Update README.md

README.md

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 [![CI](https://github.com/Astronomax/vrptw-powerful-route-minimization-heuristic/actions/workflows/test.yml/badge.svg)](https://github.com/Astronomax/vrptw-powerful-route-minimization-heuristic/actions/workflows/test.yml)
-# vrptw-powerful-route-minimization-heuristic
+
+# vrptw-powerful-route-minimization-heuristic
+
+Efficient and fast implementation of the algorithm described in the following articles:
+* A powerful route minimization heuristic for the vehicle routing problem with time windows, Operations Research Letters, Volume 37, Issue 5, 2009,
+Pages 333-338 [[link]](https://doi.org/10.1016/j.orl.2009.04.006)
+* A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows, Computers & Operations Research, Volume 37, Issue 4, 2010, Pages 724-737 [[link]](https://doi.org/10.1016/j.cor.2009.06.022)
+
+> The vehicle routing problem with time windows (VRPTW) is one of the most important and widely studied problems in the operations research. The objective of the
+VRPTW is to minimize the number of routes (primary objective) and, in case of ties, the
+total travel distance (secondary objective). Given the hierarchical objective, most of the
+recent and best heuristics for the VRPTW use a two-stage approach where the number
+of routes is minimized in the first stage and the total distance is then minimized in the
+second stage. It has also been shown that minimizing the number of routes is sometimes
+the most time consuming and challenging part of solving VRPTWs.
+https://www.sintef.no/globalassets/project/vip08/abstract_nagata.pdf
+
+This algorithm does exactly that: it minimizes the primary objective.
+
+Various implementations that minimize the secondary objective can be attached to it. For example, the algorithm described in the following:
+* Edge assembly‐based memetic algorithm for the capacitated vehicle routing problem, Networks, Volume 54, 2009 [[link]](https://doi.org/10.1002/net.20333)
+
+Its implementation may also appear in this repository at some point, although this is not so interesting.
+
+## Benchmark Problem Sets
+
+* [Solomon's problem sets] (https://www.sintef.no/projectweb/top/vrptw/solomon-benchmark/) (25, 50, and 100 customers)
+* [Gehring & Homberger's extended benchmark] (http://www.sintef.no/Projectweb/TOP/VRPTW/Homberger-benchmark/) (200, 400, 600, 800, and 1000 customers)
+
+## Usage
+
+This implementation is just a simple command line utility. 
+But it's also pretty easy to borrow and use as a library in your own project. For example, to use it in conjunction with your own implementation of the algorithm that minimizes the total travel distance (secondary objective).
+
+Before running the cmake build process, you need to load some external submodules (for now these are only public Tarantool helper repositories). Run the following:
+
+```console
+$ git submodule update --init --recursive
+```
+
+After that, build in the standard way:
+
+```console
+$ mkdir build && cd build && cmake .. -DCMAKE_BUILD_TYPE=RelWithDebInfo && make && cd ..
+```
+
+Then you can run the utility:
+```console
+$ ./build/routes --help
+Usage: ./debug-build/routes <file1> <file2> [<options>]
+file1 - problem statement, file2 - where to output the solution
+
+Options:
+  --beta_correction       - Enables beta-correction mechanism.
+  --log_level=<option>    - Log level: none, normal, verbose.
+  --n_near=<value>        - Sets the preferred n_near.
+  --k_max=<value>         - Sets the preferred k_max.
+  --t_max=<value>         - Sets the preferred t_max (in secs).
+  --i_rand=<value>        - Sets the preferred i_rand.
+  --lower_bound=<value>   - Sets the preferred lower_bound
+$ ./build/routes GehringHomberger1000/C1_10_1.TXT C1_10_1.sol --lower_bound 100 --t_max 120
+```
+After completion, the current directory will contain a file with the solution, the name of which you specified when starting. In this example it is "C1_10_1.sol".