A Scala Embedded DSL for Combinatorial Optimization in Software Requirements Engineering

The goal of the presented work is to provide support for software requirements engineering domain experts in modeling combinatorial optimization problems that arise in requirements prioritization and release planning. A Domain-Specific Language (DSL), called reqT/CSP, is presented that integrates constraints modeling with requirements modeling. The DSL is embedded in the object-functional Scala programming language. The DSL is demonstrated using principal examples of priority ranking and release planning. Benefits, limitations and future work are discussed

A Stochastic Continuous Optimization Backend for MiniZinc with Applications to Geometrical Placement Problems

International audienceMiniZinc is a solver-independent constraint modeling language which is increasingly used in the constraint programming community. It can be used to compare different solvers which are currently based on either Constraint Programming, Boolean satisfiability, Mixed Integer Linear Programming, and recently Local Search. In this paper we present a stochastic continuous optimization backend for MiniZinc models over real numbers. More specifically, we describe the translation of FlatZinc models into objective functions over the reals, and their use as fitness functions for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) solver. We illustrate this approach with the declarative modeling and solving of hard geometrical placement problems, motivated by packing applications in logistics involving mixed square-curved shapes and complex shapes defined by Bézier curves

Optimising Training for Service Delivery

We study the problem of training a roster of engineers, who are scheduled to respond to service calls that require a set of skills, and where engineers and calls have different locations. Both training an engineer in a skill and sending an engineer to respond a non-local service call incur a cost. Alternatively, a local contractor can be hired. The problem consists in training engineers in skills so that the quality of service (i.e. response time) is maximised and costs are minimised. The problem is hard to solve in practice partly because (1) the value of training an engineer in one skill depends on other training decisions, (2) evaluating training decisions means evaluating the schedules that are now made possible by the new skills, and (3) these schedules must be computed over a long time horizon, otherwise training may not pay off. We show that a monolithic approach to this problem is not practical. Instead, we decompose it into three subproblems, modelled with MiniZinc. This allows us to pick the approach that works best for each subproblem (MIP or CP) and provide good solutions to the problem. Data is provided by a multinational company

Exploring Software Product Management decision problems with constraint solving - opportunities for prioritization and release planning

Decision-making is central to Software Product Management (SPM) and includes deciding on requirements priorities and the content of coming releases. Several algorithms for prioritization and release planning have been proposed, where humans with or without machine support enact a series of steps to produce a decision outcome. Instead of applying some specific algorithm to find an acceptable solution to a decision problem, we propose to model SPM decision-making as a Constraint Satisfaction Problem (CSP), where relative and absolute priorities, interdependencies, and other constraints are expressed as relations among variables representing entities such as feature priorities, stakeholder preferences, and resource constraints. The solution space is then explored with the help of a constraint solver without humans needing to care about specific algorithms. This paper discusses advantages and limitations of CSP modeling in SPM and gives principal examples as a proof-of-concept of CSP modeling in requirements prioritization and release planning. A discussion of further research on constraint solving in SPM is also given

Planificación y técnicas de mejora de planes para Smart Cities

[EN] Esta propuesta se basa en el concepto de las “Smart Cities” y sostenibilidad, las cuales se han convertido en una tendencia en las ciudades que invierten en tecnología, como también para aquellas que la ven como una necesidad para resolver problemas. En el presente trabajo de fin de máster, se ha analizado la dificultad en la que se encuentran actualmente los habitantes de Daule (provincia Guayas – Ecuador). De este modo para solucionar esta situación se ha planificado la gestión y distribución de camiones recolectores de desechos, tanto orgánicos como inorgánicos, donde se busca mejorar el servicio y la calidad de vida de las personas. El problema se resolvió mediante un modelo informático que utiliza una biblioteca de funciones predefinidas mediante “Minizinc”, un software libre que utiliza un lenguaje sencillo. Asimismo, mediante la “Programación de satisfacción de restricciones” (CSPs), se crearon condiciones para gestionar las visitas de los camiones mediante su carga. Además, se ofreció información del orden de visita, horarios de entrada y de salida de cada una de las unidades desde el punto de partida al de llegada entre los sectores de las zonas rurales previamente mencionadas. Para alcanzar la solución, el objetivo es visitar la mayor cantidad de sectores por camiones recolectores dependiendo de la prioridad del sector. Como resultado se consiguió evaluar lo siguiente: Analizar diferentes niveles de optimización que ofrece el compilador; analizar la eficiencia de los resultados del tiempo de respuestas y analizar las soluciones con el usuario.[EN] This proposal is based on the concept of “Smart Cities” and sustainability, which have become a trend for cities that invest in technology, as well as those that see in it as a need to solve problems. In the present Master’s degree final project, the current situation in which the inhabitants of rural areas of the city of Daule (Guayas-Ecuador) live, was analyzed in order to later plan the management and distribution of waste collection trucks, both organic and inorganic, with which it is sought to improve the service and people's quality of life. The problem was modeled using a library of predefined functions with "Minizinc", an open source software that uses simple language. Likewise, through the "Constraint Satisfaction Programming" CSPs, restrictions were entered to limit and sort the visits of the collector trucks through their cargo. Additionaly, information was provided on the order of visit, entry and exit schedules of every unit from the point of departure to the point of arrival among the previously mentioned sectors. In order to achieve the proposed solution, the main objective is to visit the largest number of destinations by collection trucks depending on the priority of the sectors. As a result, the following was achieved: analyze different optimization levels offered by the compiler; analyze the response time results efficiency and; analyze the solutions in front of the user.[CA] Aquesta proposta és fonamenta en el concepte de les “Smart Cities” i sostenibilitat, les quals s'han convertit en una tendència en ciutats que inverteixen en tecnologia, com també per a aquelles que veuen la tecnologia com una necessitat per a resoldre problemes. En el present treball de fi de màster, s'ha analitzat la situació actual en la qual viuen els habitants de zones rurals del voltant de Daule, (província de Guayas - l'Equador), per a posteriorment planificar la gestió i distribució de camions recol·lectors de deixalles, tant orgànics com inorgànics, on es busca millorar el servei i la qualitat de vida de les persones. El problema es va plantejar emprant un model informàtic que utilitza una biblioteca de funcions predefinides mitjançant “Minizinc”, un programari lliure amb un llenguatge senzill. Així mateix, en la “Programació de satisfacció de restriccions” (CSPs) es van aplicar mètodes per a gestionar les visites dels camions mitjançant la seua càrrega i el seu horari; a més, d'oferir informació de l'ordre de visita, horaris d'entrada i d'eixida de cadascuna de les unitats des del punt de partida al d'arribada entre els sectors de les zones rurals esmentades. Per a aconseguir la solució proposada, l'objectiu principal és visitar la major quantitat de sectors per camions recol·lectors depenent de la prioritat de les àrees. Com a resultat es va aconseguir avaluar el següent: L'usuari podrà analitzar diferents nivells d'optimització que ofereix el compilador, l'eficiència dels resultats del temps de respostes i analitzar les solucions de cara amb l'usuari.Agradezco a la Beca del Senescyt (Secretaría de Educación Superior, Ciencia, Tecnología e Innovación) del Ecuador.Jiménez Valencia, LS. (2019). Planificación y técnicas de mejora de planes para Smart Cities. http://hdl.handle.net/10251/125972TFG

ASlib: A Benchmark Library for Algorithm Selection

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa