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Algo/Louage/Louage.ipynb
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Algo/Louage/Louage.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Mobility and Smart Cities\n",
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"\n",
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"## GILA - Greedy Incremental Louage’s Algorithm\n",
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"\n",
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"### Presentation\n",
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"\n",
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"Louage is a transportation system in Tunisia to overcome the lack of public transportation. This system is spread across all the Tunisian territory and is used by a lot of people. In this system their are two types of trips : regional and cross-regional.\n",
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"\n",
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"This transportation system is very cheap and have a very high occupation level (97%). \n",
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"\n",
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"Drivers propose their itineraries and stops with a time window of departure and arrival and a price per seat. \n",
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"\n",
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"Passengers can ask to book a trip from a location to a destination with a desire departure and an arrival time window.\n",
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"\n",
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"Operators assigned passengers to a trip if the trip is compatible with their time window and if there is enough seats available. First come first served (FIFO). \n",
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"\n",
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"Other criteria could also be used like pricing, number of stops, number of changes require to achieve the trip etc. \n",
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"\n",
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"### Problem\n",
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"\n",
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"The Louage probleme is a multi-constraints and multi-objectives problem.\n",
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"\n",
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"Objectives :\n",
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"- Maximize the number of passengers per vehicle (High occupation rate) to maximize the profit of the driver\n",
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"- Maximize the number of satisfied requests (e.g. maximize the number of clients)\n",
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"- Respect first in first out (FIFO) rule\n",
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"- Minimize the waiting time of the passengers\n",
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"- Minimize the overall sum of running itineraries duration\n",
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"- As soon as a louage is full, it should leave\n",
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"\n",
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"Constraints :\n",
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"- Coherence of time windows and travel time with normalization\n",
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"- Louage’ s lines constraints\n",
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"- Itinerary constraints\n",
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"- Stop position depencies respected between request and organization (set of journeys)\n",
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"- Respect the order of status and conditions on state transition for each journey\n",
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"- Capacity of the vehicle\n",
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"- Price\n",
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"\n",
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"How can we assigned travelers to their corresponding while respecting the constraints and maximizing the objectives ?\n",
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"\n",
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"### Conception\n",
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"\n",
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"To solve this problem the GILA algorithm uses a greedy approach. We makes the best decision at each step to reach a local optimum.\n",
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"\n",
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"#### Inputs\n",
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"\n",
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"We have two types of input in different lists : demands and offers.\n",
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"\n",
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"##### Demands\n",
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"\n",
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"Each client create a demand with the following information :\n",
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"- Itinerary / Path (origin, destination)\n",
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" - Contains all the stops of the itinerary\n",
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" - Time window of departure (hd_minus, hd_plus)\n",
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" - Time window of arrival (ha_minus, ha_plus)\n",
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"- Booked placed (NP)\n",
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"- Bill\n",
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"- State (initialized, validated, paired, contractualized, running, realized, non realized and archived), this is used to keep track of the state of the demand during the algorithm.\n",
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"\n",
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"##### Offers\n",
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"\n",
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"Each driver realized a trip with the following information :\n",
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"- Itinerary / Path (origin, destination)\n",
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" - Contains all the stops of the itinerary\n",
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" - Time window of departure (hd_minus, hd_plus)\n",
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" - Time window of arrivals (ha_minus, ha_plus)\n",
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"- Number of available seats (PlacesL)\n",
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"- The driver\n",
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"- The vehicle\n",
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"- Status\n",
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"\n",
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"#### Output\n",
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"\n",
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"As a result, the algorithm returns an organization of journeys composed of associated the demands and offers.\n",
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"Each journey is composed of :\n",
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"- an itinerary\n",
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"- an offer\n",
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"- a list of demands\n",
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"- a state\n",
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"- a maximal number of seats occupied by passengers at any step of the itinerary\n",
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"- the number of available seats at any step of the itinerary \n",
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"\n",
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"#### Normalization\n",
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"\n",
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"When clients are affected to a trip, their time window of departure and arrival are updated to take into account the traveling time and the time window of departure and arrival of the louage.\n",
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"\n",
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"We have an offer leaving between 8h and 8h30 for a trip that takes 30min and arriving between 8h to 9h30.\n",
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"\n",
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"Normalization will shift the arrival time window of the offer to 8h30 and 9h30.\n",
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"\n",
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"Trips must also be coherent:\n",
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"- each status of journey must be in the defined order\n",
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"- it's impossible to drop someone before picking him up so pickup location have to be before dropoff location in the itinerary \n",
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"\n",
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"### Examples\n",
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"\n",
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"#### Status\n",
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"\n",
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"\n",
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"\n",
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"#### Pairing \n",
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"\n",
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"\n",
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"\n",
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"#### Normalization\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Implementation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Analysis\n",
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"\n",
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"This solution allows to solve the problem in a reasonable time even it does not reach a perfect solution all the time.\n",
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"\n",
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"This algorithm can also not fit real life situations. For instance, if a vehicle has 6 passengers seat, that does not mean that you can transport only 6 persons. More person could be fitted inside regardless of the number of seats and regulations."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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Algo/Louage/Louage_GuillaumeMARTIN_description_v1.zip
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Algo/Louage/Louage_GuillaumeMARTIN_description_v1.zip
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Algo/Louage/louage.md
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108
Algo/Louage/louage.md
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# Mobility and Smart Cities
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## GILA - Greedy Incremental Louage’s Algorithm
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### Presentation
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Louage is a transportation system in Tunisia to overcome the lack of public transportation. This system is spread across all the Tunisian territory and is used by a lot of people. In this system their are two types of trips : regional and cross-regional.
|
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|
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This transportation system is very cheap and have a very high occupation level (97%).
|
||||
|
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Drivers propose their itineraries and stops with a time window of departure and arrival and a price per seat.
|
||||
|
||||
Passengers can ask to book a trip from a location to a destination with a desire departure and an arrival time window.
|
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|
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Operators assigned passengers to a trip if the trip is compatible with their time window and if there is enough seats available. First come first served (FIFO).
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Other criteria could also be used like pricing, number of stops, number of changes require to achieve the trip etc.
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### Problem
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The Louage probleme is a multi-constraints and multi-objectives problem.
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Objectives :
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- Maximize the number of passengers per vehicle (High occupation rate) to maximize the profit of the driver
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- Maximize the number of satisfied requests (e.g. maximize the number of clients)
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- Respect first in first out (FIFO) rule
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- Minimize the waiting time of the passengers
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- Minimize the overall sum of running itineraries duration
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- As soon as a louage is full, it should leave
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Constraints :
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- Coherence of time windows and travel time with normalization
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- Louage’ s lines constraints
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- Itinerary constraints
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- Stop position depencies respected between request and organization (set of journeys)
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- Respect the order of status and conditions on state transition for each journey
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- Capacity of the vehicle
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- Price
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How can we assigned travelers to their corresponding while respecting the constraints and maximizing the objectives ?
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### Conception
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To solve this problem the GILA algorithm uses a greedy approach. We makes the best decision at each step to reach a local optimum.
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#### Inputs
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We have two types of input in different lists : demands and offers.
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##### Demands
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Each client create a demand with the following information :
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- Itinerary / Path (origin, destination)
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- Contains all the stops of the itinerary
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- Time window of departure (hd_minus, hd_plus)
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- Time window of arrival (ha_minus, ha_plus)
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- Booked placed (NP)
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- Bill
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- State (initialized, validated, paired, contractualized, running, realized, non realized and archived), this is used to keep track of the state of the demand during the algorithm.
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##### Offers
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Each driver realized a trip with the following information :
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- Itinerary / Path (origin, destination)
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- Contains all the stops of the itinerary
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- Time window of departure (hd_minus, hd_plus)
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- Time window of arrivals (ha_minus, ha_plus)
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- Number of available seats (PlacesL)
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- The driver
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- The vehicle
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- Status
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#### Output
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As a result, the algorithm returns an organization of journeys composed of associated the demands and offers.
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Each journey is composed of :
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- an itinerary
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- an offer
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- a list of demands
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- a state
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- a maximal number of seats occupied by passengers at any step of the itinerary
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- the number of available seats at any step of the itinerary
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#### Normalization
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When clients are affected to a trip, their time window of departure and arrival are updated to take into account the traveling time and the time window of departure and arrival of the louage.
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|
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We have an offer leaving between 8h and 8h30 for a trip that takes 30min and arriving between 8h to 9h30.
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Normalization will shift the arrival time window of the offer to 8h30 and 9h30.
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Trips must also be coherent:
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- each status of journey must be in the defined order
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- it's impossible to drop someone before picking him up so pickup location have to be before dropoff location in the itinerary
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### Examples
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#### Status
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#### Pairing
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#### Normalization
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BIN
Algo/Louage/louage.pdf
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BIN
Algo/Louage/louage.pdf
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