Enriching Solutions to Combinatorial Problems via Solution Engineering

International audienceExisting approaches to identify multiple solutions to combinatorial problems in practice are at best limited in their ability to simultaneously incorporate both diversity among generated solutions, as well as problem-specific desires that may only be discovered or articulated by the user after further analysis of solver output. We propose a general framework for problems of a combinatorial nature that can generate a set of of multiple (near-)optimal, diverse solutions, that are further infused with desirable features. We call our approach solution engineering. A key novelty is that desirable solution properties need not be explicitly modeled in advance. We customize the framework to both the mathematical programming and constraint programming technologies, and subsequently demonstrate its prac-ticality by implementing and then conducting computational experiments on existing test instances from the literature. Our computational results confirm the very real possibility of generating sets of solutions infused with features that might otherwise remain undiscovered

Survey of scientific programming techniques for the management of data-intensive engineering environments

The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.The current work has been partially supported by the Research Agreement between the RTVE (the Spanish Radio and Television Corporation) and the UC3M to boost research in the field of Big Data, Linked Data, Complex Network Analysis, and Natural Language. It has also received the support of the Tecnologico Nacional de Mexico (TECNM), National Council of Science and Technology (CONACYT), and the Public Education Secretary (SEP) through PRODEP

Scheduling scientific experiments for comet exploration

International audienceThe Rosetta/Philae mission was launched in 2004 by the European Space Agency (ESA). It is scheduled to reach the comet 67P/Churyumov-Gerasimenko in November 2014 after traveling more than six billion kilometers. The Philae module will then be separated from the orbiter (Rosetta) to attempt the first ever landing on the surface of a comet. If it succeeds, it will engage a sequence of scientific exploratory experiments on the comet. In this paper, we describe a constraint programming model for scheduling the different experiments of the mission. A feasible plan must satisfy a number of constraints induced by energetic resources, precedence relations on tasks, and in-compatibility between instruments. Moreover, a very important aspect is related to the transfer (to the orbiter then to the Earth) of all the data produced by the instruments. The capacity of inboard memories and the limitation of transfers within visibility windows between lander and orbiter, make the transfer policy implemented on the lander CPU prone to data loss. We introduce a global con-straint to handle data transfers. The purpose of this constraint is to ensure that data-producing tasks are scheduled in such a way that no data is lost. Thanks to this constraint and to the filtering rules we propose, mission control is now able to compute feasible plans in a few seconds for scenarios where minutes were previously often required. Moreover, in many cases, data transfers are now much more accurately simulated, thus increasing the reliability of the plans