document

Machine learning based trajectory prediction to support demand forecasting: A Transformer Neural Network Approach

Vos, Reinier (author)

Air traffic sector demand and capacity balancing is an important process to enable safe and efficient flight execution. In current operations, demand and capacity are determined based on schedules and flight plans. In reality, disruptions to flights create a different situation that may not have been anticipated by the Air Navigation Service...

master thesis 2023

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Two phase implementation of a machine learning (ML) model in demand forecasting for organisations

van Spengler, Olivier (author)

Demand forecasting plays a critical role in organizational planning, encompassing inventory management, capacity allocation, and financial decision-making. However, achieving accurate forecasts can be challenging, particularly in industries characterized by high demand volatility, such as semiconductor assembly equipment manufacturing,...

master thesis 2023

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Kiosk Strategic Demand Forecasting with Scenario Planning: A case study at SITA

LIU, JIshan (author)

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order...

master thesis 2022

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Design, Development, and Implementationof a Demand Forecast Model: A case Study to Improve Cargill’s Demand Management

Acosta Martinez, Victor Acosta (author)

This thesis project studied the demand planning process of Cargill Global Edible Oil Solutions (GEOS) in the EMEA region and had the objective of developing a demand forecast model for the Retail Food Services customers to improve their satisfaction levels. The scope of this covered the commercial and contractual relationships, for the delivery...

master thesis 2022

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Post-pandemic office real estate: A pilot study of using scenario planning to develop scenarios with quantitative office space demand for corporate real estate

Cheng, Ziyao (author)

The influence of the COVID-19 pandemic is profound and enduring to the entire world. A ‘recovery’ to go back in time is unlikely to happen due to the lack of uncertainty in the future. The darkest time will eventually pass, whereas post-pandemic, the era in which the pandemic is perceived to have subsided, can still accelerate existing trends...

master thesis 2022

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A cooperative strategy for optimizing vehicle relocations and staff movements in cities where several carsharing companies operate simultaneously

Chang, X. (author), Wu, Jianjun (author), Correia, Gonçalo (author), Sun, Huijun (author), Feng, Ziyan (author)

Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation...

journal article 2022

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Privacy-Preserving Techniques for Machine Learning Applications in Supply Chains

Joshi, Ayush (author)

Supply chains are vital to the global economy, and so, increasing efficiency in supply chain management is of utmost importance. Modernizing technology has allowed for various uses of machine learning to be possible in several aspects of supply chains, specifically in demand forecasting with prediction models, and customer relations with chat...

bachelor thesis 2021

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Forecasting spare parts of free-floating shared vehicles: A search to a data driven approach to improve the availability of spare parts

Broekman, Marleen (author)

For the shared mobility service providers, it is essential to have an economic operation and therefore, it is necessary to maximise their fleet utilisation. Of influence on the latter is the availability of the spare parts. The topics for which spare part forecasting research is already performed are different in several ways compared to the...

master thesis 2021

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Relocating operational and damaged bikes in free-floating systems: A data-driven modeling framework for level of service enhancement

Chang, Ximing (author), Wu, Jianjun (author), Sun, Huijun (author), Correia, Gonçalo (author), Chen, JianHua (author)

Free-floating bike sharing is an innovative and sustainable travel mode, where shared bikes can be picked up and returned at any proper place on the streets and not just at docking stations. Nevertheless, in these systems, two major problems arise. One is the imbalance of free-floating shared bikes (FFSB) between zones due to one-way trips,...

journal article 2021

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Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach

Xenochristou, Maria (author), Hutton, Chris (author), Hofman, Jan (author), Kapelan, Z. (author)

This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the interpretability of statistical methods. For this reason, a random forest model is used to predict daily demands 1 day...

journal article 2021

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On the quality requirements of demand prediction for dynamic public transport

Peled, Inon (author), Lee, Kelvin (author), Jiang, Yu (author), Dauwels, J.H.G. (author), Pereira, Francisco C. (author)

As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any...

journal article 2021

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Dynamic human resource management decision support model based on tactical aircraft maintenance demand forecasts

Slangen, Bram (author)

The long and therefore expensive training of aircraft maintenance technicians underline the need for accurate demand forecasts that allow for dynamic control of acquisition and training rate of personnel. This control enables human resource management to react swiftly to increases in workforce demand at times of technician shortages. To help...

master thesis 2020

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Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty

Ghotge, R. (author), Snow, Yitzi (author), Safaei Farahani, S. (author), Lukszo, Z. (author), van Wijk, A.J.M. (author)

Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the...

journal article 2020

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An ensemble stacked model with bias correction for improved water demand forecasting

Xenochristou, Maria (author), Kapelan, Z. (author)

Water demand forecasting is an essential task for water utilities, with increasing importance due to future societal and environmental changes. This paper suggests a new methodology for water demand forecasting, based on model stacking and bias correction that predicts daily demands for groups of ~120 properties. This methodology is compared...

journal article 2020

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Water Demand Forecasting Accuracy and Influencing Factors at Different Spatial Scales Using a Gradient Boosting Machine

Xenochristou, Maria (author), Hutton, C. (author), Hofman, J. (author), Kapelan, Z. (author)

Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode...

journal article 2020

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Waste collection inventory routing with non-stationary stochastic demands

Markov, Iliya (author), Bierlaire, Michel (author), Cordeau, Jean François (author), Maknoon, M.Y. (author), Varone, Sacha (author)

We solve a rich routing problem inspired from practice, in which a heterogeneous fixed fleet is used for collecting recyclable waste from large containers over a finite planning horizon. Each container is equipped with a sensor that communicates its level at the start of the day. Given a history of observations, a forecasting model is used to...

journal article 2020

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Investigation of active learning techniques for dynamic Time-of-Use (dToU) tariff policy design for residential users

Nikte, Shreyas (author)

The active learning approach is a special case of semi-supervised machine learning which is able to interactively query the user to reduce the uncertainty of the machine learning model. The approach is useful to minimize the data labeling cost. The project aims to study and use this method to characterize residential electricity users’ demand...

master thesis 2019

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Forecast-based mechanisms for demand response

Methenitis, G. (author), Kaisers, Michael (author), la Poutré, J.A. (author)

We study mechanisms to incentivize demand response in smart energy systems. We assume agents that can respond (reduce their demand) with some probability if they prepare prior to the real-ization of the demand. Both preparation and response incur costs to agents. Previous work studies truthful mechanisms that select a minimal set of agents to...

conference paper 2019

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Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics

Nieuwenhuijsen, J.A.H. (author), Correia, Gonçalo (author), Milakis, D. (author), van Arem, B. (author), van Daalen, C. (author)

This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this...

journal article 2018

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Using metro smart card data to model location choice of after-work activities: An application to Shanghai

Wang, Y. (author), Correia, Gonçalo (author), de Romph, E. (author), Timmermans, H. J.P.(Harry) (author)

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip...

journal article 2017