A Novel Multiobjective Formulation of the Robust Software Project Scheduling Problem

Chicano, F., Cervantes A., Luna F., & Recio G. (2012). A Novel Multiobjective Formulation of the Robust Software Project Scheduling Problem. (Di Chio, C., Agapitos A., Cagnoni S., Cotta C., de Vega F. Fernández, Di Caro G. A., et al., Ed.).Applications of Evolutionary Computation - EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Málaga, Spain, April 11-13, 2012, Proceedings. 497–507.The Software Project Scheduling (SPS) problem refers to the distribution of tasks during a software project lifetime. Software development involves managing human resources and a total budget in an optimal way for a successful project which, in turn, demonstrates the importance of the SPS problem for software companies. This paper proposes a novel formulation for the SPS problem which takes into account actual issues such as the productivity of the employees at performing different tasks. The formulation also provides project managers with robust solutions arising from an analysis of the inaccuracies in task-cost estimations. An experimental study is presented which compares the resulting project plans and analyses the performance of four different well-know evolutionary algorithms over two sets of realistic instances representing the problem. Statistical parameters are also provided in order to help the project manager in the decision process.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish Ministry of Science and Innovation and FEDER under contract TIN2008-06491-C04. Andalusian Government under contract P07-TIC-03044

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

A robust R&D project portfolio optimization model for pharmaceutical contract research organizations

Pharmaceutical drug Research and Development (R&D) outsourcing to contract research organizations (CROs) has experienced a significant growth in recent decades and the trend is expected to continue. A key question for CROs and firms in similar environments is which projects should be included in the firm?s portfolio of projects. As a distinctive contribution to the literature this paper develops and evaluates a business support tool to help a CRO decide on clinical R&D project opportunities and revise its portfolio of R&D projects given the existing constraints, and financial and resource capabilities. A new mathematical programming model in the form of a capital budgeting problem is developed to help revising and rescheduling of the project portfolio. The uncertainty of pharmaceutical R&D cost estimates in drug development stages is captured to mimic a more realistic representation of pharmaceutical R&D projects, and a robust optimization approach is used to tackle the uncertain formulation. An illustrative example is presented to demonstrate the proposed approach

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems

Multi-objective evolutionary design of robust controllers on the grid

Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this grid-enabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H ∞ loop shaping

An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.Fil: Nesmachnow, Sergio. Facultad de Ingeniería; UruguayFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toutouh, Jamal. Massachusetts Institute of Technology. Science And Artificial Intelligence Laboratory; Estados UnidosFil: Luna, Francisco. Universidad de Málaga. Instituto de Tecnologías e Ingeniería del Software; Españ

Virtual power plant models and electricity markets - A review

In recent years, the integration of distributed generation in power systems has been accompanied by new facility operations strategies. Thus, it has become increasingly important to enhance management capabilities regarding the aggregation of distributed electricity production and demand through different types of virtual power plants (VPPs). It is also important to exploit their ability to participate in electricity markets to maximize operating profits. This review article focuses on the classification and in-depth analysis of recent studies that propose VPP models including interactions with different types of energy markets. This classification is formulated according to the most important aspects to be considered for these VPPs. These include the formulation of the model, techniques for solving mathematical problems, participation in different types of markets, and the applicability of the proposed models to real case studies. From the analysis of the studies, it is concluded that the most recent models tend to be more complete and realistic in addition to featuring greater diversity in the types of electricity markets in which VPPs participate. The aim of this review is to identify the most profitable VPP scheme to be applied in each regulatory environment. It also highlights the challenges remaining in this field of study

Optimización de la planificación energética en hogares inteligentes: Un enfoque multi-objetivo

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning problem considering user preferences, which in turn are proposed to be solved using a multiobjective evolutionary approach, in order to minimize energy consumption and maximize quality of service offered to users. Promising results are reported on realistic instances of the problem, compared with situations where no intelligent energy planning are used (i.e., ?Bussiness as Usual? model) and also with a greedy algorithm developed in the framework of the reference project. The proposed evolutionary approach was able to improve up to 29.0% in energy utilization and up to 65.3% in user preferences over the reference methods.Este artículo presenta los avances en el diseño e implementación de un sistemade recomendación para planificar el uso de electrodomésticos, enfocado en mejorarla eficiencia energética desde el punto de vista tanto de las compañías de energíacomo de los usuarios finales. El sistema propone el uso de información histórica ydatos de sensores para definir instancias del problema de planificación considerandolas preferencias del usuario, que a su vez se proponen resolver mediante un enfoqueevolutivo multiobjetivo, para minimizar el consumo de energía y maximizar la calidaddel servicio ofrecido a los usuarios. Se informan resultados prometedores en casosrealistas del problema, en comparación con situaciones en las que no se utiliza unaplanificación energética inteligente (es decir, modelo ‘Bussiness as Usual’) y tambiéncon un algoritmo goloso desarrollado en el marco del proyecto de referencia. El enfoqueevolutivo propuesto fue capaz de mejorar hasta el 29.0 % en la utilización de energía yhasta el 65,3 % en las preferencias del usuario sobre los métodos de referencia.Fil: Nesmachnow, Sergio. Facultad de Ingeniería; UruguayFil: Colacurcio, Giovanni. Facultad de Ingeniería; UruguayFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toutouh, Jamal. Massachusetts Institute of Technology. Computer Science And Artificial Intelligence Laboratory; Estados UnidosFil: Luna, Francisco. Universidad de Málaga. Departamento Lenguajes y Ciencias de la Computación; Españ

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02