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This study aims to empirically study the effect of process constraints and the combinative effects of different constraints on enterprise digital transformation.

This paper selects the World Bank's business environment survey of Chinese enterprises in 2012 as the research sample to empirically study the effect of process constraints and different kinds of constraints on enterprise digital transformation.

The authors find that process constraints have an inverted U-shaped effect on enterprise digital transformation and that employee digital literacy plays an intermediary role in this process. That is, process constraints have a too-much-of-a-good-thing effect on employees' digital literacy, which further affects enterprise digital transformation. The increase in the number of input and output constraints will make the inverted U-shaped relationship between the process constraint and digital transformation steeper.

The constraints faced by enterprises are everywhere and of many kinds. This paper not only discusses the influence of process constraints on enterprise digital transformation but also analyzes the interactive influence of different kinds of constraints on enterprise digital transformation and explores its micromechanism. This approach is helpful for enterprise managers in thinking about how to make full use of different kinds of constraints to activate the power of enterprise digital transformation, regard constraints as challenges and opportunities, and use them to stimulate the ability to improve the resource integration and utilization.

The purpose of this paper is to present a new method for solving parametric programming problems; a new scheme of constraints fuzzification. In the proposed approach, constraints are learned based on deductive learning.

Adaptive neural‐fuzzy inference system (ANFIS) is used for constraint learning by generating input and output membership functions and suitable fuzzy rules.

The experimental results show the ability of the proposed approach to model the set of constraints and solve parametric programming. Some notes in the proposed method are clustering of similar constraints, constraints generalization and converting crisp set of constraints to a trained system with fuzzy output. Finally, this idea for modeling of constraint in the support vector machine (SVM) classifier is used and shows that this approach can obtain a soft margin in the SVM.

Properties of the new scheme such as global view of constraints, constraints generalization, clustering of similar constraints, creation of real fuzzy constraints, study of constraint strength and increasing the degree of importance to constraints are different aspects of the proposed method.

The purpose of this study is to improve vehicles’ brake stability, the problem of constraint control for an antilock braking system (ABS) with asymmetric slip ratio constraints is concerned. A nonlinear control method based on barrier Lyapunov function (BLF) is proposed not only to track the optimal slip ratio but also to guarantee no violation on slip ratio constraints.

A quarter vehicle braking model and Burckhardt’s tire model are considered. The asymmetric BLF is introduced into the controller for solving asymmetric slip ratio constraint problems.

The proposed controller can implement ABS zero steady-state error tracking of the optimal wheel slip ratio and make slip ratio constraints flexible for various runway surfaces and runway transitions. Simulation and experimental results show that the control scheme can guarantee no violation on slip ratio constraints and avoid self-locking.

The slip rate equation with uncertainties is established, and BLF is introduced into the design process of the constrained controller to realize the slip rate constrained control.

Each production unit is responsible for the protection of the environment. The restricted undesirable production effects lower environmental damage. This paper emphasizes a proportional reduction of the undesirable outputs, and it supports the growth of desirable outputs as much as possible as well. The two-stage proposed model not only considers the viewpoint of the managers to follow the environmental regulations but also it assigns some bounds on producing undesirable factors according to international environmental protocols. Additionally, the restricted bounds on the undesirable outputs, in both stages, enhance the discriminatory ability of the model.

Two-stage network structure based on Data Envelopment Analysis (DEA) is applied as the main methodology for this paper. The advantages of the proposed model are appointed to assess the environmental units.

Comparing with the existing models, the proposed approach presents a new two-stage model to deal with the environmental issues. Furthermore, the discriminatory ability of the efficiency scores is improved. The distribution of this model is greater than the existing ones.

This paper is fully written, submitted and revised during limitations caused by coronavirus .

The proposed method is employed in two different cases. The efficiency scores of 25 power plants and 13 poultry farms are determined. In fact, the undesirable outputs never meet zero in the process of production but they can be reduced. The results of this research support the effect of the undesirable factors' restriction on the reduction scenario. Both of the examples show that imposing the upper bounds for the undesirable products provide low-efficiency results in comparison with the existing model. On the other hand, the results cover the arguments of sustainability in the evaluation of environmental efficiency.

In the production process, desirable outputs and undesirable factors are produced jointly so undesirable factors never meet zero. This paper develops a new two-stage method to reduce the undesirable outputs at each stage. First, the model confirms the reduction of undesirable outputs. Second, this model imposes restrictions on intermediate and final undesirable outputs according to environmental rights and the concerns of the managers. The model increases the discrimination of the efficiency assessment of real-life two-stage environmental systems as well. Then it focuses on the production of desirable outputs. The new objective function is defined according to the aim of the proposed model that not only declares better efficiency decomposition to the individual system but also the efficiency score is evaluated for each stage.

To manage cash flow in supply chains, the purpose of this paper is to propose inverse data envelopment analysis (DEA) model.

This paper develops an inverse range directional measure (RDM) model to deal with positive and negative values. The proposed model is developed to estimate input and output variations such that not only efficiency score of decision making unit (DMU) remains unchanged, but also efficiency score of other DMUs do not change.

Given that auto making industry deals with huge variety and volumes of parts, cash flow management is so important. In this paper, inverse RDM models are developed to manage cash flow in supply chains. For the first time, the authors propose inverse DEA models to deal with negative data. By applying the inverse DEA models, managers distinguish efficient DMUs from inefficient ones and devise appropriate strategies to increase efficiency score. Given results of inverse integrated RDM model, other combinations of cash flow strategies are proposed. The suggested strategies can be taken into account as novel strategies in cash flow management. Interesting point is that such strategies do not lead to changes in efficiency scores.

In this paper, inverse input and output-oriented RDM model is developed in presence of negative data. These models are applied in resource allocation and investment analysis problems. Also, inverse integrated RDM model is developed.

Examines output determination in a sample of 50 small engineering firms with up to 100 employees in the Hampshire, Sussex, Dorset and Wiltshire region during 1992‐1997. Face‐to‐face interviews using a personal, open, unstructured approach were conducted in all firms with selected follow‐up discussions during the five‐year period. The findings indicate disconcerting problems of output determination within the sample. As a result of non‐optimum approaches to output determination displayed by the firms, financial returns will inevitably tend to remain disappointing for the small engineering firm. Seeks to provide all those interested in the promotion of business activity in the small firm sector, such as industry itself, higher education, government departments, the advisory bodies, consultants and local authorities, with a clearer appreciation of the real motivations and weaknesses behind output decision making.

The paper aims to describe and compare multiple methods for estimating the technical efficiency of 113 US oil refineries in operation in 2006 and 2007, considering undesirable output in a production process.

A technology that satisfies weak disposability between desirable and undesirable outputs is constructed by allowing different abatement factors across all refineries. Several measures based on data envelopment analysis approaches are implemented and compared to study the impact of disposability assumptions and to investigate the effects of using non‐uniform abatement factors. A hyperbolic efficiency measure is used to analyze the potential output loss of each refinery due to environmental regulations.

The results indicate that domestic refineries can improve efficiencies regardless of the disposability assumptions and that environmental regulations reduce the amount of potentially desirable outputs produced by some facilities. However, refineries in the western USA appear to be the most affected by regulations. In general, efficient refineries are less likely to be affected.

Undesirable outputs are limited to toxic release. Undesirable outputs generated from refining crude oil, such as greenhouse gases, can be used when data are available. The desirable outputs in this paper do not include premium products, such as lubricants, which could raise the efficiency estimates of complex refineries.

To the authors' knowledge, this paper is the first implementation of the weakly disposable technology constructed by different uniform abatement factors. Further, the paper investigates the effects of various disposability assumptions on efficiency estimation. The result clearly identifies refineries that use their resources efficiently. The paper suggests that the data may be used to augment managerial decision‐making regarding benchmarking and best practices.

This paper aims to introduce an original application of the corrected response surface method (CRSM) in the context of the optimal design of a permanent magnet synchronous machine used as an integrated starter generator. This method makes it possible to carry out this design in a very efficient manner, in comparison with conventional optimization approaches.

The search for optimal conditions is achieved by the joint use of two multi-physics models of the machine to be optimized. The former models most finely the physical functioning of the machine; it is called “fine model”. The second model describes the same physical phenomena as the fine model but must be much quicker to evaluate. Thus, to minimize its evaluation time, it is necessary to simplify it considerably. It is called “coarse model”. The lightness of the coarse model allows it to be used intensively by conventional optimization algorithms. On the other hand, the fine reference model makes it possible to recalibrate the results obtained from the coarse model at any instant, and mainly at the end of each classical optimization. The difference in definition between fine and coarse models implies that these two models do not give the same output values for the same input configuration. The approach described in this study proposes to correct the values of the coarse model outputs by constructing an adjustment (correcting) response surface. This gives the name to this method. It then becomes possible to have the entire load of the optimization carried over to the coarse model adjusted by the addition of this correction response surface.

The application of this method shows satisfactory results, in particular in comparison with those obtained with a traditional optimization approach based on a single (fine) model. It thus appears that the approach by CRSM makes it possible to converge much more quickly toward the optimal configurations. Also, the use of response surfaces for optimization makes it possible to capitalize the modeling data, thus making it possible to reuse them, if necessary, for subsequent optimal design studies. Numerous tests show that this approach is relatively robust to the variations of many important functioning parameters.

The CRSM technique is an indirect multi-model optimization method. This paper presents the application of this relatively undeveloped optimization approach, combining the features and benefits of (Indirect) efficient global optimization techniques and (multi-model) space mapping methods.

This paper uses various Data Envelopment Analysis (SBM-DEA) approaches to study the efficiency of major airlines in Asia-Pacific region. To evaluate the operation efficiency of fourteen major airlines in Asia-Pacific region from 2003-2011, Available Seat Kilometers(ASK), Available Ton Kilometers(ATK), the number of employees are used as input factors, Revenue Passenger Kilometers(RPK), Revenue Ton Kilometers(RTK), the amount of Sales are used as output factors.

The non-radial SBM-DEA (Slacks-based Measure of Efficiency) model was able to provide a more comprehensive efficiency of combining economic performance and regional difference. And it was also able to capture slack values in input excess and output shortage.

The results demonstrate that Korea and Japan airlines are operated efficiently and could be regarded as the benchmarking airlines. On the other hand, most of the China and ASEAN airlines are deemed to be inefficient. Also analyzing slacks may be more suitable way for the evaluation or suggestion of an improvement scheme for the inefficient airlines. The excess of labor is the major cause of the airlines’ inefficiency.

The purpose of this article is to evaluate performance of minimum energy controllers thoroughly on a tiltrotor aircraft.

Minimum energy controllers are designed for tiltrotor aircraft models for helicopter and airplane modes. Performance of minimum energy controllers is evaluated with respect to several criteria.

Minimum energy controllers can be used for tiltrotor aircraft flight control system design. These controllers show satisfactory performance when noise intensities and variance bounds vary.

Minimum energy controllers can be implemented for tiltrotor aircraft flight control system design.

In this paper, minimum energy controllers are applied for tiltrotor aircraft flight control system design and the performance of minimum energy controllers is evaluated deeply on a complex physical system (i.e. tiltrotor aircraft).