FinalYearProject

DATASETS:

0. California distance chart
1. Supply Chain Shipment Pricing Data

• Freight cost, mode, weight, dates, vendor, manufacture site
• SAfrica, Africa

2. E Commerce Dataset Analysis

• Brazil
• Big dataset n comprehensive

3. Supply chain based
4. Freight data
5. Route
6. Route
7. Europe Dataset
8. Route-> separate source and destination nodes
9. https://archive-beta.ics.uci.edu/ml/datasets/3d+road+network+north+jutland+denmark
10. https://archive-beta.ics.uci.edu/ml/datasets/vehicle+routing+and+scheduling+problems
11. https://networkrepository.com/road.php
12. https://www.cs.utah.edu/~lifeifei/SpatialDataset.htm
13. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CUWWYJ
14. https://snap.stanford.edu/data/index.html#road
15. https://archive-beta.ics.uci.edu/ml/datasets/cargo+2000+freight+tracking+and+tracing

Interesting links:

• Routing IN R
• ARCMAP
  • Checkout ARCMAP
• GOOGLE OR

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Algorithms

• VRP algoss
• Genetic algo(GA).
• Tabu search(TS).
• Simulated Annealing(SA).
• Ant colony system.
• Particle Swarm Optimisation
• RPS - DRP (distribution req planning)
• RPS - MAS
• Dynamic programming approaches
• SVRP
• DQN (Deep Q Network - combination of RL and CNN)
• OCaPi
• Grey Wolf Optimizer (CGWO)

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IDEA:

• INPUT: SRC, DEST, Depots (list)
• STANDARD DATA: Google API (place -> lat, long)
  • Depot - frequency of item deliveries; traffic
  • Cost - freight weight (balancing load) and transportation cost
  • Distance - time => cost factor 1
  • Delivery urgency - priority based (Time window penalty cost)
  • Fuel efficiency, Carbon emissions
  • Freshness degradation (for cold logistics alone)
  • Road, traffic, weather
  • Vehicle conditions
  • Driving hours and rest
  • (Fleet management)
  • Energy use
  • Safety and security
  • Economic Health
  • Ecosystem impacts
• Factors:

  • Depot: Frequencies of item-calculated from dataset using DepotId Used for quick transport of goods Frequent depot-> delivery started as and when the orders are placed Less frequent depot-> delivery process must wait until some amt -> reached.

  • Cost:

  • Distance and time: Optimal path -> less distance and less time.

  • Delivery urgency: If an order needs urgent delivery(Ex: amazon prime subscribers vs normal users), routing must be planned accordingly. cost -> increase

  • Fuel efficiency, carbon emissions: (is there any cnxn bw traffic condition and fuel efficiency?) Suggest route which decrease the carbon emissions. and same with fuel efficiency.

  • Freshness Degradation: When the order is veggies,.. consider the time

  • Road, Traffic and weather: Main problem is we have to get real time data for predicting the current traffics, weather and road condition.

  • Vehicle conditions: Depends on the size of vehicle. if it is a big truck, we can only suggest highways....

  • Driving hours and rest: Consider rest time

  • Safety and security: may be we can consider the safety of the goods ( type of goods) Packaging cost

  • Economic health:

  • Ecosystem impacts: similiar to carbon emissions

  • Capacity of the vehicle

IP: Set of orders and also factors

OP: Total cost after considering the factors and Optimal path

(Hierarchy)

• OUTPUT: Routes + time factor => best k solutions

• METHODOLOGY: Equation based on all factors - try to optimise the score

• Challenges

• IDEA: diverse adaption for any country based on standard data

• IDEA: SPARK DISTRIBUTED processing

1. Finalise mode: Air route, ship route, road route ; or mixed mode
2. Finalise scope: India or US or specific country; worldwide, intercontinent

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1. Identify more factors
2. Find equation

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To decide:

1. Route optimise --- > existing route => optimise

2. Graph dataset =>

3. Lat and Longitude

4. Distributed data.... spark

5. Maximise delivery Minimise time Minimise cost

-- > dataset depend