A New Image Steganography Method using Message Bits Shuffling


Prithwish Das,Kushal Chakraborty,Sayak Sinha,Atanu Das,



Steganography has been considered as a technique of message hiding within another carrier multimedia data. Messages in the form of image (with embedded handwritten or typed texts) are often embedded in several ways within another image in image steganography. DCT based schemes are undertaken in the frequency domain methods in addition to usual plain text message embedding. Most of the message image hiding techniques embeds image bit string without considering any shuffling schemes to deal with the said string before embedding. Present work targeted to incorporate message hiding essentially with shuffled and re-shuffled bit strings in different ways prior to DCT operation. A new method has been proposed with these shuffling schemes to enhance the security level of the encryption. Investigations with the proposed image steganography method show that the new methods performed better than normal image steganography techniques without shuffling schemes. Performance of the proposed method is evaluated using Peak-Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). Results show that the shuffling bit steganography method outperformed the common DCT based schemes without shuffling.


Image Steganograph, DCT,Message Bit Shuffling,


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Free-Space Optical channel turbulence analysis based on lognormal distribution and stochastic differential equation


TayyabaGul Tareen,Shahryar Shafique,Mehr-e-Munir,



An Optical wave propagating through a free-space optical channel may severely experience the intensity fluctuations that can result in channel gain fluctuations and fading. This paper provide a model that can analyze the influence of inevitable turbulence effect on a free-space channel which is based on the stochastic differential equation to synthesis lognormal distributed samples with a corresponding correlation time. The numerical analysis of theoretical model is presented and compared for performance evaluation. To examine the resemblance between numerical and theoretical analysis, two properties of free-space optical channel is considered including the probability density function and auto-covariance property. The model showed distinctive performance results when modelling typical channel situations.


Auto-covariance,Free-space optica,lognormal distribution,stochasticdifferential equation (SDE),Turbulence effects ,


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Phong Thanh Nguyen,Thu Anh Nguyen,Quyen Le Hoang Thuy To Nguyen,Vy Dang Bich Huynh,



Building Information Modelling (BIM) has made considerable progress over the past few decades regarding information technology applied in the construction industry. In developed countries, governmental organizations and private companies had published many valuable and quality academic studies regarding BIM. However, few studies have mentioned the application of SWOT modelling to develop a strategy for applying the BIM 360 Field in construction and engineering companies. This paper presents an overview of the BIM 360 Field application in construction quality management. Suitable strategies could be used to enhance the quality assurance of construction project management.



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Relationship between Organizational Environment and Teacher’s Citizenship Behaviour


Muhammad Tahir Khan Farooqi,Dr. Shehzad Ahmed,Dr. AsifIqbal,Sabahat Parveen,



The aim of the study was to investigate the correlation between organizational environment and teachers’ citizenship behaviour. The research study was quantitative and correlational design was used. Survey technique was used. The population of the study comprises Elementary School Teachers (ESTs) of Mathematics. Multistage random sampling was used to select four districts (Faisalabad, Multan, Sargodha and Jhang). Further, 20 schools (10 males & 10 females) and 4 teachers from each school were randomly selected. The data from selected sample were collected using survey method. SPSS version 24 was used to analyze the data. Pearson r and ANOVA were used. The analysis revealed that there exist significant and positive relationship between organizational environment and teachers’ citizenship behaviour.


Organizational environment,Citizenship behaviour,Multistage random sampling,


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Vy Dang Bich Huynh,Quyen Le Hoang Thuy To Nguyen,Phuc Van Nguyen,Phong Thanh Nguyen,



The positive impact of social capital on job search success has been supported in the literature, however the research community has not reached a consensus because social capital is not always good, especially in terms of bonding. This paper explores the role of bonding social capital on several dimensions of job search success. The partial least square structural equation model was used with input data from 400 undergraduates, obtained from a field survey in Ho Chi Minh City, Vietnam. The results confirm the positive role of bonding social capital on acquired job quality, job search cost, and job search convenience. Keywords: education, job search success, partial least square structural equation model (PLS-SEM), social capital




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XL.Zou, T., Su, Y., & Wang, Y. (2018). Examining relationships between social capital, emotion experience and life satisfaction for sustainable community. Sustainability, 10(8), 2651.


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Muhammad Hasnain,Adeed khan,Saqib Shah,Muhammad Majid Naeem,Marvan Raza,



Developed economies have realized construction health and safety issue and have improved the working site condition by continuously emphasizing on the issue. Sadly, the case is different in developing countries particularly in the Indian subcontinent where the injury and death rate is high due to poor health and safety conditions. The paper examines the current health and safety practices, legislations and the management of Health and safety of Pakistan, a country in the Indian subcontinent. The data reviewed is organized around developing countries and the culture affecting health and safety in these countries is discussed. Moreover, the secondary data focuses on health and safety management system, behavioral aspects of the stakeholders, general health conditions of workers associated to the construction industry and the construction industry of Pakistan is also discussed. For the achievement of objectives, both, qualitative and quantitative methodologies are adopted (i-e questionnaire survey and interviews). The questionnaire and the interviews mainly focus on the contractors, workers, designers and the clients. The findings from these methods indicates that majority of the respondents have a poor degree of health and safety awareness. It also reveals that there are general health problems faced by the workers, people are hesitant to record and report the accident at site and showed the key behavioral aspects affecting the health and safety.




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III.Awan, T. (2001). Pakistan Institute of Labour Education and Research (FILER), Occupational Health and Safety in Pakistan, Asian Labour Update (ALU) issue, Nathan Road, Kowloon, Hong Kong, No.39, pp.5-7.

IV.Awan, T., 2007, Occupational Health and Safety in Pakistan [Online]. Pakistan Institute of Labour Education and Research, Asian Labour Update Issue 39. V.Alhajeri, M. (2011). Health and safety in the construction industry: challenges and solutions in the UAE. PHD. Coventry University.VI.Arndt,

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XII.Choudhry, R., Fang, D. and Mohamed, S. (2007). Developing a Model of Construction Safety Culture. Journal of Management in Engineering, [online] 23(4), pp.207-212.

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XIV.Coble, R.J. and Haupt, T.C., 1999, March. Construction safety in developing countries. In 2nd International Conference of CIB on W (Vol. 99, pp. 903-908).

XV.Creswell, J.W., 2014. A concise introduction to mixed methods research. Sage Publications.

XVI.Devine, C.M., Jastran, M., Jabs, J., Wethington, E., Farell, T.J. and Bisogni, C.A., 2006. “A lot of sacrifices:” Work–family spillover and the food choice coping strategies of low-wage employed parents. Social science & medicine, 63(10), pp.2591-2603.

XVII.DiDomenico, A. and Nussbaum, M. (2011). Effects of different physical workload parameters on mental workload and performance. International Journal of Industrial Ergonomics, 41(3), pp.255-260.

XVIII.Dong, X.S., Wang, X., Fujimoto, A. and Dobbin, R., 2012. Chronic back pain among older construction workers in the United States: a longitudinal study. International journal of occupational and environmental health, 18(2), pp.99-109.

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Complexity Based Approach for Architecture Evaluation


Maushumi Lahon,Uzzal Sharma,



Architecture Evaluation is a means to reduce risk and save cost. It holds the key to success of the system being developed. Various evaluation methods exist which have specific objectives and basis and all contribute to enhance product quality. In this paper a Complexity UML Based Architecture Evaluation (CUBAE) approach is proposed to evaluate the architecture of a system built using CBSD approach. . The proposed approach estimates the complexity of the architecture from the UML representation of different views of the architecture. Earlier works on complexity measures of UML representations found in literature are used along with proposed measures for complexity calculation. This complexity measure may be used to assess and compare architecture representing the same system along with other measures like modifiability and different quality attributes used for evaluating the architecture.


CBSD,Architecture evaluation,UML,Complexity,Metrics,


I. B. Xu, D. Kang and J.Lu, ―”A structural complexity measure for UML class diagrams”, International Conference on Computational Science (ICCS 2004), Krakow Poland, June 2004, pp.431-435.
II. D.Kang, B. Xu, J. Lu and W.C. Chu, ―”A complexity measure for ontology based on UML”, IEEE 10th International Workshop on Future Trends in Distributed Computing Systems (FTDCS 2004), Suzhou, China, May 2004, pp.222-228.
III. E. Bouwers, C. Lilienthal, J. Visser, and A.V. Deursen , “A Cognitive Model for Software Architecture Complexity “,Proceedings of the International Conference on Program Comprehension (ICPC), IEEEComputerSociety, 2010. Software Engineering Research Group Technical Reports:
IV. J.D. Thomas , Ph.D. Thesis, “Architecture Assessment of InformationSystem Families”, Department of Technology Management, Eindhoven University of Technology, February 2002.
V. M. Marchesi, OOA metrics for the unified modeling languages. In Proceedings of 2ndEuromicro Conference on Software Maintenance and Reengineering (CSMR’98), Palazzo degli Affari, Italy, March, 1998,pp.67- 73.
VI. Nico Lassing, “Architecture-Level Modifiability Analysis”, Ph.D. thesis, Free University Amsterdam, February 2002.
VII. P. Kruchten,‖ Architectural Blueprints—The ―4+1” View Model of Software Architecture‖, IEEE Software 12 (6),November 1995, pp. 42-50.
VIII. P. Bengtsson, Ph..D. Thesis, “Architecture Level Modifiability Analysis‖, Department of Software Engineering and Computer Science, Blekinge Institute of Technology, Sweden 2002.
IX. R. Kazman, M. Klein, and P. Clements, “ATAM: Method for Architecture Evaluation”, CMU/ SEI- 200 0- TR-0 04,ES C- TR- 200 0- 004.
X. R. Kazman,G. Abowd, L. Bass, & M. Webb, “SAAM: A Method for Analyzing the Properties of Software Architectures”,81-90. Proceedings of the 16th International Conference on Software Engineering. Sorrento, Italy, May 1994.
XI. R. Kazman and M, Burth, “Assessing Architectural Complexity”, Proceedings of 2nd Euromicro Working Conference on Software Maintenance And Reengineering (CSMR 98), IEEE Computer Society Press, 1998.
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XIII. T.Yi,F. Wu,and C. Gan, “A Comparison of Metrics for UML Class Diagrams”, ACM SIGSOFT Software Engineering Notes, Vol 25, Sept’2004.
XIV. df
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Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques


Srihari J,Santhi B,



Global warming has been a severe threat to humanityand greenhouse gases emitted from power plants is one of the major causes of global warming. In this paper, we use machine learning to incorporate energy efficiency techniques to buildings by predicting the Heating and Cooling Load using eight input features.Heating load is the amount of heat per unit time that a building needs to maintain the temperature at an established level whereas Cooling load is the amount of heat per unit time that must be removed. Heating, cooling, and ventilation systems are used to handle heating and cooling load. We train four regression (linear regression, Lasso, Ridge, and Elastic-Net) and three gradient boosting models (GBM, XGBoost, and LightGBM) and test them to compare their performance using 768 rows of data of residential buildings. We observe that the gradient boosting models perform significantly better than the standard regression models for both Heating Load and Cooling Load. XGBoost achieves the highest R-squared score of 0.99 for Heating Load and 0.99 for Cooling Load. From the results of this study, we conclude that machine learning techniques can predict Heating Load and Cooling Load with high accuracy. The obtained Heating load and cooling load values can be used to install efficient heating, cooling and ventilation systems and thus reduce both energy consumption and money.


Energy efficiency,Heating Load,Cooling Load,Machine Learning,


I.Al Fardan, A. S., Al Gahtani, K. S., and Asif, M. (2017). Demand side management solution through new tariff structure to minimize excessive load growth and improve system load factor by improving commercial buildings energy performance in Saudi Arabia. 2017 5th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2017, pages 302–308.

II.Bizjak, M., Zalik, B.,ˇ Stumberger, G., and Lukaˇc, N. (2018). Estimation andˇ optimisation of buildings’ thermal load using LiDAR data. Building and Environment, 128:12–21.

III.Borgstein, E. H., Lamberts, R., and Hensen, J. L. (2018). Mapping failures in energy and environmental performance of buildings. Energy and Buildings, 158:476–485.

IV.Caputo, P., Costa, G., and Ferrari, S. (2013). A supporting method for defining energy strategies in the building sector at urban scale. Energy Policy, 55:261 –270. Special section: Long Run Transitions to Sustainable Economic Structures in the European Union and Beyond.V.Cetin, K. S., Tabares-Velasco, P. C., and Novoselac, A. (2014). Appliance daily energy use in new residential buildings: Use profiles and variation in time-ofuse. Energy and Buildings, 84:716–726.

VI.Chen, T. and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System.VII.Cheng, V. and Steemers, K. (2011). Modelling domestic energy consumption at district scale: A tool to support national and local energy policies. Environmental Modelling Software, 26(10):1186 –1198.VIII.Dai, C., Zhang, H., Arens, E., and Lian, Z. (2017). Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions. Building and Environment, 114:1–10.IX.Deng, H., Fannon, D., and Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163:34–43.X.Dheeru, D. and KarraTaniskidou, E. (2017). UCI machine learning repository.XI.Flett, G. and Kelly, N. (2017). A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand. Energy and Buildings, 140:171–187.XII.Fonseca, J. A.and Schlueter, A. (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142:247 –265.XIII.Guo, Y., Li, G., Chen, H., Wang, J., and Huang, Y. (2017). A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods. Energy Procedia, 142:1003–1008.XIV.Gupta, N. and Shet, H. N. (2016). Analysis of Measures to Improve EnergyXV.Performance of a Commercial Building by Energy Modeling.2016 Online International Conference on Green Engineering and Technologies (IC-GET) Analysis, pages 1–4.XVI.Hamid, M. F. A., Ramli, N. A., and Syawal Nik Mohd Kamal, N. M. F. (2017). An analysis of energy performance of a commercial building using energy modeling. In 2017 IEEE Conference on Energy Conversion (CENCON), pages 105–110. IEEE.XVII.Holmegaard, E., Johansen, A., and Kjærgaard, M. B. (2016). Towards a metadata discovery, maintenance and validation process to support applications that improve the energy performance of buildings. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016.XVIII.Jaffal, I. and Inard, C. (2017). A metamodel for building energy performance. Energy and Buildings, 151:501–510.

XIX.Jeong, Y.-k., Kim, T., Nam, H.-S., and Lee, I.-w. (2016). Implementation of energy performance assessment system for existing building. 2016 International Conference on Information and Communication Technology Convergence (ICTC), (20142010102370):393–395.XX.Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 3146–3154. Curran Associates, Inc.XXI.Kim, J., Zhou, Y., Schiavon, S., Raftery, P., and Brager, G. (2018). Personal comfort models: Predicting individuals’ thermal preferenceusing occupant heating and cooling behavior and machine learning. Building and Environment, 129:96–106.XXII.Konis, K. and Annavaram, M. (2017). The Occupant Mobile Gateway: A participatory sensing and machine-learning approach for occupant-aware energy management. Building and Environment, 118:1–13.XXIII.Kwok, S. S. K., Yuen, R. K. K., and Lee, E. W. M. (2011). An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Building and Environment, 46(8):1681–1690.XXIV.Onose, B.-a. (2016). Control optimization for increasing energy performance of existing buildings. 2016 Eleventh International Conference on Ecological Vehicles and Renewable Energies (EVER), pages 1–4.XXV.Parise, G., Martirano, L., and Parise, L. (2014). Energy performance of buildings: An useful procedure to estimate the impact of the lighting control systems. Conference Record -Industrial and Commercial Power Systems Technical Conference, pages 1–7.XXVI.Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., and Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208(May):889–904.XXVII.Shimoda, Y., Fujii, T., Morikawa, T., and Mizuno, M. (2004). Residential enduse energysimulation at city scale. Building and Environment, 39(8):959 –967. Building Simulation for Better Building Design.XXVIII.Song, M., Niu, F., Mao, N., Hu, Y., and Deng, S. (2018). Review on building energy performance improvement using phase change materials. Energy and Buildings, 158:776–793.

XXIX.Talebi, B., Haghighat, F., and Mirzaei, P. A. (2017). Simplified model to predict the thermal demand profile of districts. Energy and Buildings, 145:213 –225.XXX.Talebi, B., Haghighat, F., Tuohy, P., and Mirzaei, P. A. (2018). Validation of a community district energy system model using field measured data. Energy, 144:694 –706.XXXI.Touzani, S., Granderson, J., and Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158:1533–1543.XXXII.Tsanas, A. and Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49:560–567.XXXIII.Tuominen, P., Holopainen, R.,Eskola, L., Jokisalo, J., and Airaksinen, M. (2014). Calculation method and tool for assessing energy consumption in the building stock. Building and Environment, 75:153 –160.XXXIV.Vujoˇsevi ́c, M. and Krsti ́c-Furundˇzi ́c, A. (2017). The influence of atrium on energy performance of hotel building. Energy and Buildings, 156:140–150.XXXV.Wang, Z., Wang, Y., and Srinivasan, R. S. (2018). A novel ensemble learning approach to support building energy use prediction. Energy and Buildings, 159:109–122.

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Adil Afridi,Atif Afridi,Farhan Zafar,



Pervious concrete pavement could be a distinctive and effective thanks to capture storm water and permit it to course into the bottom therefore recharging groundwater, reducing storm water runoff, and meeting U.S. Environmental Protection Agency (EPA) storm water laws. this technique has been counseled by independent agency and geotechnical engineers as a Best Management Practices (BMPs) for the management of storm water runoff. This pavement technology creates additional economical land use by eliminating the necessity for retention ponds, swales, and alternative storm water management devices. receptive surface treatments retain the water sub-surface because it bit by bit infiltrates into the soil; holding the storm water in multiple air voids or cells conjointly aiding in water quality through degradation of hydrocarbons into greenhouse emission and water, and retentive metals within the structure keeps them from the groundwater table Despite the employment of receptive systems for nearly thirty years within the USA, not tons of analysis has been performed on the long run absorption of contaminants within the concrete microstructure. many studies showcase the removal potency of those pavements within the 1st few years of service, stating it’s shown higher than seventy five p.c potency in removal of contaminants, this investigation targeted on varied receptive concrete treatments decisive optimum strength, voids, infiltration and voids. in addition geochemical work on trace metal sorption, major component adverse effects and water quality edges was performed on existing tons on MTSU field.


concrete pavemen, water runoff,optimum strength,


I.Construction and Maintenance Assessment of Pervious Concrete Pavements, RMC Foundation, January 2007,

II.G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references).

III.Hydraulic Performance Assessment of Pervious Concrete Pavements for Storm water Management Credit, RMC Foundation,

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An inventory model of flexible demand for price, stock and reliability with deterioration under inflation incorporating delay in payment


Sudip Adak,G.S. Mahapatra,



This paper presents an inventory model for deteriorating items with a constant rate of deterioration and the demand rate is flexible which depends on the price, stock as well as the reliability of the products. This model allowing the shortage under inflation, and delay in payment is also taken into account. We consider situation of the credit period is less than or greater than the cycle time for settling the account. Numerical example is given for different cases and sensitivity analysis is carried out to analyze the effect of the parameters on the optimal solution.


Deterioration,Reliability,Credit period,Inflation,Delay payment,


I.A. Guria, B. Das, S. Mondal and M. Maiti,“Inventory policy for an item with inflation induced purchasing price, selling price and demand with immediate part payment”, Applied Mathematical Modelling, 37 (1-2), 240-257, 2013.

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III.C.J. Chung and H.M. Wee, “Scheduling and replenishment plan for an integrated deteriorating inventory model with stock dependent selling rate”, International Journal of Advanced Management Technology, 35 (7-8), 665-679, 2008.

IV.C.K. Jaggi, P.K. Kapur, S.K. Goyal and S.K. Goel,”Optimalreplenishment and credit policy in EOQ model under two-levels of trade credit policy when demand is influenced by credit period”, International Journal of System Assurance Engineering and Management, 3(4), 352-359, 2012.

V.E.A. Elsayed and C. Teresi, “Analysis of inventory systems with deteriorating items”, International Journal of Production research, 21(4), 449-460, 1983.

VI.G. Janakiram, S. Sridhar, J.G. Shanthikumar, “A comparison of the optimal costs of two canonical inventory systems”, Operations Research, 55(5), 866-875, 2007.

VII.G.A. Widyadana and H.M. Wee, “Optimal deteriorating items production inventory models with random machine breakdown and stochastic repair time”,Applied Mathematical Modelling, 35, 3495-3508, 2011.

VIII.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “A production inventory model with fuzzy coefficients using parametric geometric programming approach”, International Journal of Machine Learning and Cybernetics, 2(2), 99-105, 2011.

IX.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “Fuzzy parametric geometric programming with application in fuzzy EPQ model under flexibility and reliability consideration”, Journal of Information and Computing Science, 7(3), 223-234, 2012.

X.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “An EPQ model with imprecise space constraint based on intuitionistic fuzzy optimization technique”, Journal of Multiple-Valued Logic and Soft Computing, 19(5-6), 409-423, 2012.

XI.G.S. Mahapatra, T.K. Mandal and G.P. Samanta, “EPQ model with fuzzy coefficient of objective and constraint via parametric geometric programming”, International Journal of Operational Research, 17(4), 436-448, 2013.

XII.G.S. Mahapatra, S. Adak, T.K. Mandal and S. Pal, “Inventory model for deteriorating items with time and reliability dependent demand and partial backorder”, International Journal of Operational Research, 29 (3), 344-359, 2017.

XIII.H.C. Liao, C.H. Tsai and C.T. Su, “An inventory model with deteriorating items under inflation when delay in payment is permissible”, International Journal of Production Economics, 63, 207-214, 2000.

XIV.H.J. Chang and C.Y. Dye, “An EOQ model for deteriorating items with time vary demand and partial backlogging”, Journal of Operational Research Society, 50, 1176-1182, 2001.

XV.H.M. Wee and S.T. Law, “Replenishment and pricing policy for deteriorating items taking into account the time value of money”, International Journal of Production Economics, 71, 213-220, 2001.

XVI.N.H.Shah and H. Soni, “A Multi-Object Production Inventory Model with Backorder for Fuzzy Random Demand Under Flexibility and Reliability”,Journal of Mathematical Modelling and Algorithms, 10 (4), 341-356, 2011.

XVII.K.J. Chung and C.N. Lin, “Optimal inventory replenishment models for deteriorating items taking account of time discounting”, Computer and Operations Research, 28, 67-83, 2001.XVIII.K.J. Chung and P.S. Ting, “A heuristic for replenishment for deteriorating items with a linear trend in demand”, Journal of Operational Research Society, 44, 1235-1241, 1993.

XIX.K.L. Hou, “An inventory model for deteriorating items with stock-dependent consumption rate and shortage under inflation and time discounting”,European Journal of Operational research, 168, 463-474, 2006.

XX.J.J. Liao and K.N. Huang, “An inventory model for deteriorating items with two levels of trade credit taking account of time discounting”, Acta Application Mathematics, 110(1), 313-326, 2010.

XXI.J.M. Chen, “An EOQ model for deteriorating items withtime-proportional demand and shortages under inflation and time discounting”, International Journal of Production Economics, 55, 21-30, 1998.

XXII.J. Sicilia, L.A. San-Jose and J. Garcia-Laguna, “An inventory model where backordered demand ratio is exponentially decreasing with the waiting time”, Annals of operations research, 19 (1), 137-155, 2012.

XXIII.P.K. Tripathy, W.M.Wee and P.R. Majhi, “An EOQ model with process reliability consideration”, Journal of Operational Research Society, 54, 549-554, 2003.

XXIV.R.B. Misra, “Optimum production lot size model for a system with deteriorating inventory”, International Journal of Production Research, 13, 495-505, 1975.

XXV.R.I. Levin, C.P. McLaughlin, R.P. Lamone and J.F. Kottas, “Production/Operations Management: Contemporary Policy for Managing Operating System”, McGraw-Hill, New York.

XXVI.S. Khanra, S.K. Ghosh and K.S. Chaudhuri,”An EOQ model for a deteriorating item with time dependent quadratic demand rate under permissible delay in payment”, Applied Mathematics and Computation, 218, 1-9, 2011.

XXVII.S. Pal, A. Goswami and K.S. Chaudhuri, “A deterministic inventory model for deteriorating items with stock dependent demand rate”, International Journal of Production Economics, 32, 291-99, 1993.

XXVIII.S. Pal, G.S. Mahapatra and G.P. Samanta, “An EPQ model of ramp type demand withWeibull deterioration under inflation and finite horizon in crisp and fuzzy environment”, International Journal of Production Economics, 156, 159-166, 2014.

XXIX.S. Pal, G.S. Mahapatra and G.P. Samanta, “An Inventory Model of Price and Stock dependent Demand Rate with Deterioration under Inflation and Delay in payment”, International Journal of System Assurance Engineering and Management, 5(4), 591-601, 2014.

XXX.S. Pal, G.S. Mahapatra and G.P. Samanta, “A production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness”, Economic Modelling, 46, 334-345, 2015.

XXXI.S. Pal, G.S. Mahapatra, G.P. Samanta, “A Three-Layer Supply Chain EPQ Model for Price-and Stock-Dependent Stochastic Demand with Imperfect Item Under Rework”, Journal of Uncertainty Analysis and Applications, 4 (1), 10, 2016.

XXXII.S. Pal, and G.S. Mahapatra, “A manufacturing-oriented supply chain model for imperfect quality with inspection errors, stochastic demand under rework and shortages”, Computers & Industrial Engineering, 106, 299-314, 2017.

XXXIII.S.H. Kim, M.A. Cohen and S. Netessine, “Performance contracting in after-sales service supply chains”, Management Science, 53 (12), 1843-1858, 2007.

XXXIV.S.K. Goyal, “EOQ under conditions of permissible delay in payments”, Journal of Operation Research Society, 36, 335-338, 1985.

XXXV.S.K. Manna, K.S. Chaudhuri, “An EOQ model with ramp type demand rate time dependent deterioration rate, unit production cost and shortage”, European Journal of Operational research, 171, 557-566, 2006.

XXXVI.S.S. Sana and K.S. Chaudhuri, “A deterministic EOQ model with delays in payments and price discount offers”, European Journal of Operational research, 184, 509-533, 2008.

XXXVII.T. Jin and H. Liao,“Spare parts inventory control considering stochastic growth of an installed base”, Computers & Industrial Engineering, 56 (1), 452-460, 2009.

XXXVIII.T. Roy and K.S. Chaudhuri,“An EPLS model for a variable production rate with stock-pricesensitive demand and deterioration”,Yugoslav Journalof Operations Research, 21, 1-13, 2011.

XXXIX.T.K. Datta, A.K. Pal, “Deterministic inventory system for deteriorating items with inventory level-dependent demand rate and shortages”, Opsearch, 27, 213.224, 1990.

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