add readme

README.md

@@ -1,11 +1,24 @@
-# LightBenders.jl
+# **LightBenders**.jl
 
-[![CI](https://github.com/psrenergy/LightBenders.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/psrenergy/LightBenders.jl/actions/workflows/CI.yml)
-[![codecov](https://codecov.io/gh/psrenergy/LightBenders.jl/graph/badge.svg?token=zfYd247kfX)](https://codecov.io/gh/psrenergy/LightBenders.jl)
+[![CI](https://github.com/psrenergy/**LightBenders**.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/psrenergy/**LightBenders**.jl/actions/workflows/CI.yml)
+[![codecov](https://codecov.io/gh/psrenergy/**LightBenders**.jl/graph/badge.svg?token=zfYd247kfX)](https://codecov.io/gh/psrenergy/LightBenders.jl)
 [![Aqua](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
 
 ## Introduction
 
+**LightBenders** is a Julia package that provides a flexible and efficient implementation of the two-stage Benders decomposition method. Designed for solving large-scale optimization problems, **LightBenders** is especially suited for problems where decisions are divided into a main (first-stage) problem and a set of scenario-dependent (second-stage) problems.
+
+With support for both serialized and parallel processing of second-stage scenarios, **LightBenders** allows users to efficiently tackle computationally demanding problems. By leveraging Julia's high-performance capabilities and parallel computing features, the package offers robust performance for solving a wide range of applications, including energy systems, logistics, and supply chain optimization.
+
+### Key Features
+- Generic Implementation: Fully customizable for diverse optimization models.
+- Scenario Management: Scenarios are handled either sequentially or in parallel, enabling scalability for large instances.
+- User-Friendly Interface: Seamlessly integrates with Julia's optimization ecosystem, such as JuMP.
+- Parallel Computing Support: Efficiently utilize multiple cores or distributed systems for solving second-stage problems in parallel.
+
+
+**LightBenders** is ideal for researchers and practitioners looking to adopt a modular and high-performance approach to Benders decomposition in Julia.
+
 ## Getting Started
 
 ### Installation
@@ -14,4 +27,84 @@
 julia> ] add LightBenders
 ```
 
-### Example
+### Example: Newsvendor Model
+```julia
+using LightBenders
+using JuMP
+using HiGHS
+
+## definitions
+Base.@kwdef mutable struct Inputs
+    buy_price::Real
+    sell_price::Real
+    return_price::Real
+    max_storage::Int
+    demand::Vector{<:Real}
+end
+function state_variables_builder(inputs)
+    model = Model(HiGHS.Optimizer)
+    set_silent(model)
+    sp = LightBenders.SubproblemModel(model)
+    # state variable
+    @variable(sp, 0 <= bought <= inputs.max_storage)
+    LightBenders.set_state(sp, :bought, bought)
+    return sp
+end
+function first_stage_builder(sp, inputs)
+    bought = sp[:bought]
+
+    @constraint(sp, bought <= inputs.max_storage)
+    @objective(sp, Min, bought * inputs.buy_price)
+    return sp
+end
+function second_stage_builder(sp, inputs)
+    bought = sp[:bought]
+
+    @variable(sp, dem in MOI.Parameter(0.0))
+    @variable(sp, sold >= 0)
+    @variable(sp, returned >= 0)
+    @constraint(sp, sold_dem_con, sold <= dem)
+    @constraint(sp, balance, sold + returned <= bought)
+    @objective(sp, Min, -sold * inputs.sell_price - returned * inputs.return_price)
+    return sp
+end
+function second_stage_modifier(sp, inputs, s)
+    dem = sp[:dem]
+    JuMP.set_parameter_value(dem, inputs.demand[s])
+    return nothing
+end
+
+## call LightBenders
+inputs = Inputs(5, 10, 1, 100, [10, 20, 30])
+num_scenarios = length(inputs.demand)
+policy_training_options = LightBenders.PolicyTrainingOptions(;
+    num_scenarios = num_scenarios,
+    lower_bound = -1e6,
+    implementation_strategy = LightBenders.SerialTraining(),
+    stopping_rule = LightBenders.GapWithMinimumNumberOfIterations(;
+        abstol = 1e-1,
+        min_iterations = 2,
+    ),
+    cut_strategy = LightBenders.CutStrategy.MultiCut,
+)
+policy = LightBenders.train(;
+    state_variables_builder,
+    first_stage_builder,
+    second_stage_builder,
+    second_stage_modifier,
+    inputs = inputs,
+    policy_training_options,
+)
+results = LightBenders.simulate(;
+    state_variables_builder,
+    first_stage_builder,
+    second_stage_builder,
+    second_stage_modifier,
+    inputs,
+    policy,
+    simulation_options = LightBenders.SimulationOptions(
+        policy_training_options;
+        implementation_strategy = LightBenders.BendersSerialSimulation(),
+    ),
+)
+```