Robust Optimization Model for Single Line Dynamic Bus Dispatching

For effective bus operations, it is important to flexibly arrange the departure times of buses at the first station according to real-time passenger flows and traffic conditions. In dynamic bus dispatching research, existing optimization models are usually based on the prediction and simulation of passenger flow data. The bus departure schemes are formulated accordingly, and the passenger arrival rate uncertainty must be considered. Robust optimization is a common and effective method to handle such uncertainty problems. This paper introduces a robust optimization method for single-line dynamic bus scheduling. By setting three scenarios—the benchmark passenger flow, high passenger flow, and low passenger flow—the robust optimization model of dynamic bus departures is established with consideration of different passenger arrival rates in different scenarios. A genetic algorithm (GA) is improved for minimizing the total passenger waiting time. The results obtained by the proposed optimization method are compared with those from a stochastic programming method. The standard deviation of the relative regret value with stochastic optimization is 5.42%, whereas that of the relative regret value with robust optimization is 0.62%. The stability of robust optimization is better, and the fluctuation degree is greatly reduced.

Primed Prediction: A Critical Examination of the Consequences of Exclusion of the Ontological Now in AI Protocol

Revisiting Norbert Wiener’s cybernetic prediction as the theoretical foundation of AI this chapter makes a plea how we need to uncover the black box of what is behind prediction and simulation. It explores the shortcomings of cybernetic prediction, the theoretical foundation of Artificial Intelligence, through the lens of Jean Baudrillard’s simulacra and simulation. Specifically, what prediction excludes – namely, an accounting for the ontological now – is what Baudrillard warned against in his analysis of the role technological innovations play in untethering reality from the material plane, leading to a crisis of simulacrum of experience. From this perspective, any deep-learning system rooted in the Wiener’s view of cybernetic feedback loops risks creating behaviour more so than predicting it. As this chapter will argue, such prediction is a narrow, self-referential system of feedback that ultimately becomes a self-fulfilling prophecy girded by the psycho-social effects of the very chaos it seeks to rationalise.

Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability

In the modern digitalised era, the total number of businesses and organisations utilising crowdsourcing services has risen, leading to an increase of their website traffic. In this way, there is plenty of space for marketers and strategists to capitalise big data from both their own and the crowdsourcer’s websites. This can lead to a comprehension of factors affecting their brand name, sustainability (gross profit) and consequently visitor influence. The first of the three staged contexts, based on web data, includes the retrieval of web data analytics and metrics from five air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model, through Fuzzy Cognitive Mapping (FCM), and in the last stage, an Agent-Based Model is deployed for data prediction and simulation. We concluded that crowdsourcing referral traffic increases air forwarders’ top 3 keywords volume, and decreases social traffic and total keywords volume, which then boosts their global web rank and gross profit. The exact opposite results occur with crowdsourcing search traffic. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers about SEO and SEM strategies for brand name and profit enhancement, based on harvesting crowdsourcing platform traffic.

Prediction of the Miscibility of PBAT/PLA Blends

Designing polymer structures and polymer blends opens opportunities to improve the performance of plastics. Blending poly(butylene adipate-co-terephthalate) (PBAT) and polylactide (PLA) is a cost-effective approach to achieve a new sustainable material with complementary properties. This study aimed to predict the theoretical miscibility of PBAT/PLA blends at the molecular level. First, the basic properties and the structure of PBAT and PLA are introduced, respectively. Second, using the group contribution methods of van Krevelen and Hoy, the Hansen and Hildebrand solubility parameters of PBAT and PLA were calculated, and the effect of the molar ratio of the monomers in PBAT on the miscibility with PLA was predicted. Third, the dependence of the molecular weight on the blend miscibility was simulated using the solubility parameters and Flory–Huggins theory. Next, the glass transition temperature of miscible PBAT/PLA blends, estimated using the Fox equation, is shown graphically. According to the prediction and simulation, the blends with a number-average molecular weight of 30 kg/mol for each component were thermodynamically miscible at 296 K and 463 K with the possibility of spinodal decomposition at 296 K and 30% volume fraction of PBAT. This study contributes to the strategic synthesis of PBAT and the development of miscible PBAT/PLA blends.

Prandtl’s Secondary Flows of the Second Kind. Problems of Description, Prediction, and Simulation

Abstract— The occurrence of turbulent pulsations in straight pipes of noncircular cross-section leads to the situation, when the average velocity field includes not only the longitudinal component but also transverse components that form a secondary flow. This hydrodynamic phenomenon discovered at the twenties of the last century (J. Nikuradse, L. Prandtl) has been the object of active research to the present day. The intensity of the turbulent secondary flows is not high; usually, it is not greater than 2–3% of the characteristic flow velocity. Nevertheless, their contribution to the processes of transverse transfer of momentum and heat is comparable to that of turbulent pulsations. In this paper, a review of experimental, theoretical, and numerical studies of secondary flows in straight pipes and channels is given. Emphasis is placed on the issues of revealing the physical mechanisms of secondary flow formation and developing the models of the apriori assessment of their forms. The specific features of the secondary flow development in open channels and channels with inhomogeneously rough walls are touched upon. The approaches of semiempirical simulation of turbulent flows in the presence of secondary flows are discussed.