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This article focusses on the hybrid strategy, a simultaneous combination of cost leadership and differentiation strategy. The study aims to examine the impact of hybrid strategy on firm performance through its anticipated positive effects on process and product innovation. In addition, we study the moderating role of adaptive capacity in the direct relationships of hybrid strategy with process and product innovation.

Structural equation modelling was used to analyse 1,842 Spanish firms with fewer than 250 employees. We randomly selected small and medium-sized enterprises (SMEs) operating in Spain from the Spanish Central Business Directory (2021) database. The overall sample design was based on stratified sampling.

We found that hybrid strategy is positively related to firm performance and to process and product innovation. Additionally, in firms implementing hybrid strategies, process innovation fostered firm performance. Finally, adaptive capacity strengthened the relationships of hybrid strategy with process and product innovation. This sheds light on how and when hybrid strategy is most effective in fostering SME performance.

We highlight that SMEs need to establish strategies that use diverse resources and capabilities and not just generate competitive advantage using one strategy (cost leadership or differentiation strategy). This requires an agile and flexible systems and structures.

Our research provides novel results by proposing the adoption of hybrid strategies instead of pure strategies (cost leadership and differentiation strategy) as a way for SMEs to survive during crises. Unlike “stuck in the middle” strategies, our study demonstrates the importance of hybrid strategies in a comprehensive model that links them to innovation and firm performance, with adaptive capacity being a determining factor.

Hydrodynamic parameter estimation is significant for the velocity prediction of unmanned surface vehicles. Considering the field data’s uncertain nonlinearities (environmental disturbances and measurement noise), this paper aims to propose a hybrid adaptive parameter estimation (HAPE) strategy.

First, a rough estimation of hydrodynamic parameters is used by the least squares method. Second, an improved adaptive parameter estimation algorithm is applied to compensate for the influence of uncertain nonlinearities and adjust the parameters within the rough range. Finally, it is proved that the calculated velocity asymptotically converges to the actual value during the parameter estimation procedure.

The numerical simulation and pool experiments are conducted in two scenarios of steady turning and sinusoidal thrust to verify the effectiveness of the proposed HAPE method. The results validate that the accuracy of the predicted velocity using the hydrodynamic model obtained by the HAPE strategy is better than the APE algorithm. In addition, the hydrodynamic parameters estimated with the sinusoidal thrust data are more applicable than the steady turning data.

This study proposes a HAPE strategy that considers the uncertain nonlinearities of the field data. This method provides a more accurate predicted velocity. Besides, as far as we know, it is the first time to analyze the influence of different test conditions on the accuracy of the predicted velocity.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems.

For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping.

The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation.

The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems.

The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature.

It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The purpose of this paper is to provide a simulation model for assessing innovation deployment strategies by evaluating and comparing their outcomes using a hybrid modeling and simulation of agent‐based modelling and simulation (ABMS) and system dynamics (SD). Since successful deployment of innovations in any organization is as important as the innovations themselves, how to choose a suitable deployment strategy and assess its effectiveness before actual implementation is a critical task.

This paper adopts a hybrid modeling and simulation approach combining the advantages of agent‐based modeling and system dynamics to study the activities and strategies involved in innovation deployment. The developed model was verified and validated with the data from GM's OnStar project.

The research demonstrates that evaluating various deployment strategies for desirable results through hybrid modeling and simulation is possible and useful by finding critical factors and making appropriate forecast, which would aid managers in assessment of innovation deployment strategies and deployment decision‐making processes.

The hybrid modeling and simulation approach provides a powerful tool in various study fields, not only in industries, but also suitable to evaluate the system outputs in both macroscopic and microscopic point of view for many strategy‐making or consulting firms. The presented work is meant for proof‐of‐concept, so numerous expansions are necessary for use in different projects.

For the case evaluated in this paper, it is found that a customer‐oriented strategy outperforms a cost‐oriented strategy in market share as well as customer satisfaction and profit. Cooperation and R&D are essential innovation drivers, but from the study, customer satisfaction does not solely depend on technical advantages. Alliance is a key factor to reduce the cost and risk of uncertainties. The structure and approach of the presented hybrid simulation model can be adopted for other cases to derive conclusions.

The originality of the paper lies in the way of adopting a hybrid modeling and simulation approach to study the effects of innovation deployment strategies. The model can be modified and used in a wide range of organizations to evaluate different strategies and aid decision‐making processes.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This paper aims to understand the challenges of managing projects in hybrid organizations. The authors explore how organizations with persistent competing institutional logics strive to balance competing priorities, and the authors craft a research agenda to examine the capabilities to manage projects in hybrid organizations.

The authors focus on the social enterprise hybrid organizational form to study how such organizations manage persistent competing social and economic logics. The authors review the project management and social enterprise literature to generate new insights and suggest future research directions for theory development for project management.

The understanding of the influences of the institutional context on the management of projects is still quite limited. The authors propose that project managers need adaptive capabilities to address how the dual logics, and their corresponding different expectations, can be flexibly combined. The objective is not to reduce the complexity due to the different logics, which is the focus of much of the literature on institutional complexity. Instead, the focus is on how to incorporate dual logics into a successfully blended hybrid organization.

There is a dearth of literature about how projects are successfully managed in hybrid organizations with persistent competing institutional logics, like social enterprises, and important questions remain to be answered. This paper offers new insights on the capabilities required to flexibly combine dual logics that would generally compete and create conflict on projects in hybrid organizations.

This paper aims to present a smooth transition adaptive hybrid impedance control for compliant connector assembly.

The dynamics of the manipulator is firstly presented with linear property. The controller used in connector assembly is inspired by human operation habits in similar tasks. The hybrid impedance control is adopted to apply force in the assembly direction and provide compliance in rest directions. The reference trajectory is implemented with an adaptive controller. Event-based switching strategy is conducted for a smooth transition from unconstrained to constrained space.

The method can ensure both ideal compliance behaviour with dynamic uncertainty and a smooth transition from unconstrained to constrained space. Also, the method can ensure compliant connector assembly with a good tolerance to the target estimation error.

The method can be applied in the connector assembly by “pushing” operation. The controller devotes efforts on force tracking and smooth transition, having potential applications in contact tasks in delicate environment.

As far as the authors know, the paper is original in providing a uniform controller for improving force and position control performance in both unconstrained and constrained space with dynamic uncertainty. The proposed controller can ensure a smooth transition by only adjusting parameters.

The purpose of this paper is to take transient contact force response, overshoots and steady-state force tracking error problems into account to form an excellent force controller.

The basic impedance function with a pre-PID tuner is designed to improve the force response. A dynamic adaptive adjustment function that combines the advantages of hybrid impedance and adaptive hybrid impedance control is presented to achieve both force overshoots suppressing and tracking ability.

The introduced pre-PID tuner impedance function can achieve more than the pure impedance function in aspects of converging to the desired value and reducing the force overshoots. The performance of force overshoots suppression and force tracking error are maintained by introducing the dynamic adaptive sigma adjustment function. The simulation and experimental results both show the achieved control performance by comparing with the previous control methods.

The implementation of the controller is easy and convenient in practical manufacture scenes that require force control using industrial robots.

A superior robot controller adapting to a variety of complex tasks owing to the following characteristics: maintenance of high-accuracy position tracking capability in free-space (basic capabilities of modern industrial robots); maintenance of high speed, stability and smooth contact performance in collision stage; and presentation of high-precision force tracking capability in steady contact.