1 K Convexity and The Optimality of the (s, S) Policy.
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Transcript of 1 K Convexity and The Optimality of the (s, S) Policy.
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KKConvexity Convexity
and and
The Optimality of the (The Optimality of the (ss, , SS) Policy ) Policy
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OutlineOutline
optimal inventory policies for multi-period problems (s, S) policy
K convexity
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General Idea of Solving General Idea of Solving a Two-Period Base-Stock Problema Two-Period Base-Stock Problem
Di: the random demand of period i; i.i.d.
x(): inventory on hand at period () before ordering
y(): inventory on hand at period () after ordering
x(), y(): real numbers; X(), Y(): random variables
D1
x1
D2
X2 = y1 D1
y1 Y2
*1 1 1 1 1 1 1 1 1 2 2( , ) ( ) ( ) ( ) [ ( )]f x y c y x hE y D E D y E f X
2 2
*2 2 2 2 2( ) min ( , )
y xf x f x y
2 2 2 2 2 2 2 2 2( , ) ( ) ( ) ( )f x y c y x hE y D E D y
1 1
*1 1 1 1 1( ) min ( , )
y xf x f x y
discounted factor , if applicable
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General Idea of Solving General Idea of Solving a Two-Period Base-Stock Problema Two-Period Base-Stock Problem
problem: to solve need to calculate need to have the solution of
for every real number x2
D2D1
x1
y1
X2 = y1 D1
Y2
2 2
*2 2 2 2 2( ) min ( , )
y xf x f x y
*1 1 1 1 1 1 1 1 1 2 2( , ) ( ) ( ) ( ) [ ( )]f x y c y x hE y D E D y E f X
2 2 2 2 2 2 2 2 2( , ) ( ) ( ) ( )f x y c y x hE y D E D y
1 1
*1 1 1 1 1( ) min ( , )
y xf x f x y
*1 1( )f x
*2 2[ ( )]E f X
*2 2( )f x
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General Idea of Solving General Idea of Solving a Two-Period Base-Stock Problema Two-Period Base-Stock Problem
convexity optimality of base-stock policy
convexity convex
convexity convex in y1
convexity convex in y1
D2D1
x1
y1
X2 = y1 D1
Y2
2 2
*2 2 2 2 2( ) min ( , )
y xf x f x y
*1 1 1 1 1 1 1 1 1 2 2( , ) ( ) ( ) ( ) [ ( )]f x y c y x hE y D E D y E f X
2 2 2 2 2 2 2 2 2( , ) ( ) ( ) ( )f x y c y x hE y D E D y
1 1
*1 1 1 1 1( ) min ( , )
y xf x f x y
*2 1 1[ ( )]E f y D
*2 2( )f x
*1 1 1 1 1 2 1 1( ) ( ) [ ( )]cy hE y D E D y E f y D
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General ApproachGeneral Approach
FP: functional property of cost-to-go function fn of period n SP: structural property of inventory policy Sn of period n what FP of fn leads to the optimality of the (s, S) policy? How does the structural property of the (s, S) policy preserve
the FP of fn?
period Nperiod N-1period N-2period 2period 1 …
FP of fN
SP of SN
FP of fN-1
SP of SN-1
FP of fN-2
SP of SN-2
FP of f2
SP of S2
FP of f1
SP of S1
…
attainment preservation
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Optimality of Base-Stock PolicyOptimality of Base-Stock Policy
period Nperiod N-1period N-2period 2period 1 …
convex fN
optimality of BSP
convex fN-1
optimality of BSP
convex fN-2
optimality of BSP
convex f2
optimality of BSP
convex f1
optimality of BSP
…
attainment preservation
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Functional Properties of G
for the Optimality of the (s, S) Policy
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A Single-Period Problem A Single-Period Problem with Fixed-Costwith Fixed-Cost
convex G(y) function: optimality of (s, S) policy G0(x) = actual expected cost of the period, including fixed and
variable ordering costs G0(x) not necessarily convex even if G(y) being so convex fn insufficient to ensure optimal (s, S) in all periods what should the sufficient conditions be?
G(y)
by
ea s S
K
x
G0(x)
s S
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Another Example Another Example on the Insufficiency of Convexity in Multiple Periodson the Insufficiency of Convexity in Multiple Periods
convex Gt(y) c = $1.5, K = $6 (s, S) policy with s = 8, S = 10 no longer convex neither ft(x)
(10, 30)
(8, 36)
(0, 60) (20, 60)
Gt(y)
y
y
min{ ( ),
min[ ( )]}t
ty x
G x
K G y
(10, 30)
(8, 36)
(0, 36)
(20, 60)
( )
min{ ( ),
min[ ( )]}
t
t
ty x
f x cx
G x
K G y
y(10, 15)
(8, 24)(0, 36) (20, 30)
min{ ( ), min[ ( )]}t ty x
G x K G y
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s S
Feeling for the Functional Property Feeling for the Functional Property for the Optimality of (for the Optimality of (ss, , SS) Policy) Policy
Is the (s, S) policy optimal for this G?
YesK
K
y
G(y)
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ld eba
Feeling for the Functional Property Feeling for the Functional Property for the Optimality of (for the Optimality of (ss, , SS) Policy) Policy
Are the (s, S) policies optimal for these G?
y
KKG(y)
No No
b da le
y
KK
G(y)
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13as S
Feeling for the Functional Property Feeling for the Functional Property for the Optimality of (for the Optimality of (ss, , SS) Policy) Policy
key factors: the relative positions and magnitudes of the minima
Is the (s, S) policy optimal for this G?
y
K
G(y)
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Sufficient Conditions Sufficient Conditions for the Optimality of (for the Optimality of (ss, , SS) Policy) Policy
set S to be the global minimum of G(y) set s = min{u: G(u) = K+G(S)} sufficient conditions (***) to hold simultaneously
(1) for s y S: G(y) K+G(S); (2) for any local minimum a of G such that S < a, for S y a: G(y)
K+G(a)
no condition on y < s (though by construction G(y) K+G(S)) properties of these conditions
sufficient for a single period not preserving by itself functions with additional properties
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fn satisfying condition ***
What is needed?What is needed?
optimality of (s, S) policy in period n
fn satisfying condition
*** plus an additional property
optimality of (s, S) policy in
period n
fn-1 with all the desirable properties
additional property: K-
convexity
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KConvexity
and
KConvex Functions
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Definitions Definitions of of KK-Convex Functions-Convex Functions
(Definition 8.2.1.) for any 0 < < 1, x y,
f(x + (1-)y) f(x) + (1-)(f(y) + K)
(Definition 8.2.2.) for any 0 < a and 0 < b,
or, for any a b c,
(differentiable function) for any x y,
f(x) + f '(x)(y-x) f(y) + K
Kaxfbxfxfxfba )())()(()(
bcKbfcf
abafbf
)()()()(
Interpretation: x y, function f lies
below f(x) and f(y)+K for all points
on (x, y)
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Properties Properties of of KK-Convex Functions-Convex Functions
possibly discontinuous
no positive jump, nor too big a negative jump
satisfying sufficient conditions ***
(a)
K
(b)
K
(c)
K
(a): A K-convex function; (b) and (c) non-K-convex functions
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Properties Properties of of KK-Convex Functions-Convex Functions
(a). A convex function is 0-convex.
(b). If K1 K2, a K1-convex function is K2-convex.
(c). If f is K-convex and c > 0, then cf is k-convex for all k cK.
(d). If f is K1-convex and g is K2-convex, then f+g is (K1+K2)-convex.
(e). If f is K-convex and c is a constant, then f+c is K-convex
(f). If f is K-convex and c is a constant, then h where h(x) = f(x+c) is K-convex.
(g). If f is K-convex and D is random, then h where h(x) = E[f(x-D)] is K-convex.
(h). If f is K-convex, x < y, and f(x) = f(y) + K, then for any z [x, y], f(z) f(y)+K. f crosses f(y) + K only once (from above) in (-, y)
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KK-Convexity Being Sufficient, -Convexity Being Sufficient, not Necessary, for the Optimality of (not Necessary, for the Optimality of (ss, ,
SS)) non K-convex functions with optimal (s, S) policy
K
K
y
G(y)
K
K
y
G(y)
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Technical Proof
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Results and ProofsResults and Proofs
assumption: h+ 0 and vT is K-convex
conclusion: optimal (s, S) policy for all periods (possible with different (s, S)-values)
dynamics of DP: Gt(y) = cy + hE(yD)+ + E(Dy)+ + E[ft+1(yD)]
approach ft+1 K-convex Gt(y) K-convex (Lemma 8.3.1)
Gt K-convex an (s, S) policy optimal (Lemma 8.3.2)
Gt K-convex K-convex (Lemma 8.3.3)
K-convex ft K-convex desirable result (Theorem 8.3.4)
..
,for
),(
),()(min),(min)(*
wo
sx
xG
SGKyGKxGxG
t
tt
xytt
)]([min),(min)( yGKxGcxxf t
xytt
)(* xGt
)(* xGt