Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness

[EN] There are many models in the literature that have been proposed in the last decades aimed at assessing the reliability, availability and maintainability (RAM) of safety equipment, many of them with a focus on their use to assess the risk level of a technological system or to search for appropriate design and/or surveillance and maintenance policies in order to assure that an optimum level of RAM of safety systems is kept during all the plant operational life. This paper proposes a new approach for RAM modelling that accounts for equipment ageing and maintenance and testing effectiveness of equipment consisting of multiple items in an integrated manner. This model is then used to perform the simultaneous optimization of testing and maintenance for ageing equipment consisting of multiple items. An example of application is provided, which considers a simplified High Pressure Injection System (HPIS) of a typical Power Water Reactor (PWR). Basically, this system consists of motor driven pumps (MDP) and motor operated valves (MOV), where both types of components consists of two items each. These components present different failure and cause modes and behaviours, and they also undertake complex test and maintenance activities depending on the item involved. The results of the example of application demonstrate that the optimization algorithm provide the best solutions when the optimization problem is formulated and solved considering full flexibility in the implementation of testing and maintenance activities taking part of such an integrated RAM model.Authors are grateful to the Spanish Ministry of Science and Innovation for the financial support of this work (research project ENE2013-45540-R) and the Doctoral fellow (BES-2011-043906 and BES-2014-067602).Martón Lluch, I.; Martorell Aigües, P.; Mullor, R.; Sánchez Galdón, AI.; Martorell Alsina, SS. (2016). Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness. Reliability Engineering and System Safety. 153:151-158. https://doi.org/10.1016/j.ress.2016.04.015S15115815

Flexible Job Shop Scheduling with Sequence-dependent Setup and Transportation Times by Ant Colony with Reinforced Pheromone Relationships

This paper proposes a swarm intelligence approach based on a disjunctive graph model in order to schedule a manufacturing system with resource flexibility and separable setup times. Resource flexibility assigns each operation to one of the alternative resources (assigning sub-problem) and, consequently, arranges the operation in the right sequence of the assigned resource (sequencing sub-problem) in order to minimize the makespan. Resource flexibility is mandatory for rescheduling a manufacturing system after unforeseen events which modify resource availability. The proposed method considers parallel (related) machines and enforces in a single step both the assigning and sequencing sub-problems. A neighboring function on the disjunctive graph is enhanced by means of a reinforced relation-learning model of pheromone involving more effective machine-sequence constraints and a dynamic visibility function. It also considers the overlap between the jobs feeding and the machine (anticipatory) setup times. It involves separable sequence-independent and dependent setup phases. The algorithm performance is evaluated by modifying the well-known benchmark problems for JOB shop scheduling. Comparison with other systems and lower bounds of benchmark problems has been performed. Statistical tests highlight how the approach is very promising. The performance achieved when the system addresses the complete problem is quite close to that obtained in the case of the classical job-shop problem. This fact makes the system effective in coping with the exponential complexity especially for sequence dependent setup times

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

A universal and improved mutation strategy for iterative wavefront shaping

Recent advances in iterative wavefront shaping (WFS) techniques have made it possible to manipulate the light focusing and transport in scattering media. To improve the optimization performance, various optimization algorithms and improved strategies have been utilized. Here, a novel guided mutation (GM) strategy is proposed to improve optimization efficiency for iterative WFS. For both phase modulation and binary amplitude modulation, considerable improvements in optimization effect and rate have been obtained using multiple GM-enhanced algorithms. Due of its improvements and universality, GM is beneficial for applications ranging from controlling the transmission of light through disordered media to optical manipulation behind them.Comment: 5 pages with 6 figure

Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization

This is an Accepted Manuscript of an article published in Hydrological Sciences Journal on MAY 1 2013, available online: http://dx.doi.org/10.1080/02626667.2013.779777[EN] An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.The authors wish to thank the Confederacion Hidrogrofica del Jucar (Spanish Ministry of the Environment) for the data provided in developing this study and the Comision Interministerial de Ciencia y Tecnologia, CICYT (Spanish Ministry of Science and Innovation) for funding the projects INTEGRAME (contract CGL2009-11798) and SCARCE (programme Consolider-Ingenio 2010, project CSD2009-00065). The authors also thank the European Commission (Directorate-General for Research and Innovation) for funding the project DROUGHT-R&SPI (programme FP7-ENV-2011, project 282769) and the Seventh Framework Programme of the European Commission for funding the project SIRIUS (FP7-SPACE-2010-1, project 262902). We are grateful to the reviewers for their valuable comments, which have improved this paper.Lerma Elvira, N.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A. (2013). Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal. 58(4):797-812. https://doi.org/10.1080/02626667.2013.779777S79781258