Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design

This paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well as with two popular genetic algorithms, NSGA-II and SPEA2. We extend an existing case study of green supply chain design in the South Eastern Europe region by optimizing simultaneously costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. The results show that in the considered case the scalarization methods outperform genetic algorithms in finding efficient solutions and that the CO2 and PM emissions can be lowered by accepting a marginal increase of costs over their global minimum

Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure

In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate twodimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another.We apply thismethodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform ameta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms

External validation of a prediction model and decision tree for sickness absence due to mental disorders

Purpose: A previously developed prediction model and decision tree were externally validated for their ability to identify occupational health survey participants at increased risk of long-term sickness absence (LTSA) due to mental disorders. Methods: The study population consisted of N = 3415 employees in mobility services who were invited in 2016 for an occupational health survey, consisting of an online questionnaire measuring the health status and working conditions, followed by a preventive consultation with an occupational health provider (OHP). The survey variables of the previously developed prediction model and decision tree were used for predicting mental LTSA (no = 0, yes = 1) at 1-year follow-up. Discrimination between survey participants with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC). Results: A total of n = 1736 (51%) non-sick-listed employees participated in the survey and 51 (3%) of them had mental LTSA during follow-up. The prediction model discriminated (AUC = 0.700; 95% CI 0.628–0.773) between participants with and without mental LTSA during follow-up. Discrimination by the decision tree (AUC = 0.671; 95% CI 0.589–0.753) did not differ significantly (p = 0.62) from discrimination by the prediction model. Conclusion: At external validation, the prediction model and the decision tree both poorly identified occupational health survey participants at increased risk of mental LTSA. OHPs could use the decision tree to determine if mental LTSA risk factors should be explored in the preventive consultation which follows after completing the survey questionnaire

Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population

PurposeThis study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders.MethodsCohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples.Results30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692-0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615-0.804).ConclusionsAn 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice

Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions

Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (a parts per thousand yen30) SA days and high (a parts per thousand yen3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures. This was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures. In total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable. In the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA

The trade off between diversity and quality for multi-objective workforce scheduling

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences

Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems.

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time convergence behaviour over a set of 1200 problem instances. The new benchmarks are created by fusing previously proposed single-objective interpolated continuous optimisation problems (ICOPs) via a common set of Pareto non-dominated seeds. They thus inherit the ICOP property of having tunable fitness landscape features. The benchmarks are of intrinsic interest as they derive from interpolation methods and so can approximate general problem instances. This property is revealed to be of particular importance as our extensive set of numerical experiments indicates that choices pertaining to (i) the weighting of the inverse distance interpolation function and (ii) the problem dimension can be used to construct problems that are challenging to all tested multi-objective search paradigms. This in turn means that the new multi-objective ICOPs problems (MO-ICOPs) can be used to construct well-balanced benchmark sets that discriminate well between the run-time convergence behaviour of different solvers

Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges

Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents. In this paper we record our research experience on formal modelling of MAS. We review our research throughout the last decade, by describing the problems we have encountered and the decisions we have made towards resolving them and providing solutions. Much of this work involved membrane computing and classes of P Systems, such as Tissue and Population P Systems, targeted to the modelling of MAS whose dynamic structure is a prominent characteristic. More particularly, social insects (such as colonies of ants, bees, etc.), biology inspired swarms and systems with emergent behaviour are indicative examples for which we developed formal MAS models. Here, we aim to review our work and disseminate our findings to fellow researchers who might face similar challenges and, furthermore, to discuss important issues for advancing research on the application of membrane computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Temporal development of the humoral immune response to surface antigens of Moraxella catarrhalis in young infants

The primary Moraxella catarrha/is-specific humoral immune response, and its association with nasopharyngeal colonization, was studied in a cohort of infants from birth to 2 years of age. Results indicated that the levels of antigen-specific IgG, IgA and IgM showed extensive inter-individual variability over time, with IgM and IgA levels to all 9 recombinant domains, from 7 different OMPs, being relatively low throughout the study period. In contrast, the level of antigen-specific IgG was significantly higher for the recombinant domains Hag(385-863), MID764-913, MID962-1200, UspA1(557-704) and UspA2(165-318) in cord blood compared to 6 months of age (P <= 0.001). This was a most likely a consequence of maternal transmission of antigen-specific IgG to newborn babies, possibly indicating a future role for these 3 surface antigens in the development of an effective humoral immune response to M. catarrhalis. Finally, at 2 years of age, the levels of antigen-specific IgG still remained far below that obtained from cord blood samples, indicating that the immune response to M. catarrhalis has not matured at 2 years of age. We provide evidence that a humoral antibody response to OMPs UspA1,UspA2 and Hag/MID may play a role in the immune response to community acquired M. catarrhalis colonization events. (C) 2011 Elsevier Ltd. All rights reserved

Enhancing Perception of Complex Sculptural Forms using Interactive Real-time Ray tracing

This paper looks at experiments into using real-time ray tracing to significantly enhance shape perception of complex three-dimensional digitally created structures. The author is a computational artist whose artistic practice explores the creation of intricate organic three-dimensional forms using simulation of morphogenesis. The generated forms are often extremely detailed, comprising tens of millions of cellular primitives. This often makes depth perception of the resulting structures difficult. His practice has explored various techniques to create presentable artefacts from the data, including high resolution prints, animated videos, stereoscopic installations, 3D printing and virtual reality. The author uses ray tracing techniques to turn the 3D data created from his morphogenetic simulations into visible artefacts. This is typically a time-consuming process, taking from seconds to minutes to create a single frame. The latest generation of graphics processing units offer dedicated hardware to accelerate ray tracing calculations. This potentially allows the generation of ray traced images, including self-shadowed complex structures and multiple levels of transparency, from new viewpoints at frame rates capable of real-time interaction. The author presents the results of his experiments using this technology with the aim of providing significantly enhanced perception of his generated three-dimensional structures by allowing user-initiated interaction to generate novel views, and utilizing depth cues such as stereopsis, depth from motion and defocus blurring. The intention is for these techniques to be usable to present new exhibitable works in a gallery context