table 2, according to Equation (10), as presented in

Table A1. Appraisal matrix of criteria by the four decision-makers by utilizing T2NNs.

Alternatives	DMs	Criteria
		${\mathbf{C}}_1$	${\mathbf{C}}_2$
${\mathrm{A}\mathrm{V}}_1$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
${\mathrm{A}\mathrm{V}}_2$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_3$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$
${\mathrm{A}\mathrm{V}}_4$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
Alternatives	DMs	Criteria
		${\mathbf{C}}_3$	${\mathbf{C}}_4$
${\mathrm{A}\mathrm{V}}_1$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_2$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
${\mathrm{A}\mathrm{V}}_3$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
${\mathrm{A}\mathrm{V}}_4$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
Alternatives	DMs	Criteria
		${\mathbf{C}}_5$	${\mathbf{C}}_6$
${\mathrm{A}\mathrm{V}}_1$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$
${\mathrm{A}\mathrm{V}}_2$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_3$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_4$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
Alternatives	DMs	Criteria
		${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$
${\mathrm{A}\mathrm{V}}_2$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_3$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_4$	${\mathrm{D}\mathrm{M}}_1$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_2$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_3$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$
	${\mathrm{D}\mathrm{M}}_4$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$

;

Step 6: The four T2NN decision matrices were aggregated by using the T2NNWA operator according to Equation (11), as presented in

Table A2. Aggregated appraisal matrix of criteria by the four decision-makers by utilizing T2NNs.

Alternatives	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$
${\mathrm{A}\mathrm{V}}_1$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
${\mathrm{A}\mathrm{V}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_3$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.38, 0.32, 0.26\right);\left(0.50, 0.55, 0.67\right);\left(0.47, 0.51, 0.62\right)〉$
${\mathrm{A}\mathrm{V}}_4$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.47, 0.38, 0.37\right);\left(0.37, 0.35, 0.48\right);\left(0.28, 0.39, 0.39\right)〉$
Alternatives	Criteria
	${\mathbf{C}}_3$	${\mathbf{C}}_4$
${\mathrm{A}\mathrm{V}}_1$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.38, 0.32, 0.26\right);\left(0.50, 0.55, 0.67\right);\left(0.47, 0.51, 0.62\right)〉$
${\mathrm{A}\mathrm{V}}_2$	$〈\left(0.70, 0.75, 0.80\right);\left(0.15, 0.15, 0.25\right);\left(0.10, 0.15, 0.15\right)〉$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$
${\mathrm{A}\mathrm{V}}_3$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$
${\mathrm{A}\mathrm{V}}_4$	$〈\left(0.95, 0.90, 0.95\right);\left(0.10, 0.10, 0.05\right);\left(0.05, 0.05, 0.05\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
Alternatives	Criteria
	${\mathbf{C}}_5$	${\mathbf{C}}_6$
${\mathrm{A}\mathrm{V}}_1$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.78, 0.71, 0.78\right);\left(0.19, 0.16, 0.18\right);\left(0.11, 0.15, 0.14\right)〉$
${\mathrm{A}\mathrm{V}}_2$	$〈\left(0.38, 0.32, 0.26\right);\left(0.50, 0.55, 0.67\right);\left(0.47, 0.51, 0.62\right)〉$	$〈\left(0.38, 0.32, 0.26\right);\left(0.50, 0.55, 0.67\right);\left(0.47, 0.51, 0.62\right)〉$
${\mathrm{A}\mathrm{V}}_3$	$〈\left(0.20, 0.20, 0.10\right);\left(0.65, 0.80, 0.85\right);\left(0.45, 0.80, 0.70\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_4$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.60, 0.45, 0.50\right);\left(0.20, 0.15, 0.25\right);\left(0.10, 0.25, 0.15\right)〉$
Alternatives	Criteria
	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	$〈\left(0.78, 0.71, 0.78\right);\left(0.19, 0.16, 0.18\right);\left(0.11, 0.15, 0.14\right)〉$	$〈\left(0.78, 0.71, 0.78\right);\left(0.19, 0.16, 0.18\right);\left(0.11, 0.15, 0.14\right)〉$
${\mathrm{A}\mathrm{V}}_2$	$〈\left(0.50, 0.45, 0.50\right);\left(0.40, 0.35, 0.50\right);\left(0.35, 0.30, 0.45\right)〉$	$〈\left(0.38, 0.32, 0.26\right);\left(0.50, 0.55, 0.67\right);\left(0.47, 0.51, 0.62\right)〉$
${\mathrm{A}\mathrm{V}}_3$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$	$〈\left(0.40, 0.30, 0.35\right);\left(0.50, 0.45, 0.60\right);\left(0.45, 0.40, 0.60\right)〉$
${\mathrm{A}\mathrm{V}}_4$	$〈\left(0.37, 0.34, 0.28\right);\left(0.50, 0.56, 0.68\right);\left(0.43, 0.53, 0.59\right)〉$	$〈\left(0.35, 0.35, 0.10\right);\left(0.50, 0.75, 0.80\right);\left(0.50, 0.75, 0.65\right)〉$

. Subsequently, the score function was applied to convert the T2NN into real values, according to Equation (12);

Step 7: The normalized decision matrix was computed according to the benefit criteria and the cost criteria by applying Equation (13), as presented in

Table A3. Normalized matrix of selected criteria by using the MEREC method.

Alternatives	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	0.875	0.797	1.000	0.708	0.520	0.502	0.511	0.380
${\mathrm{A}\mathrm{V}}_2$	1.000	1.000	1.000	1.000	0.585	1.000	0.720	0.756
${\mathrm{A}\mathrm{V}}_3$	1.000	0.647	0.875	0.414	1.000	0.889	0.904	0.672
${\mathrm{A}\mathrm{V}}_4$	1.000	0.832	0.875	0.797	0.414	0.575	1.000	1.000

;

Step 8: The overall performance of each substitute was calculated by applying Equation (14), as presented in

Table A4. Overall performance of selected criteria by using the MEREC method.

Alternatives	Criteria								Overall Performance
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	0.875	0.797	1.000	0.708	0.520	0.502	0.511	0.380	0.379
${\mathrm{A}\mathrm{V}}_2$	1.000	1.000	1.000	1.000	0.585	1.000	0.720	0.756	0.134
${\mathrm{A}\mathrm{V}}_3$	1.000	0.647	0.875	0.414	1.000	0.889	0.904	0.672	0.230
${\mathrm{A}\mathrm{V}}_4$	1.000	0.832	0.875	0.797	0.414	0.575	1.000	1.000	0.221

, in which higher ratings of overall performances were produced by normalizing the combined estimations with lower values;

Step 9: The partial performance of each substitute was calculated by applying Equation (15), as presented in

Table 4. Partial performance and the weights of the criteria using the MEREC method.

Criterion	Partial Performance				Removal Effect	Weights	Rank
	${\mathbf{A}\mathbf{V}}_1$	${\mathbf{A}\mathbf{V}}_2$	${\mathbf{A}\mathbf{V}}_3$	${\mathbf{A}\mathbf{V}}_4$
Price ${\mathrm{C}}_1$	0.368	0.134	0.230	0.221	0.012	0.014	8
$\mathrm{Environmental} \mathrm{friendliness} {\mathrm{C}}_2$	0.360	0.134	0.185	0.203	0.083	0.095	6
$\mathrm{Battery} \mathrm{capacity} \mathrm{of} \mathrm{the} \mathrm{autonomous} \mathrm{vehicles} {\mathrm{C}}_3$	0.379	0.134	0.216	0.208	0.027	0.031	7
$\mathrm{Lane} \mathrm{management} {\mathrm{C}}_4$	0.349	0.134	0.138	0.198	0.145	0.166	3
$\mathrm{Velocity} \mathrm{of} \mathrm{autonomous} \mathrm{vehicles} {\mathrm{C}}_5$	0.322	0.073	0.230	0.129	0.211	0.242	1
$\mathrm{Park} \mathrm{and} \mathrm{ride} \mathrm{system} {\mathrm{C}}_6$	0.318	0.134	0.218	0.164	0.130	0.149	4
$\mathrm{Vehicular} \mathrm{communication} \mathrm{systems} {\mathrm{C}}_7$	0.320	0.097	0.220	0.221	0.106	0.122	5
$\mathrm{Capacity} \mathrm{of} \mathrm{the} \mathrm{autonomous} \mathrm{vehicles} {\mathrm{C}}_8$	0.293	0.103	0.189	0.221	0.158	0.181	2

;

Step 10: The removal influence of the eight criteria was determined according to Equation (16), as presented in Table 4. Finally, the eight criteria weights were calculated according to Equation (17), as exhibited in Table 4, and shown in Figure 4.

Step 11: In the first step of the T2NN–CoCoSo method for ranking the four logistic autonomous vehicles selected for the study, the normalized extended decision matrix was computed according to Equation (18), as presented in

Table A5. Normalized matrix of selected four autonomous vehicles by using the CoCoSo method.

Alternatives	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	0.875	0.797	1.000	0.708	0.520	0.502	0.511	0.380
${\mathrm{A}\mathrm{V}}_2$	1.000	1.000	1.000	1.000	0.585	1.000	0.720	0.756
${\mathrm{A}\mathrm{V}}_3$	1.000	0.647	0.875	0.414	1.000	0.889	0.904	0.672
${\mathrm{A}\mathrm{V}}_4$	1.000	0.832	0.875	0.797	0.414	0.575	1.000	1.000

;

Step 12: The sum of the weighted comparability matrix was computed according to Equation (19), as presented in

Table A6. Weighted normalized matrix of selected four autonomous vehicles by using the CoCoSo method.

Alternatives	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	0.000	0.044	0.000	0.083	0.158	0.149	0.122	0.181
${\mathrm{A}\mathrm{V}}_2$	0.014	0.000	0.000	0.166	0.121	0.000	0.049	0.036
${\mathrm{A}\mathrm{V}}_3$	0.014	0.095	0.031	0.000	0.000	0.019	0.013	0.054
${\mathrm{A}\mathrm{V}}_4$	0.014	0.035	0.031	0.108	0.242	0.111	0.000	0.000

;

Step 13: The entire value of the power weight of comparison matrix was determined according to Equation (20), as presented in

Table A7. Matrix for values of power weight of selected four autonomous vehicles by using the CoCoSo method.

Alternatives	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
${\mathrm{A}\mathrm{V}}_1$	0.000	0.744	0.000	0.662	0.640	0.753	0.774	0.734
${\mathrm{A}\mathrm{V}}_2$	0.943	0.000	0.000	0.742	0.601	0.000	0.693	0.547
${\mathrm{A}\mathrm{V}}_3$	0.943	0.800	0.897	0.000	0.000	0.553	0.592	0.589
${\mathrm{A}\mathrm{V}}_4$	0.943	0.728	0.897	0.691	0.709	0.721	0.000	0.000

;

Step 14: The proportional weight of the four logistic AVs selected was determined with the support of score strategies, ${\psi }_{ia}$, ${\psi }_{ib}$, and ${\psi }_{ic}$ for each logistic AV by applying Equations (21)–(23), as presented in

Table 5. Final ranking of four intelligent autonomous vehicles using the CoCoSo method.

Alternatives	${\mathit{\psi }}_{\mathit{i}\mathit{a}}$	${\mathit{\psi }}_{\mathit{i}\mathit{b}}$	${\mathit{\psi }}_{\mathit{i}\mathit{c}}$	${\mathit{\psi }}_{\mathit{i}}$	$\mathbf{R}\mathbf{a}\mathbf{n}\mathbf{k}$
${\mathrm{A}\mathrm{V}}_1$	0.269	4.480	0.930	2.930	1
${\mathrm{A}\mathrm{V}}_2$	0.208	2.707	0.721	1.953	3
${\mathrm{A}\mathrm{V}}_3$	0.245	2.241	0.848	1.886	4
${\mathrm{A}\mathrm{V}}_1$	0.278	3.722	0.964	2.654	2

. The value of parameter λ was set to 0.5, based on the opinions of the four decision-makers;

Step 15: The whole score was computed according to Equation (24), as presented in Table 5. The four logistic AVs were ranked in descending order according to ${\psi }_i$ value, where the maximum value refers to the logistic ${\mathrm{A}\mathrm{V}}_1$, and the minimum value refers to the logistic ${\mathrm{A}\mathrm{V}}_3$, as shown in Figure 5.

5.2. Results Analysis and Discussion

Recently, a great number of innovations that have an impact on the logistics industry have emerged. The use of AVs is one of the more recent technological developments that have the potential to significantly alter the dynamics of the logistics industry. These types of cars, with their built-in computer technology, artificial intelligence, global positioning system technology, and sensor technology minimize the impact of human meddling to the greatest extent feasible. AVs have the potential to not only reduce the risk of accidents caused by drivers and boost overall safety but also to ensure the safe delivery of products to their final destination. AVs are capable of making choices in a matter of seconds, which is something that is surely not conceivable for humans. According to all these data, it was necessary to present a study in which the evaluation and arrangement of four logistic AVs were discussed. The results extracted from the proposed T2NN–MEREC–CoCoSo model for evaluating several logistic AVs are discussed. The results are divided into two parts: results related to the evaluation of the selection criteria, and results related to the arrangement of the four logistic AVs.

According to the results of the evaluation of the eight criteria selected for the study using the T2NN–MEREC method, with a weight of 0.242, the velocity of AVs criterion is the most important criterion in selecting a logistic AV, followed by the capacity of AVs criterion, with a weight of 0.181; the price criterion is the least influential, with a weight of 0.014, as shown in Table 4 and Figure 4.

Accordingly, four logistic AVs were evaluated using the T2NN–CoCoSo method. According to the strategy of the arithmetic average of sums of the score ${\psi }_{ia}$, ${\mathrm{A}\mathrm{V}}_4$ is the most appropriate for adoption in the logistic locations, followed by ${\mathrm{A}\mathrm{V}}_1$, while ${\mathrm{A}\mathrm{V}}_2$ is the least appropriate. According to the strategy of the total of proportional scores ${\psi }_{ib}$, ${\mathrm{A}\mathrm{V}}_1$ is the most appropriate for adoption in the logistic locations, followed by ${\mathrm{A}\mathrm{V}}_4$, while ${\mathrm{A}\mathrm{V}}_3$ is the least appropriate. According to the strategy of the score value of balanced compromise ${\psi }_{ic}$, ${\mathrm{A}\mathrm{V}}_4$ is the most appropriate for adoption in the logistic locations, followed by ${\mathrm{A}\mathrm{V}}_1$, while ${\mathrm{A}\mathrm{V}}_2$ is the least appropriate. Finally, according to the whole score ${\psi }_i$, ${\mathrm{A}\mathrm{V}}_1$ is the most appropriate for adoption in the logistic locations, followed by ${\mathrm{A}\mathrm{V}}_4$, while ${\mathrm{A}\mathrm{V}}_3$ is the least appropriate, as presented in Table 5. In addition, the coefficient $\lambda$ adopted by the decision-makers was 0.5.

5.3. Sensitivity Analysis

In this part, a sensitivity analysis was performed to examine the effect of changes in the arrangement of the four logistic AVs. The results of the analysis were divided into two parts. The first part is related to the change in the weight of the velocity of autonomous vehicles criterion ${\mathrm{C}}_5$ as this criterion had the highest weight. The second part is related to the change in the coefficient of $\lambda$ which is found in one of the steps of the CoCoSo method.

According to the first part, twenty cases of change in the weight of the velocity of the AVs criterion were created, as presented in

Table A8. Changing weights in the ${\mathrm{C}}_5$ criterion.

Cases	Criteria
	${\mathbf{C}}_1$	${\mathbf{C}}_2$	${\mathbf{C}}_3$	${\mathbf{C}}_4$	${\mathbf{C}}_5$	${\mathbf{C}}_6$	${\mathbf{C}}_7$	${\mathbf{C}}_8$
Main case	0.014	0.095	0.031	0.166	0.242	0.149	0.122	0.181
Case 0.05	0.046	0.123	0.063	0.191	0.050	0.154	0.149	0.205
Case 0.10	0.044	0.116	0.059	0.181	0.100	0.146	0.141	0.194
Case 0.15	0.041	0.110	0.056	0.171	0.150	0.138	0.133	0.184
Case 0.20	0.039	0.104	0.053	0.161	0.200	0.130	0.125	0.173
Case 0.25	0.036	0.097	0.049	0.151	0.250	0.122	0.117	0.162
Case 0.30	0.034	0.091	0.046	0.141	0.300	0.114	0.109	0.151
Case 0.35	0.031	0.084	0.043	0.130	0.350	0.106	0.102	0.140
Case 0.40	0.029	0.078	0.040	0.120	0.400	0.098	0.094	0.130
Case 0.45	0.027	0.071	0.036	0.110	0.450	0.089	0.086	0.119
Case 0.50	0.024	0.065	0.033	0.100	0.500	0.081	0.078	0.108
Case 0.55	0.022	0.058	0.030	0.090	0.550	0.073	0.070	0.097
Case 0.60	0.019	0.052	0.026	0.080	0.600	0.065	0.063	0.086
Case 0.65	0.017	0.045	0.023	0.070	0.650	0.057	0.055	0.076
Case 0.70	0.015	0.039	0.020	0.060	0.700	0.049	0.047	0.065
Case 0.75	0.012	0.032	0.016	0.050	0.750	0.041	0.039	0.054
Case 0.80	0.010	0.026	0.013	0.040	0.800	0.033	0.031	0.043
Case 0.85	0.007	0.019	0.010	0.030	0.850	0.024	0.023	0.032
Case 0.90	0.005	0.013	0.007	0.020	0.900	0.016	0.016	0.022
Case 0.95	0.002	0.006	0.003	0.010	0.950	0.008	0.008	0.011

. The main case is one of these cases. As for the rest of the nineteen cases, for example in case 0.05, a weight of 0.05 was assigned to the velocity of the AVs criterion, and the rest of the basic criterion weight value after taking 0.05 from it, which equaled 0.192, was then divided equally among the rest of the criteria, and so on, for the rest of the cases.

Figure 6 shows the results of the sensitivity analysis according to the change in the weight of the velocity of AVs criterion. Accordingly, the order of the AVs remains in the same order as in the main case until case 0.35, where ${\mathrm{A}\mathrm{V}}_1$ is the highest rank followed by ${\mathrm{A}\mathrm{V}}_4$, while ${\mathrm{A}\mathrm{V}}_3$ is the lowest rank. In contrast, the order of alternatives changes from case 0.40 until the end of the cases, where ${\mathrm{A}\mathrm{V}}_4$ becomes the highest rank, followed by ${\mathrm{A}\mathrm{V}}_1$, while ${\mathrm{A}\mathrm{V}}_3$ is the lowest rank.

According to the second part, twenty cases of change in the weight coefficient λ were generated, as presented in

Table A9. Changes in the coefficient $\lambda$ value.

$\mathbf{Change} \mathbf{in} \mathit{\lambda }$	${\mathbf{A}\mathbf{V}}_1$	${\mathbf{A}\mathbf{V}}_2$	${\mathbf{A}\mathbf{V}}_3$	${\mathbf{A}\mathbf{V}}_4$	$\mathbf{Change} \mathbf{in} \mathit{\lambda }$	${\mathbf{A}\mathbf{V}}_2$	${\mathbf{A}\mathbf{V}}_3$	${\mathbf{A}\mathbf{V}}_4$	${\mathbf{A}\mathbf{V}}_2$
$\lambda$ = 0.05	2.924	1.971	1.933	2.676	$\lambda$ = 0.55	2.932	1.949	1.876	2.650
$\lambda$ = 0.10	2.924	1.969	1.929	2.674	$\lambda$ = 0.60	2.934	1.944	1.864	2.644
$\lambda$ = 0.15	2.925	1.968	1.925	2.672	$\lambda$ = 0.65	2.936	1.939	1.849	2.638
$\lambda$ = 0.20	2.925	1.966	1.920	2.670	$\lambda$ = 0.70	2.938	1.932	1.832	2.630
$\lambda$ = 0.25	2.925	1.966	1.920	2.670	$\lambda$ = 0.75	2.941	1.924	1.810	2.621
$\lambda$ = 0.30	2.926	1.964	1.915	2.667	$\lambda$ = 0.80	2.945	1.914	1.783	2.609
$\lambda$ = 0.35	2.927	1.962	1.909	2.665	$\lambda$ = 0.85	2.950	1.900	1.746	2.593
$\lambda$ = 0.40	2.928	1.959	1.902	2.662	$\lambda$ = 0.90	2.956	1.882	1.694	2.571
$\lambda$ = 0.45	2.929	1.956	1.895	2.658	$\lambda$ = 0.95	2.965	1.855	1.617	2.540
$\lambda$ = 0.50	2.930	1.953	1.886	2.654	$\lambda$ = 1.00	2.980	1.813	1.483	2.491

. The results indicate that the order of the AVs is identical to the same order obtained from the application of the proposed methodology T2NN–MEREC–CoCoSo, as exhibited in Figure 7.

5.4. Comparative Analysis

In this part of the study, a comparison analysis was performed to confirm the robustness, reliability, and validity of the proposed T2NN–MEREC–CoCoSo approach. Several comparisons were made between the results of the T2NN–MEREC–CoCoSo approach with two other MCDM hybrid approaches. In the other two approaches to decision-making, T2NN–MEREC was employed to calculate the weights of the criteria while VIKOR and TOPSIS were applied in a normal fuzzy environment to rank the AVs. A comparison of the three approaches T2NN–MEREC–CoCoSo, T2NN–MEREC–fuzzy VIKOR, and T2NN–MEREC–fuzzy TOPSIS is presented in

Table 6. Comparison results.

AVs	Models
	T2NN–MEREC–CoCoSo	T2NN–MEREC–Fuzzy VIKOR	T2NN–MEREC–Fuzzy TOPSIS
${\mathrm{A}\mathrm{V}}_1$	1	1	1
${\mathrm{A}\mathrm{V}}_2$	3	4	3
${\mathrm{A}\mathrm{V}}_3$	4	3	4
${\mathrm{A}\mathrm{V}}_4$	2	2	2

and Figure 8. The results show that the three models have the same arrangement with slight differences, as presented in Table 6, although the same weights were applied in each model. Although different fuzzy sets were used, the results had similar arrangements.

From here, and to confirm the strength of the proposed methodology, T2NN–MEREC–CoCoSo, and its correlation with the other models, T2NN–MEREC–fuzzy VIKOR and T2NN–MEREC–fuzzy TOPSIS, a correlation analysis was conducted using Pearson’s correlation parameter.

Table 7. The Pearson correlation parameter.

Models	T2NN–MEREC–CoCoSo	T2NN–MEREC–Fuzzy VIKOR	T2NN–MEREC–Fuzzy TOPSIS
T2NN–MEREC–CoCoSo	1.000	0.800	1.000
T2NN–MEREC–fuzzy VIKOR		1.000	0.800
T2NN–MEREC–fuzzy TOPSIS			1.000

presents the results of the analysis. Based on the results, it appears that there are strong correlations between the three models used (T2NN–MEREC–CoCoSo, T2NN–MEREC–fuzzy VIKOR and T2NN–MEREC–fuzzy TOPSIS). These findings demonstrate the inability of conventional fuzzy sets to deal with uncertainty in MCDM issues. In addition, the results may be used as a reference to illustrate that the usage of MCDM techniques in a T2NN environment is a suitable way to handle uncertainty concerns in MCDM methods and make them more consistent when evaluating criteria and substitutes.

6. Concluding Remarks

Today, AVs are emerging as one of the solutions for the future of smart mobility. Also, expectations indicate that the future of switching to AVs is close; with the “autopilot” feature, AVs are about to become an essential part of our daily lives. There are many social, environmental, and economic impacts that make the future of AVs interesting for sustainability advocates. The AV can be defined as a car that is able to sense the environment around it almost completely through cameras and sensors that give commands to a computer that determines the speed of the car, the extent of its deviations, the appropriate time to use the brakes, and all the things that the driver does while driving, which makes it able to fully drive itself. The AV is a mode of transportation that is both effective and convenient. The introduction of AVs marks the beginning of yet another pivotal phase in the development of the logistics industry, our day-to-day lives, and the overall progression of human civilization. There are certain ethical and safety problems that come along with the use of AVs, in addition to the advantages.

In this paper, a comprehensive approach was presented for the evaluation and selection of intelligent AVs for adoption in the logistics industry. Initially, a committee of three decision-makers was formed to participate in the study and express their views on the subject and its aspects. Also, seven semantic terms were identified for use in assessing aspects of a problem, because decision-makers often make assessments under conditions of near certainty. In addition, the evaluation criteria were defined as follows: price, environmental friendliness, the battery capacity of the autonomous vehicles, lane management, the velocity of autonomous vehicles, the park and ride system, vehicular communication systems, and the capacity of the autonomous vehicles. In this regard, the approach used in the study was a hybrid approach using MCDM methods. The hybrid approach was implemented in a neutrosophic environment using T2NNs. The hybrid approach was composed of two decision-making methods: the T2NN–MEREC method and the T2NN–CoCoSo method. First, the T2NN–MEREC method was applied to determine the priorities and weights of the eight criteria. The T2NN–CoCoSo method was applied to rank the four selected alternatives.

Accordingly, the study provides some information, including that the velocity of autonomous vehicles criterion is the most influential criterion in choosing a logistic AV. Sensitivity and comparison analyses were performed on the results of the study to prove the strength and stability of the proposed approach. The results of the two analyses indicate that the order of the candidate alternatives is stable, with some slight changes in the order in a few cases.

Regarding future research, suggestions will be made regarding the limitations of, and applications for, this study. First, the interdependencies between criteria were not considered, which may have affected the final results. The Analytic Network Process (ANP) method can then be used to investigate the influential network relationship between criteria. Furthermore, the outcomes of the proposed method were found to be heavily influenced by the subjective opinions of the decision-makers. Therefore, it is imperative to conduct interviews with a greater number of decision-makers involved in the evaluation process in order to address this issue effectively in subsequent instances. Ultimately, while the suggested approach was successfully utilized in the context of AV selection, it can also be effectively employed to address MCDM challenges in various other domains, including, but not limited to, location selection and service selection.