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Article
Publication date: 25 July 2023

Gerasimos G. Rigatos, Masoud Abbaszadeh, Bilal Sari and Jorge Pomares

A distinctive feature of tilt-rotor UAVs is that they can be fully actuated, whereas in fixed-angle rotor UAVs (e.g. common-type quadrotors, octorotors, etc.), the associated…

Abstract

Purpose

A distinctive feature of tilt-rotor UAVs is that they can be fully actuated, whereas in fixed-angle rotor UAVs (e.g. common-type quadrotors, octorotors, etc.), the associated dynamic model is characterized by underactuation. Because of the existence of more control inputs, in tilt-rotor UAVs, there is more flexibility in the solution of the associated nonlinear control problem. On the other side, the dynamic model of the tilt-rotor UAVs remains nonlinear and multivariable and this imposes difficulty in the drone's controller design. This paper aims to achieve simultaneously precise tracking of trajectories and minimization of energy dissipation by the UAV's rotors. To this end elaborated control methods have to be developed.

Design/methodology/approach

A solution of the nonlinear control problem of tilt-rotor UAVs is attempted using a novel nonlinear optimal control method. This method is characterized by computational simplicity, clear implementation stages and proven global stability properties. At the first stage, approximate linearization is performed on the dynamic model of the tilt-rotor UAV with the use of first-order Taylor series expansion and through the computation of the system's Jacobian matrices. This linearization process is carried out at each sampling instance, around a temporary operating point which is defined by the present value of the tilt-rotor UAV's state vector and by the last sampled value of the control inputs vector. At the second stage, an H-infinity stabilizing controller is designed for the approximately linearized model of the tilt-rotor UAV. To find the feedback gains of the controller, an algebraic Riccati equation is repetitively solved, at each time-step of the control method. Lyapunov stability analysis is used to prove the global stability properties of the control scheme. Moreover, the H-infinity Kalman filter is used as a robust observer so as to enable state estimation-based control. The paper's nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Finally, the nonlinear optimal control approach for UAVs with tilting rotors is compared against flatness-based control in successive loops, with the latter method to be also exhibiting satisfactory performance.

Findings

So far, nonlinear model predictive control (NMPC) methods have been of questionable performance in treating the nonlinear optimal control problem for tilt-rotor UAVs because NMPC's convergence to optimum depends often on the empirical selection of parameters while also lacking a global stability proof. In the present paper, a novel nonlinear optimal control method is proposed for solving the nonlinear optimal control problem of tilt rotor UAVs. Firstly, by following the assumption of small tilting angles, the state-space model of the UAV is formulated and conditions of differential flatness are given about it. Next, to implement the nonlinear optimal control method, the dynamic model of the tilt-rotor UAV undergoes approximate linearization at each sampling instance around a temporary operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. The linearization process is based on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modelling error, which is due to the truncation of higher-order terms from the Taylor series, is considered to be a perturbation that is asymptotically compensated by the robustness of the control scheme. For the linearized model of the UAV, an H-infinity stabilizing feedback controller is designed. To select the feedback gains of the H-infinity controller, an algebraic Riccati equation has to be repetitively solved at each time-step of the control method. The stability properties of the control scheme are analysed with the Lyapunov method.

Research limitations/implications

There are no research limitations in the nonlinear optimal control method for tilt-rotor UAVs. The proposed nonlinear optimal control method achieves fast and accurate tracking of setpoints by all state variables of the tilt-rotor UAV under moderate variations of the control inputs. Compared to past approaches for treating the nonlinear optimal (H-infinity) control problem, the paper's approach is applicable also to dynamical systems which have a non-constant control inputs gain matrix. Furthermore, it uses a new Riccati equation to compute the controller's gains and follows a novel Lyapunov analysis to prove global stability for the control loop.

Practical implications

There are no practical implications in the application of the nonlinear optimal control method for tilt-rotor UAVs. On the contrary, the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed to the linear parameter varying (LPV) form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. The stability properties of the Galerkin series expansion-based optimal control approaches are still unproven.

Social implications

The proposed nonlinear optimal control method is suitable for using in various types of robots, including robotic manipulators and autonomous vehicles. By treating nonlinear control problems for complicated robotic systems, the proposed nonlinear optimal control method can have a positive impact towards economic development. So far the method has been used successfully in (1) industrial robotics: robotic manipulators and networked robotic systems. One can note applications to fully actuated robotic manipulators, redundant manipulators, underactuated manipulators, cranes and load handling systems, time-delayed robotic systems, closed kinematic chain manipulators, flexible-link manipulators and micromanipulators and (2) transportation systems: autonomous vehicles and mobile robots. Besides, one can note applications to two-wheel and unicycle-type vehicles, four-wheel drive vehicles, four-wheel steering vehicles, articulated vehicles, truck and trailer systems, unmanned aerial vehicles, unmanned surface vessels, autonomous underwater vessels and underactuated vessels.

Originality/value

The proposed nonlinear optimal control method is a novel and genuine result and is used for the first time in the dynamic model of tilt-rotor UAVs. The nonlinear optimal control approach exhibits advantages against other control schemes one could have considered for the tilt-rotor UAV dynamics. For instance, (1) compared to the global linearization-based control schemes (such as Lie algebra-based control or flatness-based control), it does not require complicated changes of state variables (diffeomorphisms) and transformation of the system's state-space description. Consequently, it also avoids inverse transformations which may come against singularity problems, (2) compared to NMPC, the proposed nonlinear optimal control method is of proven global stability and the convergence of its iterative search for an optimum does not depend on initialization and controller's parametrization, (3) compared to sliding-mode control and backstepping control the application of the nonlinear optimal control method is not constrained into dynamical systems of a specific state-space form. It is known that unless the controlled system is found in the input–output linearized form, the definition of the associated sliding surfaces is an empirical procedure. Besides, unless the controlled system is found in the backstepping integral (triangular) form, the application of backstepping control is not possible, (4) compared to PID control, the nonlinear optimal control method is of proven global stability and its performance is not dependent on heuristics-based selection of parameters of the controller and (5) compared to multiple-model-based optimal control, the nonlinear optimal control method requires the computation of only one linearization point and the solution of only one Riccati equation.

Details

International Journal of Intelligent Unmanned Systems, vol. 12 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 1 February 2024

Gerasimos G. Rigatos, Pierluigi Siano, Mohammed S. Al-Numay, Bilal Sari and Masoud Abbaszadeh

The purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet…

Abstract

Purpose

The purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet synchronous motors and five-phase asynchronous induction motors (IMs) are among the types of multiphase motors one can consider for the traction system of electric vehicles (EVs). By distributing the required power in a large number of phases, the power load of each individual phase is reduced. The cumulative rates of power in multiphase machines can be raised without stressing the connected converters. Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases.

Design/methodology/approach

A novel nonlinear optimal control approach has been developed for five-phase IMs. The dynamic model of the five-phase IM undergoes approximate linearization using Taylor series expansion and the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance. For the linearized model of the motor, an H-infinity feedback controller is designed. This controller achieves the solution of the optimal control problem under model uncertainty and disturbances.

Findings

To select the feedback gains of the nonlinear optimal (H-infinity) controller, an algebraic Riccati equation has to be solved repetitively at each time-step of the control method. The global stability properties of the control loop are demonstrated through Lyapunov analysis. Under moderate conditions, the global asymptotic stability properties of the control scheme are proven. The proposed nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

Research limitations/implications

Comparing to other nonlinear control methods that one could have considered for five-phase IMs, the presented nonlinear optimal (H-infinity) control approach avoids complicated state-space model transformations, is of proven global stability and its use does not require the model of the motor to be brought into a specific state-space form. The nonlinear optimal control method has clear implementation stages and moderate computational effort.

Practical implications

In the transportation sector, there is progressive transition to EVs. The use of five-phase IMs in EVs exhibits specific advantages, by achieving a more balanced distribution of power in the multiple phases of the motor and by providing fault tolerance. The study’s nonlinear optimal control method for five-phase IMs enables high performance for such motors and their efficient use in the traction system of EVs.

Social implications

Nonlinear optimal control for five-phase IMs supports the deployment of their use in EVs. Therefore, it contributes to the net-zero objective that aims at eliminating the emission of harmful exhaust gases coming from human activities. Most known manufacturers of vehicles have shifted to the production of all-electric cars. The study’s findings can optimize the traction system of EVs thus also contributing to the growth of the EV industry.

Originality/value

The proposed nonlinear optimal control method is novel comparing to past attempts for solving the optimal control problem for nonlinear dynamical systems. It uses a novel approach for selecting the linearization points and a new Riccati equation for computing the feedback gains of the controller. The nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 43 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 12 January 2024

Wei Xiao, Zhongtao Fu, Shixian Wang and Xubing Chen

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this…

Abstract

Purpose

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

Design/methodology/approach

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

Findings

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

Originality/value

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Book part
Publication date: 5 April 2024

Mike G. Tsionas

In this chapter, we consider the possibility that a firm may use costly resources to improve its technical efficiency. Results from static analyses imply that technical efficiency…

Abstract

In this chapter, we consider the possibility that a firm may use costly resources to improve its technical efficiency. Results from static analyses imply that technical efficiency is determined by the configuration of factor prices. A dynamic model of the firm is developed under the assumption that managerial skill contributes to technical efficiency. Dynamic analysis shows that the firm can never be technically efficient if it maximizes profits, the steady state is always inefficient, and it is locally stable. In terms of empirical analysis, we show how likelihood-based methods can be used to uncover, in a semi-non-parametric manner, important features of the inefficiency-management relationship using a flexible functional form accounting for the endogeneity of inputs in a production function. Managerial compensation can also be identified and estimated using the new techniques. The new empirical methodology is applied in a data set previously analyzed by Bloom and van Reenen (2007) on managerial practices of manufacturing firms in the UK, US, France and Germany.

Article
Publication date: 6 February 2024

Junghee Han

Quite often than not, a new industry can be created, thanks to the countless entrepreneurs and innovative activities across the globe. Smart city (SC) is one such industry and a…

Abstract

Purpose

Quite often than not, a new industry can be created, thanks to the countless entrepreneurs and innovative activities across the globe. Smart city (SC) is one such industry and a living lab using the key roles of the digital platform that enable a seamless flow of information and knowledge for innovation within the SC. The purpose of this paper is to illustrate how SC can be a new regional industry engine through an “open collective innovation system” as its new concept. In particular, SC provides efficient transaction costs and knowledge flows. Eventually, SC can be an innovation hub for entrepreneurship through openness.

Design/methodology/approach

To frame the research goals, the authors used qualitative research methodologies based on grounded theory. In particular, the author used inductive reasoning to generate arguments and conclusions about the future of an SC as a new growth engine in the era of the fourth industrial revolution. Numerous documents and prior literature were used for the preliminary conceptualization of an SC. Interview data were then coded for reasoning in an open collective innovation system based on “openness”.

Findings

SC maximizes efficiency in practicing innovation. In the perspective of innovation costs, SC can minimize transaction costs, specifically the information processing costs, through data openness. In this context, transaction costs can be considered an economic equivalent of friction in a physical system. So, as the friction is low, some movements of an object on the surface are likely to be easy. SC is optimized for innovation activities through an “open collective innovation system”. In terms of innovation networks, an SC results in an innovation efficiency derived from both the network and the spatial agglomerations in physical and cyberspace. The efficiency-based SC itself overlaps knowledge creation, dissemination and absorption, providing an open innovation (OI) ecosystem.

Research limitations/implications

This paper remarkably extends that SC can be an “open collective innovation system model” and a new conceptualization. Eventually, SC will play a crucial role in developing regional industries as a new growth engine. To operate as a new growth engine fully-fledged, the SC is needed to accumulate innovative assets such as the critical mass of residents, numerous firms, etc. However, this study has some limitations. First, difficulties in any analytic approach to SC resulted from their many interdependent facets, such as social, economic, infrastructural and spatial complex systems, which exist in similar but changing forms over a huge range of scales. Also, this research is at a quite an early stage. Thus, its theoretical stability is weak. So, this paper used the qualitative methodology with a grounded theory. Another limitation is in the research methodology. The limitation of using grounded theory adapted by this work is that the results of this study may not be generalizable beyond the context of this study. This non-generalizability occurs because ours is an inductive approach to research, meaning that the findings are based on data collected and analyzed. As such, the results of this study may not be applicable to other contexts or situations. In addition, the analysis of data in the grounded theory is based on researcher’s subjective interpretations. This means that the researcher’s own biases, preferences and assumptions may influence the results of the study. The quality of the data collected is another potential limitation. If the data is incomplete or of poor quality, it can cause researcher’s own subjective interpretations.

Practical implications

Findings of this study have some practical implications for enterprises, practitioners and governors. First, firms should use value networks instead of value chains. Notably, the firms that pursue new products or services or startups that try to find a new venture business should take full advantage of SC. This taking advantage is possible because SC not only adapts state-of-the-art information technology (e.g. sensor devices, open data analytics, IoT and fiber optic networks) but also facilitates knowledge flow (e.g. between universities, research centers, knowledge-based partner firms and public agencies). More importantly, with globalized market competition in recent years, sustainability for firms is a challenging issue. In this respect, managers can take the benefits of SC into consideration for strategic decisions for sustainability. Specifically, industrial practitioners who engage in innovation activities have capabilities of network-related technologies (e.g. data analysis, AI, IoT and sensor networks). By using these technologies in an SC, enterprises can keep existing customers as well as attract potential customers. Lastly, the findings of this study contribute to policy implementation in many aspects. At first, for SC to become a growth engine at regional or natural levels, strong policy implementation is crucial because SC is widely regarded as a means of entrepreneurship and an innovation plaza (Kraus et al., 2015). To facilitate entrepreneurship, maker spaces used for making the prototypes to support entrepreneurial process were setup within universities. The reason for establishing maker spaces in universities is to expand networking between entrepreneurs and experts and lead to innovation through a value network. One of the policy instruments that can be adapted is the “Data Basic Income Scheme” suggested by this research to boost the usage of data, providing content and information for doing business. Also, a governor in SC as an intermediator for the process of the knowledge flow should initiate soft configuration for SC.

Social implications

This work makes two theoretical contributions to OI aspects: (1) it explores dynamic model archetypes; and (2) it articulates and highlights how SC with digital technology (i.e. in the AI, IoT and big data context) can be used to create collective knowledge flow efficiently. First, the findings of this study shed light on the OI dynamic model. It reveals important archetypes of new sub-clustering creation, namely, a system that underpins the holistic process of innovation by categorization in amongst the participating value network (Aguilar-Gallegos et al., 2015). In innovation studies, scholars have particularly paid attention to a cluster’s evolution model. In the process of innovation, the “open innovation dynamic model” suggested by this study illustrates sub-clustering that happens in value networks by taking the benefits of SC. Eventually, the evolution or development of sub-clusters can bring in a new system, namely, an OI system. Second, the findings of this study contribute to the understanding of the role of digital technologies in promoting knowledge flow. The usage and deployment of digital technologies in SC may enormously and positively influence innovative activities for participants. Furthermore, the rising of digital economy, in the so-called platform business, may occur depending on advanced technologies and OI. In doing so, the findings can further tow innovation research through juxtaposition between SC and innovation research (Mehra et al., 2021).

Originality/value

This paper shows that the function of an SC not only improves the quality of life but also acts as an engine of new industry through an open collective innovation setting using dynamic and ecological models.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 18 January 2024

Zaihua Luo, Juliang Xiao, Sijiang Liu, Mingli Wang, Wei Zhao and Haitao Liu

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too…

Abstract

Purpose

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

Design/methodology/approach

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Findings

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

Originality/value

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 29 November 2023

Na Zhang, Haiyan Wang and Zaiwu Gong

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of…

Abstract

Purpose

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Design/methodology/approach

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

Findings

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

Originality/value

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Open Access
Article
Publication date: 5 October 2023

Babitha Philip and Hamad AlJassmi

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International…

Abstract

Purpose

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

Design/methodology/approach

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

Findings

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

Originality/value

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 18 January 2024

Minglang Zhang, Xue Zuo and Yuankai Zhou

The purpose of this paper is to reveal the dynamic contact characteristics of the slip ring. Dynamic contact resistance models considering wear and self-excited were established…

Abstract

Purpose

The purpose of this paper is to reveal the dynamic contact characteristics of the slip ring. Dynamic contact resistance models considering wear and self-excited were established based on fractal theory.

Design/methodology/approach

The effects of tangential velocity, stiffness and damping coefficient on dynamic contact resistance are studied. The relationships between fractal parameters, wear time and contact parameters are revealed.

Findings

The results show that the total contact area decreases with the friction coefficient and fractal roughness under the same load. Self-excited vibration occurs at a low speed (less than 0.6 m/s). It transforms from stick-slip motion at 0.4 m/s to pure sliding at 0.5 m/s. A high stiffness makes contact resistance fluctuate violently, while increasing the damping coefficient can suppress the self-excited vibration and reduce the dynamic contact resistance. The fractal contact resistance model considering wear is established based on the fractal parameters models. The validity of the model is verified by the wear tests.

Originality/value

The results have a great significance to study the electrical contact behavior of conductive slip ring.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0300/

Details

Industrial Lubrication and Tribology, vol. 76 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 20 July 2023

Shahin Rajaei Qazlue, Ahmad Mehrabian, Kaveh Khalili-Damghani and Mohammad Amirkhan

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this…

Abstract

Purpose

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

Design/methodology/approach

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

Findings

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

Originality/value

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

Details

Journal of Modelling in Management, vol. 19 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

1 – 10 of over 4000