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Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Key words: forest inventory, hierarchical Bayes model, Kalman filter, Markov chain Monte Carlo (MCMC), credible interval. L'evaluation des ressources forestieres fondee sur des donnees de nombreuses sources et multitemporelles est devenue plus frequente. Par consequent, l'amelioration des capacites de previsions de la dynamique forestiere au moyen du regroupement et de l'analyse efficaces des donnees sequentielles spatiales et de series chronologiques est maintenant plus cruciale. Le filtrage et le nivellement bayesiens fournissent un formalisme bien defini pour la fusion ou l'assimilation des diverses donnees. Nous avons determine la frequence a laquelle le cadre bayesien generique et normalise est utilise dans la litterature scientifique et nous avons evalue si une telle approche est avantageuse pour les applications forestieres. Une revue de la litterature a montre que l'utilisation des methodes bayesiennes semble etre moins frequente en foresterie que dans d'autres disciplines, particulierement la teledetection. On a constate plus particulierement que les analyses des series chronologiques favorisaient les methodes ad hoc. Notre revue n'a pas revele de preuves numeriques importantes pour une meilleure performance par diverses approches bayesiennes, mais ce resultat peut etre du en partie a la difficulte de comparer un eventail de methodes pour differentes taches de previsions. Nous avons identifie des defis methodologiques relies a l'assimilation des previsions du developpement forestier; plus particulierement, en combinant la croissance modelisee aux perturbations dues aux operations forestieres et aux phenomenes naturels. Neanmoins, les cadres bayesiens offrent des possibilites de combiner efficacement et de faire une mise a jour avant et apres les distributions predictives et de calculer les mesures connexes de l'incertitude qui semblent sous-utilisees en foresterie. [Traduit par la Redaction] Mots-cles : inventaire forestier, modele hierarchique bayesien, filtre de Kalman, Monte Carlo par chaine de Markov (MCCM), intervalle de credibilite., 1. Introduction 1.1. Motivation and objectives Systematic forest inventories have been carried out for over a century (Kangas et al. 2018a) and have utilized remote sensing and other digital map [...]

Abstract. We propose a novel two-stage data-analytic modeling approach combining theories, statistical analysis, and optimization techniques to model player engagement as a function of motivation to maximize customer game-play via [...]

Abstract. Low engagement rates and high attrition rates have been formidable challenges to mobile apps and their long-term success, especially for those whose revenues derive mainly from in-app purchases. To [...]

Abstract. We develop an automated teller machine (ATM) replenishment policy for a bank that operates multiple ATMs. The aim is to minimize the cost of replenishments and stockouts, taking into [...]

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data. Key words: travel time, short-term prediction, wavelet neural network, multi- regime based model, Markov chain, volatility analysis. La prediction a court terme du temps de deplacement constitue un apport essentiel aux systemes de transport intelligent. La prevision opportune et precise du trafic est necessaire au niveau des systemes avances de gestion de la circulation et des systemes avances d'information des voyageurs. Bien qu'on ait propose plusieurs approches de prediction a court terme du temps de deplacement au cours de la derniere decennie, particulierement pour les modeles hybrides qui comportent des modeles d'apprentissage automatique et des modeles statistiques, peu d'etudes se concentrent sur le probleme de surapprentissage cause par les modeles hybrides. Le probleme de surapprentissage diminue l'exactitude de prediction particulierement durant les heures de pointe. Dans le cadre de cette etude, on propose un modele hybride qui comprend un reseau neuronal en ondelettes (WNN), la chaine de Markov (MAR) et le modele de volatilite (VOA) aux fins de prediction a court terme du temps de deplacement des usagers d'un systeme d'autoroutes. Le but de cette etude vise a apporter des connaissances plus approfondies par rapport aux modeles de trafic dynamiques sous-jacents et a ameliorer l'exactitude et la robustesse de prediction. Dans cette methode, on tient compte de l'analyse periodique, de la correction d'erreurs et de l'extraction sonore et on ameliore les predictions aux heures de pointe. La methodologie proposee prevoit le temps de deplacement en decomposant les donnees sur le temps de deplacement en trois volets: l'analyse periodique presentee par le reseau neuronal en ondelettes modifie, une partie residuelle modelisee par chaine de Markov et la partie volatilite evaluee par le modele autoregressif conditionnellement heteroscedastique generalise modifie. On examine l'efficacite de prediction en utilisant les donnees du temps de deplacement sur les autoroutes de Houston au Texas et en l'analysant au moyen de trois mesures: l'erreur moyenne absolue, le pourcentage d'erreur absolue moyenne et l'ecart-type. Les resultats montrent que les temps de deplacement prevus par la methode WNNMAR-VOA sont robustes et precis. Entre-temps, la methode proposee est en mesure de refleter les caracteristiques periodiques sous-jacentes et la nature de volatilite des donnees sur le temps de deplacement. [Traduit par la Redaction] Mots-cles: temps de deplacement, prediction a court terme, reseau neuronal en ondelettes, modele a multiples regimes, chaine de Markov, analyse de volatilite., Introduction Short-term travel time prediction is a fundamental input for intelligent transportation systems (ITS). Traffic congestion is a thorny problem in many cities, especially in those megacities because of the [...]

We propose a partitioning algorithm to solve a class of linear-quadratic Markov decision processes with inequality constraints and nonconvex stagewise cost; within each region of the partitioned state space, the value function and the optimal policy have analytical quadratic and linear forms, respectively. Compared to grid-based numerical schemes, the partitioning algorithm gives the closed-form solution without discretization error, and in many cases does not suffer from the curse of dimensionality. The algorithm is applied to two applications. In the main application, we present a model for limit order books with stochastic market depth to study the optimal order execution problem; stochastic market depth is consistent with empirical studies and necessary to accommodate various order activities. The optimal execution policy obtained by the algorithm significantly outperforms that of a deterministic market depth model in numerical examples. In the second application, we use the algorithm to compute the exact optimal solution to the renewable electricity management problem, for which previously only an approximate solution was known. As a comparison, we show that the approximate solution can be quite inaccurate for some initial states and thus demonstrate an advantage of the exact solution. History: Accepted by Yinyu-Ye, optimization. Funding: The research of S. Kou was supported in part by the Ministry of Education in Singapore [Grant MOE2014-T2-1-006] and by the National Natural Science Foundation of China [Grant R-2015-S-005]. The research of N. Chen was supported in part by the Hong Kong University of Science and Technology [Start-up Fund R9382]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2016.2639. Keywords: Markov chains * large order execution * electricity trading/production * partitioning * quadratic stochastic programming, 1. Introduction Linear-quadratic-Gaussian control is one of the few examples in stochastic dynamic programming that yields a closed-form solution (see, e.g., Bertsekas 1995, Athans 1971). It has quadratic costs and [...]

We analyze a tractable model of a limit order book on short time scales, where the dynamics are driven by stochastic fluctuations between supply and demand. We establish the existence of a limiting distribution for the highest bid, and for the lowest ask, where the limiting distributions are confined between two thresholds. We make extensive use of fluid limits to establish recurrence properties of the model. We use the model to analyze various high-frequency trading strategies, and comment on the Nash equilibria that emerge between high-frequency traders when a market in continuous time is replaced by frequent batch auctions. Funding: The second author's research was partially supported by the National Science Foundation [Grant DMS-1204311] and NSF Graduate Research Fellowship. Keywords: limit order book * queueing * fluid limit * high-frequency trading * Nash equilibrium, 1. Introduction A limit order book (LOB) is a trading mechanism for a single-commodity market. The mechanism is of significant interest to economists as a model of price formation. It [...]

Abstract. This work addresses operational management optimization problems in wastewater treatment plants. We developed a novel technology that allows control of such plants, based on real-time sensor readings, with cloud [...]

Abstract. A queuing system resulting from a signalized intersection regulated by pretimed control in a network of urban traffic is considered. Modeling the queue length and the delay of vehicles [...]

Abstract. This paper proposes an analytical model to approximate the transient aggregate joint queue-length distribution of tandem finite (space) capacity Markovian networks. The methodology combines ideas from transient aggregation-disaggregation techniques [...]

Abstract--Natural mortality (M) is one of the most important life history attributes of functioning fish populations. The most common methods to estimate M in fish populations provide point estimates which [...]

Background trans-fatty acids (TFAs) are a well-known risk factor of ischemic heart disease (IHD). In Australia, the highest TFA intake is concentrated to the most socioeconomically disadvantaged groups. Elimination of industrial TFA (iTFA) from the Australian food supply could result in reduced IHD mortality and morbidity while improving health equity. However, such legislation could lead to additional costs for both government and food industry. Thus, we assessed the potential cost-effectiveness, health gains, and effects on health equality of an iTFA ban from the Australian food supply. Methods and findings Markov cohort models were used to estimate the impact on IHD burden and health equity, as well as the cost-effectiveness of a national ban of iTFA in Australia. Intake of TFA was assessed using the 2011-2012 Australian National Nutrition and Physical Activity Survey. The IHD burden attributable to TFA was calculated by comparing the current level of TFA intake to a counterfactual setting where consumption was lowered to a theoretical minimum distribution with a mean of 0.5% energy per day (corresponding to TFA intake only from nonindustrial sources, e.g., dairy foods). Policy costs, avoided IHD events and deaths, health-adjusted life years (HALYs) gained, and changes in IHD-related healthcare costs saved were estimated over 10 years and lifetime of the adult Australian population. Cost-effectiveness was assessed by calculation of incremental cost-effectiveness ratios (ICERs) using net policy cost and HALYs gained. Health benefits and healthcare cost changes were also assessed in subgroups based on socioeconomic status, defined by Socio-Economic Indexes for Areas (SEIFA) quintile, and remoteness. Compared to a base case of no ban and current TFA intakes, elimination of iTFA was estimated to prevent 2,294 (95% uncertainty interval [UI]: 1,765; 2,851) IHD deaths and 9,931 (95% UI: 8,429; 11,532) IHD events over the first 10 years. The greatest health benefits were accrued to the most socioeconomically disadvantaged quintiles and among Australians living outside of major cities. The intervention was estimated to be cost saving (net cost Conclusions Our model estimates that a ban of iTFAs could avert substantial numbers of IHD events and deaths in Australia and would likely be a highly cost-effective strategy to reduce social-economic and urban-rural inequalities in health. These findings suggest that elimination of iTFA can cost-effectively improve health and health equality even in countries with low iTFA intake., Author(s): Matti Marklund 1,2,3,*, Miaobing Zheng 4, J. Lennert Veerman 5, Jason H. Y. Wu 1 Introduction Ischemic heart disease (IHD) is the single most common cause of death in [...]

We prove a central limit theorem for a class of additive processes that arise naturally in the theory of finite horizon Markov decision problems. The main theorem generalizes a classic [...]

Quasi-open-loop policies consist of sequences of Markovian decision rules that are insensitive to one component of the state space. Given a semi-Markov decision process (SMDP), we distinguish between exogenous and [...]

Given a Markov decision process (MDP) with n states and a total number m of actions, we study the number of iterations needed by policy iteration (PI) algorithms to converge [...]

This paper describes sufficient conditions for the existence of optimal policies for partially observable Markov decision processes (POMDPs) with Borel state, observation, and action sets, when the goal is to [...]

This paper is concerned with the optimality of a trend following trading rule. The underlying market is modeled like a bull-bear switching market in which the drift of the stock [...]

In this paper, we study discounted stochastic games with Borel state and compact action spaces depending on the state variable. The primitives of our model satisfy standard continuity and measurability [...]

Over the past decade, observations of giant exoplanets (Jupiter-size) have provided key insights into their atmospheres (1,2), but the properties of lower-mass exoplanets (sub-Neptune) remain largely unconstrained because of the [...]

We develop and implement a Bayesian semiparametric model of demand under interproduct competition that enables us to assess the respective contributions of brand-SKU (stock keeping unit) hierarchy and interproduct similarity to explaining and predicting demand. To incorporate brand-SKU hierarchy effects, we use Bayesian hierarchical clustering inherent in a nested Dirichlet process to simultaneously partition brands, and SKUs conditional on brands, into groups of 'similarity clusters.' We examine cluster memberships and postprocess the Markov chain Monte Carlo output to infer cluster properties by accounting for parameter uncertainty. Our proposed approach lends to a spatial competition interpretation in latent attribute space and helps uncover the extent to which competition across SKUs in the latent attribute space is local or global. In a related vein, we discuss the implications of well-defined groups of similar SKUs as subcategory or submarket boundaries in latent attribute space. We empirically test our model using aggregate beer category sales data from a midsize U.S. retail chain. We find that branding hierarchy effects dominate those from product similarity. We find that the model partitions the 15 brands in the data into 4 brand clusters and the 96 SKUs into 25 SKU clusters conditional on brand cluster membership. In estimating a set of models of spatial interproduct competition, we find that SKU competition is more local than global in that only subsets of products compete within groups of comparable products. Finally, we discuss the substantive implications of our results. Keywords: competition; nested Dirchlet process; brand-SKU clustering; product similarity; spatial models History: Received June 29, 2012; accepted March 6, 2014, by Pradeep Chintagunta, marketing. Published online in Articles in Advance February 19, 2015., 1. Introduction Competitive effects are integral to market outcomes (e.g., unit sales), particularly in the consumer packaged goods (CPG) industry where there is abundant brand and stock keeping unit (SKU) [...]

We investigate a game of singular control and strategic exit in a model of competitive market share control. In the model, each player can make irreversible investments to increase his [...]

The mechanochemical protein dynamin is the prototype of the dynamin superfamily of large GTPases, which shape and remodel membranes in diverse cellular processes (1). Dynamin forms predominantly tetramers in the cytosol, which oligomerize at the neck of clathrin-coated vesicles to mediate constriction and subsequent scission of the membrane (1). Previous studies have described the architecture of dynamin dimers (2,3), but the molecular determinants for dynamin assembly and its regulation have remained unclear. Here we present the crystal structure of the human dynamin tetramer in the nucleotide-free state. Combining structural data with mutational studies, oligomerization measurements and Markov state models of molecular dynamics simulations, we suggest a mechanism by which oligomerization of dynamin is linked to the release of intramolecular autoinhibitory interactions. We elucidate how mutations that interfere with tetramer formation and autoinhibition can lead to the congenital muscle disorders Charcot-Marie-Tooth neuropathy (4) and centronuclear myopathy (5), respectively. Notably, the bent shape of the tetramer explains how dynamin assembles into a right-handed helical oligomer of defined diameter, which has direct implications for its function in membrane constriction., The three highly conserved vertebrate isoforms of dynamin contain five distinct domains (Extended Data Fig. 1a): an N-terminal GTPase (G) domain mediating nucleotide binding and hydrolysis, a bundle signalling element [...]

In a stochastic interdiction game a proliferator aims to minimize the expected duration of a nuclear weapons development project, and an interdictor endeavors to maximize the project duration by delaying some of the project tasks. We formulate static and dynamic versions of the interdictor's decision problem where the interdiction plan is either precommitted or adapts to new information revealed over time, respectively. The static model gives rise to a stochastic program, whereas the dynamic model is formalized as a multiple optimal stopping problem in continuous time and with decision-dependent information. Under a memory-less probabilistic model for the task durations, we prove that the static model reduces to a mixed-integer linear program, whereas the dynamic model reduces to a finite Markov decision process in discrete time that can be solved via efficient value iteration. We then generalize the dynamic model to account for uncertainty in the outcomes of the interdiction actions. We also discuss a crashing game where the proliferator can use limited resources to expedite tasks so as to counterbalance the interdictor's efforts. The resulting problem can be formulated as a robust Markov decision process. Keywords: interdiction game; PERT network; Markov decision process; robust optimization History: Received April 15, 2013; accepted March 28, 2014, by Dimitris Bertsimas, optimization. Published online in Articles in Advance September 23, 2014., 1. Introduction Since its inception in 1968, a total of 190 states have joined the Treaty on the Non-Proliferation of Nuclear Weapons, whose objective is to prevent the spread of [...]

We consider curbside bus stops of the kind that serve multiple bus routes and that are isolated from the effects of traffic signals and other stops. A Markov chain embedded [...]

We study the dynamic of the Gaussian quantum discord in a continuous-variable system subject to a common non-Markovian environment with zero-temperature. By considering an initial two-mode Gaussian symmetric squeezed thermal state, we show that Gaussian discord has a very different dynamic characteristic in a non-Markovian evolution versus a Markov process, and can be created by the memory effect, which features non-Markovianity. We also study the relationship between Gaussian discord and the non-Markovian degree of the environment. The results may offer us an effective experimental method to get more quantum correlations. PACS Nos.: 03.65.Yz, 03.65.Ta, 03.67.Mn. Nous etudions la discorde quantique gaussienne dans un systeme a variable continue soumis a un environnement commun non markovien a temperature nulle. En considerant un etat thermique comprime symetrique et gaussien a deux etats, nous montrons que la discorde gaussienne a une dynamique dans une evolution non markovienne qui est tres differente d'un processus markovien. Elle peut etre creee par effet de memoire avec proprietes non markoviennes. Nous etudions aussi la relation entre la discorde gaussienne et le niveau non markovien de l'environnement. Les resultats pourraient nous offrir une methode efficace dans des experiences pour obtenir plus de correlations quantiques. [Traduit par le Redaction], 1. Introduction Quantum correlations (QCs), which have very different features than classical correlations, are a fundamental resource for quantum information processing [1]. In the early years, it was believed that [...]

Researchers from diverse disciplines have examined the many factors that contribute to the influence of published research papers. Such influence dynamics are in essence a marketing of science issue. In this paper, we propose that in addition to known established, overt drivers of influence such as journal, article, author, and Matthew effects, a latent factor 'citability' influences the eventual impact of a paper. Citability is a mid-range latent variable that captures the changing relationship of an article to a field. Our analysis using a discretized Tobit model with hidden Markov processes suggests that there are two states of citability, and these dynamic states determine eventual influence of a paper. Prior research in marketing has relied on models where the various effects such as author and journal effects are deemed static. Unlike ours, these models fail to capture the continuously evolving impact dynamics of a paper and the differential effect of the various drivers that depend on the latent state a paper is in at any given point of time. Our model also captures the impact of uncitedness, which other models fail to do. Our model is estimated using articles published in seven leading marketing journals during the years 1996-2003. Findings and implications are discussed. Keywords Citation * Citability * Matthew effect * Uncitedness * Scientometrics * Hidden Markov model * Tobit, Introduction The perceived quality of research is important both for individual scholars and for journals as they are intrinsically linked to the standing of both and are at essence a [...]

Advanced traveler information systems provide travelers with pre-trip and en route travel information necessary to improve the trip decision making process based on various criteria (e.g., avoiding the negative impacts of traffic congestion, selecting specific travel modes, etc.). This study investigates an adaptive routing methodology for multimodal transportation networks. To integrate transit networks, the model takes into account both the predefined timetables of public transportation services and the variability of travel times. A graph theory based methodology is proposed to capture travel behavior within a multimodal network. The study advances a routing algorithm based on Markov decision processes. Special network modeling elements were defined to allow the developed algorithm to select the most efficient transportation mode at each junction along a given route. The proposed methodology is applied to a small real-world network located in the central business district area of Montreal, Quebec. The network includes bus, subway, and bicycle transportation facilities. The simulations were run under the assumption that users do not use private vehicles to travel between arbitrary selected origin and destination points. The developed routing algorithm was applied to several simulation scenarios. The results identified what is the most efficient combination of transportation modes that the travelers have to use given certain traffic and transit service conditions. Larger and more complex networks of motorized and non-motorized modes with stochastic properties will be investigated in subsequent work. Key words: Markov decision process, adaptive routing, multimodal transportation, Canadian traveler problem. Les systemes avances de renseignements sur les voyages fournissent aux voyageurs l'information necessaire, avant et pendant le voyage, pour ameliorer la prise de decisions concernant le voyage en se basant sur plusieurs criteres (p.ex. eviter les impacts negatifs de la congestion routiere, choisir des moyens de transport specifiques, etc.). La presente etude examine une methode adaptative de trace pour les reseaux de transport multimodal. Pour integrer les reseaux de transport, le modele tient compte a la fois des tableaux-horaires predefinis des services de transport public et de la variabilite des durees de trajets. Une methodologie basee sur une theorie graphique est proposee pour saisir le comportement de trajet dans un reseau multimodal. L'etude propose un algorithme de trace base sur les processus de decision de Markov. Des elements speciaux de modelisation du reseau sont definis afin de permettre a l'algorithme de choisir le mode de transport le plus efficace a chaque jonction le long d'une route donnee. La methode proposee est appliquee a un petit reseau reel situe dans le quartier d'affaires de Montreal, Quebec. Le reseau comprend l'autobus, le metro et le transport par bicyclette. Les simulations ont ete effectuees en presumant que les utilisateurs ne se servaient pas de vehicules prives pour voyager entre un point d'origine et une destination arbitraires. L'algorithme de trace developpe a ete utilise sur plusieurs scenarios de simulation. Les resultats ont identifie la combinaison la plus efficace de moyens de transport que les voyageurs devaient utiliser etant donne certaines conditions de circulation et etats du service de transport en commun. Des reseaux plus vastes et plus complexes de moyens de transport motorises et non motorises ayant des proprietes stochastiques seront examines lors d'un travail ulterieur. [Traduit par la Redaction] Mots-cles: processus de decision de Markov, trace adaptatif, transport multimodal, probleme du voyageur canadien., Introduction The term intelligent transportation system (ITS) refers to methods used to add information and communication technology to travelers, transportation infrastructure, and vehicles to improve safety and reduce cost, travel [...]

This paper addresses the problems of finite-time stochastic stability and stabilization for linear Markovian jump systems subject to partial information on the transition probabilities. By introducing bounded finite time and stochastic character, sufficient conditions that can ensure bounded finite time and [H.sub.∞] finite-time bounded filtering are derived. Finally, an example is given to illustrate the efficiency of the proposed method. PACS No.: 45.80.+r. Nous etudions ici la stabilite stochastique a temps finis et la stabilisation pour des systemes de sauts de Markov lineaires, dans les cas ou l'information sur les probabilites de transition est partielle. En introduisant le temps fini borne et la stochasticite, nous rejoignons les conditions qui garantissent un temps fini borne et son filtrage Received 9 July 2012. Accepted 2 July 2013. [H.sub.∞]. Nous donnons finalement un exemple pour illustrer l'efficience de la methode propose. [Traduit par la Redaction], 1. Introduction The filtering problem has long been one of the fundamental problems in the field of signal processing, communication, and control application. A problem can be briefly described to [...]

Since its founding in 1992, the Croatian Operational Research Society (CRORS) has aimed to motivate its members and other researchers to develop new methods of operations research (O.R.), as well [...]

Ports-of-entry are critical components of the modern international supply chain infrastructure, particularly container seaports and airfreight hubs. The potential operational and economic impact resulting from their temporary closure is unknown [...]

Over the past couple of decades the advancements in the areas of information and computational technology allowed for a variety of intelligent transportation systems developments and deployments. This study investigates an advanced traveler information system (ATIS) and (or) an advanced public transit system (APTS) adaptive and real-time transit routing component. The proposed methodology is applied to bus routes with fixed, predefined bus line alignments. It is shown that routing buses on such systems can be modeled in real-time by employing an associated Markov chain with reward model to minimize the impact of congested traffic conditions on the travelers and the overall operation cost of the transit system. A case study using a traffic and transit data from a real-world bus line was used to apply the proposed bus routing approach. It was found that under certain traffic congestion conditions buses should be re-routed to minimize their travel time and the associated system costs. The hypothetical congestion scenarios investigated show that individual bus travel time delays range between 50 and 740 s when the proposed adaptive routing is employed. The proposed methodology is also suitable for application to transit systems that run on a demand-adaptive basis (the bus line alignment changes with the travelers demand). Additional calibration and future integration of the system into specific ATIS and (or) APTS user services will be investigated. Key words: adaptive routing, transit system, Markov chain, Markov decision process. Les avancements realises au cours des recentes decennies dans les domaines de l'information et des technologies de calcul ont permis le developpement et la mise en place de nombreux systemes de transport intelligents. La presente etude examine la composante d'acheminement de transit adaptatif et en temps reel d'un systeme avance d'information du voyageur (ATIS) et (ou) un systeme avance de transport en commun (APTS). La methodologie proposee est appliquees aux lignes d'autobus avec des alignements de circuits d'autobus fixes et predefinis. Il a ete demontre que l'assignement de circuits d'autobus dans de tels systemes peut etre modelise en temps reel au moyen d'une chaine de Markov associee a un modele de recompense afin de minimiser l'impact du trafic congestionne sur les voyageurs et le cout global de fonctionnement du systeme de transport en commun. Une etude de cas utilisant les donnees de circulation et de transport en commun d'une ligne d'autobus existante a ete utilisee pour appliquer l'approche proposee de circuits d'autobus. Il a ete trouve que, sous certaines conditions de congestion de circulation, les circuits autobus devraient etre modifiees afin de minimiser leur temps de voyage ainsi que les couts de systemes connexes. Les scenarios hypothetiques de congestion etudies montrent que les delais individuels du temps de voyage d'un autobus se situent entre 50 et 740 secondes lorsqu'un un circuit adaptif est propose. La methode proposee est egalement adequate pour l'application aux systemes de transport en commun qui fonctionnent sur une base adaptee a la demande (le circuit d'autobus change selon la demande des voyageurs). Un etalonnage additionnel et une integration a venir dans les services aux utilisateurs d'un systeme ATIS et (ou) APTS specifique seront etudies. Mots-cles : acheminement adaptatif, systeme de transport en commun, chaine de Markov, processus markovien de prise de decision. [Traduit par la Redaction], Introduction and problem statement Deployment of intelligent transportation systems (ITS) improves the performance of the transportation system by using modern electronics and communications technologies. Advanced traveler information system (ATIS) and [...]

We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm fnr finding the optimal state costs or Q-factors. The main difference is in [...]

In this paper, we address the problem of making optimal product offers to customers of a retail bank by using techniques including Markov chains, genetic algorithms, mathematical programming, and design [...]

Forecasting the likelihood of recessions and slowdowns is an important issue for many who use economics in business. While there has been a large and growing body of literature on [...]

This paper studies the problem of purchasing and allocating copies of movies to multiple stores of a movie rental chain. A unique characteristic of this problem is the return process [...]