Support Multiple Big-M reformulation.

README.md

@@ -172,6 +172,10 @@ The following reformulation methods are currently supported:
 
 3. [Indicator](https://jump.dev/JuMP.jl/stable/manual/constraints/#Indicator-constraints): This method reformulates each disjunct constraint into an indicator constraint with the Boolean reformulation counterpart of the Logical variable used to define the disjunct constraint.
 
+4. [MBM](https://doi.org/10.1016/j.compchemeng.2015.02.013): The multiple big-m method creates multiple M values for each disjunct constraint. The 'MBM' struct is created with the following required argument:
+
+    - `optimizer`: Optimizer to use when solving subproblems to determine M values. This is a required value.
+
 ## Release Notes
 
 Prior to `v0.4.0`, the package did not leverage the JuMP extension capabilities and was not as robust. For these earlier releases, refer to [Perez, Joshi, and Grossmann, 2023](https://arxiv.org/abs/2304.10492v1) and the following [JuliaCon 2022 Talk](https://www.youtube.com/watch?v=AMIrgTTfUkI).

src/DisjunctiveProgramming.jl

@@ -6,7 +6,6 @@ Reexport.@reexport using JuMP
 
 # Use Meta for metaprogramming
 using Base.Meta
-
 # Create aliases
 import JuMP.MOI as _MOI
 import JuMP.MOIU.CleverDicts as _MOIUC
@@ -22,6 +21,7 @@ include("macros.jl")
 include("reformulate.jl")
 include("bigm.jl")
 include("hull.jl")
+include("mbm.jl")
 include("indicator.jl")
 include("print.jl")
 

src/constraints.jl

@@ -23,6 +23,29 @@ _vec_to_scalar_set(::_MOIExactly) = _MOI.EqualTo
 _vec_to_scalar_set(::_MOIAtLeast) = _MOI.GreaterThan
 _vec_to_scalar_set(::_MOIAtMost) = _MOI.LessThan
 
+#helper function to create variables
+# Helper function to copy variable properties from an existing variable
+function _copy_variable(
+    target_model::JuMP.AbstractModel,
+    original_var::JuMP.AbstractVariableRef,
+    )
+    new_var = JuMP.@variable(target_model, base_name = JuMP.name(original_var))
+
+    JuMP.has_lower_bound(original_var) && JuMP.set_lower_bound(new_var, JuMP.lower_bound(original_var))
+    JuMP.has_upper_bound(original_var) && JuMP.set_upper_bound(new_var, JuMP.upper_bound(original_var))
+    JuMP.has_start_value(original_var) && JuMP.set_start_value(new_var, JuMP.start_value(original_var))
+    JuMP.is_integer(original_var) && JuMP.set_integer(new_var)
+    JuMP.is_binary(original_var) && JuMP.set_binary(new_var)
+
+    if JuMP.is_fixed(original_var)
+        JuMP.fix(new_var, JuMP.fix_value(original_var); force=true)
+    end
+
+    return new_var
+end
+
+
+
 ################################################################################
 #                         BOILERPLATE EXTENSION METHODS
 ################################################################################

src/datatypes.jl

@@ -367,6 +367,25 @@ struct BigM{T} <: AbstractReformulationMethod
     end
 end
 
+"""
+    MBM{O} <: AbstractReformulationMethod
+
+A type for using the multiple big-M reformulation approach for disjunctive constraints.
+
+**Fields**
+- 'optimizer::O': Optimizer to use when solving mini-models (required).
+"""
+struct MBM{O} <: AbstractReformulationMethod
+    optimizer::O
+
+    # Constructor with optimizer (required)
+    function MBM(optimizer::O) where {O}
+        new{O}(optimizer)
+    end
+end
+
+
+
 """
     Hull{T} <: AbstractReformulationMethod