README.md

Network-Design

Repository for the implementation of a solution to the modified Two Echelon Vehicle Routing Problem with Transfer Optimizations.

Problem Statement

The classical Vehicle Routing Problem (VRP) is a combinatorial optimization problem that is NP-hard. It consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers.
The Two Echelon Vehicle Routing Problem (2E-VRP), is an advanced version of the classical Vehicle Routing Problem. It involves the delivery of goods to customers in a two tier fashion - first, from the main depot to certain satellite locations (warehouses) and second, from these warehouses to the customers. Separate fleets are used for the two tiers. Hence, it at least as hard as the classical VRP.
The problem, being targeted in the repository, is a modified version of the two echelon VRP. In the current statement, instead of warehouses, we have car-parks, where the goods can only be transferred from the first level vehicle to the second level vehicle. Hence, no storage is possible and transfer optimization is needed to be done.

Solution Method

The solution method that is followed for the current problem, makes use of a local search heuristic on a solution based improvement method.
The initial solution is obtained by applying the Clarke and Wright's Savings Algorithm on the two levels (or tiers) separately. This initial solution is then, repeatedly improved using the local search heuristic, involving the move, iterated swap and segment exchange operators.
Once, the local optimum has been obtained, the solution space, is then, explored by a perturb and shake operator on the obtained best Solution.
This process of improvement and perturbation is iterated several times, so as to yield the global best solution.

Structure

The directory provides a Java package for running the code.
The package is placed inside the src/main folder. The directory structure is as follows:

Network-Design  
├───bin  
│   ├───test  
│   └───vrp  
├───files  
│   ├───input  
│   ├───interface  
│   ├───output  
└───src  
    ├───main  
    │   └───vrp  
    └───test  

The source code is placed in main/vrp/ and the compiled .CLASS files are placed in bin/vrp/.

Dependencies

The package uses Java as the primary language for algorithms, and Python 3 as a secondary language for data visualization and test case generation.
The python library dependencies are mentioned in the requirements.txt file.
Also the project makes use of making a server side application that can be called from a http call using a JSON object. For the integration of JSON with Java, JSON Simple has been used. The jar file can be downloaded from here and added in the external JARs option in Eclipse.

Setup

1. Install the required python 3 libraries using:
   py -m pip install -r requirements.txt
2. Download the json-simple-1.1.1.jar from the source mentioned in the previous section and add the path of JAR file to the Build Path of the Eclpise Project.
3. Run the start.sh file in the top directory.
4. A random test case can be generated using the following command:
   py generate.py [On Windows]
   python generate.py [On Linux]
5. The generated input file is located at (workspace)/files/input/custom/generated.dat by default.

Usage

1. Providing the filename of the input file as an argument to the main() method of the Main Class, the project is run on Eclipse IDE.
2. The output solution is sent to ./files/output/solution.txt by default.
3. The resulting final solution can be visualaized using the following command:
   py display.py [On Windows]
   python display.py [On Linux]

Hyper Parameters

1. numUselessIterations : Number of no-improvement iterations before a restart
2. numRestarts : Number of random restarts
3. probRemoval : The probability of choosing either of perturb insertion operators

JSON Format Structure

The input JSON object to the java code must be in the following format:

{
        "l1cap" : 1000,
        "l2cap" : 200,
        "numVehicles1" : 10
        "coordinates" : [
                // Array of coordinate objects
                {
                        "id" : 1, "x" : 1, "y" : 2,
                },
                ...
        ],
        "customers" : [
                // Array of customer objects
                {
                        "id" : 2,
                        "demand" : 100,
                },
                ...
        ],
}

Assumptions

1. The goods are to be delivered in a to tier hierarchy. First, the level 1 vehicles shall take the goods to certain carparks, where the goods shall be transferred into the smaller vehicles. These smaller vehicles shall deliver the goods to the customers.
2. The capacity of level 1 vehicles is more than the capacity of level 2 vehicles.
3. The number of vehicles available at level 1 and level 2 are known beforehand.

To-do

• Improve perturbation function so as to explore the solution space.
• Restart from original solution, when no improvement observed.
• Check Iterated Swap Procedure, without the dynamically changing solution with every iteration.
• Perturbation Algorithm :- Satellite Swap and Worst Removal Operations to be implemented first. Regret Insertion also done.
• Modify the current problem such that the constraints match the classical Two Echelon VRP, and check the results.
• Check for larger test cases - Appropriate input format for the test cases of set 2, set 3, etc.
• Change nodesDistances[][] to double[][].
• Python Code to generate random test cases and plot them for visualization.
• Implement Route Removal and Route Redistribution for perturbation.
• Local Search for first level vehicles.
• Making new choices for the location of first level vehicles.
• Separate the functionalities of generate.py and nodecluster.py
• Implement restrictions on the number of vehicles available in a carparks.
• Separate out the test case files in a separate src/test/ folder
• Code clean-up for private and public access modifiers of functions.
• Java-Python Integration for calling cluster functions OR Use clustering methods from Java.