A continuous whole-link travel time model with occupancy constraint

The continuous dynamic network loading problem (CDNLP) aims to compute link travel times and path travel times on a congested network, given time-dependent path flow rates for a given time period. A crucial element of CDNLP is a model of the link performance. Two main modeling frameworks have been used in link loading models: The so-called whole-link travel time (WTT) models and the kinematic wave model of Lighthill–Whitham–Richards (LWR) for traffic flow.In this paper, we reformulate a well-known whole-link model in which the link travel time, for traffic entering a time t, is a function of the number of vehicles on link. This formulation does not require the satisfying of the FIFO (first in, first out) condition. An extension of the basic WTT model is proposed in order to take explicitly into account the maximum number of vehicles that the link can accommodate (occupancy constraint). A solution scheme for the proposed WTT model is derived.Several numerical examples are given to illustrate that the FIFO condition is not respected for the WTT model and to compare the travel time predictions effected by LWR and WTT models.     

A network-consistent time-dependent travel time layer for routing optimization problems

In the vehicle routing literature, there is an increasing focus on time-dependent routing problems, where the time (or cost) to travel between any pair of nodes (customers, depots) depends on the departure time. The aim of such algorithms is to be able to take recurring congestion into account when planning logistics operations. To test algorithms for time-dependent routing problems, time-dependent problem data is necessary. This data usually comes in the form of three-dimensional travel time matrices that add the departure time as an extra dimension. However, most currently available time-dependent travel time matrices are not network-consistent, i.e., the travel times are not correlated both in time and in space. This stands in contrast to the behavior of real life congestion, which generally follows a specific pattern, appearing in specific areas and then affecting all travel times to and from those areas. As a result of the lack of available network-consistent travel time matrices, it is difficult to develop algorithms that are able to take this special structure of the travel time data into account.     

A better project performance prediction model using fuzzy time series and data envelopment analysis

Earned value management (EVM) is a critical project management methodology that evaluates and predicts project performance from cost and schedule perspectives. The novel theoretical framework presented in this paper estimates future performance of a project based on the past performance data. The model benefits from a fuzzy time series forecasting model in the estimation process. Furthermore, fuzzy-based estimation is developed using linguistic terms to interpret different possible conditions of projects. Eventually, data envelopment analysis is applied to determine the superior model for forecasting of project performance. Multiple illustrative cases and simulated data have been used for comparative analysis and to illustrate the applicability of theoretical model to real situations. Contrary to EVM-based approach, which assumes the future performance is the same as the past, the proposed model can greatly assist project managers in more realistically assessing prospective performance of projects and thereby taking necessary and on-time appropriate actions.     

A hybrid approach to combine fuzziness and randomness in travel choice prediction

Several approaches have been developed in order to deal with uncertainty, in the prediction of travellers’ choices. Uncertainty almost always affects travel alternatives in several different choice contexts. However, the way in which this uncertainty affects choice options may consistently vary. Two main types of uncertainty can be identified: randomness and fuzziness.     

A comparison of kriging with nonparametric regression methods

“Kriging” is the name of a parametric regression method used by hydrologists and mining engineers, among others. Features of the kriging approach are that it also provides an error estimate and that it can conveniently be employed also to estimate the integral of the regression function. In the present work, the kriging method is described and some of its statistical characteristics are explored. Also, some extensions of the nonparametric regression approach are made so that it too displays the kriging features. In particular, a “data driven” estimator of the expected square error is derived. Theoretical and computational comparisons of the kriging and nonparametric regressors are offered.     

A travel time estimation model for a high-level picker-to-part system with class-based storage policies

Most previous related studies on warehouse configurations and operations only investigated single-level storage rack systems where the height of storage racks and the vertical movement of the picking operations are both not considered. However, in order to utilize the space efficiently, high-level storage systems are often used in warehouses in practice. This paper presents a travel time estimation model for a high-level picker-to-part system with the considerations of class-based storage policy and various routing policies. The results indicate that the proposed model appears to be sufficiently accurate for practical purposes. Furthermore, the effects of storage and routing policies on the travel time and the optimal warehouse layout are discussed in the paper.     

Nonparametric time series prediction: A semi-functional partial linear modeling

There is a recent interest in developing new statistical methods to predict time series by taking into account a continuous set of past values as predictors. In this functional time series prediction approach, we propose a functional version of the partial linear model that allows both to consider additional covariates and to use a continuous path in the past to predict future values of the process. The aim of this paper is to present this model, to construct some estimates and to look at their properties both from a theoretical point of view by means of asymptotic results and from a practical perspective by treating some real data sets. Although the literature on the use of parametric or nonparametric functional modeling is growing, as far as we know, this is the first paper on semiparametric functional modeling for the prediction of time series.     

Minimax-robust prediction of discrete time series

Summary We discuss a robust approach for predicting a weakly stationary discrete time series whose spectral density f is not exactly known. We assume that we know that f ), where is a convex set of spectral densities fulfilling some not too stringent conditions. We proof the existence of a most indeterministic density f ⁰ in , and we show that the classical optimal linear predictor for a time series with spectral density f ⁰ is mini-max-robust in the sense that it minimizes the maximal possible prediction error.We investigate some special models , and, in doing so, we illustrate a generally applicable method for determining the robust predictor. In particular, we discuss model sets which are defined by conditions on a finite part of the autocovariance sequence of the corresponding time series. These examples are of particular interest as the most indeterministic density is an autoregressive one, i.e. the robust predictor is finite. We discuss connections between this type of model set and maximum entropy and generalized maximum entropy spectral estimates.     

Recursive prediction of chaotic time series

Summary Considerable progress has been made in recent years in the analysis of time series arising from chaotic systems. In particular, a variety of schemes for the short-term prediction of such time series has been developed. However, hitherto all such algorithms have used batch processing and have not been able to continuously update their estimate of the dynamics using new observations as they are made. This severely limits their usefulness in real time signal processing applications. In this paper we present a continuous update prediction scheme for chaotic time series that overcomes this difficulty. It is based on radial basis function approximation combined with a recursive least squares estimation algorithm. We test this scheme using simulated data and comment on its relationship to adaptive transversal filters, which are widely used in conventional signal processing.     

Vehicle routing with soft time windows and Erlang travel times

This paper investigates vehicle-routing problems in which the travel times are random variables, and deliveries are made subject to soft time-window constraints. In particular, we model the travel time using a shifted gamma distribution. Penalties are incurred for deviations from the customers' time windows—early or late—and are developed using a fixed cost, a linear cost penalty, and/or a quadratic loss penalty. Alternatively, specifying a given probability of meeting the time-window constraints is considered. A tabu-search metaheuristic is developed, and computational results on test problems from the literature are reported.     

A kriging procedure for processes indexed by graphs

We provide a new kriging procedure of processes on graphs. Based on the construction of Gaussian random processes indexed by graphs, we extend to this framework the usual linear prediction method for spatial random fields, known as kriging. We provide the expression of the estimator of such a random field at unobserved locations as well as a control for the prediction error.     

A new method to mitigate data fluctuations for time series prediction

Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.     

MODWT-ARMA model for time series prediction

Many time series in the applied sciences display a time-varying second order structure and long-range dependence (LRD). In this paper, we present a hybrid MODWT-ARMA model by combining the maximal overlap discrete wavelet transform (MODWT) and the ARMA model to deal with the non-stationary and LRD time series. We prove theoretically that the details series obtained by MODWT are stationary and short-range dependent (SRD). Then we derive the general form of MODWT-ARMA model. In the experimental study, the daily rainfall and Mackey–Glass time series are used to assess the performance of the hybrid model. Finally, the normalized error comparison with DWT-ARMA, EMD-ARMA and ARIMA model indicates that this combined model is an effective way to improve forecasting accuracy.     

A universal functional equation

It is shown that the S-chains solving Rubel's universal fourth-order differential equation also satisfy a third-order functional equation.