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1 OR II GSLM 52800

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2 Outline introduction to discrete-time Markov Chain introduction to discrete-time Markov Chain problem statement long-term average cost pre unit time solving the MDP by linear programming

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3 States of a Machine StateCondition 0Good as new 1Operable – minor deterioration 2Operable – major deterioration 3Inoperable – output of unacceptable quality

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4 Transition of States State /81/16 103/41/8 2001/

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5 Possible Actions DecisionActionRelevant States 1Do nothing0, 1, 2 2 Overhaul (return to state 1) 2 3 Replace (return to state 0) 1, 2, 3

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6 Problem adopting different collections of actions leading to different long-term average cost per unit time problem: to find the policy that minimizes the long-term average cost per unit time

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7 Costs of Problem cost of defective items state 0: 0; state 1: 1000; state 2: 3000 cost of replacing the machine = 4000 cost of losing production in machine replacement = 2000 cost of overhauling (at state 2) = 2000

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8 Policy R d : Always Replace When State 0 half of the time at state 0, with cost 0 half of the time at other states, all with cost 6000, because of machine replacement average cost per unit time = /16 7/ /16

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9 Long-Term Average Cost of a Positive, Irreducible Discrete-time Markov Chain a positive, irreducible discrete-time Markov chain with M+1 states, 0, …, M only M of the balance eqt plus the normalization eqt

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10 Policy R a : Replace at Failure but Otherwise Do Nothing /16 7/8 1 1/2 1 1/16 3/4 1/8 1/2

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11 Policy R b : Replace in State 3, and Overhaul in State /16 7/ /16 3/4 1/8 1

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12 Policy R c : Replace in States 2 and /16 7/ /16 3/4 1/8 1 1

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13 Problem R b, i.e., replacing in State 3 and overhauling in State 2 in this case the minimum-cost policy is R b, i.e., replacing in State 3 and overhauling in State 2 question: Is there any efficient way to find the minimum cost policy if there are many states and different types of actions? impossible to check all possible cases

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14 Linear Programming Approach for an MDP let D ik be the probability of adopting decision k at state i i be the stationary probability of state i y ik = P(state i and decision k) C ik = the cost of adopting decision k at state i

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15 Linear Programming Approach for an MDP

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16 Linear Programming Approach for an MDP at optimal, D ik = 0 or 1, i.e., a deterministic policy is used

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17 Linear Programming Approach for an MDP actions possibly to adopt at state 0: do nothing (i.e., k = 1) 1: do nothing or replace (i.e., k = 1 or 3) 2: do nothing, overhaul, or replace (i.e., k = 1, 2, or 3) 3: replace (i.e., k = 3) variables: y 01, y 11, y 13, y 21, y 22, y 23, and y 33

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18 Linear Programming Approach for an MDP Stat e /81/16 103/41/8 2001/

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19 Linear Programming Approach for an MDP solving, y 01 = 2/21, y 11 = 5/7, y 13 = 0, y 21 = 0, y 22 = 2/21, y 23 = 0, y 33 = 2/21 optimal policy at state 0: do nothing state 1: do nothing state 2: overhaul state 3: replace

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