In this paper, we present the research issues, the representation models, as well as the methodological and algorithmic approaches that have been proposed for cross-docking systems in the literature. More specifically, we have conducted a systematic literature review so as to analyze the contribution of Artificial Intelligence (AI), and more generally AI-based techniques, to cross-docking systems. One immediate interest of this work is that it allows us to identify some new potential uses of AI-based techniques for solving cross-docking problems. To conduct our analysis, we have extended the standard approach of systematic literature review called SALSA; e-SALSA is the novel derived and enhanced approach we propose in our study. It consists of seven steps for carrying out a systematic literature review based on a meta-analysis of the available data on the subject of AI-based techniques applied to the domain of cross docking (AI4 CD: Artificial Intelligence for Cross-Docking). In the light of the results of our review and analysis, several new scientific issues are identified on AI4 CD, giving us the opportunity of suggesting some new directions of research.

In this paper, a new bi-objective model is proposed to deal with the Home Health Care Routing and Scheduling Problem. The considered problem combined the Vehicle Routing Problem with the Personnel scheduling Problem. Two well-known multi-objective Evolutionary algorithms are suggested to solved it with test instances taking from the literature. The obtained results show the effectiveness and the suitability of evolutionary algorithms to solve the problem.

This paper deals with the tracking of multiple objects since it has become one of the most interesting and active fields of research. The aim of the aircraft application is to localize, track and classify different aerial maneuvering aircraft using the Interacting Multiple Model (IMM) combined with the assignment Global Nearest Neighbor (GNN) algorithm based on the aircraft's kinematic behavior. The GNN represents the combinatorial optimization solution based on the comparison of different assignments.

This paper proposes a new assignment solution based on the Generalized Bayes’ Theorem (GBT) which aims to establish the best matching between two sets of uncertain data. In order to estimate the effectiveness of the proposition, it is compared to the best credal assignment solutions and the well known Global Nearest Neighbor (GNN) algorithm, through synthetic data and a literature example of multi-target tracking scenarios. Given the same input data, the proposed solution gives better assignment results, especially when sensor imprecision increases. However, the proposed solution stills actually computationally more complex than the GNN and the solution proposed by Denoeux et al.

Since a few years, LMIs conditions associated to the control of continuous Takagi Sugeno (TS) fuzzy models have used non quadratic Lyapunov functions. Indeed they are much more general than classical quadratic functions. However, there are requirements about the derivative of the membership functions appearing in the LMIs. Whereas, this problem doesn't exist with discrete time models. This study tries to put a bridge between the continuous and discrete cases for the class of continuous Takagi Sugeno fuzzy models which can be exactly discretized. Indeed, for this particular class, ones the stability of the discrete model is ensured, the same control law applied to the continuous model will ensure the stability too. The interest of such an approach is that complex control laws can be applied with no hypothesis on the membership functions. Simulation examples illustrate the effectiveness of the proposed approach.

This paper introduces a system for exchanging and managing imperfect information about events in vehicular networks (VANET). Using belief functions, this model is developed through an application using smartphones.

This paper aims to present a new architecture of control law called multi-model controller. Indeed, it uses constant linear models as with a classical TS approach, but the interpolation between the models is adaptive based on an observer structure. A comparison with an output TS feedback controller for uncertain models shows the interest of the proposed method.

Conditions about the stabilization of Takagi-Sugeno fuzzy models can systematically be derived and solved usually using linear matrix inequality problems. Current research tries to lower any pessimism. However, often it leads to an important increase in the number of decision variables, and problems become unsolvable. In this correspondence, we choose to reduce the number of decision variables while not raising the conservatism in comparison with previous results. This correspondence deals with the discrete case, which is harder to solve. We supply results about the stabilization, the regulator problem, and also H( infinity) control.

Fuzzy models of the Takagi-Sugeno type enable representation of a wide class of nonlinear models. Conditions about their stabilization can be derived systematically. Usually these conditions are written as linear matrix inequality (LMI) problems. In this paper, we use a collection of properties concerning matrices to extend the area of solutions involving the feedback control laws. We will work out with continuous fuzzy models, and supply results about the stabilization, the regulator problem, and also Hinfincontrol. Solutions based on a reduced number of decision variables, implying a reduced number of LMI conditions, are also proposed. We show that a good tradeoff can been obtained.

This paper explains how multisensor data fusion and target identification can be performed within the transferable belief model (TBM), a model for the representation of quantified uncertainty based on belief functions. We present the underlying theory, in particular the general Bayesian theorem needed to transform likelihoods into beliefs and the pignistic transformation needed to build the probability measure required for decision making. We present how this method applies in practice. We compare its solution with the classical one, illustrating it with an embarrassing example, where the TBM and the probability solutions completely disagree. Computational efficiency of the belief-function solution was supposedly proved in a study that we reproduce and we show that in fact the opposite conclusions hold. The results presented here can be extended directly to many problems of data fusion and diagnosis.

This paper presents control and stabilization of an interconnected tank system that can be used in irrigation systems and chemical processes for mixing liquids. We propose a nonlinear controller with a state space representation based on Takagi-Sugeno fuzzy models [Takagi, T. and Sugeno, M., 1985]. This controller is based on an affine PDC (parallel distributed and compensation) control law and an augmented fuzzy observer to reject disturbances. Stability and stabilization of the closed loop is proved by a Lyapunov approach using a quadratic function. The whole conditions of stabilization can be put in the form of a LMI problem that can be conveniently solved.

Deals with the stability and stabilization of Takagi-Sugeno fuzzy models by the means of a special type of control law, called the compensation and division for fuzzy models. In order to prove the stability results, we use a Lyapunov approach. The whole approach can be put in the form of a list of LMIs that can be conveniently computed. We show that the CDF we get is less conservative than previous control laws.

There are strong views in the literature between the advocates of the Bayesian approach on one hand and the advocates of the belief functions on the other. This paper refers to a few previously published studies showing that, for decision-aimed problems, algorithms using belief functions were much slower than those using a Bayesian approach. Thus, if beliefs are appealing intellectually, they are in fact useless for real applications. This article shows that most of these studies are simply false, because they are based on an erroneous use of the belief functions. © 2001 John Wiley & Sons, Inc.

Possibility theory aims at representing and handling uncertain information. An important property of this theory is the ability to merge different data sources in order to increase the quality of the information. Different fusion rules have been defined in the literature, each with its own advantages and drawbacks. However, these rules usually cannot deal rigorously with contradictory data. Therefore, this paper proposes a new approach, based on a fusion rule using a vector expressing the reliability of the data sources. Comparisons are made with classical fusion rules. An algorithm assessing the indexes of reliability of the data used by the new role is given, and an index of the quality of the result is proposed. These three tools define a new method dealing with the reliability of the data in the fusion field, and enable a clear distinction between the data and their quality.

The use of a nonlinear context dependent operator based on possibility theory is studied in order to provide a generic fusion mechanism in different parts of the control of a robot in an uncertain, ill-defined environment. Its main interest consists in the definition of a reliability degree for each partial result which may allow a more pertinent adaptive mechanism.