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An optimal Bayesian acceptance sampling plan using decision tree method 基于决策树方法的最优贝叶斯验收抽样方案
Q3 Business, Management and Accounting Pub Date : 2023-01-01 DOI: 10.1504/ijams.2023.134457
Julia T. Thomas, Mahesh Kumar
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引用次数: 0
Influence of organisational career management variables: mentoring on career success of faculty academics - an empirical study from an Indian perspective 组织职业管理变量的影响:导师对教师学术生涯成功的影响——一项基于印度视角的实证研究
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-07-26 DOI: 10.1504/ijams.2021.116497
A. Seema
The main purpose of the present study is to determine the influence and relationship between mentoring and career success of faculties. Career success is termed as the individual's subjective or intrinsic feelings of accomplishment and ultimate satisfaction pertaining to his or her career. This study also highlights on one of the organisational career management practices, that is, mentoring in bringing its importance and linkage with career success from an individual point of view in support with literature review. Descriptive research design has been adopted for the study. Stratified random sampling method followed by random sampling technique was used for the study. It was decided to conduct the data collection with 450 (around 59% proportionate) faculty members of selected 17 arts and science colleges at Vellore District, Tamil Nadu, India. This present study has utilised structural equation modelling (SEM) through smart partial least square (PLS) 2.0 Version. The study outcomes show the significant influence and correlation between mentoring and career success in terms of career prospect, career commitment and career satisfaction.
本研究的主要目的是确定教师指导与职业成功之间的影响和关系。职业成功被称为个人对自己职业生涯的主观或内在成就感和最终满足感。这项研究还强调了组织职业管理实践之一,即从个人角度支持文献综述的辅导,以提高其重要性并将其与职业成功联系起来。本研究采用描述性研究设计。研究采用分层随机抽样法和随机抽样技术。决定对印度泰米尔纳德邦Vellore区选定的17所艺术和科学学院的450名教员(约占59%)进行数据收集。本研究通过智能偏最小二乘(PLS)2.0版本使用了结构方程建模(SEM)。研究结果显示,在职业前景、职业承诺和职业满意度方面,辅导与职业成功之间存在显著影响和相关性。
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引用次数: 1
Optimum prediction and forecasting of wheat demand in Iran 伊朗小麦需求优化预测与预报
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-07-26 DOI: 10.1504/ijams.2021.10039601
R. Babazadeh, Meisam Shamsi, Fatemeh Shafipour
Wheat is the staple food source in most countries and is grown in bad climatic conditions such as cold areas. Wheat contains about 55% carbohydrates and 20% calories. Optimum prediction of wheat demand would help policy makers to take optimum strategic decisions about the amount of domestic wheat production, import, and export for mid and long terms. In this study, firstly, the factors affecting demand for wheat are identified according to market analysis. Then, artificial neural network (ANN) method is employed for optimum forecasting of wheat demand in Iran. Different regression methods are used to justify the efficiency of the ANN model. The mean absolute percentage error (MAPE) of the ANN method is achieved equal to 4.64% which shows about 95% precision of the ANN method. According to acquired results, the ANN method could be efficiently applied for wheat demand prediction in order to take appropriate related strategic decisions.
小麦是大多数国家的主食来源,生长在寒冷地区等恶劣的气候条件下。小麦含有大约55%的碳水化合物和20%的卡路里。对小麦需求的最佳预测将有助于决策者对国内小麦产量、进口和出口做出中长期的最佳战略决策。在本研究中,首先,根据市场分析,确定了影响小麦需求的因素。然后,采用人工神经网络方法对伊朗小麦需求量进行优化预测。使用不同的回归方法来证明ANN模型的有效性。该方法的平均绝对百分误差(MAPE)达到4.64%,表明该方法的精度约为95%。研究结果表明,该方法可以有效地应用于小麦需求预测,从而做出相应的战略决策。
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引用次数: 0
An energy-efficient cluster formation in wireless sensor network using grey wolf optimisation 基于灰狼优化的无线传感器网络中高效簇的形成
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-07-15 DOI: 10.1504/ijams.2021.10039602
Rajakumar R, K. Dinesh, T. Vengattaraman
With the emerging technology, wireless sensor network (WSNs) plays a vital role in monitoring day-to-day life activities which suffers from various issues such as routing, intrusion, and topology control. However, to address these issues an energy-efficient cluster formation is quite important. Thus, the successive cluster formation improves the lifetime of the networks to reduce routing overheads. Our contribution in this work includes selecting energy-efficient cluster heads with the aid of the Grey Wolf Optimisation (GWO) algorithm. This algorithm attracts several researchers with its efficient leadership capability and hunting methodology but it lags in exploration and exploitation which leads to poor clustering in WSN when it is applied. The proposed methodology includes a tuning parameter for efficient exploration and exploitation later used to solve the issue which resides in WSN. The experimental results show that the proposed algorithm provides better results over cluster head selection and minimised energy consumption in WSN.
随着无线传感器网络技术的发展,无线传感器网络在日常生活活动监控中发挥着至关重要的作用,它面临着路由、入侵和拓扑控制等各种问题。然而,要解决这些问题,高效节能的集群形成是非常重要的。因此,连续集群的形成提高了网络的生命周期,从而减少了路由开销。我们在这项工作中的贡献包括在灰狼优化(GWO)算法的帮助下选择节能簇头。该算法以其高效的领导能力和猎取方法吸引了众多研究人员的注意,但由于其在探索和开发上的滞后,导致其在应用时聚类效果不佳。提出的方法包括一个调优参数,用于有效的勘探和开发,随后用于解决存在于WSN中的问题。实验结果表明,该算法具有较好的簇头选择效果和最小的能量消耗。
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引用次数: 4
Trust, commitment and business expansion in automotive supply chains in a developing country: a principal-agency perspective 发展中国家汽车供应链中的信任、承诺和业务扩张:主体机构视角
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-02-23 DOI: 10.1504/IJAMS.2021.10035824
J. Badenhorst-Weiss, A. Tolmay
The automotive industry is important for sustaining developing countries' economies. Literature states the South African automotive buyer-seller relationships are hampered by conflict where both parties reveal self-serving behaviour. This results in a decrease of trust and commitment, and increased supply chain uncertainty that is hampering business expansion. Hence, this study aimed to investigate the relationships between trust, commitment and business expansion through buyer-seller relationships. A quantitative study was conducted through a structured close-ended questionnaire among 114 managers from automotive component manufacturers. The empirical research found a strong presence of trust and commitment in automotive buyer-seller relationships. The influence of trust and commitment on possible business expansion was determined through a regression-based analysis. Findings revealed trust in a seller (agent) results in business expansion and commitment to a seller acts as a mediator between trust and business expansion. Action plans for both agents and principals (buyers) are recommended to sustain business.
汽车工业对维持发展中国家的经济至关重要。文献表明,南非汽车买卖关系受到冲突的阻碍,双方都表现出自私的行为。这导致信任和承诺减少,供应链不确定性增加,阻碍了业务扩张。因此,本研究旨在通过买卖关系来调查信任、承诺和业务扩张之间的关系。通过一份结构化的封闭式问卷对来自汽车零部件制造商的114名管理人员进行了定量研究。实证研究发现,在汽车买卖关系中存在着强烈的信任和承诺。通过基于回归的分析确定了信任和承诺对可能的业务扩张的影响。调查结果显示,对卖方(代理人)的信任会导致业务扩张,对卖方的承诺是信任和业务扩张之间的中介。建议为代理商和委托人(买方)制定行动计划,以维持业务。
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引用次数: 0
Self-organisation migration technique for enhancing the permutation coded genetic algorithm 增强排列编码遗传算法的自组织迁移技术
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-02-23 DOI: 10.1504/IJAMS.2021.10035828
K. Dinesh, Rajakumar R, R. Subramanian
Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.
遗传算法(GA)是用于解决各种优化问题的众所周知的优化算法。遗传算法基于进化原理,有效地解决了大规模问题。此外,它还结合了各种混合技术,以在复杂问题中获得最佳性能。然而,自组织是一种流行的模式,它从个体之间的局部互动中获得全球秩序。采用自组织和遗传算法相结合的方法来提高算法的收敛性能。本文提出了一种双向自组织迁移技术来改进遗传算法,以实现种群的收敛性和均衡多样性。在旅行商问题的标准试验台上进行了实验,并从TSPLIB中获得了实例。因此,在各种参数度量方面,该算法与现有的经典遗传算法相比显示出了优势。
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引用次数: 1
Multi-objective hazardous materials routing and scheduling for balancing safety and travel time 平衡安全和旅行时间的多目标危险品路线和时间表
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-02-23 DOI: 10.1504/IJAMS.2021.10035826
Kamran S. Moghaddam
This research develops a new multi-objective multi-period optimisation model to find optimal links and routes to maintain a balance between safe and fast distribution of hazmats between origins and destinations through the transport network. The transport network includes multiple origins and destinations along with multiple hazmat classes to better mimic the challenges faced by practitioners. We consider unknown probabilities for hazmat incidents along with a game-theoretic demon approach and formulate a link-based routing and scheduling hazmat shipment problem. The objective functions are defined to minimise both the probability of population exposure affected by hazmat transport risks and the total transportation time in the distribution network. This paper also proposes a solution method based on an integrated Monte Carlo simulation and fuzzy goal programming to obtain Pareto-optimal solutions. A numerical example is provided to evaluate the effectiveness of the developed mathematical model and the solution method in obtaining Pareto-optimal solutions.
这项研究开发了一个新的多目标多周期优化模型,以找到最佳链路和路线,从而通过运输网络在起点和目的地之间保持危险品安全快速分布之间的平衡。运输网络包括多个起点和终点,以及多个危险品类别,以更好地模拟从业者面临的挑战。我们考虑了危险品事件的未知概率以及博弈论恶魔方法,并提出了一个基于链路的危险品运输路线和调度问题。目标函数的定义旨在最大限度地降低受危险品运输风险影响的人群暴露概率和配送网络中的总运输时间。本文还提出了一种基于蒙特卡洛模拟和模糊目标规划的求解方法,以获得Pareto最优解。给出了一个数值例子来评估所开发的数学模型和求解方法在获得Pareto最优解方面的有效性。
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引用次数: 0
A cost allocation model based on the combination of data envelopment analysis and Shannon entropy (case study: branches of the central post of Isfahan) 基于数据包络分析和香农熵相结合的成本分配模型(案例研究:伊斯法罕中心邮政分局)
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-02-23 DOI: 10.1504/IJAMS.2021.10035827
Zohreh Safa, Reza Maddahi
The method used for allocation of costs in this study has several stages. In stage 1, various types of data envelopment analysis (DEA) models were used to assess the efficiency of decision-making units (DMUs), including constant return to scale and variable return to scale with a variety of input and output types in radial and non-radial states. In stage 2, Shannon entropy method was used to combine the obtained efficiencies for each decision making unit. In stage 3, the allocation of costs was done based on the combined efficiency number for each unit obtained in phase 2. The proposed cost allocation model was then implemented in an example and its fairness was compared with similar methods using the Gini coefficient method. Finally, the proposed model was investigated in Central Post of Isfahan.
本研究中用于成本分配的方法有几个阶段。在第一阶段,使用各种类型的数据包络分析(DEA)模型来评估决策单元(DMU)的效率,包括在径向和非径向状态下具有各种输入和输出类型的恒定规模回报率和可变规模回报率。在第二阶段,使用香农熵方法来组合每个决策单元的效率。在第3阶段,根据第2阶段获得的每个单元的组合效率数进行成本分配。然后通过实例实现了所提出的成本分配模型,并将其公平性与使用基尼系数法的类似方法进行了比较。最后,在伊斯法罕中央邮政对所提出的模型进行了研究。
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引用次数: 0
An application of integer programming to producing aircraft engine parts 整数规划在飞机发动机零件生产中的应用
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2021-02-23 DOI: 10.1504/IJAMS.2021.10035825
Daniel Solow, Qi Wu, Daniel Magri
Integer programming models are developed for optimising the production of a part used in aircraft engines. A real-world problem is solved to optimality; however, for some potentially large real-world problems, one of these models can require too much time, so appropriate heuristics are developed. These heuristics are shown computationally to be both effective and efficient using randomly generated data.
整数规划模型是为优化飞机发动机零件的生产而开发的。现实世界中的问题被解决到最优性;然而,对于一些潜在的大型现实世界问题,其中一个模型可能需要太多时间,因此开发了适当的启发式方法。使用随机生成的数据,这些启发式算法在计算上被证明是有效和高效的。
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引用次数: 0
Application of Soft Computing Techniques Rough Set Theory and Formal Concept Analysis for analysing Investment Decisions in Gold-ETF 软计算技术在黄金etf投资决策分析中的应用——粗糙集理论和形式概念分析
IF 0.7 Q3 Business, Management and Accounting Pub Date : 2020-04-20 DOI: 10.1504/ijams.2020.10025173
Biswajit Acharjya, Subhashree Natarajan
Complex and noisy financial eco-system requires reliable models and proven techniques to predict the market movements and investor decisions. This study uses competent soft computing techniques: rough set theory (RST) and formal concept analysis (FCA) to study the investors' preferences, behavioural drivers and their actual behaviour in Gold-ETF (G-ETF) market. G-ETF, though a safe-haven and an alternate for reducing portfolio risks, inherits all complexities of financial markets. The employed RST helps in generating decision rules; and FCA to identify key factors affecting investment decision. This study is first of its kind, as integration of the foresaid techniques was not employed to study financial behaviour, earlier. The study has analysed 250 responses of G-ETF investors, in 12 listed G-ETFs, to conclude with a rich insight on the investment decisions discretised by different decision rules, strongly recommending the combined use of RST and FCA for data driven decisions.
复杂而嘈杂的金融生态系统需要可靠的模型和行之有效的技术来预测市场走势和投资者决策。本研究采用胜任的软计算技术:粗糙集理论(RST)和形式概念分析(FCA)来研究投资者在黄金ETF(G-ETF)市场中的偏好、行为驱动因素及其实际行为。G-ETF虽然是一个避风港和降低投资组合风险的替代品,但它继承了金融市场的所有复杂性。所采用的RST有助于生成决策规则;以及FCA,以确定影响投资决策的关键因素。这项研究是同类研究中的第一项,因为早期没有将上述技术结合起来研究金融行为。该研究分析了12只上市G-ETF中G-ETF投资者的250份回复,以对由不同决策规则离散的投资决策有着丰富的见解,强烈建议将RST和FCA结合用于数据驱动决策。
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引用次数: 0
期刊
International Journal of Applied Management Science
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