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Production Planning
Basic Inventory Model Workforce Scheduling
Enhance Modeling SkillsDynamic Models
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Dynamic Inventory Model
•Modeling Time•Modeling Inventory•Unusual Network Example
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Singapore Electric Generator
Singapore Electric Generator Production Unit Costs Jan Feb Mar Apr. May Production $ 28.00 $ 27.00 $ 27.80 $ 29.00 Inventory $ 0.30 $ 0.30 $ 0.30 $ 0.30 Production Qty 0 0 0 0 Production Limits 60 62 64 66
Beginning Inventory 15 -43 -79 -113 Delivery Reqmts 58 36 34 59 MinimumEnding Inventory (43) (79) (113) (172) 7
Production Cost $ -$ -$ -$ -Inventory Cost $ (4.20) $ (18.30) $ (28.80) $ (42.75) Total Total Cost $ (4.20) $ (18.30) $ (28.80) $ (42.75) $ (94.05)
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Inventory
•Balancing Your Checkbook Previous Balance + Income -Expenses =
New Balance •Modeling Dynamic Inventory
Starting Inv. + Production -Shipments = Ending Inv.
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Average Balances
•Assuming Smooth Cash Flows
Averages (Starting + Ending)/2
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Challenge
•Formulate a Solver Model
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Singapore Electric Generator
Singapore Electric Generator Production Unit Costs Jan Feb Mar Apr. May Production $ 28.00 $ 27.00 $ 27.80 $ 29.00 Inventory $ 0.30 $ 0.30 $ 0.30 $ 0.30 Production Qty 0 0 0 0 Production Limits 60 62 64 66
Beginning Inventory 15 -43 -79 -113 Delivery Reqmts 58 36 34 59 MinimumEnding Inventory (43) (79) (113) (172) 7
Production Cost $ -$ -$ -$ -Inventory Cost $ (4.20) $ (18.30) $ (28.80) $ (42.75) Total Total Cost $ (4.20) $ (18.30) $ (28.80) $ (42.75) $ (94.05)
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A Network Formulation
Dec. Inv.
Jan. Inv.
Apr. dem.
Feb. dem.
Mar. dem.
Jan. dem.
Feb.Inv.
Mar. Inv.
Apr. Inv.
May Inv.
Apr. mfg.
Mar. mfg.
Feb. mfg.
Jan. mfg.
Production Variables
Supply ≤Prod. Limits
Demand ≥ req
Inventory Variables
Shipment Quantities
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A Network Formulation
Singapore Electric Generator Production
Unit Costs Dec Jan Feb Mar Apr. May Production $ 28.00 $ 27.00 $ 27.80 $ 29.00 Inventory $ 0.30 $ 0.30 $ 0.30 $ 0.30 Production Qty 0 0 0 0Production Limits 60 62 64 66 Delivery Reqmts 58 36 34 59 Calc. Ending Inv. -43 (36) (34) (59) Minimum Ending Inventory 15 - - - - 7
Production Cost $ - $ - $ - $ -Inventory Cost $ 2.25 $ - $ - $ - Total Total Cost $ 2.25 $ - $ - $ - $ 2.25
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Another View
s.t. InitialBalance: Production['Jan'] -EndingInv['Jan'] = 43
s.t. MonthlyBalances['Feb']: Production['Feb'] + EndingInv['Jan'] -EndingInv['Feb'] = 36
s.t. MonthlyBalances['Mar']: Production['Mar'] + EndingInv['Feb'] -EndingInv['Mar'] = 34
s.t. MonthlyBalances['Apr']: Production['Apr'] + EndingInv['Mar'] -EndingInv['Apr'] = 59
s.t. FinalBalance: EndingInv['Apr'] >= 7
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Scheduling Postal Workers
•Each postal worker works for 5 consecutive days, followed by 2 days off, repeated weekly.
Day Mon Tues Wed Thurs Fri Sat Sun Demand 17 13 15 19 14 16 11
•Minimize the number of postal workers (FTE’s)
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Challenge
•Formulate a Solver Model
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Formulating the LP
Scheduling Postal Workers
Shift Mon - Tues - Wed - Thurs - Fri - Sat -Sun –Fri Sat Sun Mon Tues Wed Thurs
Day Demand Mon 1 1 1 1 1 17 Tues 1 1 1 1 1 13 Wed 1 1 1 1 1 15 Thurs 1 1 1 1 1 19 Fri 1 1 1 1 1 14 Sat 1 1 1 1 1 16 Sun 1 1 1 1 1 11
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Formulating as an LP
•The Objective Total Work
ers Required Minimize $I
$5 •The decision variables
The number of workers assigned to each shift
$B$5:$H$5 •The Constraints
Enough workers each day $I$6:$I$12 >= $J$6:$J$12
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Minimize z = MF + TS + WSu+ ThM+ FT + SW + SuTh
subject to MF + ThM+ FT + SW + SuTh ≥ 17 MF + TS + FT + SW + SuTh ≥ 13 MF + TS + WSu+ SW + SuTh ≥ 15 MF + TS + WSu+ ThM+ SuTh ≥ 19 MF + TS + WSu + ThM + FT ≥ 14
TS + WSu + ThM + FT + SW ≥ 16 WSu + ThM+ FT + SW + SuTh ≥ 11
Non-negativity
The linear program
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The Decision Variable Decision
•Would it be possible to have the variables be the number of workers on each day? •Conclusion: sometimes the decision variables incorporate constraints of the problem.
Hard to do this well, but worth keeping in mind We will see more of this in integer programming.
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Enhancement
•Some days we will have too many workers•Excess •Only concerned with the largest excess•Minimize the largest Excess
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Challenge
•Formulate a Solver Model
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Formulating the LP
Scheduling Postal Workers
Shift Mon - Tues - Wed - Thurs - Fri - Sat -Sun –Fri Sat Sun Mon Tues Wed Thurs
Day Demand Mon 1 1 1 1 1 17 Tues 1 1 1 1 1 13 Wed 1 1 1 1 1 15 Thurs 1 1 1 1 1 19 Fri 1 1 1 1 1 14 Sat 1 1 1 1 1 16 Sun 1 1 1 1 1 11
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Minimize the Maximum
•Min Max{XS[Mon], XS[Tues], …}•Min Z•S.t. Z ≥ XS[Mon]•S.t. Z ≥ XS[Tues]•…•S.t. MF + ThM + FT + SW + SuTh– XS[Mon] = 17 •S.t. MF + TS + FT + SW + SuTh– XS[Tues] = 13 •….
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Enhancement
•Ensure at least 30% of the workers have Sunday off•Formulate a Solver Model
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Formulating the LP
Scheduling Postal Workers
Shift Mon - Tues - Wed - Thurs - Fri - Sat -Sun –Fri Sat Sun Mon Tues Wed Thurs
Day Demand Mon 1 1 1 1 1 17 Tues 1 1 1 1 1 13 Wed 1 1 1 1 1 15 Thurs 1 1 1 1 1 19 Fri 1 1 1 1 1 14 Sat 1 1 1 1 1 16 Sun 1 1 1 1 1 11
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Minimize z = MF + TS + WSu+ ThM+ FT + SW + SuTh
subject to MF + ThM+ FT + SW + SuTh ≥ 17 MF + TS + FT + SW + SuTh ≥ 13 MF + TS + WSu+ SW + SuTh ≥ 15 MF + TS + WSu+ ThM+ SuTh ≥ 19 MF + TS + WSu + ThM + FT ≥ 14
TS + WSu + ThM + FT + SW ≥ 16 WSu + ThM+ FT + SW + SuTh ≥ 11
Non-negativity
The linear program
.7(MF + TS) -0.3*(WSu + ThM + FT + SW + SuTh) ≥ 0
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Summary
•More LP Modeling•LPs are more general than Networks•Modeling Time•Clever choices of decision variables