A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events.

Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate and reliable means to model the response of urban catchments under extreme precipitation events produced by Climate Change. This research aimed at optimizing the stormwater modelling of urban catchments using Design of Experiments (DOE), in order to identify the parameters that most influenced their discharge and simulate their response to severe storms events projected for Representative Concentration Pathways (RCPs) using a statistics-based Climate Change methodology. The application of this approach to an urban catchment located in Espoo (southern Finland) demonstrated its capability to optimize the calibration of stormwater simulations and provide robust models for the prediction of extreme precipitation under Climate Change.This paper was possible thanks to the research projects RHIVU (Ref. BIA2012-32463) and SUPRIS-SUReS (Ref. BIA 2015-65240-C2-1-R MINECO/FEDER, UE), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). The authors wish to express their gratitude to all the entities that provided the data necessary to develop this research: Helsinki Region Environmental Services Authority HSY, Map Service of Espoo, National Land Survey of Finland, Geological Survey of Finland, EURO-CORDEX and European Climate Assessment & Dataset

The Characteristics of Demand Rates in Inventory Routing Problem

In today's business landscape, demand variability plays a crucial role in determining the success of companies across various industries. This article explores the concept of demand variability, encompassing both deterministic and stochastic demand patterns. We delve into the differences between these demand types and their implications for businesses. The article emphasizes the significance of accurate demand forecasting and its role in strategic decision-making. Deterministic demand, characterized by predictable patterns, allows businesses to forecast with certainty. On the other hand, stochastic demand introduces uncertainty, requiring statistical methods and probability theory for estimation and management. Furthermore, we explore the distinction between stochastic stationary demand and stochastic nonstationary demand. While the former maintains consistent statistical properties over time, the latter experiences fluctuations in its characteristics due to external factors. We highlight the challenges faced by businesses in forecasting and managing nonstationary demand and the need for adaptive forecasting methods. To successfully navigate today's dynamic market, companies must embrace advanced analytics and data-driven approaches. By leveraging historical data, statistical models, and forecasting techniques, businesses can gain valuable insights into demand patterns, optimize inventory management, and make informed strategic decisions. Ultimately, understanding and managing demand variability is paramount for businesses seeking to improve customer satisfaction, optimize operations, and enhance their competitive advantage. This article aims to provide a comprehensive understanding of demand variability and equip readers with insights and strategies to tackle the challenges posed by an ever-changing market landscape