FedEx Parcel Delivery Scheduler

This program schedules parcels for delivery by assigning parcels to trucks and determining the route that each truck will take. Multiple scheduling options are offered so that a decision can be made about which route to take, with well packed trucks and short, efficient routes.

The program takes three files, one containing map data, one containing parcel data, and one containing truck data. The corresponding files must be named map-data.csv, parcel-data.csv, and truck-data.csv respectively.

Every parcel must have an ID, source city, destination city, and a volume in cubic centimeters. This must be entered into the parcel file in this exact order with values separated by commas and each parcel on a new line. A parcels ID is unique, that is, no two parcels can have the same ID. An example of a parcel-data.csv file is as follows.

18	London	Hamilton	19
15	Belleville	Ottawa	16
14	Ottawa	Guelph	20
6	Belleville	Ottawa	12
1	Guelph	Hamilton	10
0	Toronto	Hamilton	5
7	London	Guelph	8
8	Ottawa	London	21
12	London	Hamilton	24
16	London	Guelph	22
5	Ottawa	London	21
11	Toronto	Hamilton	8
3	Guelph	Hamilton	20
9	London	Belleville	7
13	Guelph	Hamilton	15

Every truck also has a unique ID. Trucks also have a maximum capacity in cubic centimeters, and as you load parcels into the truck the total volume of all parcels added cannot exceed the maximum capacity. A truck will also have a route that it will take to deliver the parcels that get loaded. An empty truck has an ID and maximum capacity. This must be entered into the truck file in this exact order with values separated by commas and each truck on a new line. An example of a truck-data.csv file is as follows.

3	35
8	49
5	33
6	27
0	42

All trucks and all parcels will start from a common location called the depot. This depot must be a city in the map-data.csv file and there must de distance measures between the depot and all other relevant cities in th map. The depot is set by the user as an input argument. For example, if you want to run the program with the depot set to Toronto you would run ./main Toronto.

A map is constructed from the map data file using the class distanceMap. Each entry in this file must contain two cities and the respective distance between them in kilometers. There must be a map entry for every city that a parcel must be delivered to, and this city must be connected to all other cities. An example of a map-data.csv file is as follows.

Belleville	Guelph	265
Belleville	Hamilton	256
Belleville	Toronto	187
Belleville	Ottawa	269
Belleville	Kingston	85
Belleville	London	366
Belleville	Mississauga	209
Belleville	Oakville	223
Belleville	Windsor	543
Belleville	Woodstock	314
Guelph	Hamilton	53
Guelph	Toronto	93
Guelph	Woodstock	67
Guelph	Ottawa	528
Guelph	Kingston	344
Guelph	London	120
Guelph	Mississauga	71
Guelph	Oakville	70
Guelph	Windsor	297
Hamilton	Toronto	69
Hamilton	Woodstock	79
Hamilton	Ottawa	517
Hamilton	Kingston	354
Hamilton	London	128
Hamilton	Mississauga	48
Hamilton	Oakville	33
Hamilton	Windsor	306
Toronto	Woodstock	142
Toronto	Ottawa	449
Toronto	Kingston	242
Toronto	London	192
Toronto	Mississauga	28
Toronto	Oakville	38
Toronto	Windsor	369
Woodstock	Ottawa	576
Woodstock	Kingston	391
Woodstock	London	55
Woodstock	Mississauga	118
Woodstock	Oakville	118
Woodstock	Windsor	232
Ottawa	Kingston	196
Ottawa	London	627
Ottawa	Mississauga	470
Ottawa	Oakville	485
Ottawa	Windsor	804
Kingston	London	441
Kingston	Mississauga	284
Kingston	Oakville	298
Kingston	Windsor	618
London	Mississauga	172
London	Oakville	157
London	Windsor	192
Mississauga	Oakville	22
Mississauga	Windsor	349
Oakville	Windsor	333

In this program the file domain.hpp defines the classes necessary to represent the parcels, trucks, and fleet of trucks. These classes are parcels, trucks, and fleet. A fleet keeps track of the trucks and also can report on statistics about the trucks such as average distance travelled and average capacity used of all the trucks in this fleet.

The file schedule.hpp defines three different scheduling algorithms to be implemented. These algorithms take the parcels and trucks that a user uploads and then sorts them into priority queues to be used for loading parcels onto trucks. The three different scheduling algorithms are implemented as follows:

1. The random scheduler randomScheduler implements a scheduling algorithm that will load parcels onto trucks by randomly picking a truck to load a given parcel onto until all parcels have been loaded.
2. The mostparcelScheduler implements a scheduling algorithm that will order the parcels to be loaded onto trucks in a priority sequence where parcels with smaller volumes are loaded first. This priority queue of parcels is loaded onto trucks one by one. A truck is chosen based on the priority queue of available trucks. The potential trucks are ordered based on largest capacity, and then we take a subset of of these trucks who have enough available space to fit the parcel. Priority is given to trucks who have the parcel destination city already in their route. This algorithm generates a route that packs the most parcels onto the least trucks by prioritizing smaller parcels and larger trucks.
3. The shortrouteScheduler implements a scheduling algorithm that will order the parcels to be loaded onto trucks in a priority sequence that loads the parcels with the same destination sequentially. This priority queue of parcels is loaded onto trucks one by one. A truck is chosen based on the priority queue of available trucks. The potential trucks are ordered based on largest capacity, and then we take a subset of this list of trucks who have enough available space to fit the parcel. Priority is given to trucks who have the parcel destination city already in their route. This algorithm generates a route that packs trucks with parcels all headed to the same destination. This will result in shorter routes.

The program main.cpp runs these various scheduling algorithms for the given parcels and trucks and outputs performance statistics regarding the average and standard deviation for free volume in loaded trucks, the average and standard deviation for the capacity used in loaded trucks, and the average and standard deviation for the distance travelled for loaded trucks in this fleet. An example of the performance statistics written to the route-stats.csv file for the input data described above is as follows:

Scheduler	Free Volume in Used Trucks (cm^3)	Average Capacity Used (%)	Std Dev Average Capacity	Avg Distance (km)	Std Dev Distance
Random Parcels	25	84.5357	+-12.5665	536.8	+-132.59
Most Parcels	46	75.3034	+-13.8122	536.8	+-396.749
Short Route	16	91.4323	+-6.7635	275	+-224.113