Population-based simulation optimization for urban mass rapid transit networks

In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin–destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results. Document type: Articl

Event-by-event fluctuations in collective quantities

We discuss an event-by-event fluctuation analysis of particle production in heavy ion collisions. We compare different approaches to the evaluation of the event-by-event dynamical fluctuations in quantities defined on groups of particles, such quantities as mean transverse momentum, transverse momentum spectra slope, strength of anisotropic flow, etc.. The direct computation of the dynamical fluctuations and the sub-event method are discussed in more detail. We also show how the fluctuation in different variables can be related to each other.Comment: LaTex, 14 pages and 5 figures. 2 references adde

Cold truths: how winter drives responses of terrestrial organisms to climate change

Winter is a key driver of individual performance, community composition, and ecological interactions in terrestrial habitats. Although climate change research tends to focus on performance in the growing season, climate change is also modifying winter conditions rapidly. Changes to winter temperatures, the variability of winter conditions, and winter snow cover can interact to induce cold injury, alter energy and water balance, advance or retard phenology, and modify community interactions. Species vary in their susceptibility to these winter drivers, hampering efforts to predict biological responses to climate change. Existing frameworks for predicting the impacts of climate change do not incorporate the complexity of organismal responses to winter. Here, we synthesise organismal responses to winter climate change, and use this synthesis to build a framework to predict exposure and sensitivity to negative impacts. This framework can be used to estimate the vulnerability of species to winter climate change. We describe the importance of relationships between winter conditions and performance during the growing season in determining fitness, and demonstrate how summer and winter processes are linked. Incorporating winter into current models will require concerted effort from theoreticians and empiricists, and the expansion of current growing-season studies to incorporate winter

A new MRI rating scale for progressive supranuclear palsy and multiple system atrophy: validity and reliability

AIM To evaluate a standardised MRI acquisition protocol and a new image rating scale for disease severity in patients with progressive supranuclear palsy (PSP) and multiple systems atrophy (MSA) in a large multicentre study. METHODS The MRI protocol consisted of two-dimensional sagittal and axial T1, axial PD, and axial and coronal T2 weighted acquisitions. The 32 item ordinal scale evaluated abnormalities within the basal ganglia and posterior fossa, blind to diagnosis. Among 760 patients in the study population (PSP = 362, MSA = 398), 627 had per protocol images (PSP = 297, MSA = 330). Intra-rater (n = 60) and inter-rater (n = 555) reliability were assessed through Cohen's statistic, and scale structure through principal component analysis (PCA) (n = 441). Internal consistency and reliability were checked. Discriminant and predictive validity of extracted factors and total scores were tested for disease severity as per clinical diagnosis. RESULTS Intra-rater and inter-rater reliability were acceptable for 25 (78%) of the items scored (≥ 0.41). PCA revealed four meaningful clusters of covarying parameters (factor (F) F1: brainstem and cerebellum; F2: midbrain; F3: putamen; F4: other basal ganglia) with good to excellent internal consistency (Cronbach α 0.75-0.93) and moderate to excellent reliability (intraclass coefficient: F1: 0.92; F2: 0.79; F3: 0.71; F4: 0.49). The total score significantly discriminated for disease severity or diagnosis; factorial scores differentially discriminated for disease severity according to diagnosis (PSP: F1-F2; MSA: F2-F3). The total score was significantly related to survival in PSP (p<0.0007) or MSA (p<0.0005), indicating good predictive validity. CONCLUSIONS The scale is suitable for use in the context of multicentre studies and can reliably and consistently measure MRI abnormalities in PSP and MSA. Clinical Trial Registration Number The study protocol was filed in the open clinical trial registry (http://www.clinicaltrials.gov) with ID No NCT00211224

Scheduling recurring radiotherapy appointments in an ion beam facility

Ion beam radiotherapy is a modern form of cancer treatment that is offered in specialized facilities. Treatment consists of multiple, almost daily irradiation appointments, followed by optional imaging and control assignments. The corresponding problem of scheduling these recurring radiotherapy treatment appointments can be classified as a complex job shop scheduling problem with custom constraints, such as recurring activities, optional activities, and special time window constraints. The objective is to minimize the operation time of the bottleneck resource, the particle beam, while simultaneously minimizing any penalties arising from violations of time window constraints. The authors model the problem mathematically and introduce various customized constraints. Three metaheuristic solution approaches—namely a genetic algorithm with tailor-made feasibility-preserving crossover operators, an iterated local search, and a combination of the two approaches—all perform well on both small and large problem instances. However, the simple combination of the two stand-alone algorithms leads to best results when applied to real-world inspired problem instances.© The Author(s) 201

Population-based simulation optimization for urban mass rapid transit networks

In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin–destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results.© The Author(s) 201

Multi-objective simulation optimization for complex urban mass rapid transit systems

In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a non-dominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our two-phase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.© The Author(s) 201