Lagrangian multipliers and to decompose the original problem into some simple subproblems. Given the Lagrangian multipliers, the relaxed problem provides a lower bound for the optimal primal objective value in a minimization problem. Generally, by means of updating the Lagrangian multipliers effectively, the lower bound can be improved gradually.
5.1. Lagrangian decomposition and coordination method
Applying a Lagrangian decomposition to the model, we choose to relax coupling constraints (2), (4), (5), and (15). This leads to a decomposition with as many independent subproblems as items.
Using non-negative Lagrange multipliers , , , we can generate the following relaxed problem (LRP). Let the set of items, the set of periods and the set of locations be I, T, L, respectively.
(LRP)
Minimize ( , , , , ), with
( , , , , ) = ∑ ∑ + ∑ ∑ ∑(( + ) + ( + − ) + ℎ ) −1
=1 =1 =1 =1 =1
+ ∑∑( ) + ∑ ∑( − 1) + ∑ ∑ ∑( − 1) + ∑∑ ∑(
=1 =1 =1 =1 =1 =1 =1 =1 =1 =1
+ ∑ ∑ (∑ − )
=1 =1 =1
subject to constraints (6), (10), (11), (16), (19).
The problem (LRP) can be decomposed into independent subproblems, 1, 2,⋯, | |, as follows.
( )
Minimize ( , , , , ), with
(20)
= ∑
=1
∑ ∑(( + ) + ( + −1 − ) + ℎ ) + ∑( )
=1 =1 =1
+ ∑∑ + ∑∑ + ∑ (21)
=1 =1 =1 =1 =1
subject to constraints (6), (10), (11), (16), (19).
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Problem is equivalent to the single item production planning and layout problem. This problem is a mixed integer linear programming problem and can be solved optimally using standard optimization software.
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