Add more information about algorithms

docs/src/SUMMARY.md

@@ -70,4 +70,5 @@
     * [Development practices](internals/development/best-practices.md)
     * [Testing](internals/development/testing.md)
   * [Algorithms](internals/algorithms/index.md)
+    * [Heuristics](internals/algorithms/heuristics.md)
     * [Rosomaxa](internals/algorithms/rosomaxa.md)

docs/src/internals/algorithms/heuristics.md

@@ -0,0 +1,80 @@
+# Heuristics
+
+This page provides some high level overview of general heuristic used to solve VRP. Some information can be found on
+vrp-core crate's [documentation page](https://docs.rs/vrp-core/latest/vrp_core/solver/index.html)
+
+
+## Starting from scratch: constructive heuristics
+
+To build initial solutions to start with, the solver internally can use different built-in constructive heuristics, such as:
+
+- variation of Clark & Wright Savings algorithm
+- regret insertion
+- insertion with blinks
+- nearest neighbor
+- random insertions
+- etc.
+
+[Related documentation](https://docs.rs/vrp-core/latest/vrp_core/construction/heuristics/index.html)
+
+Typically, the solver builds four initial solutions and then they are memorized as initial population state by the one
+of the population algorithms:
+
+- `greedy`: only best solution is kept
+- `elitism`: N best solutions are kept which are met to some diversification criteria
+- `rosomaxa`: a custom population-based algorithm which focuses on improving exploration/exploitation ratio.
+
+The latter is default, however, others can be used if amount of available CPU is low.
+
+[Related documentation](https://docs.rs/rosomaxa/latest/rosomaxa/population/index.html)
+
+## Searching for better solution: meta heuristics
+
+The goal of metaheuristic (or just heuristic for simplicity) is to refine one (or many) of the known solutions. Currently available heuristics:
+
+- `ruin and recreate` principle: ruin parts of solution and recreates them. Key ideas:
+  - use multiple ruin/recreate methods and combine them differently
+  - make a larger moves in solution space
+- `local search`: use different local search operators. The main difference from R&R:
+  - try to avoid making a big steps in solution space
+  - target to improve specific aspects in solution
+- `explorative heuristics`: these can be seen as generators for explorative moves in solutions space:
+  - `redistribute search`: removes jobs from specific route and prevents their insertion back to it
+  - `infeasible search`: allows constraint violations to explore infeasible solutions space. It has recovery step
+     to move back to feasible space.
+- `decomposition search`: splits existing solution into multiple smaller ones (e.g. not more than 2-4 routes) and tries
+   to improve them in isolation. Typically, it uses all heuristics just mentioned.
+
+Each heuristic accept one of solution from population (not necessary the best known one) and tries to improve it (or diversify).
+During one of refinement iterations, many solutions are picked at the same time and many heuristics are called then. This step
+is called a `generation`.
+
+[Related documentation](https://docs.rs/vrp-core/latest/vrp_core/solver/search/index.html)
+
+
+## What heuristic to pick: hyper heuristic
+
+As the solver is not limited to one heuristic, there is a problem: what is the best strategy to pick one of the pre-defined
+heuristics at given search state? To solve that problem, there are two heuristics are available:
+
+- `static selective`: associate with every heuristic some probability weight and use it to decide which one to pick next
+- `dynamic selective`: try to learn probability dynamically based on search progression over time.
+
+The latter is used by default.
+
+[Related documentation](https://docs.rs/rosomaxa/latest/rosomaxa/hyper/index.html)
+
+
+## When to stop: termination criteria
+
+The search is terminated and the best known solution is returned when some termination criteria is met. The following
+termination criteria are supported:
+
+- `time`: stop after some specified amount of seconds
+- `generation`: stop after some specified amount of generations
+- `coefficient variation`: stop if there is no `significant` improvement in specific time or amount of generations
+- `user interrupted` from command line, e.g. by pressing Ctrl + C
+
+Interruption when building initial solutions is supported. Default is 300 seconds or 3000 generations max.
+
+[Related documentation](https://docs.rs/rosomaxa/latest/rosomaxa/termination/index.html)

docs/src/internals/algorithms/index.md

@@ -1,3 +1,14 @@
 # Algorithms
 
 This chapter describes some used algorithms (WIP).
+
+
+## References
+
+TODO: An incomplete list of most important references:
+
+- Clarke, G & Wright, JW 1964. Scheduling of vehicles from a Central Depot to a Number of the Delivery Point. Operations Research, 12 (4): 568-581
+- Schrimpf, G., Schneider, K., Stamm-Wilbrandt, H., Dueck, V.: Record Breaking Optimization Results Using the Ruin and Recreate Principle. J. of Computational Physics 159 (2000) 139–171
+- Jan Christiaens, Greet Vanden Berghe: Slack Induction by String Removals for Vehicle Routing Problems
+- Thibaut Vidal: Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
+- Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband and Zheng Wen: A Tutorial on Thompson Sampling https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf

docs/src/internals/algorithms/rosomaxa.md

@@ -56,6 +56,15 @@ This helps to address [exploration-exploitation dilemma](https://en.wikipedia.or
 in applying a strategy of picking heuristics.
 
 
+### Additional used techniques
+
+TODO: describe additional explorative techniques:
+
+- tabu list usage in ruin methods
+- alternative objectives manipulation
+- ..
+
+
 ## Further research
 
 * experiment with different solution characteristics