/Profecta

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FinalYearProject

Truck details: Truck

DATASETS:

  1. California distance chart
  2. Supply Chain Shipment Pricing Data
  • Freight cost, mode, weight, dates, vendor, manufacture site
  • SAfrica, Africa
  1. E Commerce Dataset Analysis
  • Brazil
  • Big dataset n comprehensive
  1. Supply chain based
  2. Freight data
  3. Route
  4. Route
  5. Europe Dataset
  6. Route-> separate source and destination nodes
  7. https://archive-beta.ics.uci.edu/ml/datasets/3d+road+network+north+jutland+denmark
  8. https://archive-beta.ics.uci.edu/ml/datasets/vehicle+routing+and+scheduling+problems
  9. https://networkrepository.com/road.php
  10. https://www.cs.utah.edu/~lifeifei/SpatialDataset.htm
  11. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CUWWYJ
  12. https://snap.stanford.edu/data/index.html#road

Interesting links:

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)

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
  1. Identify more factors
  2. Find equation

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