Pub Date : 2023-01-01DOI: 10.1504/ijams.2023.134457
Julia T. Thomas, Mahesh Kumar
{"title":"An optimal Bayesian acceptance sampling plan using decision tree method","authors":"Julia T. Thomas, Mahesh Kumar","doi":"10.1504/ijams.2023.134457","DOIUrl":"https://doi.org/10.1504/ijams.2023.134457","url":null,"abstract":"","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135156448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-26DOI: 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.
{"title":"Influence of organisational career management variables: mentoring on career success of faculty academics - an empirical study from an Indian perspective","authors":"A. Seema","doi":"10.1504/ijams.2021.116497","DOIUrl":"https://doi.org/10.1504/ijams.2021.116497","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49315300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-26DOI: 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.
{"title":"Optimum prediction and forecasting of wheat demand in Iran","authors":"R. Babazadeh, Meisam Shamsi, Fatemeh Shafipour","doi":"10.1504/ijams.2021.10039601","DOIUrl":"https://doi.org/10.1504/ijams.2021.10039601","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46652000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-15DOI: 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.
{"title":"An energy-efficient cluster formation in wireless sensor network using grey wolf optimisation","authors":"Rajakumar R, K. Dinesh, T. Vengattaraman","doi":"10.1504/ijams.2021.10039602","DOIUrl":"https://doi.org/10.1504/ijams.2021.10039602","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46012185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 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.
{"title":"Trust, commitment and business expansion in automotive supply chains in a developing country: a principal-agency perspective","authors":"J. Badenhorst-Weiss, A. Tolmay","doi":"10.1504/IJAMS.2021.10035824","DOIUrl":"https://doi.org/10.1504/IJAMS.2021.10035824","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48926713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 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.
{"title":"Self-organisation migration technique for enhancing the permutation coded genetic algorithm","authors":"K. Dinesh, Rajakumar R, R. Subramanian","doi":"10.1504/IJAMS.2021.10035828","DOIUrl":"https://doi.org/10.1504/IJAMS.2021.10035828","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49392088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 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.
{"title":"A cost allocation model based on the combination of data envelopment analysis and Shannon entropy (case study: branches of the central post of Isfahan)","authors":"Zohreh Safa, Reza Maddahi","doi":"10.1504/IJAMS.2021.10035827","DOIUrl":"https://doi.org/10.1504/IJAMS.2021.10035827","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42305361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 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.
{"title":"Multi-objective hazardous materials routing and scheduling for balancing safety and travel time","authors":"Kamran S. Moghaddam","doi":"10.1504/IJAMS.2021.10035826","DOIUrl":"https://doi.org/10.1504/IJAMS.2021.10035826","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49256405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 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.
{"title":"An application of integer programming to producing aircraft engine parts","authors":"Daniel Solow, Qi Wu, Daniel Magri","doi":"10.1504/IJAMS.2021.10035825","DOIUrl":"https://doi.org/10.1504/IJAMS.2021.10035825","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41449864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-20DOI: 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.
{"title":"Application of Soft Computing Techniques Rough Set Theory and Formal Concept Analysis for analysing Investment Decisions in Gold-ETF","authors":"Biswajit Acharjya, Subhashree Natarajan","doi":"10.1504/ijams.2020.10025173","DOIUrl":"https://doi.org/10.1504/ijams.2020.10025173","url":null,"abstract":"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.","PeriodicalId":38716,"journal":{"name":"International Journal of Applied Management Science","volume":"12 1","pages":"207-241"},"PeriodicalIF":0.7,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41623212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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