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Publication date: 30 January 2023

Benedetta Esposito, Ornella Malandrino, Maria Rosaria Sessa and Daniela Sica

The improvement of the agri-food supply chain sustainability plays pivotal role in the planet’s survival and in overcoming of climate disasters. Digital technologies that support…

Abstract

The improvement of the agri-food supply chain sustainability plays pivotal role in the planet’s survival and in overcoming of climate disasters. Digital technologies that support the collection of Big Data produced along the agri-food supply chain (SC) emerge as powerful tools to accelerate the ecological transition of the sector. Digital technologies can support the implementation of circular business models by sharing data across the SC, monitoring in real time the materials flow, automatizing some agricultural practices and improving the decision-making through the development of decision support systems. Despite the relevance of these arguments, there is a lack of shared frameworks and guidelines for the effective development of a “data-driven circular economy” in the agri-food SC. In this scenario, this chapter examines how scholars investigate data-oriented strategies to accelerate the ecological transition and the adoption of circular economy (CE) models in the agri-food sector (AFS). To this end, a systematic literature review (SLR) was performed. Twenty-nine papers were selected following a rigorous sampling process. Both bibliometric and descriptive results are provided in the first part of this chapter. According to the analytical framework developed, the selected papers were examined in light of the “reduce, reuse and recycle” (3R) paradigm. Moreover, an additional R was retrieved from the systematic review (i.e., redesign), broadening the analytical perspective. The results indicate that scholars have predominantly provided theoretical contributions concerning the role of digital technologies and big data for the agri-food circular transition from a macro-perspective. The findings are useful for policy-makers and managers, who can promote and implement the big data-oriented approach to facilitate the circular transition. Limitations and future research directions are also provided.

Details

Big Data and Decision-Making: Applications and Uses in the Public and Private Sector
Type: Book
ISBN: 978-1-80382-552-6

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Article
Publication date: 26 December 2023

An Thi Binh Duong, Teck Lee Yap, Vu Minh Ngo and Huy Truong Quang

The growing awareness of climate risks associated with food safety issues has drawn the attention of stakeholders urging the food industry to carry out a sustainable food safety…

733

Abstract

Purpose

The growing awareness of climate risks associated with food safety issues has drawn the attention of stakeholders urging the food industry to carry out a sustainable food safety management system (FSMS). This study aims to investigate whether the critical success factors (CSFs) of sustainable FSMS can contribute to achieving climate neutrality, and how the adoption of FSMS 4.0 supported by the Industry Revolution 4.0 (IR 4.0) technologies moderates the impact of the CSFs on achieving climate neutrality.

Design/methodology/approach

Survey data from 255 food production firms in China and Vietnam were utilised for the empirical analysis. The research hypotheses were examined using structural equations modelling (SEM) with route analysis and bootstrapping techniques.

Findings

The results show that top management support, human resource management, infrastructure and integration appear as the significant CSFs that directly impact food production firms in achieving climate neutrality. Moreover, the results demonstrate that the adoption of FSMS 4.0 integrated with the three components (ecosystems, quality standards and robustness) significantly moderates the impact of the CSFs on achieving climate neutrality with lower inputs in human resources, infrastructure investment, integration and external assistance, and higher inputs in strengthening food safety administration.

Originality/value

This study provides empirical findings that fill the research gap in understanding the relationship between climate neutrality and the CSFs of sustainable FSMS while considering the moderating effects of the FSMS 4.0 components. The results provide theoretical and practical insights into how the food production sector can utilise IR 4.0 to attain sustainable FSMS for achieving climate neutrality.

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The International Journal of Logistics Management, vol. 35 no. 3
Type: Research Article
ISSN: 0957-4093

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Article
Publication date: 7 November 2024

Elyas Baboli Nezhadi, Mojtaba Labibzadeh, Farhad Hosseinlou and Majid Khayat

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

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Abstract

Purpose

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

Design/methodology/approach

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

Findings

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

Originality/value

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

Details

International Journal of Structural Integrity, vol. 15 no. 6
Type: Research Article
ISSN: 1757-9864

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Article
Publication date: 29 July 2014

M.Q. Chau, X. Han, C. Jiang, Y.C. Bai, T.N. Tran and V.H. Truong

The performance measure approach (PMA) is widely adopted for reliability analysis and reliability-based design optimization because of its robustness and efficiency compared to…

335

Abstract

Purpose

The performance measure approach (PMA) is widely adopted for reliability analysis and reliability-based design optimization because of its robustness and efficiency compared to reliability index approach. However, it has been reported that PMA involves repeat evaluations of probabilistic constraints therefore it is prohibitively expensive for many large-scale applications. In order to overcome these disadvantages, the purpose of this paper is to propose an efficient PMA-based reliability analysis technique using radial basis function (RBF).

Design/methodology/approach

The RBF is adopted to approximate the implicit limit state functions in combination with latin hypercube sampling (LHS) strategy. The advanced mean value method is applied to obtain the most probable point (MPP) with the prescribed target reliability and corresponding probabilistic performance measure to improve analysis accuracy. A sequential framework is proposed to relocate the sampling center to the obtained MPP and reconstruct RBF until a criteria is satisfied.

Findings

The method is shown to be better in the computation time to the PMA based on the actual model. The analysis results of probabilistic performance measure are accurately close to the reference solution. Five numerical examples are presented to demonstrate the effectiveness of the proposed method.

Originality/value

The main contribution of this paper is to propose a new reliability analysis technique using reconstructed RBF approximate model. The originalities of this paper may lie in: investigating the PMA using metamodel techniques, using RBF instead of the other types of metamodels to deal with the low efficiency problem.

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Engineering Computations, vol. 31 no. 6
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 12 October 2020

Ali Kaveh, Hossein Akbari and Seyed Milad Hosseini

This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of…

326

Abstract

Purpose

This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of the proposed method in comparison to other optimization methods, two sets of test problems consisting of 13 constrained benchmark functions and 6 benchmark trusses are investigated numerically. The results indicate that the performance of the proposed method is competitive with other considered state-of-the-art optimization methods.

Design/methodology/approach

In this paper, a new physically-based metaheuristic algorithm called plasma generation optimization (PGO) algorithm is developed for solving constrained optimization problems. PGO is a population-based optimizer inspired by the process of plasma generation. In the proposed algorithm, each agent is considered as an electron. Movement of electrons and changing their energy levels are based on simulating excitation, de-excitation and ionization processes occurring through the plasma generation. In the proposed PGO, the global optimum is obtained when plasma is generated with the highest degree of ionization.

Findings

A new physically-based metaheuristic algorithm called the PGO algorithm is developed that is inspired from the process of plasma generation.

Originality/value

The results indicate that the performance of the proposed method is competitive with other state-of-the-art methods.

Details

Engineering Computations, vol. 38 no. 4
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 7 July 2021

Amirhessam Tahmassebi, Mehrtash Motamedi, Amir H. Alavi and Amir H. Gandomi

Engineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find…

233

Abstract

Purpose

Engineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap.

Design/methodology/approach

The essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models.

Findings

The proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance.

Originality/value

Although, the current study reveals that the geometric input features and reinforcement indices are the most important variables in failure modes detection, better model can be achieved with employing more robust strategies to establish proper database to decrease the errors in some of the failure modes identification.

Details

Engineering Computations, vol. 39 no. 2
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 30 December 2022

Aishwarya Narang, Ravi Kumar and Amit Dhiman

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and…

437

Abstract

Purpose

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Design/methodology/approach

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

Findings

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

Originality/value

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

Details

Multidiscipline Modeling in Materials and Structures, vol. 19 no. 2
Type: Research Article
ISSN: 1573-6105

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Article
Publication date: 19 October 2023

Jing Gao, Yang Gao, Tao Guan, Sisi Liu and Tao Ma

This paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and…

615

Abstract

Purpose

This paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and discusses the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two.

Design/methodology/approach

This paper starts with the weakening mechanism of supply chain digitization on bullwhip effect, builds bullwhip effect models of traditional supply chain and digital supply chain, respectively, simulates the influence of supply chain digitization transformation on bullwhip effect by using Matlab software and analyzes the causes of bullwhip effect in supply chain led by T company and the digitization process.

Findings

Firstly, digitization can reduce bullwhip effect in multi-level supply chain by reducing information feedback deviation. Second, digital transformation is conducive to improving the overall performance of the supply chain. Third, government incentives can promote the digital transformation of supply chain and inhibit bullwhip effect.

Research limitations/implications

Although the study considers the heterogeneous subject -- the government's incentive effect on digital transformation and information sharing – it does not include the influence of the end node in the supply chain, that is the consumer. In addition, this paper only analyzes and discusses the bullwhip effect on the amplification of demand, without considering the situation that the market contraction will lead to the reduction of demand.

Practical implications

This paper considers the distortion degree and delay degree of information feedback, carries out quantitative analysis of bullwhip effect, builds the bullwhip effect model of traditional supply chain and digital supply chain, uses Matlab software to analyze the difference of the influence of supply chain digital transformation on bullwhip effect suppression and puts forward the corresponding control strategy.

Social implications

The research shows that digital transformation can reduce the bullwhip effect in multi-layer supply chain by reducing the information feedback deviation, which is conducive to improving the overall supply chain performance, and government support can accelerate the digital transformation of supply chain to a certain extent.

Originality/value

First, break through the limitations of traditional supply chain research, expand the research perspective to digital supply chain and discuss the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two. Second, quantify the bullwhip effect through information feedback bias and provide an analysis method for the weakening of the bullwhip effect. Third, the driving role of the government in the digital transformation of the supply chain is considered in the study, so that the model is more close to the actual situation of enterprise operation.

Details

Business Process Management Journal, vol. 30 no. 1
Type: Research Article
ISSN: 1463-7154

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Article
Publication date: 6 January 2025

Loan T. Le, Luan Duc Tran and Trieu Ngoc Phung

The study investigates determinants of willingness to pay (WTP) for laser land leveling (LLL) technology, its demand heterogeneity across individual farmers and plot…

13

Abstract

Purpose

The study investigates determinants of willingness to pay (WTP) for laser land leveling (LLL) technology, its demand heterogeneity across individual farmers and plot characteristics and the technology's empirical impact on paddy productivity.

Design/methodology/approach

The study applies the Becker-DeGroote, Marschak style to elicit the WTP for LLL technology and the Cragg model to examine the determinants of the WTP to capture both the demand decision and affordability. The randomized controlled trials (RCT) are incorporated with a production function model to analyze the technology effects on paddy productivity.

Findings

The Cragg model finds that the key demographic and behavioral traits such as age, extension services and risk acceptance significantly influence the adoption decision; however, the plot area, bank and financial capacity become predominant factors in the adoption affordability. The LLL treatment effect results in a statistically significant increase in paddy yield of 6.48%, equivalent to 492,138 kg ha-1.

Research limitations/implications

The analysis underscores the factor complexity, illustrating that the LLL-promoting interventions need to address both the adoption barriers and the enablers for greater affordability. A composite of climate-smart agricultural programs should be employed to facilitate the LLL adoption. The empirical evidence highlights the positive effect on agricultural productivity, potentially offering a significant boost to output and farmer income.

Originality/value

The study contributes to existing literature by analyzing the heterogeneous demand for LLL technology with two distinguishable features of the paddy mono-cropping system and land fragmentation and by incorporating the RCTs alongside a production function for the effects on paddy productivity.

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-0839

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Article
Publication date: 16 April 2018

Naser Safaeian Hamzehkolaei, Mahmoud Miri and Mohsen Rashki

Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and…

149

Abstract

Purpose

Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and discrete variables. The gradient-based RBDO algorithms are less than satisfactory for these cases. The simulation-based approaches could also be computationally inefficient, especially when the double-loop strategy is used. This paper aims to present a pseudo-double loop flexible RBDO, which is efficient for solving problems, including both discrete/continuous variables.

Design/methodology/approach

The method is based on the hybrid improved binary bat algorithm (BBA) and weighed simulation method (WSM). According to this method, each BBA’s movement generates proper candidate solutions, and subsequently, WSM evaluates the reliability levels for design candidates to conduct swarm in a low-cost safe-region.

Findings

The accuracy of the proposed enhanced BBA and also the hybrid WSM-BBA are examined for ten benchmark deterministic optimizations and also four RBDO problems of truss structures, respectively. The solved examples reveal computational efficiency and superiority of the method to conventional RBDO approaches for solving complex problems including discrete variables.

Originality/value

Unlike other RBDO approaches, the proposed method is such organized that only one simulation run suffices during the optimization process. The flexibility future of the proposed RBDO framework enables a designer to present multi-level design solutions for different arrangements of the problem by using the results of the only one simulation for WSM, which is very helpful to decrease computational burden of the RBDO. In addition, a new suitable transfer function that enhanced convergence rate and search ability of the original BBA is introduced.

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