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Nowadays, one of the key challenges of refrigerated transport systems is to ensure the desired level of quality of shipped products. This paper attempts to study the problems of optimal temperature control in refrigerated transport systems. First, a taxonomy of refrigerated transport systems and their characteristics will be presented. Then, the paper provides a state of the art on optimal temperature control problems in refrigerated transport systems over the last two decades - to the best of our knowledge, no similar state of the art exists in the literature - and highlights the advantages and disadvantages of associated solutions. A comparative study of these solutions is given to help decision makers make the best choice based on relevant criteria.

The coronavirus outbreak identified in Wuhan, China brought the majority of countries in the world under lockdown. The socioeconomic consequences will be, without doubt, severe and enormous. In this paper, we investigate the sharing approach as a strategy to help reduce the heavy toll of the pandemic. A state of the art is provided to describe the virus propagation, the rationale behind the emergence of the sharing concept as well as its impact in a pandemic context. Finally, we suggest a list of recommendations for policy makers in order to attempt the endorsement of the sharing economy to enrich existing measures that have been taken in the same respect.

Data classification problems have been intensively studied by several groups of researchers including computer scientists, statisticians, engineers, biologists. Within the context of widespread use of databases and the explosive growth in their sizes, “Big Data”, new challenges are introduced in order to permit to several organizations to take benefits and efficiently utilize their data. The main objective of this paper is to review main published works which propose mathematical programming approaches in order to solve data classification problems with Support Vector Machine (SVM).

Aim of this work is to characterise and compute the set of initial conditions for a system of controlled diffusion processes which allow to reach a terminal target satisfying pointwise state constraints with a given probability of success. Defining a suitable auxiliary optimal control problem, the characterization of this set is related to the solution of a particular Hamilton-Jacobi-Bellman equation. A semi-Lagrangian numerical scheme is defined and its convergence to the unique viscosity solution of the equation is proved. The validity of the proposed approach is then tested on some numerical examples.

The aim of this article is to study the Hamilton Jacobi Bellman (HJB) approach for state-constrained control problems with maximum cost. In particular, we are interested in the characterization of the value functions of such problems and the analysis of the associated optimal trajectories, without assuming any controllability assumption. The rigorous theoretical results lead to several trajectory reconstruction procedures for which convergence results are also investigated. An application to a five-state aircraft abort landing problem is then considered, for which several numerical simulations are performed to analyse the relevance of the theoretical approach.

We study an aircraft abort landing problem modelled by a five dimensional state system with state constraints and a maximum running cost function as introduced by Bulirsch, Montrone and Pesch (J. Optim. Theory Appl., Vol. 70, No 1, pp 1-23, 1991). We propose a Hamilton-Jacobi-Bellman (HJB) approach in order to compute the value function associated to the problem, as well as trajectory reconstruction procedures based on the value function, or on a related exit time function. Some numerical illustrations are included to show the relevance of our approach.

This work deals with numerical approximations of unbounded and discontinuous value functions associated to some stochastic control problems. We derive error estimates for monotone schemes based on a Semi-Lagrangian method (or more generally in the form of a Markov chain approximation). A motivation of this study consists in approximating chance-constrained reachability sets. The latters will be characterized as level sets of discontinuous value functions associated to adequate stochastic control problems. A precise analysis of the level-set approach is carried out and some numerical simulations are given to illustrate the approach.

This work focuses on a safety analysis problem under probabilistic target constraints.