Mode Selection Problem Research Articles

Analyzing Unsteady Turbomachinery Flow Simulations With Mixing Entropy

Abstract The prediction of unsteady aerodynamic loads is a central problem during the design of turbomachinery. Over the last 20 years, harmonic balance methods have been shown to be highly efficient for this task. A CPU-cost optimal setup of a harmonic balance simulation, however, requires knowledge of relevant harmonics. In the case of a single blade row with a periodic disturbance this question amounts to the classical problem of harmonic convergence, a problem which is solely due to the nonlinearity of the unsteady flow physics. In contrast, for multi-stage configurations, the choice of harmonics is further complicated by the fact that the interactions of disturbances with blade rows may give rise to a vast spectrum of harmonics that possibly have important modal content, e.g., Tyler–Sofrin modes. The aim of this paper is to show that the mixing entropy attributed to circumferential modes of a given harmonic can serve as a disturbance metric on the basis of which a criterion could be derived whether a certain harmonic should be included or not. The idea is based on the observation that the entropy due to the temporal and circumferential mixing of the flow at a blade row interface may be decomposed, up to third-order terms, into independent contributions from different frequencies and mode orders. For a given harmonic balance (and steady) flow result, the mixing entropy attributed to modes that are simply mixed out, rather than resolved in the neighboring row, is shown to be a natural indicator of a potential inaccuracy. We present important features of the mixing entropy for unsteady disturbances, in particular a close relationship to sound power for acoustic modes. The problem of mode selection in a 1.5-stage compressor configuration serves as a practical example to illustrate our findings.

A novel interval-valued intuitionistic fuzzy CRITIC-TOPSIS methodology: An application for transportation mode selection problem for a glass production company

The challenge of selecting the best transportation mode is one of the most significant issues that organizations deal with during the product delivery phase. Many criteria that influence the selection of an appropriate mode of transportation are indicative of how challenging this problem is. Therefore, in this study, a hybrid decision making methodology based on interval-valued intuitionistic fuzzy sets (IVIFS) is proposed to obtain the most suitable transportation mode by considering the determined criteria. The criteria are decided according to both experts’ opinions and literature review results. Afterwards, experts’ opinions in the company are consulted for the evaluation of the determined transportation mode alternatives. Then, relying on IVIF-CRITIC technique, criteria weights are determined. Subsequently, IVIF-TOPSIS method is applied to rank the alternatives. Because the combination of these methodologies has never been applied to solve the problem at hand, the developed methodology and the application area represent a novelty. According to the results of our study, the railway has been determined as the most proper transportation mode for the glass manufacturing company. To show the robustness of the proposed methodology, we conduct a sensitivity analysis by both changing the criteria weights and applied methodology. The results show that in all scenarios the ranking of alternatives is similar.

A condition number based group correspondence method for multi-criteria group decision making

The correspondence between decision parameters and group assessments is a new and important concept in multi-criteria group decision making (MCGDM), named as group correspondence. It characterizes the degree to which the changes in group assessments correspond to the changes in decision parameters. In the existing studies, however, group correspondence is rarely considered in MCGDM. In this paper, a group correspondence method is developed based on condition numbers. Through the analysis of the difference between sensitivity and group correspondence in MCGDM, the necessity of proposing group correspondence is demonstrated. The formal definition of group correspondence in MCGDM is first presented, and then its measurement is constructed based on the condition number of a matrix. Moreover, a feedback mechanism is developed to help accelerate the convergency of the group correspondence. The identification and suggestion rules for the two steps of aggregation of the assessments are included in the feedback mechanism to suggest that the specific decision makers renew their identified assessments and weights of the criteria. The process of the proposed method for MCGDM is then designed. A control mode selection problem for a manufacturing enterprise located in Changzhou, Jiangsu, China is analyzed using the proposed method to demonstrate its validity and applicability.

Analysis of derrikingand slewing of the tower crane with consideration to driving mechanisms characteristics

The variational problem of the movement mode selection for the load outreach change mechanism during a steady-state tower crane slewing was formulated and solved in the paper, that ensures the minimization of the drive motor power. The variational problem is nonlinear, and so we used the modified PSO-Rot-Ring particle swarm met heuristic method for its solution. Low- and high-frequency oscillations of the outreach change mechanism elements during the start-up were detected in the optimization process. These oscillations are eliminated in the section of steady-state movement due to the selection of the motion boundary conditions.
 In order to increase the productivity during a tower crane exploitation the overlapping of the mechanisms operation is used. The article considers the overlapping of operations of the derriking and slewing mechanisms of the crane. To study the dynamic processes in the joint work of the derriking and slewing of the tower crane, a dynamic model was built. It takes into account the main movement of the derriking and slewing of the crane, as well as oscillation processes in the drive mechanisms and load oscillation on flexible suspension in two planes: in the plane of the derrikingand in the plane of slewing of the crane. The developed dynamic model takes into account the elastic-dissipative characteristics of the transfer mechanisms, as well as the dynamic mechanical characteristics of the drive motors, which describe the transient electromagnetic processes. On the basis of the developed dynamic model with the help of Lagrangeʼs equations six differential equations of the second order which describe a mechanical part of the mechanisms movement were found. In addition, the transient dynamic electromagnetic processes of each of the engines are described by four differential equations. As a result, a system of fourteen differential equations was obtained. It describes the common dynamics of the mechanical part of the crane mechanisms and electromagnetic processes in the electric engines of the derriking and slewing mechanisms. The obtained system of differential equations was solved by numerical methods with the help of developed computer program. For the actual construction of the tower crane in the overlapping work of the derriking and slewing mechanisms, kinematic, power, and energy characteristics were determined, which made it possible to estimate the actual loads in the elements of the construction and drive mechanisms. From the obtained results it was established that the elements of the construction and drive mechanisms are under significant dynamic and energy overloads.High-frequency oscillations of drive mechanisms at the beginning of the movement and low-frequency oscillations of elements of the construction and load were revealed. This significantly reduces the reliability of tower cranes, increases their energy losses, and affects the work of the crane operator and maintenance staff.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks

The application of device-to-device (D2D) communication and multi-carrier (MC) non-orthogonal multiple access (NOMA) have been considered as promising technologies to enhance energy efficiency, network connectivity, and spectrum efficiency in the next generation of communication systems. This paper investigates a joint optimization problem of spectrum-efficient mode selection, fair sub-channel (SC) allocation, and power allocation of user pairs for a D2D-enabled MC-NOMA network. In the proposed network, user pairs are enabled to transmit and receive data in either underlaid D2D mode or cellular mode, and the NOMA technique is adopted in both downlink and uplink transmission. To maximize the throughput of the system and to manage the co-channel interference, the joint optimization problem is formulated under the constraint of guaranteeing all users with a certain amount of resources while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements of users. As an NP-hard problem, the optimization problem is first transformed into two sub-problems namely 1- the joint SC allocation and mode selection and 2- the power allocation. Then, to jointly solve the SC allocation and mode selection sub-problem, an enhanced three-side many-to-one matching algorithm is proposed and to obtain the solution to the power allocation problem, the known sequential convex programming method is used. Moreover, an iterative algorithm is used to enhance the performance of the system in terms of sum-rate. Finally, a mathematical expression is introduced to check the quality of service (QoS) of the proposed fair resource allocation algorithm and the results of the simulation are stated to indicate improved system performance.

Contention or Symbiosis: The Model-Based and Model-Less Transmission Mode Selections for Internet of Things in Unlicensed Band

In this work, we investigate a spectrum-sharing network, where an Internet-of-Things (IoT) system and a WiFi system coexist in the unlicensed band. The IoT devices can actively transmit signal by contending for the channel with the WiFi users. Alternatively, they can also convey information by passively backscattering the ambient WiFi signal, and thus form symbiosis with the WiFi during the transmission. Since the active and the passive transmission mode have their own merits under specific circumstances, how to select one of them adaptively and efficiently with guaranteeing the normal service of the WiFi system is of high importance. To solve this problem, we firstly investigate the effective throughput of the IoT system from the cross-layer perspective. Then, by jointly considering the effective throughput and the power consumption of the IoT system, we formulate the transmission mode selection problem. Two solutions are given depending on whether the signaling between the two systems is available. By entailing the inter-system parameter sharing, the model-based transmission mode selection scheme is proposed, which gives the closed-form optimal solution. When the inter-system parameter sharing is absent, the model-less transmission mode selection scheme is proposed by using deep reinforcement learning (DRL) technique. Extensive simulation results validate the theoretical analysis, and demonstrate the effectiveness of the proposed transmission mode selection schemes.

Epsilon-Greedy-Based MQTT QoS Mode Selection and Power Control Algorithm for Power Distribution IoT

Employing message queuing telemetry transport (MQTT) in the power distribution internet of things (PD-IoT) can meet the demands of reliable data transmission while significantly reducing energy consumption through the dynamic and flexible selection of three different quality of service (QoS) modes and power control. However, there are still some challenges, including incomplete information, coupling of optimization variables, and dynamic tradeoff between packet-loss ratio and energy consumption. In this paper, the authors propose a joint optimization algorithm named EMMA for MQTT QoS mode selection and power control based on the epsilon-greedy algorithm. Firstly, the joint optimization problem of MQTT QoS mode selection and power control is modeled as a multi-armed bandit (MAB) problem. Secondly, the authors leverage the online learning capability of the epsilon-greedy algorithm to achieve joint optimization of MQTT QoS mode selection and power control. Finally, they verify the superior performance of the proposed algorithm through simulations.

Joint Mode Selection and Resource Allocation for D2D and Femtocell Users in Dense Heterogeneous Networks with Full Frequency Reuse

We consider the problem of joint mode selection and resource allocation for D2D and femtocell users in a three-tier dense heterogeneous network in which all the users can reuse the spectrum. The goal is to maximize their total weighted sum rate, subject to minimum rate requirements and maximum tolerable interference on the cellular system. Since the spectrum can be fully reused, the co-tier and cross-tier interferences among the users result in a mixed integer non-linear, non-convex program that is difficult to solve directly. Using insights into the structure of the interference, we derive a close, conservative approximation of the joint problem that has a convex relaxation that is tight. That enables good solutions to the joint problem to be obtained using a customized iterative algorithm employing the Lagrange dual decomposition method. Since the proposed iterative scheme is semi-distributed, the signaling overhead is mitigated. To further reduce the computational complexity, a low-complexity (primal) decomposition-based method is also introduced, in which we select the transmission mode heuristically based on the traffic level in the network, and then sequentially perform admission control, power control, and sub-channel allocation for the femtocell users and D2D users. Our simulation results indicate that the proposed iterative and heuristic algorithms, on average, achieve around 94% and 82% of the sum rate of the optimal Branch & Bound method, respectively, and do so at much lower computational costs.

Sales mode selection strategic analysis for risk-averse manufacturers under revenue sharing contracts

This paper considers a sales mode selection problem under revenue sharing contracts between resale and agency modes for risk-averse manufacturers with traditional retail channel, direct selling channel, and e-commerce platform channel. By considering the factors including price competition intensity, market share, revenue sharing ratio, commission rate, and degree of risk aversion, we construct leader-follower game models with manufacturers as leaders and traditional retailers and e-commerce platforms as followers. To obtain optimal solutions, we discuss conditions to ensure the upper and lower models to be convex and then give the optimal strategies for all members in the network. Through numerical experiments, we analyze the involved parameters’ impact on sales mode selection strategy and the changing trends of each member's optimal pricing and profit under different sales modes. The numerical results reveal the following revelations: The manufacturer should choose the agency mode when the commission rate is low and the direct selling channel has a large market share. If both the commission rate and degree of risk aversion are high, direct selling channels have a low market share, and price competition intensity is weak, the manufacturer should choose the resale mode. The degree of risk aversion has an effect on each member’s optimal decision. Regardless of which sales mode the manufacturer chooses, the optimal price of each member decreases as the degree of risk aversion increases. Under certain conditions, the manufacturer’s choice of agency mode can create win-win situations with supply chain members.

Sales Mode Selection Strategic Analysis for Manufacturers on E-Commerce Platforms under Multi-Channel Competition

This paper considers a sales mode selection problem between resale and agency modes on e-commerce platforms for a manufacturer with traditional retail channel, direct selling channel, and e-commerce platform channel. By considering the factors price competition, market shares, and commission rate, we construct two leader-follower models with the manufacturer as a leader and traditional retailer and e-commerce platform as followers. To obtain optimal solutions, we discuss the conditions under which the upper and lower models are convex and then give optimal strategies for all members in the network. Through numerical experiments, we analyze the impact of price competition intensity, market shares, and commission rate on mode selection strategies and the changing trend of each member’s optimal pricing and profit under different sales modes. The numerical results reveal the following revelations: If the market share of the traditional retail channel is lower than the direct selling channel, the manufacturer should choose the agency mode when the market share of the direct selling channel and price competition are lower or when the market share of the direct selling channel together with the price competition and the commission rate is higher; otherwise, the manufacturer should choose the resale mode. If the market share of the direct selling channel is lower than the traditional retail channel, the manufacturer should choose the agency mode when the price competition is weak and choose the resale mode when the price competition is strong. Under certain conditions, a win–win situation can be achieved no matter how the manufacturer chooses.

Drive power minimization of outreach change mechanism of tower crane during steady-state slewing mode

The variational problem of the movement mode selection for the load outreach change mechanism during a steady-state tower crane slewing was formulated and solved in the paper, that ensures the minimization of the drive motor power. We used a dynamic model of the outreach change mechanism for research work, which presented the mechanical system with three degrees of freedom. The formulated variational problem is nonlinear, and so we used the modified PSO-Rot-Ring particle swarm metaheuristic method for its solution. The analytical solution of another variational problem for the same model of the crane mechanism and an optimization criterion close in physical content was used as the solution initial approximation of the nonlinear variational problem to save calculated resources. The starting mode of the mechanism drive for the load outreach change was determined during the solution of the nonlinear variational problem, which ensures the root-mean-square value minimization of the drive motor power. Low- and high-frequency oscillations of the outreach change mechanism elements during the start-up were detected in the optimization process. The first oscillations are caused by the load rocking on the flexible suspension during the start-up, and the second – by the nature of the change in the driving moment and drive power. These oscillations are eliminated in the section of steady-state movement due to the selection of the motion boundary conditions, which are taken into account in the solution process of the variational problem.

Local lowest-rank dynamic mode decomposition for transient feature extraction of rolling bearings

The fault diagnosis mainly relies on the detection of periodic pulse components caused by the local damage. However, the impulsive component in bearing vibration signals is usually disturbed by noise and some harmonics due to the harsh working environment, which makes great challenge to fault diagnosis. To enhance the performance in fault diagnosis of rolling bearing, a local lowest-rank dynamic mode decomposition (LLRDMD) is proposed in this paper. Firstly, an improved projection operator is proposed to reduce the noise of augmented snapshot matrix and thus eliminating the bias of high-dimension dynamic system matrix. Secondly, by solving a local lowest-rank optimization problem, the impulsive component and interference components in the high-dimension dynamic system matrix are separated. Compared with DMD, the proposed method achieves better performance in the extraction of transient pulse component while avoiding the problems of parameter selection and mode selection. Lastly, the proposed method is applied to three sets of real datasets. Compared with existing methods, LLRDMD achieves a higher accuracy in fault diagnosis.

An Evolutionary Game for Mobile User Access Mode Selection in Sub-6 GHz/mmWave Cellular Networks

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-6 GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility. According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-6 GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.

A hesitant fuzzy multiplicative Base-criterion multi-criteria group decision making method

Hesitant fuzzy sets have a unique characteristic that its basic element could manifest the assessment values of different decision makers on the same option under a certain criterion. Base-criterion method is a very significant tool for calculating the weights of the criteria in multiple criteria decision-making. In this paper, we developed a novel approach hesitant fuzzy BCM based on hesitant fuzzy multiplicative preference relation for multiple criteria group decision making. The base-comparison of the preferential criterion relative to other criteria is expressed as linguistic terms, which might be converted into hesitant multiplicative elements (HMEs). HMEs are extended along the same length according to the attitude of the decision makers. Then normalized optimal hesitant fuzzy weights are calculated. The normalized optimal hesitant fuzzy weights of criteria may be transferred to crisp values by employing score function. To illustrate the applicability and suitability of hesitant fuzzy BCM, we analyse the optimal transportation mode selection problem and car selection problem under hesitant fuzzy environment. The outcomes of the proposed model indicate that the hesitant fuzzy BCM is highly consistent and can yield appreciable preference ranking of criteria and alternatives.

Enhanced Resource Allocation in D2D Communications With NOMA and Unlicensed Spectrum

Device-to-device (D2D) communications with non- orthogonal multiple access (NOMA) have been proved in enhancing the network capacity and reducing the burden of the traditional cellular network. Due to reusing the same resources, the underlying D2D communication may cause high interference to cellular users, and thus reducing the network throughput. To alleviate this issue, we consider moving D2D users to the unlicensed band. In this case, D2D users can reuse the resources in cellular networks or share the unlicensed band with WiFi users. However, a critical question is how to effectively perform NOMA user clustering and determine D2D pairs that should use unlicensed band and how to guarantee the quality service of users at both networks. Our objective is to maximize the network throughput by investigating a joint resource allocation problem of user clustering, power control, and D2D mode selection. Since the formulated problem is a mixed-integer nonlinear programming problem, finding an optimal solution is a great challenge. To solve this problem, we propose to use a swarm intelligence approach, namely whale optimization algorithm in solving our optimization problem with an efficient solution. Through simulation results, we show that our proposed scheme attains a better performance than other alternative schemes.

A novel Distributed AI framework with ML for D2D communication in 5G/6G networks

Inspired by the adoption of Artificial Intelligence (AI) and Machine Learning (ML) approaches in 5G and 6G networks, in this paper we propose a novel ML based Distributed AI (DAI) framework able to attain the ambitious goals set for emerging 5G/6G networks. The novelty of the DAI framework is that it is implemented in an autonomous, dynamic and flexible fashion, utilising Belief Desire Intention (BDI) agents, extended with ML capabilities, which reside on the mobile devices (User Equipment). We refer to these as BDIx agents. This provides a component-based framework (likened to LEGO-based building blocks), which can build on and utilise execution plans, by composing and arranging ML techniques in flexible ways within the framework, in order to achieve the desired goals. More specifically, we form a modular BDIx agent at a multi-agent system (MAS), integrated with Fuzzy Logic for the perception/cognitive part of the agents. By exploiting the capabilities of the BDIx agents in our DAI framework, we allow mobile devices to intercommunicate and cooperate in an autonomous manner, thus offering a number of attractive features, including improved performance in terms of network control execution time and message exchange, fast response in handling dynamic aspects in the network, self-organising network functionalities, and a framework that can act as the glue platform in employing one or more intelligent approaches to tackle the diverse 5G/6G technical requirements. To demonstrate the potential of the DAI framework we focus on Device to Device (D2D) communication and illustrate its flexibility in addressing diverse D2D challenges. Through example Plan Libraries and enhanced metrics, we outline DAI implementation specifics to achieve a number of identified 5G/6G D2D requirements. To embed the concept further, the specific problem of D2D transmission mode selection is expanded upon, from problem description to solution approach (DAIS) and implementation specifics, and hence comparatively evaluate over other approaches (i.e., Unsupervised learning ML techniques, centralised control techniques, random techniques). The results demonstrated that DAIS provides, among other performance metrics, improved mobile network Spectral Efficiency (SE) and Power Consumption (PC), better and more efficient cluster formation and reduced control decision delay.

Short-Packet Backscatter Assisted Wireless-Powered Relaying With NOMA: Mode Selection With Performance Estimation

Backscatter-assisted wireless-powered relaying has emerged as a promising approach to extend the transmission range of energy-constrained devices, e.g., sensors, with improved energy efficiency. Mode selection adapting to the dynamic channel conditions has a great impact on the performance of this hybrid relaying paradigm. However, designing an effective mode selection is challenging as the instantaneous channel state information (CSI) may not be available and is subject to errors and delays. Furthermore, instant CSI estimation incurs continual communication and computation overhead that burdens the operation of energy-constrained devices. This work considers the mode selection problem in a downlink cooperative scenario, wherein a hybrid relay aids the finite blocklength non-orthogonal transmissions from a source to a pair of destinations. To eliminate the need for instantaneous CSI estimation, we exploit the statistical system information to indicate the mode selection. In particular, we first derive the analytical expressions of average end-to-end block error rates in both active and passive relaying modes, based on which we can obtain the reward gap of the system. We then propose a bandit learning-based mode selection that exploits the reward gap to assess the upper confidence bound. The proposed mode selection method is light-weight and requires no knowledge of instantaneous CSI. Simulation results are presented to validate our analytical results and expose the efficiency of the proposed mode selection over conventional approaches. Besides, we demonstrate that the proposed approach is robust to estimation errors of the reward gap.

A Dynamic Algorithm for Interference Management in D2D-Enabled Heterogeneous Cellular Networks: Modeling and Analysis.

To supporting a wider and diverse range of applications, device-to-device (D2D) communication is a key enabler in heterogeneous cellular networks (HetCNets). It plays an important role in fulfilling the performance and quality of service (QoS) requirements for 5G networks and beyond. D2D-enabled cellular networks enable user equipment (UE) to communicate directly, without any or with a partial association with base stations (eNBs). Interference management is one of the critical and complex issues in D2D-enabled HetCNets. Despite the wide adoption of D2D communications, there are very few researchers addressing the problems of mode selection (MS), as well as resource allocation for mutual interference in three-tier cellular networks. In this paper, we first identify and analyze three key factors, namely outage probability, signal-to-interference and noise ratio (SINR), and cell density that influence the performance of D2D-enabled HetCNets. We then propose a dynamic algorithm based on a distance-based approach to minimize the interference and to guarantee QoS for both cellular and D2D communication links. Results obtained show that outage probability is improved by 35% and 49% in eNB and SCeNB links, respectively, when compared with traditional neighbor-based methods. The findings reported in this paper provide some insights into interference management in D2D communications that can help network researchers and engineers contribute to further developing next-generation cellular networks.

An Evolutionary Game for Mobile User Access Mode Selection in sub-6 GHz mmWave Cellular Networks

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-$6$ GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility{.} According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-$6$ GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.

Sustainable transportation mode selection from the freight forwarder's perspective in trading with western EU countries

The transportation mode selection problem is one of the most significant problems that logistics companies and freight forwarders face. The complexity of this problem is reflected in a large number of factors that affect the choice of an adequate mode of transport. In order to facilitate the decision-making process when choosing, the aim of this paper is to propose an approach for selecting sustainable transportation mode when trading with western EU countries using the Multi-Criteria Decision-Making (MCDM) methods, i.e., SWARA (Stepwise Weight Assessment Ratio Analysis) and MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) methods. SWARA was used to determine the weights of the criteria, while MARCOS was used for the final ranking of the alternatives. In this paper, nine criteria and five alternatives for the realization of transport from Serbia to Germany were observed. The developed approach represents a novelty since the combination of these methods has not been used yet to solve described problem. The results of the described methodology showed that road transport (via the Horgoš border crossing) proved to be the alternative with the best value, while road transport via the Batrovci border crossing proved to be the alternative with the lowest value.