935 resultados para Job search strategies
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This paper presents a simple Optimised Search Heuristic for the Job Shop Scheduling problem that combines a GRASP heuristic with a branch-and-bound algorithm. The proposed method is compared with similar approaches and leads to better results in terms of solution quality and computing times.
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Glucocorticoids affect physiology and behaviour, reproduction and potentially sexual selection as well. Shortterm and moderate glucocorticoid elevations are suggested to be adaptive, and prolonged and high elevations may be extremely harmful. This suggests that optimal reproductive strategies, and thus sexual selection, may be dose dependent. Here, we investigate effects of moderate and high elevations of blood corticosterone levels on intra- and intersexual behaviour and mating success of male common lizards Lacerta vivipara. Females showed less interest and more aggressive behaviour towards high corticosterone males and blood corticosterone levels affected male reproductive strategy. Males of moderate and high corticosterone elevations, compared with Control males, showed increased interest (i.e., higher number of chases, tongue extrusions, and approaches) towards females and high corticosterone males initiated more copulation attempts. However, neither increased male interest nor increased copulation attempts resulted in more copulations. This provides evidence for a best-of-a-bad-job strategy, where males with higher corticosterone levels compensated for reduced female interest and increased aggressive female behaviour directed towards them, by showing higher interest and by conducting more copulation attempts. Blood corticosterone levels affected intrasexual selection as well since moderate corticosterone levels positively affected male dominance, but dominance did not affect mating success. These findings underline the importance of female mate choice and are in line with adaptive compensatory behaviours of males. They further show that glucocorticoid effects on behaviour are dose dependent and that they have important implications for sexual selection and social interactions, and might potentially affect Darwinian fitness.
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Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.
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This paper proposes strategies to reduce the number of variables and the combinatorial search space of the multistage transmission expansion planning problem (TEP). The concept of the binary numeral system (BNS) is used to reduce the number of binary and continuous variables related to the candidate transmission lines and network constraints that are connected with them. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) and additional constraints, obtained from power flow equilibrium in an electric power system are employed for more reduction in search space. The multistage TEP problem is modeled like a mixed binary linear programming problem and solved using a commercial solver with a low computational time. The results of one test system and two real systems are presented in order to show the efficiency of the proposed solution technique. © 1969-2012 IEEE.
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Multiplication of bacteria within the central nervous system compartment triggers a host response with an overshooting inflammatory reaction which leads to brain parenchyma damage. Some of the inflammatory and neurotoxic mediators involved in the processes leading to neuronal injury during bacterial meningitis have been identified in recent years. As a result, the therapeutic approach to the disease has widened from eradication of the bacterial pathogen with antibiotics to attenuation of the detrimental effects of host defences. Corticosteroids represent an example of the adjuvant therapeutic strategies aimed at downmodulating excessive inflammation in the infected central nervous system. Pathophysiological concepts derived from an experimental rat model of bacterial meningitis revealed possible therapeutic strategies for prevention of brain damage. The insights gained led to the evaluation of new therapeutic modalities such as anticytokine agents, matrix metalloproteinase inhibitors, antioxidants, and antagonists of endothelin and glutamate. Bacterial meningitis is still associated with persistent neurological sequelae in approximately one third of surviving patients. Future research in the model will evaluate whether the neuroprotective agents identified so far have the potential to attenuate learning disabilities as a long-term consequence of bacterial meningitis.
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This chapter summarises the metabolomic strategies currently in force used in plant science and describes the methods used. The metabolite profiling and fingerprinting of plant tissues through MS- and/or NMR-based approaches and the subsequent identification of biomarkers is detailed. Strategies for the microisolation and de novo identification of unknown biomarkers are also discussed. The various approaches are illustrated by a metabolomic study of the maize response to herbivory. A review of recent metabolomic studies performed on seed and crop plant tissues involving various analytical strategies is provided.
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Abstract As librarians of the Social & Preventive Medicine Library in Bern, we help researchers perform systematic literature searches and teach students to use medical databases. We developed our skills mainly “on the job”, and we wondered how other health librarians in Europe were trained to become experts in searching. We had a great opportunity to “job shadow” specialists in this area of library service during a 5-day-internship at the Royal Free Hospital Medical Library in London, Great Britain.
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After the application form is submitted, the interview is the most important method of human resource allocation. Previous research has shown that the attractiveness of interviewees can significantly bias interview outcome. We have previously shown that female interviewers give attractive male interviewees higher status job packages compared their average looking counterparts. However, it is not known whether male interviewers exhibit such biases. In the present study, participants were asked to take part in a mock job negotiation scenario where they had to allocate either a high- or low-status job package to attractive or average looking ``interviewees.'' Before each decision was made, the participant's anticipatory electrodermal response (EDR) was recorded. The results supported our previous finding in that female participants allocated a greater number of high-status job packages to attractive men. Additionally, male participants uniformly allocated a greater number of low-status job packages to both attractive men and attractive women. Overall, the average looking interviewees incurred a penalty and received a significantly greater number of low-status job packages. In general, the EDR profile for both male and female participants was significantly greater when allocating the low-status packages to the average looking interviewees. However, the male anticipatory EDR profile showed the greatest change when allocating attractive women with low-status job packages. We discuss these findings in terms of the potential biases that may occur at the job interview and place them within an evolutionary psychology framework.
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We present a model linking perceptions of job insecurity to emotional reactions and negative coping behaviors. Our model is based on the idea that emotional variables explain, in part, discrepant findings reported in previous research. In particular, we propose that emotional intelligence moderates employees' emotional reactions to job insecurity and their ability to cope with associated stress. In this respect, low emotional intelligence employees are more likely than high emotional intelligence employees to experience negative emotional reactions to job insecurity and to adopt negative coping strategies.
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Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, nondifferentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, the primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. The paper also shows empirically that changing the order of mutating the objective variables and mutating the strategy parameters does not alter the previous conclusion significantly, and that Cauchy mutations with different scaling parameters still outperform the Gaussian mutation with self-adaptation. However, the advantage of Cauchy mutations disappears when recombination is used in evolution strategies. It is argued that the search step size plays an important role in determining evolution strategies' performance. The large step size of recombination plays a similar role as Cauchy mutation.
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Functional genomics is the systematic study of genome-wide effects of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. Here, we use a dynamic biophysical cropping systems model (APSIM-Sorg) to generate a state space of genotype performance based on 15 genes controlling four adaptive traits and then search this spice using a quantitative genetics model of a plant breeding program (QU-GENE) to simulate recurrent selection. Complex epistatic and gene X environment effects were generated for yield even though gene action at the trait level had been defined as simple additive effects. Given alternative breeding strategies that restricted either the cultivar maturity type or the drought environment type, the positive (+) alleles for 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genotypes were favored in the Severe-Terminal drought environment type, late genotypes were favored in the Mild-Terminal and Midseason drought environment types. In the Severe-Terminal environment, there was an interaction of the stay-green (SG) trait with other traits: Selection for + alleles of the SG genes was delayed until + alleles for genes associated with the transpiration efficiency and osmotic adjustment traits had been fixed. Given limitations in our current understanding of trait interaction and genetic control, the results are not conclusive. However, they demonstrate how the per se complexity of gene X gene X environment interactions will challenge the application of genomics and marker-assisted selection in crop improvement for dryland adaptation.
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This paper aims at analysing the writing of the Portuguese author António Lobo Antunes, considered one of the major writers in European Literature with 26 books published, by focusing on the strategies deployed in his texts of creating micro-narratives within the main frame, and conveying the elements of individual and collective memory, past and present, the self and the others, using various voices and silences. Lobo Antunes incorporates in his writing his background as a psychiatrist at a Mental Hospital in Lisbon, until 1985 (when he decided to commit exclusively to writing), his experience as a doctor in the Portuguese Colonial War battlefield, but also the daily routines of the pre and post 25th of April 1974 (Portuguese Revolution) with subtle and ironic details of the life of the middle and upper class of Lisbon‘s society: from the traumas of the war to the simple story of the janitor, or the couple who struggles to keep their marriage functional, everything serves as material to develop and interweave a complex plot, that a lot of readers find too enwrapped and difficult to follow through. Some excerpts taken from his first three novels and books of Chronicles and his later novel – Ontem não te Vi em Babilónia (2006) – will be put forward to exemplify the complexity of the writing and the main difficulties of the reader, lost in a multitude of narrators‘ voices. Recently, Lobo Antunes has commented on his work stating: What I write can be read in the darkness. This paper aims at throwing some light by unfolding some of the strategies employed to defy new borders in the process of reading.
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This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.
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This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.