Maintenance activities planning and grouping for complex structure systems


 Briana Bryan
 2 years ago
 Views:
Transcription
1 Maintenance activities panning and grouping for compex structure systems Hai Canh u, Phuc Do an, Anne Barros, Christophe Berenguer To cite this version: Hai Canh u, Phuc Do an, Anne Barros, Christophe Berenguer. Maintenance activities panning and grouping for compex structure systems. 11th Internationa Probabiistic Safety Assessment and Management Conference & the Annua European Safety and Reiabiity Conference  PSAM 11/ESREL 2012, Jun 2012, Hesinki, Finand. pp.cdrom, <ha > HAL Id: ha Submitted on 9 May 2012 HAL is a mutidiscipinary open access archive for the deposit and dissemination of scientific research documents, whether they are pubished or not. The documents may come from teaching and research institutions in France or abroad, or from pubic or private research centers. L archive ouverte puridiscipinaire HAL, est destinée au dépôt et à a diffusion de documents scientifiques de niveau recherche, pubiés ou non, émanant des étabissements d enseignement et de recherche français ou étrangers, des aboratoires pubics ou privés.
2 Maintenance activities panning and grouping for compex structure systems Hai Canh u a, Phuc Do an b, Anne Barros a, Christophe Bérenguer c a Troyes university of technoogy, Troyes, France b CRAN, Lorraine university, Nancy, France c Grenobe institute of technoogy, Grenobe, France Abstract: This paper presents a dynamic grouping maintenance strategy for compex systems whose structure may ead to both positive and negative economic dependence which impy that combining maintenance activities is cheaper (or more expensive respectivey) than performing maintenance on components separatey. Binary Partice Swarm Optimization (BPSO) agorithm is used to find optima grouping panning which is NPhard combinatoria probem. The proposed grouping maintenance strategy based on the roing horizon approach can hep to update the maintenance panning by taking into account shortterm information which coud be changed with time. A numerica exampe of a 10 components system is finay introduced to iustrate the use and the advantages of the proposed approach in the maintenance optimization framework. Keywords: Grouping maintenance, Economic dependence, Muticomponent system, Optimization. 1. INTRODUCTION For muticomponent systems, economic dependence which can be either positive or negative have been considered in maintenance optimization. Positive economic dependence impies that costs can be saved when severa components are jointy maintained instead of separatey. Negative economic dependence between components occurs when maintaining components simutaneousy is more expensive than maintaining components individuay. A number of maintenance optimization methods and maintenance strategies have been proposed and deveoped in the iterature, see for exampe [1, 2, 3, 4]. In such papers, they focus mainy on series structures which ead directy to positive economic dependence [5, 6, 7, 8]. Recenty, in [9, 10], both positive and negative economic dependence have been studied by introducing an opportunistic maintenance which attempts to baance the negative aspects and the positive aspects of grouping maintenance. The proposed maintenance modes are however ony appicabe on imited cass of systems with particuar structure. From a practica point of view, with growing up quicky of industry, systems become more and more compex. Hence, the proposed modes may no onger be used. The grouping maintenance probem remains widey open. Moreover, in the grouping maintenance framework, static modes with an infinite panning horizon are usuay used in case of stabe situations [5, 6, 7]. Recenty, dynamic modes have been introduced in order to change the panning rues according to shortterm information (e.g. faiures and varying deterioration of components) by using a roing horizon approach [8]. The primary objective of this paper is thus to deveop the roing horizon approach for grouping maintenance of compex structure systems with both positive and negative dependence. For such systems, to find optima grouping panning, dynamic programming (see [8, 3]) is no onger usabe since the combinatoria probem can be formuated as a set partitioning probem, which however can be NPhard due to the compex structure impact. To this end, the BPSO [11, 12, 13], recognized as a genera search strategy which is simpe for impementation and very efficient for goba search, is used. This paper is organized as foows. Section 2 is devoted to the description of genera assumptions and grouping maintenance probem. The impact of system structure on grouping maintenance is aso anayzed. Section 3 focuses on the deveopment of the roing horizon approach in the context of compex structure systems. A simpe numerica exampe is introduced in Section 4. Finay, the ast section presents the concusions drawn from this work.
3 2. PROBLEM FORMULATION 2.1. Genera assumptions Consider a compex structure coherent system consisting of N independent components in which component state is either operationa or faiure one. For the rate of occurrence of faiures, denoted r i (t), we take a poynomia function with scae parameter λ i > 0, and shape parameter β i > 1: r i (t) = β i λ i ( t λ i ) β i 1 (1) Both corrective and preventive maintenance are considered for each component and we assume aso that preventive and corrective maintenance durations can be negected. According to the system structure, we consider two kinds of components: noncritica component (the system can be sti functioning when the component stops); critica component (a shutdown of the component for whatever raison eads to a shutdown of the system). By definition, maintenance of a critica component may ead to an additiona cost that coud be reying on, for exampe, production quaity oss or/and restart system costs... see [14]. Two kinds of shutdown costs are here considered:(i) panned shutdown cost, denoted Csys, p that is incurred when executing preventive maintenance on a critica component;(ii) unpanned shutdown cost, denoted Csys, u that is incurred when executing corrective maintenance on a critica component. Since the faiure is random and preventive maintenance is panned, an unpanned shutdown is therefore more costy than a panned shutdown, Csys c Csys. p For component i (i = 1,..., N), we assume that after a preventive maintenance (PM) action the component is considered as good as new. The preventive maintenance cost can be divided into three parts: a fixed setup cost, denoted S, represents the common cost for a preventive maintenance activities. The setup cost depends ony on the system. For exampe, the setup cost can be composed the cost of crew traveing and the preparation cost (erecting a scaffoding or opening a machine); a specific cost, denoted c p i, depending on the component; an additiona cost Cp sys is incurred if component i is critica. That mean that this additiona cost depends on the component position in the system structure and the system characteristics. Let π i is an indicator function (π i = 1 if component i is a critica one, and otherwise π i = 0 if component i is a notcritica one). As consequence, if a PM of component i is carried out, we have to pay the foowing cost: C p i = S + cp i + π i C p sys (2) To define the preventive maintenance cyce for each component, a ongterm average cost based approach [15] is used and presented in Section 3. Between two preventive maintenance intervas, if component fais, a minima repair is then immediatey performed to restore the component to an operationa state as bad as od. Let c r i denote the specific repair cost of component i. As in the PM case, when a corrective action is carried out for component i, it requires a corrective maintenance cost, denoted Ci c. Ci c = S + c r i + π i Csys u (3) 2.2. Grouping maintenance and the impact of the system structure In the spirit of grouping maintenance strategies see [8, 1], grouping maintenance can reduce the maintenance cost. The setup cost can be shared when severa maintenance activities are performed simutaneousy since simutaneous maintenance executions on severa (group) components require usuay ony one setup cost [15, 3]. Note we, however that when a group of components are preventivey performed together, the maintenance cost coud be indirecty penaized with the reduction of components usefu ife if the maintenance dates are advanced and with the increasing of components faiure probabiity which coud impy a system immobiization if the maintenance dates are too ate. Moreover, the maintenance cost of a group may depend on the position of the group s components. We consider two kinds of groups: noncritica group (the system can be sti functioning when a
4 components of the group stop); critica group (the maintenance of the group eads to system stop). As consequence, maintenance on a critica group eads to a panned shutdown cost. Three foowing cases are considered: (i) if the critica group contains at east two critica components, the panned shutdown cost can then be shared; (ii) if the critica group contains one critica component, the panned shutdown cost remains then unchanged; (iii) if the critica group does not contain any critica component, the panned shutdown cost can then be penaized. In order to find the optima groups which coud baance to minimize the system maintenance costs on the scheduing horizon, the roing horizon approach introduced recenty in [8, 15], wi be deveoped by taking into account the system structure, see Section 3. An optimization agorithm, BPSO [11, 12], wi be aso used. 3. EXTENSION OF ROLLING HORIZON APPROACH The deveoped roing horizon approach consists of 5 phases: decomposition, tentative panning, economic profit formuations, grouping optimization using BPSO and roing horizon Phase 1: Decomposition For each individua component, we formuate an infinite horizon maintenance mode to obtain the optima individua preventive maintenance cyce that minimizes the component maintenance costs. Let M i (x) denote the expected deterioration cost incurred in x time units since the atest operation on component i. According to the minima repair poicy, M i (x) can be expressed as the foowing: M i (x) = C r i x From equations (1) and (4), we obtain: M i (x) = C r i ( x λ i ) β i 0 r i (y)dy. (4) If component i is preventivey maintained at x, the expected cost within interva [0, x] is: E i (x) = C p i + M i(x) = C p i + Cr i ( x λ i ) β i (5) By appying the renewa theory when executing the preventive maintenance of component i every x time units amount, the ongterm average cost of component i can be determined as the foowing: ϕ i (x) = E i(x) x = Cp i + Cr i ( x λ i ) β i x Since ϕ i (x) is a convex function, the optima interva ength for the preventive maintenances on component i, denoted x i, can be obtained by setting the derivative of ϕ i(x) to zero: x β i = arg min ϕ i (x) = λ i C p i i x Ci r(β i 1). (7) And the minima ongterm average maintenance cost of component i: (6) ϕ i = ϕ i (x i ) = Cp i β i x i (β i 1) (8) The minima ongterm average maintenance cost of the system assuming that a components are optimay and individuay maintained: N ϕ sys = ϕ i. (9) The optima interva ength x i which represents a nomina PM frequency of component i wi be used to define tentative execution times in the tentative panning phase. i=1
5 3.2. Phase 2: Tentative panning The idea of this phase is to estabish a tentative maintenance panning based on the individua preventive maintenance cyces. Let t begin denote the current date and i j be jth maintenance of component i since t begin and t e i is the operationa time eapsed from the ast preventive maintenance of component i before t begin. Without oss generaity we can set t begin = 0. Based on the individua maintenance rues of phase 1, the current state of component i, the tentative execution time of operation (or activity) i j, denoted t i j, is determined as foows, see [3]: t i 1 = t begin t e i + x i, t i j = t i j 1 + x i if j > 1 (10) In order to evauate the performance of grouping maintenance strategy, a finite panning horizon is usuay defined according to the current date t begin and the ending date t end which guarantees that a components are preventivey maintained at east one time in the horizon interva. It is shown in [3], t end can be determined as foows: t end = max t i 1. i After this phase, the tentative execution times of a individua maintenance activities in the scheduing horizon are identified. In the next phases we try to perform simutaneousy severa maintenance activities by changing their maintenance execution times to minimize tota maintenance cost Phase 3: Economic profit formuations The idea of this phase is to formuate the economic profits when preventive maintenance activities are simutaneousy carried out. To this end, we wi firsty find a costeffective groups which can hep to identify secondy an optima grouping maintenance panning. Costeffective group Assume now a group of severa different maintenance operations i j (i, j = 1, 2,...), denoted G k, are simutaneousy performed at time t. The corresponding economic profit of this group can be divided into three parts as foows: Since the execution of a group of m maintenance operations requires ony one setup cost, the group G k yieds a cost reduction: U G k = (m 1) S (11) The penaty costs due to the changing of components execution maintenance date: H 1 G (t) = h k i (t t i j) = h i ( t i j), (12) i j G k i j G k h i ( t i j) is the penaty cost due to the movement execution date of the maintenance operation i j. Since operation i j is actuay executed at time t = t i j + t i j ( t i j > x i ) instead of t i j. h i ( t i j) = E i (x i + t i j) ( E i (x i )+ t i j.ϕ ) ( i = C r x i i + t ) i j βi C ( r x ) βi ti λ i i C p i j β i i λ i x i (β i 1) (13) The optima execution time of the group G k, denoted t G k, can be found when the H 1 (.) searches G k its minima vaue H 1. That is: G k H 1 G = H 1 k G (t k G k) = min h i (t G k t i j) (14) t i j G k If G k is a critica one, the grouping can conduct to an additiona cost that is expressed as foows: H 2 G k = C G k C notg k, (15) The tota panned shutdown costs when a components of the group are separatey maintained: C notg k = Csys p π i ; (16) i j G k
6 Let π G k is an indicator function (π G k = 1 if G k is a critique group, and π G k = 0 if G k is a notcritique group). The panned shutdown cost when a components of the group are performed together: Thanks to equations (15), (16) and (17), H 2 G k C G k = C p sys π G k (17) can be written as: H 2 G k = C p sys (π G k i j G k π i ). (18) Note we that if the group G k is not critica, a components of the group are then not critica, H 2 G k is hence equa to zero. If G k is a critica group, H 2 can be positive or negative depending on the G k number of critica components of the group (see again Section 2). From Equations (11), (14), and (18), the economic profit of group G k, denoted EP (G k ), can be written as the foowing: EP (G k ) = U G k H 1 G k H 2 G k (19) (i) If EP (G k ) < 0 the grouping maintenance is more expensive than performing maintenance on components separatey, i.e. the grouping impies a negative economic dependence. As consequence, the components of the group shoud not be simutaneousy maintained; (ii) If EP (G k ) 0, the maintenance cost can be saved when components are jointy maintained, i.e. grouping eads to a positive economic dependence. And the group is caed costeffective. Grouping structure Based on a costeffective groups, a grouping structure (or partition of a maintenance operations in the scheduing interva), denoted SGM, can be identified. As consequence, the tota economic profit is cacuated as foows: EP S(SGM) = G k SGM EP (G k ) = G k SGM (U G k H 1 G k H 2 G k ). (20) An optima grouping structure when the tota economic profit searches its maxima vaue: SGM optima = arg max EP S(SGM) (21) SGM Note we that the maximum number of groups to be considered is here 2 n 1 for n maintenance activities. The finding of optima grouping structure becomes difficut since the combinatoria probem can be formuated as a set partitioning probem, which however can be NPhard. The dynamic programming agorithm proposed in [8, 3] can not therefore be used. To sove this probem, BPSO agorithm wi be used and described in next paragraph Phase 4: Grouping optimization using BPSO Binary partice swarm optimization was proposed by Kennedy and Eberhart in 1997, see [13], to sove optimization probems in binary search spaces (0 or 1). This method is oosey modeed on the focking behavior of birds. A soution to a specific probem is presented by a position of a partice. A swarm of fixed number of partices is generated and each partice is initiaized with a random position in the search space. Each partice fies through the search space with a veocity. At each iteration, a new veocity vaue of each partice is cacuated based on its current veocity, the distance from its persona best (the best position of partice so far), and the distance from the goba best (the best position of a partices). Once the veocity of each partice is updated, the partices are then moved to the new positions. The genera structure of BPSO is presented in Fig 1. Coding A coding phase determines how the probem is structured in the agorithm. In our probem, each grouping structure (a soution) is presented by a binary matrix of (n 1) rows and n coumns. Each row represents a group in which entry (k, j) = 1 if maintenance activity j is performed in group k and entry (k, j) = 0 for otherwise. For exampe considering a feasibe soution of 5 preventive
7 Start Coding Initiaize agorithm constants pmax, max, min, r1, r2 Initiaize randomy a swarm of partices and its veocities Evauate the swarm Update the veocity of each partice Update position for a partices No Evauate the swarm Stopping criterion satisfied? Yes Output resuts Stop Figure 1: The genera structure of BPSO maintenance activities contains of 3 groups G 1 = {1, 2}, G 2 = {3, 4}, G 3 = {5}. This soution can be presented by the matrix (22a) X = (22a) = (22b) (22) Create randomy a swarm of partices and its veocities A swarm of s partices is constructed for the BPSO. A sma number of partices in the swarm may resut in oca convergence, on the contrary, a arge number of partices wi increase computationa efforts and may make sow convergence. Therefore, the s is usuay chosen between 60 and 100. The initia position of the th partice is identified by a binary matrix, denoted X 0, whose eements are randomy assigned by 1 or 0. To adjust the position of partices, the initia veocities of a partices are aso randomy generated (1 0,..., s 0 ). eocity vaues are restricted to the imit vaues ( max, min ) to prevent the partice from moving too rapidy from one region in search space to another. We usuay set max = 4, min = 4. The initia veocity of the partice, denoted 0, is represented as matrix (22b). Evauate the swarm The goa of this step is to determine the persona best and goba best in the swarm. To do that, we must firsty evauate the position of each partice at iteration pth according to its fitness function which is defined as foows: { f(x p ) = if EP (G k ) < 0, G k X p EP S(X p ) otherwise The persona best, denoted ibest p, is the best position associated with the best fitness vaue of the partice obtained unti iteration p. ibest p is defined as foows: ibest p = arg max[f(x q )], where q = 1 p q
8 The goba best at iteration p, denoted gbest p, is the best position among a partices in the swarm, which is achieved so far and can be expressed as foows: gbest p = arg max[f(ibest p )], where = 1 s Update the veocity of each partice The objective of this step is to cacuate the new vaue of veocity of each partice to determine the next position of the partice. Based on the current veocity vaue, the persona best and the goba best, the veocity of each partice can be updated as foows: p+1 (k, j) = p (k, j) + r 1 U 1 [ibest p (k, j) Xp (k, j)] + r 2 U 2 [gbest p (k, j) X p (k, j)] U 1, U 2 are uniform random numbers between (0, 1); r 1, r 2 are socia and cognitive parameters which are taken as r 1 = r 2 = 2 consistent with the iterature, see [12]. In BPSO, the veocities of the partices are defined in terms of probabiities that a eement of matrix X p wi change to one. Using this definition a veocity must be restricted within the range [0, 1]. So whenever a veocity vaue is computed, the foowing piecewise function, whose range is cosed interva [ min, max ], is used to restrict them to minimum and maximum vaue. max if p+1 (k, j) > max h( p+1 (k, j)) = p+1 (k, j) if p+1 (k, j) < max min if p+1 (k, j) < min After appying the piecewise inear function, the foowing sigmoid function is used to scae the veocities between 0 and 1, which is then used for converting them to the binary vaues. sigmoid( p+1 (k, j)) = 1 + e 1 p+1 (k,j) Update position for a partices Based on new veocities obtained and et U is uniform random numbers between (0, 1), we update the new position for each partice by using the equation beow: { X p+1 1 if U < sigmoid( p+1 (k, j) = (k, j)) 0 otherwise Stopping criterion Stopping criterion is introduced to stop the agorithm process. Herein, imited iterations number (p max ) is used as a stopping criteria Phase 5: Roing horizon The maintenance manager can change the panning when some new information appears or when a panning for a new period and go back to step 4. Due to the previous phase, we have an optima grouping structure within the finite panning horizon [t begin, t end ]. However, with time some new information (ike maintenance resources constraints reying on for exampe management and new technoogy, opportunities,... ) by which this optima grouping structure can be impacted may become avaiabe. To update the maintenance panning, a new optima grouping structure within a new period must be identified. To this end, we simpy go back to phase NUMERICAL EXAMPLES The purpose of this section is to show how the proposed grouping maintenance strategy can be used in preventive maintenance optimization of compex systems. The impact of the system structure on the grouping maintenance panning wi be anayzed. A short comparison with previous grouping approach [8] wi be aso discussed. Consider a 10 components system whose structure is shown in Figure 2. When a component fais, it is immediatey maintained according to a minimarepair poicy. Corrective maintenance restores the component invoved into a state as bad as od. We assume faiure rate of a component i (i = 1,..., 10) is described by a Weibu distribution with scae parameter λ i > 0, and shape parameter β i > 1. Tabe 1 reports the random data for 10 components. For setup cost and shutdown system costs, we take S = 10 and Csys p = 40, Csys u = 45 respectivey.
9 o o Figure 2: Structure system Tabe 1: Data of 10 components Component i λ i β i c p i c r i t e i Maintenance panning with negigibe system structure Consider now maintenance strategies in which the system structure is not considered. We assume that the system is stopped when performing maintenance on any component, i.e. π i = 1, i = (1,..., 10). Individua maintenance panning We consider an optima maintenance panning in which a components preventive maintenance are separatey performed. To define the optima preventive maintenance cyce of components, the ongterm maintenance cost mode was used. C p i, Cc i, x i, ϕ i and t 1 i are cacuated by substitution of the data input and π i in Equations (2),(3),(7),(6),(10) respectivey. A resuts are shown in tabe 2. Tabe 2: aues of C p i, Cc i, x i, ϕ i and t1 i with negigibe system structure Component i π i C p i Ci r x i ϕ i t 1 i The average maintenance cost of the system when a components are individuay maintained is finay: ϕ SG sys = N ϕ i = (23) i=1 Grouping maintenance panning To estabish an optima grouping maintenance, the proposed grouping maintenance strategy was used. Based on the components individua maintenance execution date, the scheduing horizon is [0, ] (t begin = 0, t end = max t 1 i = ). After 500 iterations, BPSO optimization agorithm provided an optima grouping maintenance panning. The resuts are reported in Tabe 3. The tota cost saving is EP S = which is equivaent to the average saving ϕ sys = EP S t end = = Hence, the average maintenance cost of the system with proposed grouping maintenance panning is: ϕ SG sys = ϕ SG sys ϕ sys = 7.41 (24) The resut shows that grouping maintenance is cheaper than performing maintenance on components separatey. Note we that under the assumptions above, the system can be considered as a series
10 structure for which the roing horizon approach [8] can be used. obtain the same resuts shown in Tabe 3. By appying this approach, we Tabe 3: Optima grouping panning with negigibe system structure Group Group components Group execution date (t G ) Group cost saving 1 1,2,3,4,5,6,7,8,9, Maintenance panning with taking into account the system structure Individua maintenance panning According to the system structure, indicator functions π i (i = 1,..., 10) can be easiy determined. In the same manner, C i p, C r i, x i, ϕ i, t1 i are then cacuated and showed in Tabe 4. The average maintenance cost of the system when a components are individuay maintained is finay: ϕ SG sys = N ϕ i = (25) i=1 The resut shows that the consideration of the system structure in maintenance can save the maintenance cost. If the system structure is taken into account, the individua maintenance panning is even cheaper than an optima grouping maintenance panning in which the system structure is ignored. Tabe 4: aues of individua optimization with taking into account the system structure Component i π i C p i Ci r x i ϕ i t 1 i Grouping maintenance panning In order to estabish a grouping maintenance panning, we used again the proposed grouping approach. According to the components individua maintenance execution date, the panning horizon is [0, 300]. Tabe 5 reports the resuts obtained by using BPSO optimization agorithm with 500 iterations. Tabe 5: Optima grouping panning with taking into account the system structure Group Group components Group execution date (t G ) Group cost saving 1 1,5, ,3,4,6, , The tota maintenance savings cost is EP S = = and the maintenance cost saving par time unit is: ϕ SG sys = ϕ SG sys = (26) 300 From (23), (24), (25) and (26), we obtain an important ranking for the average maintenance cost of the system: ϕ SG sys > ϕsys SG > ϕ SG sys > ϕ SG sys. The resuts assert that grouping can save the maintenance cost and that the system structure can take an important rue and shoud be taken into account in maintenance panning optimization. 5. CONCLUSIONS In this work, the roing horizon approach introduced recenty in [8] is deveoped for grouping maintenance panning of compex systems whose structure can take both positive and negative impact
11 on maintenance grouping. The numerica resuts show that taking into account the system structure into grouping maintenance procedure may reduce significanty maintenance cost. In order to find an optima grouping panning, Binary Partice Swarm Optimization agorithm is proposed to use. This optimization agorithm can hep to sove combinatoria grouping probem which can be NPhard. Our future research work wi focus on the deveopment of the proposed method for systems which have the stochasticay and structuray dependent between components and aso with the taking into account the duration of maintenance activities. REFERENCES [1] R.P. Nicoai and R. Dekker. Optima maintenance of muticomponent systems: a review. Compex system maintenance handbook, pages , [2] D.I. Cho and M. Parar. A survey of maintenance modes for mutiunit systems. European Journa of Operationa Research, 51(1):1 23, [3] P. Do an, K. Bouvard, F. Brissaud, A. Barros, and C. Berenguer. Dynamic grouping maintenance strategy with time imited opportunities. In Avance in Safety, Reiabiity and Risk Management  Proc.ESREL 2011, September 2011, Troyes, France., [4] K. Bouvard, S. Artus, C. Berenguer, and. Cocquempot. Conditionbased dynamic maintenance operations panning & grouping. appication to commercia heavy vehices. Reiabiity Engineering & System Safety, [5] R. Dekker. Integrating optimisation, priority setting, panning and combining of maintenance activities. European Journa of Operationa Research, 82(2): , [6] R. Dekker, R.E. Wideman, and R. van Egmond. Joint repacement in an operationa panning phase. European Journa of Operationa Research, 91(1):74 88, [7] R. Laggoune, A. Chateauneuf, and D. Aissani. Preventive maintenance scheduing for a muticomponent system with nonnegigibe repacement time. Internationa Journa of Systems Science, 41(7): , [8] R.E. Wideman, R. Dekker, and A. Smit. A dynamic poicy for grouping maintenance activities. European Journa of Operationa Research, 99(3): , [9] H. Pham and H. Wang. Optima (τ, t) opportunistic maintenance of a koutofn: G system with imperfect pm and partia faiure. Nava Research Logistics (NRL), 47(3): , [10] SheyHuei Sheu and ChungMing Kuo. Optimization probems in koutofn systems with minima repair. Reiabiity Engineering and System Safety, 44(1):77 82, [11] J. Kennedy and R. Eberhart. Partice swarm optimization. In Neura Networks, Proceedings., IEEE Internationa Conference on, voume 4, pages IEEE, [12] N. Franken and AP Engebrecht. Investigating binary pso parameter infuence on the knights cover probem. In Evoutionary Computation, The 2005 IEEE Congress on, voume 1, pages IEEE, [13] J. Kennedy and R.C. Eberhart. A discrete binary version of the partice swarm agorithm. In Systems, Man, and Cybernetics, Computationa Cybernetics and Simuation., 1997 IEEE Internationa Conference on, voume 5, pages vo.5, oct [14] WMJ Geraerds. The cost of downtime for maintenance: preiminary considerations. Maint. Manage. Int., 5(1):13 21, [15] R. Dekker, J.R. an Der Meer, R.P. Pasmeijer, R.E. Wideman, and J.J. De Bruin. Maintenance of ightstandardsa casestudy. Journa of the Operationa Research Society, pages , 1998.
Secure Network Coding with a Cost Criterion
Secure Network Coding with a Cost Criterion Jianong Tan, Murie Médard Laboratory for Information and Decision Systems Massachusetts Institute of Technoogy Cambridge, MA 0239, USA Emai: {jianong, medard}@mit.edu
More informationFast Robust Hashing. ) [7] will be remapped (and therefore discarded), due to the loadbalancing property of hashing.
Fast Robust Hashing Manue Urueña, David Larrabeiti and Pabo Serrano Universidad Caros III de Madrid E89 Leganés (Madrid), Spain Emai: {muruenya,darra,pabo}@it.uc3m.es Abstract As statefu fowaware services
More informationCONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS
Dehi Business Review X Vo. 4, No. 2, Juy  December 2003 CONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS John N.. Var arvatsouakis atsouakis DURING the present time,
More informationTeamwork. Abstract. 2.1 Overview
2 Teamwork Abstract This chapter presents one of the basic eements of software projects teamwork. It addresses how to buid teams in a way that promotes team members accountabiity and responsibiity, and
More informationHYBRID FUZZY LOGIC PID CONTROLLER. Abstract
HYBRID FUZZY LOGIC PID CONTROLLER Thomas Brehm and Kudip S. Rattan Department of Eectrica Engineering Wright State University Dayton, OH 45435 Abstract This paper investigates two fuzzy ogic PID controers
More informationComparison of Traditional and OpenAccess Appointment Scheduling for Exponentially Distributed Service Time
Journa of Heathcare Engineering Vo. 6 No. 3 Page 34 376 34 Comparison of Traditiona and OpenAccess Appointment Scheduing for Exponentiay Distributed Service Chongjun Yan, PhD; Jiafu Tang *, PhD; Bowen
More informationSimultaneous Routing and Power Allocation in CDMA Wireless Data Networks
Simutaneous Routing and Power Aocation in CDMA Wireess Data Networks Mikae Johansson *,LinXiao and Stephen Boyd * Department of Signas, Sensors and Systems Roya Institute of Technoogy, SE 00 Stockhom,
More informationCERTIFICATE COURSE ON CLIMATE CHANGE AND SUSTAINABILITY. Course Offered By: Indian Environmental Society
CERTIFICATE COURSE ON CLIMATE CHANGE AND SUSTAINABILITY Course Offered By: Indian Environmenta Society INTRODUCTION The Indian Environmenta Society (IES) a dynamic and fexibe organization with a goba vision
More informationA Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation
A Simiarity Search Scheme over Encrypted Coud Images based on Secure Transormation Zhihua Xia, Yi Zhu, Xingming Sun, and Jin Wang Jiangsu Engineering Center o Network Monitoring, Nanjing University o Inormation
More informationMultiRobot Task Scheduling
Proc of IEEE Internationa Conference on Robotics and Automation, Karsruhe, Germany, 013 MutiRobot Tas Scheduing Yu Zhang and Lynne E Parer Abstract The scheduing probem has been studied extensivey in
More informationVendor Performance Measurement Using Fuzzy Logic Controller
The Journa of Mathematics and Computer Science Avaiabe onine at http://www.tjmcs.com The Journa of Mathematics and Computer Science Vo.2 No.2 (2011) 311318 Performance Measurement Using Fuzzy Logic Controer
More informationChapter 3: ebusiness Integration Patterns
Chapter 3: ebusiness Integration Patterns Page 1 of 9 Chapter 3: ebusiness Integration Patterns "Consistency is the ast refuge of the unimaginative." Oscar Wide In This Chapter What Are Integration Patterns?
More informationA Description of the California Partnership for LongTerm Care Prepared by the California Department of Health Care Services
2012 Before You Buy A Description of the Caifornia Partnership for LongTerm Care Prepared by the Caifornia Department of Heath Care Services Page 1 of 13 Ony ongterm care insurance poicies bearing any
More informationThe Computation of the Inverse of a Square Polynomial Matrix
The Computation of the Inverse of a Square Poynomia Matrix Ky M. Vu, PhD. AuLac Technoogies Inc. c 2008 Emai: kymvu@auactechnoogies.com Abstract An approach to cacuate the inverse of a square poynomia
More informationDynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud
Dynamic Pricing Trade Market or Shared Resources in IIU Federated Coud Tongrang Fan 1, Jian Liu 1, Feng Gao 1 1Schoo o Inormation Science and Technoogy, Shiiazhuang Tiedao University, Shiiazhuang, 543,
More informationA Supplier Evaluation System for Automotive Industry According To Iso/Ts 16949 Requirements
A Suppier Evauation System for Automotive Industry According To Iso/Ts 16949 Requirements DILEK PINAR ÖZTOP 1, ASLI AKSOY 2,*, NURSEL ÖZTÜRK 2 1 HONDA TR Purchasing Department, 41480, Çayırova  Gebze,
More informationAn Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming Problems through a Probability Maximization Model
Cahit Perkgoz et a./asia Pacific Management Review (005) 10(1), 935 An Interactive Fuzzy Satisficing Method for Mutiobective Stochastic Integer Programming Probems through a Probabiity Maximization Mode
More informationTERM INSURANCE CALCULATION ILLUSTRATED. This is the U.S. Social Security Life Table, based on year 2007.
This is the U.S. Socia Security Life Tabe, based on year 2007. This is avaiabe at http://www.ssa.gov/oact/stats/tabe4c6.htm. The ife eperiences of maes and femaes are different, and we usuay do separate
More informationA BranchandPrice Algorithm for Parallel Machine Scheduling with Time Windows and Job Priorities
A BranchandPrice Agorithm for Parae Machine Scheduing with Time Windows and Job Priorities Jonathan F. Bard, 1 Siwate Rojanasoonthon 2 1 Graduate Program in Operations Research and Industria Engineering,
More informationArtificial neural networks and deep learning
February 20, 2015 1 Introduction Artificia Neura Networks (ANNs) are a set of statistica modeing toos originay inspired by studies of bioogica neura networks in animas, for exampe the brain and the centra
More information3.3 SOFTWARE RISK MANAGEMENT (SRM)
93 3.3 SOFTWARE RISK MANAGEMENT (SRM) Fig. 3.2 SRM is a process buit in five steps. The steps are: Identify Anayse Pan Track Resove The process is continuous in nature and handed dynamicay throughout ifecyce
More informationSELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci
SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH Ufuk Cebeci Department of Industria Engineering, Istanbu Technica University, Macka, Istanbu, Turkey  ufuk_cebeci@yahoo.com Abstract An Enterprise
More informationArt of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1932394060
IEEE DISTRIBUTED SYSTEMS ONLINE 15414922 2005 Pubished by the IEEE Computer Society Vo. 6, No. 5; May 2005 Editor: Marcin Paprzycki, http://www.cs.okstate.edu/%7emarcin/ Book Reviews: Java Toos and Frameworks
More informationICAP CREDIT RISK SERVICES. Your Business Partner
ICAP CREDIT RISK SERVICES Your Business Partner ABOUT ICAP GROUP ICAP Group with 56 miion revenues for 2008 and 1,000 empoyees is the argest Business Services Group in Greece. In addition to its Greek
More informationAustralian Bureau of Statistics Management of Business Providers
Purpose Austraian Bureau of Statistics Management of Business Providers 1 The principa objective of the Austraian Bureau of Statistics (ABS) in respect of business providers is to impose the owest oad
More informationAA Fixed Rate ISA Savings
AA Fixed Rate ISA Savings For the road ahead The Financia Services Authority is the independent financia services reguator. It requires us to give you this important information to hep you to decide whether
More informationASYMPTOTIC DIRECTION FOR RANDOM WALKS IN RANDOM ENVIRONMENTS arxiv:math/0512388v2 [math.pr] 11 Dec 2007
ASYMPTOTIC DIRECTION FOR RANDOM WALKS IN RANDOM ENVIRONMENTS arxiv:math/0512388v2 [math.pr] 11 Dec 2007 FRANÇOIS SIMENHAUS Université Paris 7, Mathématiques, case 7012, 2, pace Jussieu, 75251 Paris, France
More informationIntroduction the pressure for efficiency the Estates opportunity
Heathy Savings? A study of the proportion of NHS Trusts with an inhouse Buidings Repair and Maintenance workforce, and a discussion of eary experiences of Suppies efficiency initiatives Management Summary
More informationLife Contingencies Study Note for CAS Exam S. Tom Struppeck
Life Contingencies Study Note for CAS Eam S Tom Struppeck (Revised 9/19/2015) Introduction Life contingencies is a term used to describe surviva modes for human ives and resuting cash fows that start or
More informationOverview of Health and Safety in China
Overview of Heath and Safety in China Hongyuan Wei 1, Leping Dang 1, and Mark Hoye 2 1 Schoo of Chemica Engineering, Tianjin University, Tianjin 300072, P R China, Emai: david.wei@tju.edu.cn 2 AstraZeneca
More informationINDUSTRIAL AND COMMERCIAL
Finance TM NEW YORK CITY DEPARTMENT OF FINANCE TAX & PARKING PROGRAM OPERATIONS DIVISION INDUSTRIAL AND COMMERCIAL ABATEMENT PROGRAM PRELIMINARY APPLICATION AND INSTRUCTIONS Mai to: NYC Department of Finance,
More informationFace Hallucination and Recognition
Face Haucination and Recognition Xiaogang Wang and Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {xgwang1, xtang}@ie.cuhk.edu.hk http://mmab.ie.cuhk.edu.hk Abstract.
More informationBusiness Banking. A guide for franchises
Business Banking A guide for franchises Hep with your franchise business, right on your doorstep A true understanding of the needs of your business: that s what makes RBS the right choice for financia
More informationAn Idiot s guide to Support vector machines (SVMs)
An Idiot s guide to Support vector machines (SVMs) R. Berwick, Viage Idiot SVMs: A New Generation of Learning Agorithms Pre 1980: Amost a earning methods earned inear decision surfaces. Linear earning
More information500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 3, MARCH 2013
500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 3, NO. 3, MARCH 203 Cognitive Radio Network Duaity Agorithms for Utiity Maximization Liang Zheng Chee Wei Tan, Senior Member, IEEE Abstract We
More informationBusiness schools are the academic setting where. The current crisis has highlighted the need to redefine the role of senior managers in organizations.
c r o s os r oi a d s REDISCOVERING THE ROLE OF BUSINESS SCHOOLS The current crisis has highighted the need to redefine the roe of senior managers in organizations. JORDI CANALS Professor and Dean, IESE
More informationA train dispatching model based on fuzzy passenger demand forecasting during holidays
Journa of Industria Engineering and Management JIEM, 2013 6(1):320335 Onine ISSN: 20130953 Print ISSN: 20138423 http://dx.doi.org/10.3926/jiem.699 A train dispatching mode based on fuzzy passenger demand
More informationSystems. Abstract. 2. Communication Architectures for VOD Services. 1. Introduction
Techniques for Improving Hari Kava Center for Teecommunications Research Coumbia University New York, NY 10027 the Capacity of VideoonDemand Systems Borko Furht Mutimedia Laboratory Forida Atantic University
More informationOligopoly in Insurance Markets
Oigopoy in Insurance Markets June 3, 2008 Abstract We consider an oigopoistic insurance market with individuas who differ in their degrees of accident probabiities. Insurers compete in coverage and premium.
More informationCourse MA2C02, Hilary Term 2012 Section 8: Periodic Functions and Fourier Series
Course MAC, Hiary Term Section 8: Periodic Functions and Fourier Series David R. Wikins Copyright c David R. Wikins Contents 8 Periodic Functions and Fourier Series 37 8. Fourier Series of Even and Odd
More informationEE 178/278A Probabilistic Systems Analysis. Spring 2014 Tse/Hussami Lecture 11. A Brief Introduction to Continuous Probability
EE 178/278A Probabiistic Systems Anaysis Spring 2014 Tse/Hussami Lecture 11 A Brief Introduction to Continuous Probabiity Up to now we have focused excusivey on discrete probabiity spaces Ω, where the
More informationVirtual trunk simulation
Virtua trunk simuation Samui Aato * Laboratory of Teecommunications Technoogy Hesinki University of Technoogy Sivia Giordano Laboratoire de Reseaux de Communication Ecoe Poytechnique Federae de Lausanne
More informationELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT
Initiatives Strategic Mapping Success in The Food System: Discover. Anayze. Strategize. Impement. Measure. ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT Foodservice manufacturers aocate, in
More informationA New Statistical Approach to Network Anomaly Detection
A New Statistica Approach to Network Anomay Detection Christian Caegari, Sandrine Vaton 2, and Michee Pagano Dept of Information Engineering, University of Pisa, ITALY Emai: {christiancaegari,mpagano}@ietunipiit
More informationQuery Optimization Concepts in Distributed Database
Internationa Journa of Engineering Technoogy Science and Research Voume 2 Issue 7 Juy 2015 Query Optimization Concepts in Distributed Database Rudrani sharma, Er. Pushpnee Verma, Er. Sachin Chaudhary Department
More informationLoad Balancing in Distributed Web Server Systems with Partial Document Replication *
Load Baancing in Distributed Web Server Systems with Partia Document Repication * Ling Zhuo ChoLi Wang Francis C. M. Lau Department of Computer Science and Information Systems The University of Hong Kong
More informationENERGY AND BOLTZMANN DISTRIBUTIONS
MISN159 NRGY AND BOLTZMANN DISTRIBUTIONS NRGY AND BOLTZMANN DISTRIBUTIONS by J. S. Kovacs and O. McHarris Michigan State University 1. Introduction.............................................. 1 2.
More informationUniversity of Southern California
Master of Science in Financia Engineering Viterbi Schoo of Engineering University of Southern Caifornia Dia 18664693239 (Meeting number 924 898 113) to hear the audio portion, or isten through your
More informationIntegrating Risk into your Plant Lifecycle A next generation software architecture for risk based
Integrating Risk into your Pant Lifecyce A next generation software architecture for risk based operations Dr Nic Cavanagh 1, Dr Jeremy Linn 2 and Coin Hickey 3 1 Head of Safeti Product Management, DNV
More informationACO and SVM Selection Feature Weighting of Network Intrusion Detection Method
, pp. 129270 http://dx.doi.org/10.14257/ijsia.2015.9.4.24 ACO and SVM Seection Feature Weighting of Network Intrusion Detection Method Wang Xingzhu Furong Coege Hunan, University of Arts and Science,
More informationChapter 2 Traditional Software Development
Chapter 2 Traditiona Software Deveopment 2.1 History of Project Management Large projects from the past must aready have had some sort of project management, such the Pyramid of Giza or Pyramid of Cheops,
More informationCOMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION
COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION Františe Mojžíš Department of Computing and Contro Engineering, ICT Prague, Technicá, 8 Prague frantise.mojzis@vscht.cz Abstract This
More informationAnalysis of Optimal Panel Geometry for Selfillustration Corner Reflector
Progress In Eectromagnetics Research Symposium Proceedings, Guangzhou, China, ug. 5 8, 14 145 naysis of ptima Pane Geometry for Sefiustration Corner Refector C. R. Li 1,,. S. hou 1,, and L. L. Ma 1, 1
More informationOracle. L. Ladoga Rybinsk Res. Volga. Finland. Volga. Dnieper. Dnestr. Danube. Lesbos. Auditing Oracle Applications Peloponnesus
N o r w e g i a n S e a White 60ûN ATLANTIC OCEAN UNITED KINGDOM Rio Douro Hebrid Bay of Biscay Garonne Faroe Isands Shetand Isands Orkney Isands North Loire ine Rhone Rhine Po Ebe Adriatic Batic Guf of
More informationThe Radix4 and the Class of Radix2 s FFTs
Chapter 11 The Radix and the Cass of Radix s FFTs The divideandconuer paradigm introduced in Chapter 3 is not restricted to dividing a probem into two subprobems. In fact, as expained in Section. and
More informationFinance 360 Problem Set #6 Solutions
Finance 360 Probem Set #6 Soutions 1) Suppose that you are the manager of an opera house. You have a constant margina cost of production equa to $50 (i.e. each additiona person in the theatre raises your
More informationBetting on the Real Line
Betting on the Rea Line Xi Gao 1, Yiing Chen 1,, and David M. Pennock 2 1 Harvard University, {xagao,yiing}@eecs.harvard.edu 2 Yahoo! Research, pennockd@yahooinc.com Abstract. We study the probem of designing
More informationMath: Fundamentals 100
Math: Fundamentas 100 Wecome to the Tooing University. This course is designed to be used in conjunction with the onine version of this cass. The onine version can be found at http://www.tooingu.com. We
More informationPricing Internet Services With Multiple Providers
Pricing Internet Services With Mutipe Providers Linhai He and Jean Warand Dept. of Eectrica Engineering and Computer Science University of Caifornia at Berkeey Berkeey, CA 94709 inhai, wr@eecs.berkeey.edu
More informationNormalization of Database Tables. Functional Dependency. Examples of Functional Dependencies: So Now what is Normalization? Transitive Dependencies
ISM 602 Dr. Hamid Nemati Objectives The idea Dependencies Attributes and Design Understand concepts normaization (HigherLeve Norma Forms) Learn how to normaize tabes Understand normaization and database
More informationGerman Auditors and Tax Advisors for foreign clients
München Regensburg Dachau German Auditors and Tax Advisors for foreign cients Economy Finance Financia Reports Taxes Strategies München Regensburg Dachau SH+C has focussed on consuting foreign cients with
More informationPricing and Revenue Sharing Strategies for Internet Service Providers
Pricing and Revenue Sharing Strategies for Internet Service Providers Linhai He and Jean Warand Department of Eectrica Engineering and Computer Sciences University of Caifornia at Berkeey {inhai,wr}@eecs.berkeey.edu
More informationBetting Strategies, Market Selection, and the Wisdom of Crowds
Betting Strategies, Market Seection, and the Wisdom of Crowds Wiemien Kets Northwestern University wkets@keogg.northwestern.edu David M. Pennock Microsoft Research New York City dpennock@microsoft.com
More informationProbabilistic Systems Analysis Autumn 2016 Tse Lecture Note 12
EE 178 Probabiistic Systems Anaysis Autumn 2016 Tse Lecture Note 12 Continuous random variabes Up to now we have focused excusivey on discrete random variabes, which take on ony a finite (or countaby infinite)
More informationSPOTLIGHT. A year of transformation
WINTER ISSUE 2014 2015 SPOTLIGHT Wecome to the winter issue of Oasis Spotight. These newsetters are designed to keep you uptodate with news about the Oasis community. This quartery issue features an artice
More informationEnergy Consumption Modeling and Optimization of Traction Control for Highspeed Railway Trains
Internationa Journa of Contro and Automation Vo8, No0 (05), pp094 http://dxdoiorg/0457/ica0580 Energy Consumption Modeing and Optimization of Traction Contro for Highspeed Raiway Trains Jiang Liu, *,
More informationFederal Financial Management Certificate Program
MANAGEMENT CONCEPTS Federa Financia Management Certificate Program Training to hep you achieve the highest eve performance in: Accounting // Auditing // Budgeting // Financia Management ENROLL TODAY! Contract
More informationA quantum model for the stock market
A quantum mode for the stock market Authors: Chao Zhang a,, Lu Huang b Affiiations: a Schoo of Physics and Engineering, Sun Yatsen University, Guangzhou 5175, China b Schoo of Economics and Business Administration,
More informationDesign of FollowUp Experiments for Improving Model Discrimination and Parameter Estimation
Design of FoowUp Experiments for Improving Mode Discrimination and Parameter Estimation Szu Hui Ng 1 Stephen E. Chick 2 Nationa University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Technoogy
More informationWHITE PAPER BEsT PRAcTIcEs: PusHIng ExcEl BEyond ITs limits WITH InfoRmATIon optimization
Best Practices: Pushing Exce Beyond Its Limits with Information Optimization WHITE Best Practices: Pushing Exce Beyond Its Limits with Information Optimization Executive Overview Microsoft Exce is the
More informationWe investigate a twostage serial supply chain with stationary stochastic demand and
Competitive and Cooperative Inventory Poicies in a TwoStage Suppy Chain Gérard P Cachon Pau H Zipkin The Fuqua Schoo of Business Duke University Durham North Caroina 7708 gpc@maidukeedu zipkin@maidukeedu
More informationIEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1 Scaabe MutiCass Traffic Management in Data Center Backbone Networks Amitabha Ghosh, Sangtae Ha, Edward Crabbe, and Jennifer
More informationExperiment #11 BJT filtering
Jonathan Roderick Hakan Durmus Experiment #11 BJT fitering Introduction: Now that the BJT has bn expored in static and dynamic operation, the BJT, combined with what has bn aready presented, may be used
More information(David H T Lan) Secretary for Home Affairs
Message We sha make every effort to strengthen the community buiding programme which serves to foster among the peope of Hong Kong a sense of beonging and mutua care. We wi continue to impement the District
More informationNetwork/Communicational Vulnerability
Automated teer machines (ATMs) are a part of most of our ives. The major appea of these machines is convenience The ATM environment is changing and that change has serious ramifications for the security
More informationCalculation of helicopter maneuverability in forward flight based on energy method
COMPUTER MODELLING & NEW TECHNOLOGIES 214 18(5) 554 Cacuation of heicopter maneuverabiity in forward fight based on energy method Abstract Nanjian Zhuang, Jinwu Xiang, Zhangping Luo *, Yiru Ren Schoo
More informationInformatica PowerCenter
Brochure Informatica PowerCenter Benefits Support better business decisions with the right information at the right time Acceerate projects in days vs. months with improved staff productivity and coaboration
More informationDecision support system for the batching problems of steelmaking and continuouscasting production
Omega 36 (2008 976 991 www.esevier.com/ocate/omega Decision support system for the batching probems of steemaking and continuouscasting production Lixin Tang, Gongshu Wang The Logistics Institute, Northeastern
More informationAssessing Network Vulnerability Under Probabilistic Region Failure Model
2011 IEEE 12th Internationa Conference on High Performance Switching and Routing Assessing Networ Vunerabiity Under Probabiistic Region Faiure Mode Xiaoiang Wang, Xiaohong Jiang and Achie Pattavina State
More informationMinimizing the Total Weighted Completion Time of Coflows in Datacenter Networks
Minimizing the Tota Weighted Competion Time of Cofows in Datacenter Networks Zhen Qiu Ciff Stein and Yuan Zhong ABSTRACT Communications in datacenter jobs (such as the shuffe operations in MapReduce appications
More informationApplicationAware Data Collection in Wireless Sensor Networks
AppicationAware Data Coection in Wireess Sensor Networks Xiaoin Fang *, Hong Gao *, Jianzhong Li *, and Yingshu Li +* * Schoo of Computer Science and Technoogy, Harbin Institute of Technoogy, Harbin,
More informationCI/SfB Ro8. (Aq) September 2012. The new advanced toughened glass. Pilkington Pyroclear Fireresistant Glass
CI/SfB Ro8 (Aq) September 2012 The new advanced toughened gass Pikington Pyrocear Fireresistant Gass Pikington Pyrocear, fireresistant screens in the façade: a typica containment appication for integrity
More informationThis paper considers an inventory system with an assembly structure. In addition to uncertain customer
MANAGEMENT SCIENCE Vo. 51, No. 8, August 2005, pp. 1250 1265 issn 00251909 eissn 15265501 05 5108 1250 informs doi 10.1287/mnsc.1050.0394 2005 INFORMS Inventory Management for an Assemby System wh Product
More informationWideArea Traffic Management for. Cloud Services
WideArea Traffic Management for Coud Services Joe Wenjie Jiang A Dissertation Presented to the Facuty of Princeton University in Candidacy for the Degree of Doctor of Phiosophy Recommended for Acceptance
More informationLeakage detection in water pipe networks using a Bayesian probabilistic framework
Probabiistic Engineering Mechanics 18 (2003) 315 327 www.esevier.com/ocate/probengmech Leakage detection in water pipe networks using a Bayesian probabiistic framework Z. Pouakis, D. Vaougeorgis, C. Papadimitriou*
More informationAccreditation: Supporting the Delivery of Health and Social Care
Accreditation: Supporting the Deivery of Heath and Socia Care PHARMACY E F P T O L P E D P E C M F D T G L E F R Accreditation: Supporting the Deivery of Heath and Socia Care June 9, 2015 marks Word Accreditation
More informationEuropean Journal of Operational Research
European Journa of Operationa Research 198 (2009) 435 446 Contents ists avaiabe at ScienceDirect European Journa of Operationa Research journa homepage: www.esevier.com/ocate/ejor Production, Manufacturing
More informationLearning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2
Introduction to Neura Networks Learning framework for NNs What are neura networks? Noninear function approimators How do they reate to pattern recognition/cassification? Noninear discriminant functions
More informationConstrained Optimization: Step by Step. Maximizing Subject to a set of constraints:
Constrained Optimization: Step by Step Most (if not a) economic decisions are the resut of an optimization probem subject to one or a series of constraints: Consumers make decisions on what to buy constrained
More informationLeadership & Management Certificate Programs
MANAGEMENT CONCEPTS Leadership & Management Certificate Programs Programs to deveop expertise in: Anaytics // Leadership // Professiona Skis // Supervision ENROLL TODAY! Contract oder Contract GS02F0010J
More informationLecture 5: Solution Method for Beam Deflections
Structura Mechanics.080 Lecture 5 Semester Yr Lecture 5: Soution Method for Beam Defections 5.1 Governing Equations So far we have estabished three groups of equations fuy characterizing the response of
More informationLADDER SAFETY Table of Contents
Tabe of Contents SECTION 1. TRAINING PROGRAM INTRODUCTION..................3 Training Objectives...........................................3 Rationae for Training.........................................3
More informationSimulationBased Booking Limits for Airline Revenue Management
OPERATIONS RESEARCH Vo. 53, No. 1, January February 2005, pp. 90 106 issn 0030364X eissn 15265463 05 5301 0090 informs doi 10.1287/opre.1040.0164 2005 INFORMS SimuationBased Booking Limits for Airine
More informationSubject: Corns of En gineers and Bureau of Reclamation: Information on Potential Budgetarv Reductions for Fiscal Year 1998
GAO United States Genera Accounting Office Washington, D.C. 20548 Resources, Community, and Economic Deveopment Division B276660 Apri 25, 1997 The Honorabe Pete V. Domenici Chairman The Honorabe Harry
More informationCLOUD service providers manage an enterpriseclass
IEEE TRANSACTIONS ON XXXXXX, VOL X, NO X, XXXX 201X 1 Oruta: PrivacyPreserving Pubic Auditing for Shared Data in the Coud Boyang Wang, Baochun Li, Member, IEEE, and Hui Li, Member, IEEE Abstract With
More informationPREFACE. Comptroller General of the United States. Page i
 I PREFACE T he (+nera Accounting Office (GAO) has ong beieved that the federa government urgenty needs to improve the financia information on which it bases many important decisions. To run our compex
More informationHuman Capital & Human Resources Certificate Programs
MANAGEMENT CONCEPTS Human Capita & Human Resources Certificate Programs Programs to deveop functiona and strategic skis in: Human Capita // Human Resources ENROLL TODAY! Contract Hoder Contract GS02F0010J
More informationBest Practices for Push & Pull Using Oracle Inventory Stock Locators. Introduction to Master Data and Master Data Management (MDM): Part 1
SPECIAL CONFERENCE ISSUE THE OFFICIAL PUBLICATION OF THE Orace Appications USERS GROUP spring 2012 Introduction to Master Data and Master Data Management (MDM): Part 1 Utiizing Orace Upgrade Advisor for
More informationMinimum Support Size of the Defender s Strong Stackelberg Equilibrium Strategies in Security Games
Minimum Support Size o the Deender s Strong Stackeberg Equiibrium Strategies in Security Games Jiarui Gan University o Chinese Academy o Sciences The Key Lab o Inteigent Inormation Processing, ICT, CAS
More informationMeasuring operational risk in financial institutions
Measuring operationa risk in financia institutions Operationa risk is now seen as a major risk for financia institutions. This paper considers the various methods avaiabe to measure operationa risk, and
More information