scholarly journals A theoretical multimodal assignment model for simulating intermodal trips

2011 ◽  
Author(s):  
L. D’Acierno ◽  
M. Gallo ◽  
B. Montella
Keyword(s):  
Assignment Model

Stochastic Assignment Model for Multiple Travelers Based on Proposal Theory

ICLEM 2012 ◽  
2012 ◽  
Author(s):  
Jiandong Yao ◽  
Lingjuan Chen ◽  
Zengqiang Wang
Keyword(s):  
Assignment Model ◽  
Stochastic Assignment

Simulation Tool for Assignment Models: SIMASI

2014 ◽  
Vol 13 (8) ◽  
pp. 4723-4728
Author(s):  
Pratiksha Saxena ◽  
Smt. Anjali
Keyword(s):  
Optimization Procedure ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Integrated Simulation ◽  
Assignment Model ◽  
Classical Models ◽  
Simulation Results ◽  
User Friendly ◽  
Assignment Models

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples. 

Traffic Flow Hidden Measure and Assignment Model for the Uncertain Direction Military Traffic Network

2012 ◽  
Vol 38 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Wei ZHOU ◽  
Jian-Min HE ◽  
De-Jian YU
Keyword(s):  
Traffic Flow ◽  
Traffic Network ◽  
Assignment Model

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

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