Flexible Constraints Research Articles

Behind the internationalization of family SMEs: A strategy tripod synthesis

AbstractResearch SummaryWe review and synthesize the growing literature concerning the internationalization of small and medium‐sized family enterprises (family SMEs) around a strategy tripod framework. In doing so, we identify various resource‐based, institution‐based, and industry‐based factors that contribute to family SME internationalization endeavors. We also pinpoint possible home‐country‐level and host‐country‐level institutional and industry‐based structural–cultural contingencies that may interact with family SME resource‐based factors to accentuate or impede their internationalization. Moreover, we highlight different behavioral orientations as crucial missing links that characterize the family SME internationalization scholarship. Overall, our comprehensive synthesis of the literature sheds light on how internationalization strategies of family SMEs vary based on important drivers and contextual influences captured in our tripod framework.Managerial SummaryFamily SMEs are distinct in that they possess the attributes of family businesses (family ownership and control) as well as those of SMEs (smallness, flexibility, and resource constraints). Managing these attributes may be difficult when it comes to executing important strategies such as internationalization. Family SMEs, therefore, need to be cognizant of various factors that can potentially impact their internationalization planning and success. The strategy tripod framework that we develop can provide guidance to managers in considering how resource‐based, institution‐based, and industry‐based factors drive family SME internationalization. Our integrative framework endeavors to help managers realize how their internationalization strategies and the resultant outcomes will vary depending on how they manage the three factors.

Canadian Regional Agriculture Model: PMP Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use by a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves were then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Dynamic behaviors of single- and multi-span functionally graded porous beams with flexible boundary constraints

This paper investigates the dynamic behaviors of single- and multi-span functionally graded porous (FGP) beams with flexible boundary constraints modelled by a combination of two-dimensional translational springs and a rotational spring. It is assumed that the pores are distributed either non-uniformly or uniformly according to four porosity distributions and that the material properties vary smoothly along the thickness direction of the beam. The dynamic governing equations are derived from Hamilton's principle within the framework of Timoshenko beam theory and solved by using discrete singular convolution element method (DSCEM) in conjunction with Taylor series expansion (TSE) method. To validate the accuracy of the proposed method, the present results are compared with those in open literature and obtained by finite element method (FEM). A comprehensive parametric study is conducted to investigate the effects of spring constants, boundary condition, porosity distribution, porosity coefficient and beam span ratio on the dynamic behaviors.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Temporal leeway: can it help to reduce biomechanical load for older workers performing repetitive light assembly tasks?

Current industrial production systems allow assembly of customised products which include additional elements distinguishing them from a reference model. This customisation can result in significant additional time constraints which compel workers to complete their tasks faster, which may pose problems for older workers. The objective of this laboratory study was to investigate the impact of restrictive or flexible pacing during assembly of customised products among groups of younger and older participants. The data gathered were used to analyse cycle-time, assembly performance, muscular load, and kinematic adaptations. The flexible pacing condition was found to improve production performance, increasing customised assembly cycle-time and reducing biomechanical load, for both young and older participants. However, as the task required fine manual dexterity, older participants were subjected to a higher biomechanical load, even in the flexible pacing scenario. These results should encourage assembly-line designers to allow flexible time constraints as much as possible and to be particularly attentive to the needs of older workers.

Pareto-Optimal Transit Route Planning With Multi-Objective Monte-Carlo Tree Search

Planning ideal transit routes in the complex urban environment can improve the performance and efficiency of public transportation systems effectively. However, finding such routes is computationally difficult due to the huge solution space constituted by billions of possible routes. Considering the limited scalability of exact search methods, heuristic search methods were proposed to boost the efficiency and incorporate flexible constraints. Nevertheless, the existing methods conceal multiple criteria in an objective, and thus evaluating the performance of the generated route becomes challenging due to the lack of comparable alternatives. Inspired by the prior study, we formulate the definition of pareto-optimal transit routes based on multiple criteria. However, extracting these routes remains challenging because: A) the sheer volume of possible transit routes; and B) the sparsity of pareto-optimal routes. We address these challenges by developing an efficient search framework: for challenge A, a random search method is developed based on Monte Carlo tree search where the unproductive solution subspaces are pruned progressively to reduce the search cost; and for challenge B, an estimation method is derived to guide the search process by assessing the value for each solution subspace. The superior effectiveness of our approach in approximating the pareto-optimal transit routes was demonstrated by the comprehensive evaluation based on the real-world data.

Geometric Confined Pneumatic Soft–Rigid Hybrid Actuators

In this work, we propose a new kind of soft-rigid hybrid actuator composed mainly of soft chambers and rigid frames. Compared with the well-known fiber-reinforced soft actuators, the hybrid actuators are able to ensure the design of noncircular cross-sectional shapes. It is demonstrated that rigid frames are capable of providing geometric constraints, reducing the ineffective deformation, and improving the energy utilization for the hybrid actuators with noncircular cross-sections. The essential characteristics of rigid constraints and flexible constraints are obtained by simulation and experiments on specimens with three different cross-sectional shapes. Furthermore, a spring-fluid film model is introduced to characterize the behavior of a representative hybrid linear actuator and a bending actuator with a rectangular cross-section, and it is also proved by the corresponding experiments. The change of the cross-sectional shape of fiber-reinforced soft actuators under pressurization is also explained theoretically as a contrast. Then, two application examples, namely, a robotic gripper and a caudal fin formed from linear actuators, are designed and demonstrated, showing the advantages and potential applications of the proposed geometric confined hybrid actuators. The proposed soft-rigid hybrid actuators combine the properties of soft and rigid materials, expand the design scope of the compliant actuators, and provide new solutions for robotics, especially for soft robots with specific requirements for their shapes or profiles.

An interval joint-probabilistic stochastic flexible programming method for planning municipal-scale energy-water nexus system under uncertainty

In this study, an interval joint-probabilistic stochastic flexible programming method is proposed by integrating interval joint-probabilistic stochastic programming and flexible programming into one framework. It can not only be effective for handling interval-fuzzy information associated with flexible constraints, but also be efficient for reflecting interactions among correlated random variables as well as the resulting joint risk. It is applied to the real-world energy-water nexus system management problem in Zhengzhou City, China. A series of management schemes are investigated through the proposed approaches, including (i) five satisfaction degrees linked to the flexible constraints of water resources availability and electricity demand-supply balance, (ii) five satisfaction degrees related to the flexible constraint of power output limit, and (iii) ten scenarios for different combinations of joint and individual constraint-violation levels between water resources availability and electricity demand. Results reveal that capacity expansion of renewable energy can increase with time, and the installed capacity of renewable energy at the end planning period can increase by 29.94%. Under consideration of energy and water resources availability, maximum power output limit, electricity demand-supply balance, pollutant and CO2 emissions control, and capacity expansion, decision makers in Zhengzhou can achieve a minimum system cost under the scenario of low water resources availability and high electricity demand (i.e. S10 (p=0.2, p1=0.19, p2=0.01)). These results can effectively support the 13th Five-Year Plan of Henan Province for Energy Development, which is required to choose more renewable energies with low water consumption.

An efficient method for Contingency-Constrained Transmission Expansion Planning

This paper proposes an efficient method based on a mixed-integer linear programming (MILP) model to solve the multistage Contingency-Constrained Transmission Expansion Planning (CCTEP) problem. To account for security, an iterative algorithm based on Line Outage Distribution Factors (LODF) screens the worst-case contingency (either in existing or candidate lines) and dynamically adds constraints to the Transmission Expansion Planning (TEP) formulation to enforce the security criterion, reducing both the number of decision variables and simulation times with respect to alternative approaches. Transmission losses are included through the use of piecewise linear expressions. Furthermore, operational flexibility of generation resources is considered through the use of representative daily load curves (RDLC) to allow modeling of generator ramping constraints. The method is initially illustrated and validated through numerical simulations of the classic Garver’s system. Then, the IEEE 118-bus and 300-bus systems are used to test its performance. The proposed approach can avoid under-investment in network capacity caused by neglecting transmission losses, security, and flexibility constraints.

Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach.

Weighting window functions are commonly used in Synthetic Aperture Radar (SAR) imaging to suppress the high Peak SideLobe Ratio (PSLR) at the price of probable Signal-to-Noise Ratio (SNR) loss and mainlobe widening. In this paper, based on the method of designing a mismatched filter, we have proposed a Quadratically Constrained Quadratic Program (QCQP) approach, which is a convex that can be solved efficiently, to optimize the weighting window function with both amplitude and phase, expecting to offer better imaging performance, especially on PSLR, SNR loss, and mainlobe width. According to this approach and its modified form, we are able to design window functions to optimize the PSLR or the SNR loss under different kinds of flexible and practical constraints. Compared to the ordinary real-valued and symmetric window functions, like the Taylor window, the designed window functions are complex-valued and can be asymmetric. By using Synthetic Aperture Radar (SAR) point target imaging simulation, we show that the optimized weighting window function can clearly show the weak target hidden in the sidelobes of the strong target.

Porcine Prion Protein as a Paradigm of Limited Susceptibility to Prion Strain Propagation.

Although experimental transmission of bovine spongiform encephalopathy (BSE) to pigs and transgenic mice expressing pig cellular prion protein (PrPC) (porcine PrP [PoPrP]–Tg001) has been described, no natural cases of prion diseases in pig were reported. This study analyzed pig-PrPC susceptibility to different prion strains using PoPrP-Tg001 mice either as animal bioassay or as substrate for protein misfolding cyclic amplification (PMCA). A panel of isolates representatives of different prion strains was selected, including classic and atypical/Nor98 scrapie, atypical-BSE, rodent scrapie, human Creutzfeldt-Jakob-disease and classic BSE from different species. Bioassay proved that PoPrP-Tg001-mice were susceptible only to the classic BSE agent, and PMCA results indicate that only classic BSE can convert pig-PrPC into scrapie-type PrP (PrPSc), independently of the species origin. Therefore, conformational flexibility constraints associated with pig-PrP would limit the number of permissible PrPSc conformations compatible with pig-PrPC, thus suggesting that pig-PrPC may constitute a paradigm of low conformational flexibility that could confer high resistance to the diversity of prion strains.

An Adaptive and Flexible Brain Energized Full Body Exoskeleton With IoT Edge for Assisting the Paralyzed Patients

The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients.

An integrated approach for power transformer modeling and manufacturing

Essential characteristics of smart factories, such as flexibility and resource efficiency, can be leveraged and improved by the power of machine learning and optimization techniques. For instance, the manufacturing process of a power transformer core constitutes a highly complex optimization problem. It involves creating a cost optimal slitting plan that meets all customer requirements and at the same time takes into account flexible and short-term constraints from production (e.g. current available metal bands in stock). As many of these constraints rely on forecasts, a learning system may provide the necessary predictions for these constraints. In addition, companies apply and maintain engineering software for a variety of tasks in construction, simulation, and interpretation of data. For instance, electrical engineers use a variety of tools to design an initial model of a power transformer according to customer requirements and constraints. Such tools often incorporate knowledge that serves as input for optimization and forecast models as described before. If these models are improved over time using external machine learning libraries, the newly developed models must find their way back into the implementation of engineering tools. Knowledge scattered across multiple software systems bears risk of being inconsistent. Furthermore, keeping knowledge consistent without a systematic approach is time-consuming and error-prone. In this paper, we describe an approach that leverages software engineering methods and tools and that supports knowledge transfer between software systems for optimization and modelling tasks. The approach follows the idea of a single source of knowledge together with transformation into different representations, as required by different engineering tasks. The proposed approach was introduced at an industrial site to improve the manufacturing process of power transformer cores.

Assessment of flexibility index integration into the expansion planning of clean power resources and energy storage systems in modern distribution network using benders decomposition

High penetration of renewable resources on modern distribution networks increases the net load variability in due to the uncertain nature of these resources. Hence, the system may suffer from high ramping problem, the ignorance of which leads to the power interruption. Controlling the net load variability to provide adequate ramping also increases the operation costs significantly. Therefore, a new solution should be implemented to handle this problem. The aim of this study is to incorporate the flexibility indices into the expansion planning of distributed energy resources in modern distribution networks in order to demonstrate how optimal arrangement of flexible resources results in reducing the operation cost while controlling the net load variability. To this end, the benders decomposition approach was applied to the proposed expansion planning to reduce the complexity of the problem to a level which can be solved. The uncertainty of clean resources, load consumption and electricity market price was considered in the proposed problem along with the limitations of the investment budget and carbon emission permits. The simulation process was carried out on the IEEE 69-bus test system. The results indicate that optimal placement of the flexible resources considering the flexibility constraints is profitable from both technical and economic perspectives.

Multi-step model predictive current control of permanent-magnet synchronous motor

The traditional model predictive current control (MPCC) strategy has the advantages of a fast dynamic response and flexible constraint conditions. However, this strategy only determines the optimal voltage vector in a period rather than in multiple periods, which may result in a large current ripple. To solve the above problem, this paper proposes a multi-step MPCC strategy of permanent-magnet synchronous motor. In the proposed multi-step MPCC strategy, the optimal voltage vector and the sub-optimal voltage vector are considered simultaneously. The current response in the next period is predicted on the basis of the optimal and the sub-optimal voltage vectors, respectively. To ensure the optimality of the selected voltage vector in two control periods, the current responses of the two control periods are contrasted. Compared with the traditional MPCC strategy, simulation and experimental results show that the proposed multi-step MPCC strategy can effectively reduce the current ripple and improve the steady-state performance without increasing the switching frequency.

A new approach for solving fuzzy multi-objective quadratic programming of water resource allocation problem

This paper describes an application of fuzzy multi-objective quadratic model with flexible constraints for optimal allocation of limited available water resources among different water-user sectors. Due to the fact that, water resource allocation problem is one of the practical and essential subjects in real world and many of the parameters may be faced by uncertainty. In this paper, we present α - cut approach for transforming fuzzy multi-objective quadratic programming model with flexible constraints into a crisp form. By using this approach a multi-parametric multi-objective programming model corresponding to α and parameters of flexible constraints is obtained. One of the advantages of this model is that the α - cut level is not determined by the decision makers. Actually, this model itself can calculate the α - cut level. In order to achieve a desired Pareto optimal value of multi-parametric multi-objective model, we use goal programming method for illustration of water resource allocation with sensitivity analysis of lower bound of parameters in flexible constraints. To illustrate the efficiency of the proposed approach, we apply it for a real case problem of water resource allocation.

Hyperspectral Anomaly Detection by Fractional Fourier Entropy

Anomaly detection is an important task in hyperspectral remote sensing. Most widely used detectors, such as Reed-Xiaoli (RX), have been developed only using original spectral signatures, which may lack the capability of signal enhancement and noise suppression. In this article, an effective alternative approach, fractional Fourier entropy (FrFE)-based hyperspectral anomaly detection method, is proposed. First, fractional Fourier transform (FrFT) is employed as preprocessing, which obtains features in an intermediate domain between the original reflectance spectrum and its Fourier transform with complementary strengths by space-frequency representations. It is desirable for noise removal so as to enhance the discrimination between anomalies and background. Furthermore, an FrFE-based step is developed to automatically determine an optimal fractional transform order. With a more flexible constraint, i.e., Shannon entropy uncertainty principle on FrFT, the proposed method can significantly distinguish signal from background and noise. Finally, the proposed FrFE-based anomaly detection method is implemented in the optimal fractional domain. Experimental results obtained on real hyperspectral datasets demonstrate that the proposed method is quite competitive.

The best way to perform building simulations? One-at-a-time optimization vs. Monte Carlo sampling

What is the best approach to perform building simulations as means to support decision-making and optimize building performance? Traditionally, the search for a compliant design is carried out in a manual one-at-a-time (OAT) approach where one design parameter is optimized before attention is shifted to the next one. In contrast, the Monte Carlo method makes it possible to combine many design parameters simultaneously and consider numerous input combinations. This paper presents a comparison of design approaches and their ability to optimize the performance of an office room with respect to energy demand and indoor climate. The test case is an office space with eight discretized inputs resulting in 93,750 possible designs. Bootstrapping is used to vary the baseline and fixing order for two systematic OAT approaches and to assess an increasing number of Monte Carlo samples. Summarily, OAT optimization depends heavily on the baseline and reveals only few, local optima close to this baseline, which are potentially far from the best solutions. On average, sampling 32 random simulations unveils better solutions and is less likely to suggest poor solutions. An increased sample of 1024 simulations reveals diverse solutions and favourable input ranges, while enabling sensitivity analysis, metamodeling, and flexible constraints.

Specified-Time Vehicular Platoon Control With Flexible Safe Distance Constraint

The platoon control for vehicles can efficiently reduce traffic pressure. Current control schemes are able to achieve stable platoon control, but cannot guarantee collision avoidance, connectivity preservation and convergence time at the same time. In this paper, we propose a novel control scheme that can simultaneously guarantee the safe distance and communication connectivity, which is called flexible safe distance constraint, and also can guarantee the specified convergence time. A dynamic gain based distributed control law is first designed for the vehicle with single-integral model. Using the prescribed transient and steady-state performance control approach, the flexible safe distance constraint can be satisfied. Using the time transformation method and Lyapunov stability theory, the closed-loop systems under the proposed protocols can achieve stable platoon within the specified time. Compared with some existing results, the novelty of this work is that the proposed approaches allow a vehicle to converge with the platoon within any preset time without dependence on the initial conditions or system parameters. Another contribution of this work is to propose specified-time platoon control protocols for the vehicle with double-integral model under a flexible safe distance constraint. Furthermore, in order to get closer to the actual system, the controller is extended to the triple-integral system, and some special cases are also discussed. Finally, some simulations are presented to show the effectiveness of the proposed protocols.