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International audience; This paper describes the design of an Internet of Things (IoT) system for building evacuation. There are two main design decisions for such systems: i) specifying the platform on which the IoT intelligent components should be located; and ii) establishing the level of collaboration among the components. For safety-critical systems, such as evacuation, real-time performance and evacuation time are critical. The approach aims to minimize computational and evacuation delays and uses Queuing Network (QN) models. The approach was tested, by computer simulation, on a real exhibition venue in Alan Turing Building, Italy, that has 34 sets of IoT sensors and actuators. Experiments were performed that tested the effect of segmenting the physical space into different sized virtual cubes. Experiments were also conducted concerning the distribution of the software architecture. The results show that using centralized architectural pattern with a segmentation of the space into large cubes is the only practical solution.

The Closest String Problem (CSP) calls for finding an nstring that minimizes its maximum distance from m given n-strings. Integer linear programming (ILP) proved to be able to solve large CSPs under the Hamming distance, whereas for the Levenshtein distance, preferred in computational biology, no ILP formulation has so far be investigated. Recent research has however demonstrated that another metric, rank distance, can provide interesting results with genomic sequences. Moreover, CSP under rank distance can easily be modeled via ILP: optimal solutions can then be certified, or information on approximation obtained via dual gap. In this work we test this ILP formulation on random and biological data. Our experiments, conducted on strings with up to 600 nucleotides, show that the approach outperforms literature heuristics. We also enforce the formulation by cover inequalities. Interestingly, due to the special structure of the rank distance between two strings, cover separation can be done in polynomial time.

Date of Conference: 1–4 June 2016 Conference name: ILS: International Conference on Information Systems, Logistics and Supply Chain 6th International Conference We formulate a spatial pricing problem as bilevel non-capacitated location: a leader first decides which facilities to open and sets service prices taking competing offers into account; then, customers make individual decisions minimizing individual costs that include access charges in the spirit of Hotelling. Both leader and customers are assumed to be risk-neutral. For non-metric costs (i.e., when access costs do not satisfy the triangle inequality), the problem is NP-hard even if facilities can be opened at no fixed cost. We describe an algorithm for solving the Euclidean 1-dimensional case (i.e., with access cost defined by the Euclidean norm on a line) with fixed opening costs and a single competing facility.

In the production of perishable goods, particular stress is often given to performance indicators generally less critical in such manufacturing settings as metal-cutting, or mechanical/electronic assembly. For instance, in food or biochemical productions, a prominent interest of the producer is to reduce the time from distribution to the so-called best-before end. A scheduling problem with a goal of this sort is here addressed. The decision variables considered are launching and completion times of parts in a production line with critical aspects in the initial and/or final stages. The basic problem is to find an assignment of a maximum number of products to launching and completion times, so that no two products are assigned the same launching or completion time: feasible solutions have therefore the form of three-dimensional matchings. The problem is studied under two independent respects, assuming either (i) the relative perishability of products or (ii) the feasibility of launching/completion time pairs not affected by the intermediate transformation stage. We show that the problem is NP-Complete, even under such a ranking assumption as (i), whereas is in P under assumption (ii). Polynomial-time algorithms are also proposed to solve other optimization versions of the problem (in two cases, based on the identification of a matroid structure).