Letteratura scientifica selezionata sul tema "FCFM policy"

The First-Come First-Served (FCFS) scheduling policy is the most popular scheduling algorithm used in practice. Furthermore, its usage is theoretically validated: for light-tailed job size distributions, FCFS has weakly optimal asymptotic tail of response time. But what if we don't just care about the asymptotic tail? What if we also care about the 99th percentile of response time, or the fraction of jobs that complete in under one second? Is FCFS still best? Outside of the asymptotic regime, only loose bounds on the tail of FCFS are known, and optimality is completely open. In this paper, we introduce a new policy, Nudge, which is the first policy to provably stochastically improve upon FCFS. We prove that Nudge simultaneously improves upon FCFS at every point along the tail, for light-tailed job size distributions. As a result, Nudge outperforms FCFS for every moment and every percentile of response time. Moreover, Nudge provides a multiplicative improvement over FCFS in the asymptotic tail. This resolves a long-standing open problem by showing that, counter to previous conjecture, FCFS is not strongly asymptotically optimal.

The First-Come First-Served (FCFS) scheduling policy is the most popular scheduling algorithm used in practice. Furthermore, its usage is theoretically validated: for light-tailed job size distributions, FCFS has weakly optimal asymptotic tail of response time. But what if we don't just care about the asymptotic tail? What if we also care about the 99th percentile of response time, or the fraction of jobs that complete in under one second? Is FCFS still best? Outside of the asymptotic regime, only loose bounds on the tail of FCFS are known, and optimality is completely open. In this paper, we introduce a new policy, Nudge, which is the first policy to provably stochastically improve upon FCFS. We prove that Nudge simultaneously improves upon FCFS at every point along the tail, for light-tailed job size distributions. As a result, Nudge outperforms FCFS for every moment and every percentile of response time. Moreover, Nudge provides a multiplicative improvement over FCFS in the asymptotic tail. This resolves a long-standing open problem by showing that, counter to previous conjecture, FCFS is not strongly asymptotically optimal. This paper represents an abridged version of [2].

The First Come First Served (FCFS) queuing policy is routinely assumed to be the benchmark policy for “fairness” in waiting-time performance. In this article, we propose a slight modification of the FCFS policy based on a natural extension of the well-established David and Yechiali (DY) rule and analyze it in the context of managing a waiting list for kidney transplants. In the proposed policy, the queuing agents are sequentially offered a stochastically arriving organ on a “first come, first served” basis while applying the individually optimal DY stopping rule. Through a realistic simulation, we show that the proposed policy, which we term Extended David and Yechiali (EDY), favorably compares to the FCFS policy in terms of medical efficiency while maintaining a comparable level of equity (i.e., fairness). Possible implications and practical aspects of the EDY are discussed.

The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.

This paper presents a makespan minimization of -jobs -machines re-entrant flow shop scheduling problem (RFSP) under fuzzy uncertainties using Genetic Algorithm. The RFSP objective is formulated as a mathematical programme constrained by number of jobs and resources availability with traditional scheduling policies of First Come First Serve (FCFS) and the First Buffer First Serve (FBFS). Jobs processing times were specified by fuzzy numbers and modelled using triangular membership function representations. The modified centroid defuzzification technique was used at different alpha-cuts to obtain fuzzy processing times (FPT) of jobs to explore the importance of uncertainty. The traditional GA schemes and operators were used together with roulette wheel algorithm without elitism in the selection process based on job fuzzy completion times. A test problem of five jobs with specified Job Processing and Transit Times between service centres, Job Start Times and Job Due times was posed. Results obtained using the deterministic and fuzzy processing times were compared for the two different scheduling policies, FCFS and FBFS. The deterministic optimal makespan for FBFS schedule was 61.2% in excess of the FCFS policy schedule.  The results also show that schedules with fuzzy uncertainty processing times provides shorter makespans than those for deterministic processing times and those under FCFS performing better than those under FBFS policy for early jobs while on the long run the FBFS policy performs better. The results underscore the need to take account of comprehensive fuzzy uncertainties in job processing times as a trade-off between time and costs influenced by production makespan. http://dx.doi.org/10.4314/njt.v36i3.21

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make \mathbfP [T > t], the chance a job's response time exceeds t, decay as quickly as possible in the t \to \infty limit. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail: \mathbfP [T > t] \sim C e^-γ t . FCFS achieves the optimal *decay rate* γ, but its *tail constant* C is suboptimal. Only recently have policies that improve upon FCFS's tail constant been discovered. But it is unknown what the optimal tail constant is, let alone what policy might achieve it. In this paper, we derive a closed-form expression for the optimal tail constant C, and we introduce *γ-Boost*, a new policy that achieves this optimal tail constant. Roughly speaking, γ-Boost operates similarly to FCFS, but it pretends that small jobs arrive earlier than their true arrival times. This significantly reduces the response time of small jobs without unduly delaying large jobs, improving upon FCFS's tail constant by up to 50% with only moderate job size variability, with even larger improvements for higher variability. While these results are for systems with full job size information, we also introduce and analyze a version of γ-Boost that works in settings with partial job size information, showing it too achieves significant gains over FCFS. Finally, we show via simulation that γ-Boost has excellent practical performance.

We consider a problem in which a single server must serve a stream of customers whose arrivals are distributed over a finite-size convex space. Under the assumption that the server has full information on the customer location, obvious service policies are the FCFS and the greedy (serve-the-closest-customer) approaches. These algorithms are, however, either inefficient (FCFS) or ‘unfair' (greedy).We propose and study two alternative algorithms, the gated-greedy policy and the gated-scan policy, which are more ‘fair' than the pure greedy method. We show that the stability conditions of the gated-greedy are p < 1 (where p is the expected rate at which work arrives at the system), implying that the method is at least as efficient (in terms of system stability) as any other discipline, in particular the greedy one. For the gated-scan policy we show that for any p < 1 one can design a stable gated-scan policy; however, for any fixed gated-scan policy there exists p < 1 for which the policy is unstable. We evaluate the performance of the gated-scan policy, and present bounds for the performance of the gated-greedy policy.These results are derived for systems in which the arrivals occur on a two-dimensional space (a square) but they are not limited to this configuration; rather they hold for more complex N-dimensional spaces, in particular for serving customers in (three-dimensional) convex space and serving customers on a line.

We consider a problem in which a single server must serve a stream of customers whose arrivals are distributed over a finite-size convex space. Under the assumption that the server has full information on the customer location, obvious service policies are the FCFS and the greedy (serve-the-closest-customer) approaches. These algorithms are, however, either inefficient (FCFS) or ‘unfair' (greedy). We propose and study two alternative algorithms, the gated-greedy policy and the gated-scan policy, which are more ‘fair' than the pure greedy method. We show that the stability conditions of the gated-greedy are p &lt; 1 (where p is the expected rate at which work arrives at the system), implying that the method is at least as efficient (in terms of system stability) as any other discipline, in particular the greedy one. For the gated-scan policy we show that for any p &lt; 1 one can design a stable gated-scan policy; however, for any fixed gated-scan policy there exists p &lt; 1 for which the policy is unstable. We evaluate the performance of the gated-scan policy, and present bounds for the performance of the gated-greedy policy. These results are derived for systems in which the arrivals occur on a two-dimensional space (a square) but they are not limited to this configuration; rather they hold for more complex N-dimensional spaces, in particular for serving customers in (three-dimensional) convex space and serving customers on a line.

Compared to more developed countries, the use of land resources is less efficient in China. China’s vast forest land area gives it a rich and underutilized carbon sink. This is an important way for China to achieve the goals of “carbon peaking” and “carbon neutrality”, which is of great significance to China’s sustainable development. In the past 20 years, China has designed a series of policies to serve the development of forestry carbon sinks, namely the forestry carbon sink mechanism (FCSM). However, the questions of which policy is the most important, and what is the socio-economic value it generates, have not been fully investigated. Accordingly, this paper studied 30 provincial-level regions in China from 2005 to 2020 using the difference-in-differences (DID) model. The conclusions show that: (1) the FCSM does increase the socio-economic value of land resources, thus improving the sustainability of land resources; (2) the FCSM helps to increase forest coverage, forest stock volume and the forest coverage rate, which increases the social value of land resources from the greening path; (3) the FCSM helps to increase the gross forestry product, which increases the economic value of land resources through the path of increasing production value.

Les modèles d'appariements aléatoires représentent de nombreux systèmes stochastiques concrets dans lesquels des éléments de différentes classes sont appariés selon des règles de compatibilités spécifiées. Par exemple, on peut citer les systèmes dédiés à l'allocation d'organes, les sites de recherche d'emplois, de logements, etc. De tels modèles sont toujours associés à un triptyque d'éléments : un graphe connexe, dit de compatibilités, dont les sommets représentent les classes des éléments pouvant entrer dans le systèmeet dont chaque arête relie deux classes compatibles, une politique d'appariements permettant de décider, en cas d'incertitude, quels appariements vont s'effectuer à l'intérieur du système, et un taux d'arrivées selon lequel les éléments entrent en son sein. Dans cette thèse, nous considérons des graphes généralisés, c'est-à-dire que l'on autorise l'appariement de deux éléments de la même classe, et nous étendons donc à ce cadre certains résultats déjà connus dans le cas de graphes simples. La stabilité d'un système régi par un modèle d'appariements est une propriété très importante. En effet, elle assure que les admissions au sein du système étudié sont contrôlées de sorte que les éléments ne restent pas bloqués à l'intérieur et que leur nombre n'augmente pas indéfiniment. Il est donc essentiel que le taux d'arrivées des éléments permette au système d'être stable. Dans ce manuscrit, nous caractérisons de manière algébrique cette zone de stabilité pour certains modèles d'appariements (généraux, généraux avec abandons, bipartis, bipartis étendus) ou de files d'attente, dites skill-based. Par ailleurs, nous démontrons que la politique d'appariements dite First Come, First Matched (FCFM) possède la propriété d'être maximale (généralisée), c'est-à-dire que la zone de stabilité du modèle d'appariements général associé à un graphe de compatibilités et à une politique quelconque est toujours incluse dans celle associée à ce même graphe et à FCFM. Notons que cette dernière coïncide alors avec un ensemble de mesures défini par des conditions purement algébriques. Dans ce cas, la question de l'étude des mesures permettant la stabilité des systèmes régis par un modèle d'appariements revient donc à celle, plus élémentaire, de la caractérisation d'un ensemble déterministe. Nous donnons alors un moyen de construction (simple) des mesures appartenant à celui-ci, ce qui peut s'avérer très utile pour calibrer le contrôle d'accès au système. En effet, la vérification algorithmique qu'une mesure quelconque vérifie ces conditions algébriques nécessite un nombre d'opérations polynomial en le nombre de sommets du graphe, et devient donc très coûteuse à mesure que ce cardinal augmente. Nous explicitons également, sous une forme produit, l'expression de la loi stationnaire de l'évolution temporelle du contenu d'un système stable régi par un modèle d'appariements général et sous la politique FCFM, permettant, notamment, de calculer explicitement des caractéristiques à l'équilibre de systèmes concrets et d'estimer leurs performances en temps long. On peut ainsi, par exemple, calculer la taille moyenne à l'équilibre d'une liste d'attente dans le cadre de dons croisés de reins, ou encore, estimer le temps moyen d'attente sur une interface pair-à-pair ou un site de rencontres.Enfin, les taux d'appariements associés à un modèle d'appariements (général ou biparti étendu) stable sont étudiés. Ils sont définis comme étant les fréquences asymptotiques des appariements réalisés et fournissent un critère de performance des systèmes régis par de tels modèles d'appariements, de même que les propriétés de politique-insensibilité et d'équité de ces taux, qui sont également discutées
Stochastic matching models represent many concrete stochastic systems in which elements of different classes are matched according to specified compatibility rules. For example, we can cite systems dedicated to organs allocation, job search sites, housing allocation programs, etc. Such models are typically associated to a triplet of elements: a connected graph, called compatibility graph, whose vertices represent the classes of elements that can enter the system and whose edges connect two compatible classes, amatching policy which decides the matches to be concretely executed, in case of multiple choices, and an arrival rate according to which the elements enter within it. In this thesis, we consider generalized graphs, meaning that we allow the matching of two elements of the same class, and we therefore extend to this framework some results already known in the case of simple graphs.The stability of a system governed by a matching model is a very important property. It ensures that the admissions within the system under study, are regulated, so that the elements do not accumulate in the system in the long run. It is therefore essential that the arrival rate of the elements allows the system to be stable. In this manuscript, we characterize, algebraically, this stability region for some matching models (general, general with reneging, bipartite, extended bipartite) or skill-based queueing systems.Moreover, we demonstrate that the matching policy called First Come, First Matched (FCFM) has the property of being (generalized) maximal, meaning that the stability region of the general matching model associated with a compatibility graph and with any policy is always included in the one associated with this same graph and ruled by FCFM. Note that this latter then coincides with a set of measures defined by purely algebraic conditions. In this case, the study of stability of the matching model at hand boils down to the more elementary question of characterizing of a deterministic set of measures. We then givea (simple) way to construct the measures belonging to the latter set. This turns out to be very useful for admission control, as checking the algebraic conditions requires a number of operations which is polynomial in the number of vertices of the considered compatibility graph, and therefore becomes very expensive as the number of vertices grows large.We also give, in a product form, the expression of the stationary distribution of the number-in-system process of a stable system governed by a general matching model and under the FCFM policy, allowing, in particular, to explicitly calculate characteristics at equilibrium of concrete systems and to estimate their long-time performance. We can thus, for example, calculate the size average at equilibrium of a waiting list in the case of cross-donation of kidneys, or even, estimate the average waiting time on a peer-to-peerinterface or on a dating website.Finally, the matching rates associated with a stable matching model (general or extended bipartite) are studied. They are defined as the asymptotic frequencies of the executed respective matchings, and provide an insightful performance criterion for the corresponding matching systems, as well as the policy-insensitivity and fairness properties of the matching rates, which are also discussed

AbstractAdaptations and strategies to build resilience are needed to manage current impacts and will be increasingly vital as the world continues to warm. But making adaptation decisions can be complex, requiring careful consideration of multiple factors and perspectives, and balancing different priorities over different timescales. Society is embarking on a learning process that will continue for decades. This chapter and the book it introduces aim to contribute to this process. The book draws extensively from the Future Climate for Africa (FCFA) research programme that aimed to support adaptation and resilience in sub-Saharan Africa. In this chapter, we first briefly review the planning landscape for adaptation and building resilience and then consider how applications are changing the nature of climate information and the context of its use. This is followed by a review of the current status of climate information, particularly future projections for Africa and the enduring challenge that uncertainty represents to their active use. We then ask how we can improve the use of climate information for resilience building and adaptation and present an overview of the coming chapters. The demand for information and guidance on adaptation is continuing to grow, and is highlighting the need for new types and formats of data, and more innovative interactions with users to increase usability and application. Climate plays a dynamic role within complex, rapidly evolving social-ecological systems; this requires the climate science, resilience and adaptation communities to engage widely with other sectors and actors to make the agenda relevant and tractable for policy and practice.

Food cold chain management (FCCM) is a specialized kind of supply chain supported by the two different technologies first, refrigeration and second information technology. The refrigeration equipments like cold storages and cold storing devices. They can keep the food quality cum safety intact and enhance food items shelf life. The information technology enables the tracking and tracing through data loggers etc. Despite the huge fresh produce availability in India, food inflation and food security issues are major concerns for policy makers. The food wastage in India is close to US$ 8309 million annually (US$ 60.17) due to lack of processing and cold storage facilities. The aim of this paper is to discuss status of food cold storage in India. The chapter is based on extensive review of existing literature, studying novel cold storage status and presenting benefits of cold storage. The outcome of research will be that FCCM can play vital role in supplying quality, safe food products and instrumental in reduction of food wastages.

The access control is a mechanism that a system grants, revoke the right to access the object. The subject and object can able to integrate, synchronize, communicate and optimize through read, write and execute over a UFS. The access control mechanism is the process of mediating each and every request to system resources, application and data maintained by a operating system and determining whether the request should be approve, created, granted or denied as per top management policy. The AC mechanism, management and decision is enforced by implementing regulations established by a security policy. The management has to investigate the basic concepts behind access control design and enforcement, point out different security requirements that may need to be taken into consideration. The authors have to formulate and implement several ACM on normalizing and optimizing them step by step, that have been highlighted in proposed model for development and production purpose. This research paper contributes to the development of an optimization model that aims and objective to determine the optimal cost, time and maximize the quality of services to be invested into security model and mechanisms deciding on the measure components of UFS. This model has to apply to ACM utilities over a Web portal server on object oriented and distributed environment. This ACM will be resolve the uncertainty, un-order, un formal and unset up (U^4) problems of web portal on right time and right place of any where & any time in around the globe. It will be more measurable and accountable for performance, fault tolerance, throughput, bench marking and risk assessment on any application.

In Unmanned Manufacturing Factory (UMF), we consider a workstation consisting of a machine and several workbenches, each assigned an AGV to perform the parts transporting service. Every workpiece requires multiple processes at the same workbench, and the time for the AGV to transport a part is greater than the time for the machine to assemble it. This operating model is complex, as several workpieces corresponding to several workbenches are in service at the same time. We extract the production system into a Semi-Open Queuing Networks (SOQNs) model. We use four parameters to describe the states of the system and construct the transition rate matrix. We find that the matrix has a particular structure that enables us to solve it with Matrix Geometric Method (MGM). Following a First-Come-First-Served (FCFS) policy, the performance of this production system with unlimited queuing space is evaluated in terms of service intensity, queue length, sojourn time, and throughput. The numerical experiments demonstrate a significant reduction in the sojourn time of the workpiece in the system when the number of workbenches increases to a certain value. Our work can provide important suggestions for designing UMF.

In this paper, a manufacturing work cell with a gantry that is in charge of moving materials/parts between machines and buffers is considered. With the effect of the gantry movement, the system performance becomes quite different from traditional serial production lines. In this paper, reinforcement learning is used to develop a gantry scheduling policy in order to improve system production. The gantry learns to take proper actions under different situations to reduce system production loss by using Q-Learning algorithm and finds the optimal moving policy. A two-machine one-buffer work cell with a gantry is used for case study, by which reinforcement learning is applied. Compare with the FCFS policy, the fidelity and effectiveness of the reinforcement learning method are also demonstrated.

Abstract Developing a viable data-driven policy for the management of electrical-energy consumption in campus residences is contingent on the proper knowledge of the electricity usage pattern and its predictability. In this study, an adaptive neuro-fuzzy inference systems (ANFIS) was developed to model the electrical energy consumption of students’ residence using the University of Johannesburg, South Africa as a case study. The model was developed based on the environmental conditions vis-à-vis meteorological parameters namely temperature, wind speed, and humidity of the respective days as the input variables while electricity consumption (kWh) was used as the output variable. The fuzzy c-means (FCM) is a type of clustering technique that is preferred owing to its speed boost capacity. The best FCM-clustered ANFIS-model based on a range of 2–10 clusters was selected after evaluating their performance using relevant statistical metrics namely; mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute deviation (MAD). FCM-ANFIS with 7 clusters outperformed all other models with the least error and highest accuracy. The RMSE, MAPE, MAD, and R2-values of the best models are 0.043, 0.65, 1.051, and 0.9890 respectively. The developed model will assist in optimizing energy consumption and assist in designing and sizing alternative energy systems for campus residences.