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1 – 10 of 857Krish Sethanand, Thitivadee Chaiyawat and Chupun Gowanit
This paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated…
Abstract
Purpose
This paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.
Design/methodology/approach
Crop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.
Findings
The framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.
Originality/value
This paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.
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Xiaofeng Xu, Wenzhi Liu, Mingyue Jiang and Ziru Lin
The rapid development of smart cities and green logistics has stimulated a lot of research on reverse logistics, and the diversified data also provide the possibility of…
Abstract
Purpose
The rapid development of smart cities and green logistics has stimulated a lot of research on reverse logistics, and the diversified data also provide the possibility of innovative research on location-routing problem (LRP) under reverse logistics. The purpose of this paper is to use panel data to assist in the study of multi-cycle and multi-echelon LRP in reverse logistics network (MCME-LRP-RLN), and thus reduce the cost of enterprise facility location.
Design/methodology/approach
First, a negative utility objective function is generated based on panel data and incorporated into a multi-cycle and multi-echelon location-routing model integrating reverse logistics. After that, an improved algorithm named particle swarm optimization-multi-objective immune genetic algorithm (PSO-MOIGA) is proposed to solve the model.
Findings
There is a paradox between the total cost of the enterprise and the negative social utility, which means that it costs a certain amount of money to reduce the negative social utility. Firms can first design an open-loop logistics system to reduce cost, and at the same time, reduce negative social utility by leasing facilities.
Practical implications
This study provides firms with more flexible location-routing options by dividing them into multiple cycles, so they can choose the right option according to their development goals.
Originality/value
This research is a pioneering study of MCME-LRP-RLN problem and incorporates data analysis techniques into operations research modeling. Later, the PSO algorithm was incorporated into the crossover of MOIGA in order to solve the multi-objective large-scale problems, which improved the convergence speed and performance of the algorithm. Finally, the results of the study provide some valuable management recommendations for logistics planning.
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Yangin Fan and Emmanuel Guerre
The asymptotic bias and variance of a general class of local polynomial estimators of M-regression functions are studied over the whole compact support of the multivariate…
Abstract
The asymptotic bias and variance of a general class of local polynomial estimators of M-regression functions are studied over the whole compact support of the multivariate covariate under a minimal assumption on the support. The support assumption ensures that the vicinity of the boundary of the support will be visited by the multivariate covariate. The results show that like in the univariate case, multivariate local polynomial estimators have good bias and variance properties near the boundary. For the local polynomial regression estimator, we establish its asymptotic normality near the boundary and the usual optimal uniform convergence rate over the whole support. For local polynomial quantile regression, we establish a uniform linearization result which allows us to obtain similar results to the local polynomial regression. We demonstrate both theoretically and numerically that with our uniform results, the common practice of trimming local polynomial regression or quantile estimators to avoid “the boundary effect” is not needed.
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Xiangqian Sheng, Wenliang Fan, Qingbin Zhang and Zhengling Li
The polynomial dimensional decomposition (PDD) method is a popular tool to establish a surrogate model in several scientific areas and engineering disciplines. The selection of…
Abstract
Purpose
The polynomial dimensional decomposition (PDD) method is a popular tool to establish a surrogate model in several scientific areas and engineering disciplines. The selection of appropriate truncated polynomials is the main topic in the PDD. In this paper, an easy-to-implement adaptive PDD method with a better balance between precision and efficiency is proposed.
Design/methodology/approach
First, the original random variables are transformed into corresponding independent reference variables according to the statistical information of variables. Second, the performance function is decomposed as a summation of component functions that can be approximated through a series of orthogonal polynomials. Third, the truncated maximum order of the orthogonal polynomial functions is determined through the nonlinear judgment method. The corresponding expansion coefficients are calculated through the point estimation method. Subsequently, the performance function is reconstructed through appropriate orthogonal polynomials and known expansion coefficients.
Findings
Several examples are investigated to illustrate the accuracy and efficiency of the proposed method compared with the other methods in reliability analysis.
Originality/value
The number of unknown coefficients is significantly reduced, and the computational burden for reliability analysis is eased accordingly. The coefficient evaluation for the multivariate component function is decoupled with the order judgment of the variable. The proposed method achieves a good trade-off of efficiency and accuracy for reliability analysis.
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Karim Atashgar and Leila Abbassi
Different real cases indicate that the quality of a process is better monitored by a functional relationship rather than the traditional statistical process control (SPC) methods…
Abstract
Purpose
Different real cases indicate that the quality of a process is better monitored by a functional relationship rather than the traditional statistical process control (SPC) methods. This approach is referred to as profile monitoring. A serious objective in profile monitoring is the sensitivity of a model to very small changes of the process. The rapid progress of the precision manufacturing also indicates the importance of identifying very small shift types of a process/product profile curve. This sensitivity allows one to identify the fault of a process sooner compared to the case of lack of the capability.
Design/methodology/approach
This paper proposed a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method was named as MGWMA-PF. The performance capability of the proposed approach was evaluated through several numerical examples. A real case study was also used to investigate the capability of the proposed model.
Findings
The results addressed that the proposed method was capable of detecting very small shift types effectively. The numerical report based on the average run length (ARL) term revealed the more sensitivity of the proposed model compared to other existing methods of the literature.
Originality/value
This paper proposes a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method provides detecting a very small change manifested itself to the process.
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Dante Amengual, Gabriele Fiorentini and Enrique Sentana
The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several…
Abstract
The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.
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Sam K. Formby, Manoj K. Malhotra and Sanjay L. Ahire
Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is…
Abstract
Purpose
Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is an increasing body of research based on complexity theory (CT) suggesting that constructs such as these should be viewed as variables in a complex system with inter-dependencies, interactions, and potentially nonlinear relationships. Despite the significant body of conceptual research related to CT, there is a lack of methodological research into these potentially nonlinear effects. The purpose of this paper is to demonstrate the theoretical and practical importance of non-linear terms in a multivariate polynomial model as they become more significant predictors of firm success in collaborative environments and less significant in more rigidly controlled work environments.
Design/methodology/approach
Multivariate polynomial regression methods are used to examine the significance and effect sizes of interaction and quadratic terms in operations scenarios expected to have varying degrees of complex and complex adaptive behaviors.
Findings
The results find that in highly collaborative work environments, non-linear and interaction effects become more significant predictors of success than the linear terms in the model. In more rigid, less collaborative work environments, these effects are not present or significantly reduced in effect size.
Research limitations/implications
This study shows that analytical methods sensitive to detecting and measuring nonlinearities in relationships such as multivariate polynomial regression models enhance our theoretical understanding of the relationships between constructs when the theory predicts that complex and complex adaptive behaviors are present and important.
Originality/value
This study demonstrates that complex adaptive behaviors between management and the workforce exist in certain environments and provide greater understanding of factor relationships relating to firm success than more traditional linear analytical methods.
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R. Dyczij‐Edlinger and O. Farle
The purpose of this paper is to enable fast finite element (FE) analysis of electromagnetic structures with multiple geometric design variables.
Abstract
Purpose
The purpose of this paper is to enable fast finite element (FE) analysis of electromagnetic structures with multiple geometric design variables.
Design/methodology/approach
The proposed methodology combines multi‐variable model‐order reduction with mesh perturbation techniques and polynomial interpolation of parameter‐dependent FE matrices.
Findings
The resulting reduced‐order models are of comparable accuracy as but much smaller size than the original FE systems and preserve important system properties such as reciprocity.
Research limitations/implications
The method is limited to mesh variations that are obtained from a nominal discretization by continuous deformation. Topological changes in the mesh are not permissible.
Practical implications
In contrast to the underlying FE models, the resulting reduced‐order systems are very cheap to analyze. Possible applications include parametric libraries, design optimization, and real‐time control.
Originality/value
The paper extends the scope of moment‐matching order‐reduction techniques to a class of non‐polynomial systems arising from FE models with geometric parameters.
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This paper aims to design an optimal shape for an annular S-duct, considering both energy losses and exit flow uniformity, starting from a given baseline design. Moreover, this…
Abstract
Purpose
This paper aims to design an optimal shape for an annular S-duct, considering both energy losses and exit flow uniformity, starting from a given baseline design. Moreover, this paper seeks to identify the design factors that affect the optimal annular S-duct designs.
Design/methodology/approach
The author has carried out computational fluid dynamic (CFD)-based shape optimization relative to five distinct numerical objectives, to understand their interrelations in optimal designs. Starting from a given baseline S-duct design, they have applied control node-induced shape deformations and high-order polynomial response surfaces for modeling the functional relationships between the shape variables and the numerical objectives. A statistical correlation analysis is carried out across the optimal designs.
Findings
The author has shown by single-objective optimization that the two typical goals in S-duct design, energy loss minimization and exit flow uniformity, are mutually contradictory. He has presented a multi-objective solution for an optimal shape, reducing the total pressure loss by 15.6 per cent and the normalized absolute radial exit velocity by 34.2 per cent relative to a baseline design. For each of the five numerical objectives, the best optimization results are obtained by using high-order polynomial models.
Research limitations/implications
The methodology is applicable to axisymmetric two-dimensional geometry models.
Originality/value
This paper applies a recently introduced shape optimization methodology to annular S-ducts, and, it is, to the author’s knowledge, the first paper to point out that the two widely studied design objectives for annular S-ducts are contradictory. This paper also addresses the value of using high-order polynomial response surface models in CFD-based shape optimization.
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