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∑∑ ∙ ≤ ∀

(15)










=1 =1
Constraints (15) are production capacity restrictions. The constraints ensure that the number of each item produced multiplied by the variable capacity of each item did not exceed any key resource during the planning horizon.














∑ ≤

∀ , ∀

(16)













=1
Constraints (16) are set up constraints; the constraints put a limit on production during each period. The value of M is fixed in terms of both production capacity and item demands.

, , , , ∈ {0,1} ∀ , ∀ , ∀

(17)






The model described by (1) to (17) is a mixed integer programming model. Similar to most capacitated lot-sizing problems, the problem for the real-world data is very difficult to solve.

A simpler model can be established by substituting = + −1 − into (9) leads ≥ 0

∀ , ∀ , ∀ which is clearly satisfied. Then, constraints (9) are eliminated. Constraints (6) can be rewritten as

∑ =1 + −1 − = ∀ , ∀ . Similarly, constraints (3) and (12) are satisfied by (2), (9) and (10).

Constraints (17) are also satisfied since and are binary variables. Since ∈ {0,1} ∀ , ∀ , constraints (13)

and (14) are always satisfied by (10) since ≥ 0 ∀ , ∀ , ∀ . All together implies the following simplified model.
Objective function:


=
∑∑
+ ∑ ∑ ∑((
+ ) + ( + −1
− ) + ℎ ) + ∑ ∑( )
(18)


=1 =1
=1 =1 =1
=1 =1



Subject to constraints

(2), (4), (5), (6), (10), (11), (15), (16),




, , , , ∈ {0,1} ∀ , ∀ , ∀
(19)

The approaches for solving the problem are discussed in the next section.




5 LAGRANGIAN DECOMPOSITION AND COORDINATION ALGORITHM
The Lagrangian decomposition and coordination method is one of the widely used approaches for capacitated lot-sizing problem (Buschkühl et al., 2010), integrated optimization of production scheduling and distribution planning (Nishi et al., 2007), supply chain coordination (Nishi et al., 2008), multi-product newsvendor problem with supply discount (Zhang, 2010), and so on. The key idea of this method is to relax the coupling constraints through
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