Vinod K.T., S. Prabagaran and O.A. Joseph
The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system…
Abstract
Purpose
The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.
Design/methodology/approach
A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.
Findings
The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.
Research limitations/implications
Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.
Practical implications
The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.
Originality/value
Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.
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The purpose of this paper is to examine the factors that impact assessment of police performance across the two types of policing methods, and explains the differing police public…
Abstract
Purpose
The purpose of this paper is to examine the factors that impact assessment of police performance across the two types of policing methods, and explains the differing police public dynamics at the field level.
Design/methodology/approach
This paper examines the varying police public dynamics in areas with and without community policing. For this purpose data were obtained from a survey conducted in the City of Calicut where the Janamaitri community policing program was implemented in some of the police stations. To obtain a contrasting perspective in areas without community policing, survey was also conducted in areas where community policing was not conducted. The method adopted in this study is to map people's perception of police performance and factors impacting it across the two areas and compare the same. The comparison is done by examining the OLS regression in the two areas with same independent and dependent variables, and explaining similarities and contrasts in trends.
Findings
It concludes that while community policing has great advantages over conventional policing, it has the challenge of increasing expectation among the public and diminishing impact of certain factors that are relevant in conventional police service delivery mechanisms.
Originality/value
There is one of the first studies comparing and analyzing the differing police public dynamics in areas with community policing and areas with conventional policing. It provides an insight into how public perception of police is formed in these differing environments.
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Hrishikesh Vinod, Kurt Jetta and Minaya Eric Rengifo
This study aims to highlight potential savings in advertising budgets.
Abstract
Purpose
This study aims to highlight potential savings in advertising budgets.
Design/methodology/approach
This study uses modern computer-based tools including stochastic dominance to check if advertising expenses are increasing sales by using modern causality assessment tools which allow for nonlinearities and use sophisticated assessment of causal impact of ads on sales.
Findings
This study identifies specific media spots where ad budget savings are possible. The marketing managers can take the next step to make small-scale local experiments to reassess this study’s findings.
Research limitations/implications
This study is a statistical observational assessment not based on controlled experiments.
Practical implications
The authors have tools to identify ineffective advertising which can produce huge savings for the organization. The over-the-counter cold remedies have become important due to the pandemic. The tools have wider applicability in marketing research.
Social implications
Less wasteful expenses always benefit the society.
Originality/value
To the best of the authors’ knowledge, this may be the first such attempt to use sophisticated causal identification tools. Remedies for the common cold sold by seven major US retailers help identify specific retailers and specific media with negative returns on investment.
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Amit Kumar, Vinod Kumar and Vikas Modgil
The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm…
Abstract
Purpose
The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.
Design/methodology/approach
In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.
Findings
In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.
Research limitations/implications
There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.
Originality/value
The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.
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Apeksha Balwir, Dilip Kamdi and Vinod Varghese
To find the quasi-static thermoelastic stress and displacement, the proposed model looks at how the microstructures interact with each other and how the temperature changes inside…
Abstract
Purpose
To find the quasi-static thermoelastic stress and displacement, the proposed model looks at how the microstructures interact with each other and how the temperature changes inside a rod. It uses the fractional-order dual-phase-lag (FODPL) theory to derive analytical solutions for one-dimensional problems in nonsimple media within the MDD framework. The dimensionless equations are used to analyze a finite rod experiencing the heat sources continuously distributed over a finite portion of the rod which vary with time according to the ramp-type function with other sectional heat supplies kept at zero temperature. The study introduces a technique using integral transforms for exact solutions in the Laplace transform domain for different kernel functions.
Design/methodology/approach
A novel mathematical model incorporating dual-phase-lags, two-temperatures and Riesz space-fractional operators via memory-dependent derivatives has been established to analyze the effects of thermal stress and displacement in a finite rod. The model takes into account the continuous distribution of heat sources over a finite portion of the rod and their time variation according to the ramp-type function. It incorporates the finite Riesz fractional derivative in two-temperature thermoelasticity with dual-phase-lags via memory effect, and its solution is obtained using Laplace transform with respect to time and sine-Fourier transform with respect to spatial coordinates defined over finite domains.
Findings
In memory-dependent derivatives, thermal field variables are strongly influenced by the phase-lag heat flux and temperature gradient. The non-Fourier effects of memory-dependent derivatives substantially impact the distribution and history of the thermal field response, and energy dissipation may result in a reduction in temperature without heat transfer. The temperature, displacement and stress profile exhibit a reduced magnitude with the MDD effect compared to when the memory effect is absent (without MDD). To advance future research, a new categorization system for materials based on memory-dependent derivative parameters, in accordance with the principles of two-temperature thermoelasticity theory, must be constructed.
Research limitations/implications
The one-dimensional assumption introduces limitations. For example, local heating of a one-dimensional plate will not extend radially, and heating one side will not heat the surrounding sides. Furthermore, while estimating heat transfer, object shape limits may apply.
Originality/value
This paper aims to revise the classical Fourier law of heat conduction and develop analytical solutions for one-dimensional problems using fractional-order dual-phase-lag (FODPL) theory in nonsimple media in the context of MDD.
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Anish Khobragade, Shashikant Ghumbre and Vinod Pachghare
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity…
Abstract
Purpose
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.
Design/methodology/approach
D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.
Findings
Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.
Research limitations/implications
Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.
Practical implications
Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.
Originality/value
The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.
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Bao Yong, Fan Yanqin, Su Liangjun and Zinde-Walsh Victoria
This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works…
Abstract
This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.
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Shahala Sheikh, Lalsingh Khalsa and Vinod Varghese
The influence of the temperature discrepancy parameter and higher order of the time-derivative is discussed. Classical coupled and generalized hygrothermoelasticity models are…
Abstract
Purpose
The influence of the temperature discrepancy parameter and higher order of the time-derivative is discussed. Classical coupled and generalized hygrothermoelasticity models are recovered by considering the various special cases and illustrated graphically.
Design/methodology/approach
The theory of integral transformations has been used to study a new hygrothermal model that includes higher-order time derivatives with three-phase-lags and memory-dependent derivatives (MDD). This model considers the microscopic structure’s influence on a non-simple hygrothermoelastic infinitely long cylinder. The generalized Fourier and Fick’s law was adopted to derive the linearly coupled partial differential equations with higher-order time-differential with the two-phase lag model, including memory-dependent derivatives for the hygrothermal field. The investigation of microstructural interactions and the subsequent hygrothermal change has been undertaken as a result of the delay time and relaxation time translations.
Findings
These two-phase-lag models are also practically applicable in modeling nanoscale heat and moisture transport problems applied to almost all important devices. This work will enable future investigators to gain insight into non-simple hygrothermoelasticity with different phase delays of higher order in detail.
Originality/value
To the best of my knowledge, and after completing an intensive search of the relevant literature, the author could not learn any published research that presents a general solution for a higher-order time-fractional three-phase-lag hygrothermoelastic infinite circular cylinder with memory memory-dependent derivative.
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S.M. Aparna and Sangeeta Sahney
Amidst the concerns surrounding knowledge sharing, especially in academia, the study attempts to understand its impact on Research output. To deepen our understanding, the study…
Abstract
Purpose
Amidst the concerns surrounding knowledge sharing, especially in academia, the study attempts to understand its impact on Research output. To deepen our understanding, the study considered the differing impact of age on knowledge sharing and research output through the lens of Continuity Theory and Socioemotional Selectivity Theory.
Design/methodology/approach
Data were collected from 385 faculties working in higher education institutions in India. The study uses Hierarchical regression analysis in SPSS 22.0 to test the hypotheses proposed.
Findings
A three-way interaction analysis of 385 faculties confirmed our presumption that the effect of knowledge sharing on research output was different for the two-age groups. Surprisingly, the positive effect of knowledge sharing on research output was stronger in older faculties than younger ones. However, technology usage and its effect on research output, declined with age.
Research limitations/implications
Findings gave interesting insights which contradict our earlier notions of increasing age and suggest that instead of blaming increasing age alone for the reduced output, attention must be paid towards other hidden influencing factors.
Originality/value
The study is the first of its kind that investigates the effectiveness of knowledge sharing in academia and the impact of age on the underlying phenomenon. This study makes a novel attempt to deepen our understanding of the impact of age on research output.