Constraints Of Problem Research Articles

Entrepreneurial Experience and Its Impact on the Economic Sustainability of Resource Constrained Enterprises

This study examines the mechanisms that link entrepreneurial experience (EE) to sustainable enterprise economic performance and further examines the moderating role of resource constraints on the relationship of entrepreneurs’ industry and market experience on enterprise sustainable economic performance. Resource constraints problems are not linear and need exceptional consideration. The study examines how an entrepreneur’s industry and market experience assist his/her ambition of enterprise economic sustainability in a resource-constrained environment. The study is causal and cross-sectional. A primary research survey was directed by employing structured questionnaires among 348 respondents of micro small and medium enterprises (MSMEs). The data was analyzed using SPSS version 25, and for regression results, Hayes process Macro. Findings suggested that entrepreneurs' experience plays a significant role in the sustainability of enterprise economic performance in the context of resource constraints of MSMEs. The study finds entrepreneurs' experience has a link with enterprise economic sustainability however; the resource constraints suppress business outcomes. This study contributes to the literature on entrepreneurs’ experience on organizations’ economic sustainability. Whereas, resource constraints dampen the business's economic performance. This research recommends the entrepreneur must grow the business in order to develop a network for sustainable enterprise economic performance otherwise the resource constraints will hamper the growth and sustainability of enterprise performance.

Pecha Kucha Presentation in the University English Classes: Advantages and Disadvantages

The main focus of this article is to addresses issues about the pecha kucha approach in the university English classes. It is intended here to describe the impact of the of the new presentation format in the new modern University English classrooms. In order to attain the aim of this article, we conducted a bibliographic study that pays a special attention on the advantages and disadvantages of pecha kucha in university classroom. Under advantage section we can find some important features such as the pedagogical benefits, the teacher- student roles, and last but not the least, the use of technology. On the other side, the drawback section explains the three main limitations such as time constraint, content restriction, and excessive number of rehearsals. In this new decade there is a burning desire among teachers in helping students to be technologically skilled. Through the use of pecha kucha students not only acquire the necessary tech skills but also it builds in them a critical thinking. The study concludes that there are more advantages than disadvantages in using pecha kucha in the university English classes. Moreover, the study also highlights that one possible solution to mitigate the time constraint problem is to allow the flexibility at the moment of action.

A progressive hedging-based matheuristic for the stochastic production routing problem with adaptive routing

The production routing problem (PRP) arises in the context where a manufacturing facility manages its production schedule, the delivery of goods to customers by a fleet of vehicles, and the inventory levels both at the plant and at the customers. The presence of uncertainty often complicates the planning process. In particular, production and distribution costs may significantly increase if demand uncertainty is ignored in the planning phase. Nevertheless, only a few studies have considered demand uncertainty in the PRP. In this article, we propose a two-stage stochastic programming approach for a one-to-many PRP with a single product and demand uncertainty. Unlike previous studies in the literature, we consider the case where routing decisions are made in the second stage after customer demands become known. This offers more flexibility, which can decrease transportation costs by preventing unnecessary customer visits. In addition to the static–dynamic case, in which setup decisions are made first and production quantities are decided in the second stage, we also consider the static–static setting where both sets of decisions must be made prior to the demand realization. A progressive hedging algorithm combined with a matheuristic is developed to solve the problem. The role of the progressive hedging algorithm is to decompose the stochastic problem into more tractable scenario-specific subproblems and lead the first-stage variables towards convergence by modifying their Lagrangean multipliers. However, solving the subproblems remains challenging since they include routing decisions, and we thus propose a matheuristic to exploit the structural characteristics of the subproblems. First, a Traveling Salesman Problem (TSP) is solved to find the optimal tour for all customers regardless of demand and capacity. Utilizing the sequence obtained from the TSP, we then solve a restricted PRP while taking into account the other constraints of the original problem. Finally, for each period and scenario, a vehicle routing problem is solved to improve the quality of the solutions. Computational experiments are conducted to analyze the algorithm’s efficiency and to assess the benefits that can be achieved by handling routing in the second stage.

Design optimization of offshore wind farm layout based on game theory

In offshore wind farm (OWF) planning, traditional methods usually use global optimal strategies. In this paper, game theory is applied to the multi-objective optimization of wind turbine (WT) micro-siting and cabling in an OWF. This method considers the real situation where two different teams may choose the global optimal or the local optimal solution. Firstly, the wind speed deficit in the OWF is estimated using the Gaussian wake model. The objective function and constraints of this optimization problem are formulated. Secondly, the game strategies are exhaustively enumerated. The strategies are allocated to the game space matrix and the payoffs are calculated. The above steps are repeated after redistributing the design permissions. Finally, the Nash front is formed and compared with the Pareto optimum. From simulation results, it can be observed that different Nash solutions can be obtained using different strategy permission allocation methods. So the final layout of the OWF should be determined according to different strategy allocation methods when planning the OWF.

Optimisation of collapse in functionally graded thickness honeycomb under axial impact loading

This research investigates the collapse behaviour of a honeycomb energy absorber featuring variable wall thickness. Initially, numerical simulations were conducted to analyse the effect of wall thickness in upper and lower sections of the absorber, as well as the parameter 'n‘ that characterises the type of thickness variations within the wall, as design variables. A comprehensive database was compiled, encompassing various combinations of these parameters. Subsequently, a mathematical model was developed by fitting it to the initial data using the response surface methodology. The specific absorption energy and initial crush force were computed and utilised as the objective function for optimisation. Ultimately, by employing the Particle Swarm Optimisation (PSO) technique and applying problem constraints, an optimal configuration for the honeycomb absorber was determined, with a focus on maximising the specific absorption energy. The optimised outcome obtained from the optimisation algorithm exhibited an enhancement of approximately 11% compared to the maximum values of the input samples. Furthermore, multi-objective functions were employed during the optimisation process to identify absorber parameters that would achieve both the maximum specific absorption energy and the minimum initial peak force.

Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology

In this paper, a new methodology is developed for modeling and analyzing a full-duplex UAV-assisted relay system to facilitate solving the problems of UAV energy constraints and the vulnerability of UAVs to eavesdropping in the air. Combining simultaneous wireless information and power transfer (SWIPT) technology, we model the downlink UAV eavesdropping channel and propose a secure transmission protocol for a full-duplex UAV-assisted relay system. Using this transmission protocol, we analyze and derive the connectivity and security of the entire communication link, including connection probability and lower bounds on secrecy outage probability. A key intermediate step in our analysis is to derive the signal-to-digital noise ratio of the destination and eavesdropping nodes of the full-duplex UAV relay link. The analyses show that the power allocation factor λ is a trade-off between system connectivity and security, while greater eavesdropping interference needs to be sacrificed for an equal magnitude of security performance improvement under high security demand conditions.

Ghost problem, spectrum identities and various constraints on brane-localized gravity

This paper investigates the brane-localized interactions, including DGP gravity and higher derivative (HD) gravity localized on the brane. We derive the effective action on the brane, which suggests the brane-localized HD gravity suffers the ghost problem generally. Besides, we obtain novel algebraic identities of the mass spectrum, which reveal the global nature and can characterize the phase transformation of the mass spectrum. We get a powerful ghost-free condition from the spectrum identities, which rules out one type of brane-localized HD gravity. We further prove the mass spectrum is real and non-negative m2 ≥ 0 under the ghost-free condition.Furthermore, we discuss various constraints on parameters of brane-localized gravity in AdS/BCFT and wedge holography, respectively. They include the ghost-free condition of Kaluza-Klein and brane-bending modes, the positive definiteness of boundary central charges, and entanglement entropy. The ghost-free condition imposes strict constraint, which requires non-negative couplings for pure DGP gravity and Gauss-Bonnet gravity on the brane. It also rules out one class of brane-localized HD gravity. Thus, such HD gravity should be understood as a low-energy effective theory on the brane under the ghost energy scale. Finally, we briefly discuss the applications of our results.

Does state capital equity affect ESG performance of private firms?—based on the perspective of sustainable development of Chinese enterprises

Along with the intensification of global ecological problems, corporate environmental, social, and governance (ESG) issues have attracted extensive attention from the investment community, and solving these issues is crucial for sustainable development. However, little is known about the research on equity structure reform on corporate ESG performance, especially state-owned capital on private firms' ESG performance. To this end, we utilize the panel data of 1,589 privately listed firms in China from 2013 to 2021 to explore the impact of state-owned capital on the ESG performance of private firms. The findings suggest that state-owned capital significantly improves the ESG performance of private firms. In addition, the results of mechanism analysis indicate that state-owned capital stimulates ESG performance through the dual path of improving the mitigation of external financial constraint problems and internal control environment problems. We also find that firms' external environmental shocks enhance the extent to which state capital injection into private firms enhances ESG performance, and these external environmental shocks include the strength of environmental regulation in the region, whether the firms' attribute is that of heavily polluting industries, and the strength of external media attention. In summary, our findings support the conjecture that state capital injection into private firms will be followed by enhanced ESG performance, and the external environment is a factor that influences this process. Our study may provide lessons for the sustainable development of private firms and dig deeper into the internal root causes and external shock factors.

DivideMerge: A multi-vessel optimization approach for cooperative operation and maintenance scheduling in offshore wind farm

The optimization of maintenance scheduling and routing in offshore wind farms is crucial for the intelligent operation and maintenance of offshore wind energy. It involves determining the optimal timing, assigning vessels, planning routes, and efficiently completing maintenance tasks with maximum efficiency and minimal cost. Traditional branch-and-bound search methods, often used in multi-vessel cooperative maintenance scheduling models, face limitations in finding optimal solutions efficiently, while classic heuristic methods may compromise on global optimality. This paper introduces DivideMerge, a high-performance hybrid algorithm that synergizes heuristic and exact algorithms to address large-scale multi-vessel cooperative maintenance scheduling challenges in offshore wind farms. Initially, a maintenance task constraint decomposition heuristic method is utilized to break down the collaborative optimization scheduling problem into individual vessel scheduling sub-problems, ensuring adherence to the constraints of the original problem. Subsequently, the CP-SAT solver is employed to sequentially solve these sub-problems rapidly. The solutions for individual vessel scheduling are then merged to form a comprehensive solution for the multi-vessel cooperative maintenance scheduling problem. Computational results validate the effectiveness and robustness of DivideMerge, demonstrating a solution speed nearly 1000 times faster than the commercial Gurobi solver, thus offering a significant advancement in the field of offshore wind farm maintenance optimization.

Limiting the memory consumption of caching for detecting subproblem dominance in constraint problems

Solving constraint satisfaction problems often involves a large amount of redundant exploration stemming from the existence of subproblems whose information can be reused for other subproblems. Subproblem dominance is a general notion of reusability that arises when one subproblem imposes more constraints on the remaining part of the search than another subproblem and allows the search to reuse the solutions of the dominating subproblem for the dominated subproblem. The search can exploit subproblem dominance by storing the subproblems that have been explored in a cache and abandoning the current subproblem whenever the cache contains a subproblem that dominates it. While using caching makes it possible to solve problems where subproblem dominance arises orders of magnitude faster, storing all of these subproblems can require a substantial amount of memory, making it impractical in many cases. This paper analyses the dominance between different subproblems for various constraint problems, revealing that only a relatively small number of subproblems dominate other subproblems. Based on these findings, two types of strategies are proposed for reducing the number of subproblems stored in the cache: limiting the number of subproblems that can be stored in the cache and periodically cleaning up the cache. An experimental evaluation demonstrates that these strategies provide an effective instrument for reducing the memory consumption of caching, allowing it to be used on a larger scale. However, there is a trade-off between saving memory and reducing redundant exploration, as removing subproblems from the cache may prevent dominance from being detected for certain subproblems.

Improved weighted nuclear norm with total variation for removing multiplicative noise

This paper introduces an improved weighted nuclear norm with a total variation model tailored for removing multiplicative noise. The model incorporates a weight matrix to regularize the residual matrix, effectively leveraging image redundancy to differentiate various statistical properties of the noise. Since there is no guarantee of a unique solution, the model is reformulated as a linear equality constraint problem and decomposed into two subproblems. These are addressed by using the alternating direction method of multipliers and the split Bregman method, respectively. In addition, each alternative update step has a closed-form and convergent solution. After obtaining the denoised image in the log-domain, the recovered image is given by using the exponential function and bias correction. Experimental evaluations demonstrate the efficacy of our algorithms in enhancing image restoration quality.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

A generalized disjunctive graph model for a complex production problem

This work focuses on the modeling of the features of a real-world pharmaceutical production problem, where several limited resources must be gathered and scheduled in order to mix and process active substances. The problem can be modelled as a generalization of the job-shop scheduling problem, in which each job corresponds to an order. Besides the standard precedence and capacity constraints of a classical job-shop scheduling problem, release, no-wait and time interval production constraints are added. Furthermore, the processing of each job task must be assisted by an appropriate number of operators and performed by a specific machine, out of a set of feasible machines, that is temporarily installed in a compatible clean room. An innovative generalized disjunctive graph that models all the constraints and the resource conflicts is presented.

Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution

Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution

A Lagrangian relaxation approach for resource allocation problem with capacity constraints

AbstractThis study evaluates a capacitated facility location model enhanced with distance constraints for an emergency response problem, ensuring certain neighborhoods remain within an accessible range from facilities following a hurricane. The proposed model takes into account the capacity constraints for drones and vehicles. The model determines optimal locations for facilities and the distribution of supplies across the city. It also specifies which facilities should support the needs of each neighborhood and decides on the appropriate mode of transportation—ground vehicles if possible, or drones if roadways are obstructed. To solve the problem, a Lagrangian relaxation technique is employed, relaxing the constraints related to facility capacity and distance. The numerical results confirm the quality and efficiency of the solutions. The findings indicate that ground transportation is more frequently utilized than drones at each operational facility. A comprehensive set of sensitivity analyses is conducted to examine the impact of various variables and parameters on the solution.

Driving Climate Action via Government Public Relations: The Role of Government-Citizen Relationships and Situational Perceptions

ABSTRACT The purpose of this study is to predict the active communication behaviors and their Behavioral Intentions to Support Federal Climate Policies from the view of government public relations. Using data from an online survey of 548 adults living in the United States, this study investigated the roles of Government-Citizen Relationships (GCR) and situational perceptions (i.e. Problem Recognition, Constraint Recognition, and Involvement Recognition) in predicting active communication behaviors (i.e. information seeking, forefending, and forwarding) and federal climate policies in the framework of situational theory of problem solving (STOPS). The results of this study indicate that GCR is critical in predicting active communication behaviors and public support. While Involvement Recognition among situational perceptions is significant to predict active communication behaviors and public support, program recognition and Constraint Recognition are partially significant. The results highlight the pivotal mediating roles of situational perceptions – Problem Recognition, Constraint Recognition, and Involvement Recognition – in the link between GCR and public support for federal climate policies.

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

The Finite Element Method in Time for Multibody Dynamics

Abstract The generalized-α scheme has become the approach of choice for the time integration of the equations of motion of multibody systems. Despite its simplicity, this scheme presents drawbacks: the time-step size cannot be changed easily, making it difficult to implement time adaptivity, and the solution of periodic problems cannot be found easily. This paper explores an alternative approach based on the finite element method in time. The basic principles underpinning the approach are presented and both time-continuous and time-discontinuous approaches are investigated. Two types of Galerkin schemes will be presented here: the time-continuous and the time-discontinuous schemes. In the former, the displacement field is continuous across interelement boundaries, whereas discontinuities or “jumps” are allowed across interelement boundaries for the latter. Simple problems are treated to identify the best schemes. Families of schemes of various accuracies are presented. The first family, based on time-continuous elements, features schemes that do not present numerical dissipation. Asymptotic annihilation is achieved by the time-discontinuous elements that form the second family. The problem of kinematic constraints is treated within the framework of the finite element method in time. Special emphasis is devoted to the satisfaction of the kinematic constraints and their time derivative within a time element.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

This study intends to construct and optimally run a grid-connected hybrid renewable energy system (HRES) powering a residential building. A special techno-economic multi-objective optimization technique is used for sizing the HRES. In addition, an energy management strategy (EMS) is established as a distinct multi-objective optimization problem to minimize degradation and operating costs while improving system performance. A Bi-level mixed integer non-linear programming (BMINLP) is the framework used to formulate the two-optimization problems. Sizing problem is characterized by the higher level, on contrast; the nested EMS problem within the constraints of the size problem is presented on the lower level. For this investigation, the problem is executed via the MATLAB program. The global optimization toolbox is applied via the Multi-Objective Genetic Algorithm (MOGA) to solve the sizing optimization. While the EMS is executed using the "Intlinprg" mixed integer linear programming (MILP) solver. The technical and economic performance parameters such as degradation, renewable energy fraction (REF), operating, maintenance, and investment costs are evaluated in this study. The primary results reveal that the summer week encounters the lowest total cost and the highest REF, at roughly 99484 $ and 60 %, respectively. Furthermore, the investigation shows that the lowest degradation value attained in the same week with approximately 50.

A C0 Nonconforming Virtual Element Method for the Kirchhoff Plate Obstacle Problem

This paper investigates a novel C0 nonconforming virtual element method (VEM) for solving the Kirchhoff plate obstacle problem, which is described by a fourth-order variational inequality (VI) of the first kind. In our study, we distinguish our approach by introducing new internal degrees of freedom to the traditional lowest-order C0 nonconforming VEM, which originally lacked such degrees. This addition not only facilitates error estimation but also enhances its intuitiveness. Importantly, our novel C0 nonconforming VEM naturally satisfies the constraints of the obstacle problem. We then establish an a priori error estimate for our novel C0 nonconforming VEM, with the result indicating that the lowest order of our method achieves optimal convergence. Finally, we present a numerical example to validate the theoretical result.