This page reports only the academic work registered in the databases of the Transport and Mobility Laboratory, and is not necessarily a comprehensive list of the work by Nour Dougui.
Research projects [1]
2019
OrgVisionPro: Automated organizational design and optimization
This project, joint with CLEAP S.A., is will develop advanced analytics algorithms to propose organization design (OD) scenarios based on the
existing situation, constraints, and future needs of a business. These scenarios will support organizations in
shaping their future by optimizing their structure and operating models.
Teaching assistants: Selin Atac, Janody Pougala, Marija Kukic, Negar Rezvany, Tom Haering
Fall 2021
Introduction to optimization and operations research .
Level: Bachelor
Sections: Mechanical Engineering, Civil Engineering, Environmental Sciences and Engineering, Electrical and Electronics Engineering
Lecturer: Virginie Lurkin
Teaching assistants: Nour Dougui, Negar Rezvany, Nicholas Molyneaux, Gael Lederrey, Marija Kukic, Tom Haering, Stefano Bortolomiol, Cloe Cortes Balcells
Supervision: Nour Dougui, Marija Kukic, Michel Bierlaire
Pavement maintenance is one of the major issues of public agencies. Inefficient maintenance strategies lead to high economic expenses in the long term. Public agencies rely on pavement management
system (PMS) to recommend pavement rehabilitation and maintenance. The first step of a PMS
is to create homogeneous worksites of reasonable sizes (considered practical and economically relevant). This is performed by grouping road sections based on conditions data and degradation
laws over time for the network. In practice, the grouping or clustering phase is done by a basic
algorithm whose results then need to be manually checked and repaired. This makes the task
tedious and time consuming.
The goal of this project is in a first instance to implement a deterministic clustering algorithm
that takes as input road sections information (position, type of surface, type of traffic, age...)
together with a deterministic degradation law and regroups the sections in clusters using Machine
Learning algorithms such as k-means. In a second instance, a probabilistic degradation law will
be considered and a probabilistic clustering algorithm such as Expectation–Maximization will be
implemented. Both algorithms will be tested on real world case study and the result will be
compared to the results of an existing PMS clustering methodology.
2021
The railway timetable rescheduling problem, Benoit Pahud (Civil Engineering)
Supervision: Nour Dougui, Marija Kukic, Stefano Bortolomiol
In railway networks, unexpected disruptions may occur for different reasons and cause delays, service denial, and, consequently, passenger inconvenience. This pre-study project will look at remediation strategies such as canceling, delaying or rerouting trains in case of unexpected disruptions. This pre-study project follows two main lines of research. First, the student will familiarize with state-of-the-art methodologies and algorithms to generate railway timetables in a disrupted network. Second, the student will learn how these methodologies can be integrated in the commercial software Viriato, produced by the company SMA und Partner AG.
2020-2021
Schedule repair in liner shipping, Benoit Pahud (Civil Engineering)
Supervision: Stefano Bortolomiol, Nour Dougui, Michel Bierlaire
Large liner shipping companies operate several hundred ships worldwide. These ships carry multi-modal containers on pre-established routes with a regular schedule (typically weekly). A schedule is thus an ordered list of port calls (i.e. stops in a harbour) with associated time windows. Since there are several thousands possible ports in the world, designing routes is a complex problem. Furthermore, since a rotation takes weeks or even months, roughly four journeys out of five end up requiring adjustments to accommodate delays, breakdowns or other unforeseeable but frequent events. Due to the extent of the operations, improving from an adequate schedule to an optimal one may yield enormous benefits, not only economically but also in terms of environmental impact and of quality of life for workers.
Given the nominal schedule and an additional constraint stemming from an unforeseen event, the aim of the project is to use geographical information and demand statistics in the various ports of a liner shipping network, the student applies Operational Research methodologies to generate a new optimal schedule, completing as much of the original mission as possible while minimising the ecological and economic impacts.
Supervision: Melvin Wong, Nour Dougui, Michel Bierlaire
Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Therefore, public agencies rely on pavement management system that prioritize and recommend pavement rehabilitation and maintenance to maximize results within a given budget amount.
The goal of this project is to produce a literature review on the existing pavement management systems and methodologies that optimize road maintenance on both network and project level. Besides, different road conditions degradation model should be investigated. Finally, a heuristic method for road maintenance should be implemented and tested on a case study while testing different scenarios of road condition degradation.
2020
Optimizing Organizational Chart
using local search method, Hugo Bocquet
Supervision: Nour Dougui, Selin Atac, Michel Bierlaire
The aim of this project is to produce a near optimal Organisational Chart for a real case study.
This Organisational Chart should optimize the span of control of Management in the company by
maximizing a coverage metric using clearly-de ned manipulations of the organizational design.
To achieve this goal a local search method will be used and the results should be compared to
those given by an exact method (Simplex). We will in a rst phase apply the optimization method
to a small sample of Organisational Chart without considering the role of the employees. In a
second phase, the role of the employees should be considered and a larger Organisational Chart
should be considered.
Robust routing and scheduling in liner shipping, Yannis Voet (Civil Engineering)
Supervision: Stefano Bortolomiol, Nour Dougui, Michel Bierlaire
Large liner shipping companies operate several hundred ships worldwide. These ships carry multi-modal containers on pre-established routes with a regular schedule (typically weekly). A schedule is thus an ordered list of port calls (i.e. stops in a harbour) with associated time windows. Since there are several thousands possible ports in the world, designing routes is a complex problem. Furthermore, since a rotation takes weeks or even months, roughly four journeys out of five end up requiring adjustments to accommodate delays, breakdowns or other unforeseeable but frequent events. Due to the extent of the operations, improving from an adequate schedule to an optimal one may yield enormous benefits, not only economically but also in terms of environmental impact and of quality of life for workers.
Given geographical information and demand statistics in the various ports of a network, the project aims at applying Operational Research methodologies to automatically generating shipping schedules that make sense technically, economically and environmentally, as well as resisting perturbations.
This project is proposed by Mediterranean Shipping Company (MSC).
2018-2019
Formulating and solving a dial-a-ride problem, Rym Karime (Civil Engineering)
Supervision: Yuki Oyama, Nour Dougui, Michel Bierlaire
The Dial-a-Ride Problem (DARP) is a problem to design a vehicular route and schedule, given
the passenger requests that are characterized by origins (pickup points) and destinations (delivery
points) often with the time windows. In recent years, solving the DARP is increasingly demanded,
re
ecting new technologies for mobility. On-demand transportation for elderly or disabled people
is a typical example of application.
The project is mainly dedicated to the following specific tasks. First, the literature review
will be done so that the student gets familiar with the model concepts and formulations of the
DARP. The student is expected to understand the difference among several types of models, such
as between static and dynamic models, or between single-vehicle and multi-vehicle models. At the
same time, the solution methodologies that are relevant to each type of DARP will be investigated.
Second, the student should acquire the basic skills for optimization problem. Though several
exercises, she will get used to coding and using appropriate softwares for solving the problem.
Given the knowledge and skills, finally, she should define a DARP for a specific example. The
problem will be solved by at least two methodologies and be analyzed.
Towards a techno-economic evaluation
framework for regional train propulsion
architectures, Florian Mueller
Supervision: Nour Dougui, Nikola Obrenovic, Michel Bierlaire
Railway can be the most environmental mode for land transport. However the
sector faces cost challenges. Apart from main routes, tracks are often not electrified,
requiring pollutive and operational expensive diesel propulsion systems.
In contrary to road vehicles, there are no commercial large-scale applications
of hybrid drivetrains. Furthermore, the dependencies of hybrid drivetrains on
energy supplying infrastructure, like overhead wires or recharge points, has not
been researched yet.
In order to assess the potential of hybrid drivetrains holistically, the project
Toolbox for Optimal Railway Propulsion Architectures (TORPA) has set up a
framework to define drivetrain solutions, optimize them, and compare them
among each other. Prior to this semester project, 58 drivetrain architectures
have been defined. One of them was optimized toward the objectives of driving
CO2 emissions and investment costs, on a specific use case. However, these
bare results were inconclusive and expensive to compute for more than one use
case.
In this semester project, we downselect the number of possible architectures,
outlining the currently most relevant ones. Furthermore, we include infrastructure
requirements in the definition of architectures.
The metric of investment cost is extended to display the full life cycle costs of
architectures. The model of driving CO2 emissions is attempted to display all
vehicle life cycle emissions, but left at the current state after qualitatively stating
significant CO2 contributors not included in the calculation models.
In order to assess the potential of the extended framework, it was required to
state test cases, define tangible experiments, and prioritize them. Previously, it
is researched which parameters should be chosen to be relevant for such experiments.
Thereby, representative generic test tracks are conceived.
We choose the question of viability of track electrification as first test case for
the software framework. Thus, the currently operating vehicle architectures for
diesel or full electric operation are applied. Especially the impacts of currently
existing electrification infrastructure and track parameters, like distance and stop
frequency, are investigated. Firstly, we imply a track with average constitution
and operation. It is found, that the break even of track electrification is reached
when a third of it is already electrified due to e. g. intersection with other tracks.
Furthermore, we find that stop frequency is decisive for electrification break
even: Tracks with shortened stop distance may be cheaper to be operated electrically,
even if there is no catenary infrastructure built up yet.
