Availability and Profit Optimization of Series-Parallel System with Linear Consecutive Cold Standby Units

Read  full  paper  at:http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53893#.VNr6mCzQrzE

we study availability and profit optimization of a series-parallel system consisting of three subsystems A, B and C in which A and B are cold standby. Subsystem A consists of linear consecutive k-out-of-n units while subsystems B and C consist of a single unit each. The system works if any of A or B and C work. The objective of this study is to maximize the steady-state availability and profit. To solve the optimization problem, different numbers of units for n = 2, 3, 4, 5 in subsystem A are considered. Explicit expressions for busy period of repairmen, steady-state availability and profit function are derived using linear first order differential equations. Several cases are analyzed graphically for n = 2, 3, 4, 5 to investigate the effects of various system parameters on availability and profit. The paper also presents graphical comparison for specific values of system parameters and finds that the optimal system configuration is when n = 5.

Cite this paper

Aliyu, M. , Yusuf, I. and Ali, U. (2015) Availability and Profit Optimization of Series-Parallel System with Linear Consecutive Cold Standby Units. Applied Mathematics, 6, 332-344. doi: 10.4236/am.2015.62032.

References

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http://dx.doi.org/10.1016/j.ress.2008.02.017
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http://dx.doi.org/10.1016/S0951-8320(02)00034-0
[6] Li, C.Y., Chen, X., Yi, X.S. and Tao, J.Y. (2010) Heterogeneous Redundancy Optimization for Multi-State Series-Parallel Systems Subject to Common Cause Failures. Reliability Engineering and System Safety, 95, 202-207.
http://dx.doi.org/10.1016/j.ress.2009.09.011
[7] Wang, S. and Wadata, J. (2009) Reliability Optimization of a Series-Parallel System with Fuzzy Random Lifetimes. International Journal of Innovative Computing, Information and Control, 5, 1547-1558.
[8] Wang, K.H. and Chen, Y.J. (2009) Comparative Analysis of Availability between Three Systems with General Repair Times, Reboot Delay and Switching Failures. Applied Mathematics and Computation, 215, 384-394.
http://dx.doi.org/10.1016/j.amc.2009.05.023
[9] Wang, K.-H., Yen, T.-C. and Fang, Y.-C. (2012) Comparison of Availability between Two Systems with Warm Standby Units and Different Imperfect Coverage. Quality Technology and Quantitative Management, 9, 265-282.                        eww150211lx

Availability and Profit Optimization of Series-Parallel System with Linear Consecutive Cold Standby Units

Read  full  paper  at:http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53893#.VNnDrCzQrzE

In this paper, we study availability and profit optimization of a series-parallel system consisting of three subsystems A, B and C in which A and B are cold standby. Subsystem A consists of linear consecutive k-out-of-n units while subsystems B and C consist of a single unit each. The system works if any of A or B and C work. The objective of this study is to maximize the steady-state availability and profit. To solve the optimization problem, different numbers of units for n = 2, 3, 4, 5 in subsystem A are considered. Explicit expressions for busy period of repairmen, steady-state availability and profit function are derived using linear first order differential equations. Several cases are analyzed graphically for n = 2, 3, 4, 5 to investigate the effects of various system parameters on availability and profit. The paper also presents graphical comparison for specific values of system parameters and finds that the optimal system configuration is when n = 5.

Cite this paper

Aliyu, M. , Yusuf, I. and Ali, U. (2015) Availability and Profit Optimization of Series-Parallel System with Linear Consecutive Cold Standby Units. Applied Mathematics, 6, 332-344. doi: 10.4236/am.2015.62032.

References

[1] Hu, l., Yue, D. and Li, J. (2012) Availability Analysis and Design Optimization for a Repairable Series-Parallel System with Dependencies, International Journal of Innovative Computing, Information and Control, 8, 6693-6705.
[2] Khatab, A., Nahas, N. and Nourelfath, M. (2009) Availbilty of K-Out-of-N: G Systems with Non-Identical Components Subject to Repair Priorities. Reliability Engineering & System Safety, 94, 142-151.
http://dx.doi.org/10.1016/j.ress.2008.02.017
[3] Krishnan, R. and Somasundaram, S. (2012) Reliability and Profit Analysis of Repairable K-Out-of-N System with Sensor. European Journal of Scientific Research, 67, 215-222.
[4] Juang, Y.-S., Lin, S.-S. and Kao, H.-P. (2008) A Knowledge Management System for Series-Parallel Availability Optimization and Design. Expert Systems with Applications, 34, 181-193.
http://dx.doi.org/10.1016/j.eswa.2006.08.023
[5] Levitin, G. (2002) Optimal Series-Parallel Topology of Multi-State System with Two Failure Modes. Reliability Engineering & System Safety, 77, 93-107.
http://dx.doi.org/10.1016/S0951-8320(02)00034-0
[6] Li, C.Y., Chen, X., Yi, X.S. and Tao, J.Y. (2010) Heterogeneous Redundancy Optimization for Multi-State Series-Parallel Systems Subject to Common Cause Failures. Reliability Engineering and System Safety, 95, 202-207.
http://dx.doi.org/10.1016/j.ress.2009.09.011
[7] Wang, S. and Wadata, J. (2009) Reliability Optimization of a Series-Parallel System with Fuzzy Random Lifetimes. International Journal of Innovative Computing, Information and Control, 5, 1547-1558.
[8] Wang, K.H. and Chen, Y.J. (2009) Comparative Analysis of Availability between Three Systems with General Repair Times, Reboot Delay and Switching Failures. Applied Mathematics and Computation, 215, 384-394.
http://dx.doi.org/10.1016/j.amc.2009.05.023                                          eww150210lx
[9] Wang, K.-H., Yen, T.-C. and Fang, Y.-C. (2012) Comparison of Availability between Two Systems with Warm Standby Units and Different Imperfect Coverage. Quality Technology and Quantitative Management, 9, 265-282.

Optimization of Extraction Conditions of Some Phenolic Compounds from White Horehound (Marrubium vulgare L.) Leaves

Read  full  paper  at:

http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52661#.VKCv6cCAM4

ABSTRACT

This research was aimed to optimize the extraction conditions of three phenolic compounds: total phenolics, flavonoids and condensed tannins, from White Horehound’s leaves (Marrubium vulgare L.). Distilled water and different organic solvents such as: methanol, ethanol and acetone, were used, with various concentrations (20% – 80%, v/v), temperatures (20°C – 60°C) and extraction times (30 – 450 min). Results showed that the maximum total phenolics amounts (293.34 ± 14.60 mg gallic acid equivalent/g dry weigh), were obtained with 60% aqueous methanol at 25°C for 180 min; total flavonoids (79.52 ± 0.55 mg catechin equivalent/g dry weigh) with 80% aqueous methanol at 20°C for 450 min, and condensed tannins (28.15 ± 0.80 mg catechin equivalent/g dry weigh) with 60% aqueous acetone at 50°C and for 180 min. ANOVA test showed the significant effect (***P < 0.001) of the extraction conditions tested on phenolic compounds. The Principal Component Analysis (PCA) exhibited the positive effect of low temperatures on total phenolics and flavonoids extraction, and the effect of high temperatures on the condensed tannins extraction. The Response Surface Methodology (RSM) provided predicted values of extraction conditions and maximum polyphenols amounts similar to those obtained experimentally.

Cite this paper

Bouterfas, K. , Mehdadi, Z. , Benmansour, D. , Khaled, M. , Bouterfas, M. and Latreche, A. (2014) Optimization of Extraction Conditions of Some Phenolic Compounds from White Horehound (Marrubium vulgare L.) Leaves. International Journal of Organic Chemistry, 4, 292-308. doi: 10.4236/ijoc.2014.45032.

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http://dx.doi.org/10.1016/j.foodchem.2007.06.062                                                              eww141229lx

A Decision Problem of Hotels on Setting Online-Exclusive-Rooms in E-Commerce Age

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49165#.VJJNEcnQrzE

Author(s)

Liang Xu, Xiaoyan Xu

Affiliation(s)

University of Science and Technology of China, Hefei, China.
University of Science and Technology of China, Hefei, China.

ABSTRACT

Due to the rapid development of information technology, customers’ consumption pattern in hotel industry has dramatically changed, and many people book rooms through some professional online platforms. Hotel managers need to redesign their sale channel structures by incorporating online platforms, in order to increase profit. This paper focuses on a revenue maximization problem for a hotel which cooperates with an online platform. Two cooperating forms are investigated and compared. Specifically, one intuitive form is named complete sharing, in which all the hotel rooms are shared with the online platform for booking. The other form is named setting online-exclusive-rooms (OERs), in which the hotel set a fixed quantity of rooms for exclusively booking from the online platform. By building a loss queueing model, this paper finds some properties of the optimal quantity of OERs in the latter form. Factors affecting the selection and how they affect the selection are also investigated.

KEYWORDS

Revenue Management, Hotel Room Management, Online Platform, Optimization

Cite this paper

Xu, L. and Xu, X. (2014) A Decision Problem of Hotels on Setting Online-Exclusive-Rooms in E-Commerce Age. Journal of Service Science and Management, 7, 323-336. doi: 10.4236/jssm.2014.74029.

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http://dx.doi.org/10.1017/CBO9780511804441                                                                      eww141218lx

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52263#.VI-A1cnQrzE

ABSTRACT

Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.

Cite this paper

Tantele, E. , Votsis, R. and Onoufriou, T. (2014) Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies. Open Journal of Civil Engineering, 4, 338-352. doi: 10.4236/ojce.2014.44029.

References

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Optimal Aggregate Production Plans via a Constrained LQG Model

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51362#.VGVSsmfHRK0

ABSTRACT

In this paper, a single product, multi-period, aggregate production planning problem is formulated as a linear-quadratic Gaussian (LQG) optimal control model with chance constraints on state and control variables. Such formulation is based on a classical production planning model developed in 1960 by Holt, Modigliani, Muth and Simon, and known, since then, as the HMMS model [1]. The proposed LQG model extends the HMMS model, taking into account both chance-constraints on the decision variables and data generating process, based on ARMA model, to represent the fluctuation of demand. Using the certainty-equivalence principle, the constrained LQG model can be transformed into an equivalent, but deterministic model, which is called here as Mean Value Problem (MVP). This problem preserves the main properties of the original model such as convexity and some statistical moments. Besides, it is easier to be implemented and solved numerically than its stochastic version. In addition, two very simple suboptimal procedures from stochastic control theory are briefly discussed. Finally, an illustrative example is introduced to show how the extended HMMS model can be used to develop plans and to generate production scenarios.

Cite this paper

Filho, O. (2014) Optimal Aggregate Production Plans via a Constrained LQG Model. Engineering, 6, 773-788. doi: 10.4236/eng.2014.612075.

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Stochastic Process Optimization Technique

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51266

ABSTRACT

The conventional optimization methods were generally based on a deterministic approach, since their purpose is to find out an accurate solution. However, when the solution space is extremely narrowed as a result of setting many inequality constraints, an ingenious scheme based on experience may be needed. Similarly, parameters must be adjusted with solution search algorithms when nonlinearity of the problem is strong, because the risk of falling into local solution is high. Thus, we here propose a new method in which the optimization problem is replaced with stochastic process based on path integral techniques used in quantum mechanics and an approximate value of optimal solution is calculated as an expected value instead of accurate value. It was checked through some optimization problems that this method using stochastic process is effective. We call this new optimization method “stochastic process optimization technique (SPOT)”. It is expected that this method will enable efficient optimization by avoiding the above difficulties. In this report, a new optimization method based on a stochastic process is formulated, and several calculation examples are shown to prove its effectiveness as a method to obtain approximate solution for optimization problems.

Cite this paper

Yoshida, H. , Yamaguchi, K. and Ishikawa, Y. (2014) Stochastic Process Optimization Technique. Applied Mathematics, 5, 3079-3090. doi: 10.4236/am.2014.519293.

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