constraint management constraint something that limits the performance of a process or system in...
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Constraint Constraint managementmanagement
Constraint Something that limits the performance of
a process or system in achieving its goals. Categories:
Market (demand side) Resources (supply side)
Labour Equipment Space Material and energy Financial Supplier Competency and knowledge Policy and legal environment
Steps of managing constraints
Identify (the most pressing ones) Maximizing the benefit, given the
constraints (programming) Analyzing the other portions of the
process (if they supportive or not) Explore and evaluate how to
overcome the constraints (long term, strategic solution)
Repeat the process
Linear programming
Linear programming…
…is a quantitative management tool to obtain optimal solutions to problems that involve restrictions and limitations (called constrained optimization problems).
…consists of a sequence of steps that lead to an optimal solution to linear-constrained problems, if an optimum exists.
Typical areas of problems
Determining optimal schedules Establishing locations Identifying optimal worker-job
assignments Determining optimal diet plans Identifying optimal mix of products
in a factory (!!!) etc.
Linear programming models
…are mathematical representations of constrained optimization problems.
BASIC CHARACTERISTICS: Components Assumptions
Components of the structure of a linear programming model
Objective function: a mathematical expression of the goal e. g. maximization of profits
Decision variables: choices available in terms of amounts (quantities)
Constraints: limitations restricting the available alternatives; define the set of feasible combinations of decision variables (feasible solutions space). Greater than or equal to Less than or equal to Equal to
Parameters. Fixed values in the model
Assumptions of the linear programming model
Linearity: the impact of decision variables is linear in constraints and the objective functions
Divisibility: noninteger values are acceptable
Certainty: values of parameters are known and constant
Nonnegativity: negative values of decision variables are not accepted
Model formulation
The procesess of assembling information about a problem into a model.
This way the problem became solved mathematically.
1. Identifying decision variables (e.g. quantity of a product)
2. Identifying constraints3. Solve the problem.
Graphical linear programming
1. Set up the objective function and the constraints into mathematical format.
2. Plot the constraints.3. Identify the feasible solution space.4. Plot the objective function.5. Determine the optimum solution.
1. Sliding the line of the objective function away from the origin to the farthes/closest point of the feasible solution space.
2. Enumeration approach.
Corporate system-matrix1.) Resource-product matrix
Describes the connections between the company’s resources and products as linear and deterministic relations via coefficients of resource utilization and resource capacities.
2.) Environmental matrix (or market-matrix): Describes the minimum that we must, and maximum that we can sell on the market from each product. It also describes the conditions.
Contribution margin
Unit Price - Variable Costs Per Unit = Contribution Margin Per Unit
Contribution Margin Per Unit x Units Sold = Product’s Contribution to Profit
Contributions to Profit From All Products – Firm’s Fixed Costs = Total Firm Profit
Resource-Product Relation types
P1 P2 P3 P4 P5 P6 P7
R1 a11
R2 a22
R3 a32
R4 a43 a44 a45
R5 a56 a57
R6 a66 a67
Non-convertible relations Partially convertible relations
Product-mix in a pottery – corporate system
matrixJug Plate
Clay (kg/pcs) 1,0 0,5
Weel time (hrs/pcs)
0,5 1,0
Paint (kg/pcs) 0 0,1
Capacity
50 kg/week 100 HUF/kg
50 hrs/week 800 HUF/hr
10 kg/week 100 HUF/kg
Minimum (pcs/week) 10 10
Maximum (pcs/week)
100 100
Price (HUF/pcs) 700 1060
Contribution margin (HUF/pcs)
e1: 1*P1+0,5*P2 < 50e2: 0,5*P1+1*P2 < 50e3: 0,1*P2 < 10m1, m2: 10 < P1 < 100m3, m4: 10 < P2 < 100ofCM: 200 P1+200P2=MAX200 200
Objective function
refers to choosing the best element from some set of available alternatives.
X*P1 + Y*P2 = max
variables (amount of produced
goods)
weights(depends on what we want to maximize:
price, contribution margin)
Solution with linear programming
T1
T2
33,3
33,3
33 jugs and 33 plaits a per week
Contribution margin: 13 200 HUF / week
e1: 1*P1+0,5*P2 < 50e2: 0,5*P1+1*P2 < 50e3: 0,1*P2 < 10m1,m2: 10 < P1 < 100m3, m4: 10 < P2 < 100ofCM: 200 P1+200P2=MAX
e1
e2
e3ofF
100
100
What is the product-mix, that maximizes the revenues and the
contribution to profit!
P1 P2 P3 P4 P5 P6 b (hrs/y)
R1 4 2 000
R2 2 1 3 000
R3 1 1 000
R4 2 3 6 000
R5 2 2 5 000
MIN (pcs/y) 100 200 200 200 50 100
MAX (pcs/y) 400 1100 1 000 500 1 500 2000
p (HUF/pcs) 200 270 200 30 50 150
f (HUF/pcs) 100 110 50 -10 30 20
Solution P1:
Resource constraint 2000/4 = 500 > market constraint 400
P2&P3: Which one is the better product?Rev. max.: 270/2 < 200/1 thus P3
P3=(3000-200*2)/1=2600>1000
P2=200+1600/2=1000<1100
Contr. max.: 110/2 > 50/1 thus P2
P2=(3000-200*1)/2=1400>1100
P3=200+600/1=800<1000
P4: does it worth?Revenue max.: 1000/1 > 500Contribution max.: 200
P5&P6: linear programming e1: 2*T5 + 3*T6 ≤ 6000
e2: 2*T5 + 2*T6 ≤ 5000
m1, m2: 50 ≤ T5 ≤ 1500
p3, m4: 100 ≤ T6 ≤ 2000
ofTR: 50*T5 + 150*T6 = max
ofCM: 30*T5 + 20*T6 = max
e2
e1
ofCM
ofTR
Contr. max: P5=1500, P6=1000Rev. max: P5=50, P6=1966
T5
T62000
3000
2500
2500
ExerciseExercise 1.1 1.1Set up the product-resource matrix using the following data!Set up the product-resource matrix using the following data!
RRPP coefficients: a coefficients: a1111: 10, a: 10, a2222: 20, a: 20, a2323: 30, a3: 30, a344: 10: 10 The planning period is 4 weeks (there are no holidays in it, The planning period is 4 weeks (there are no holidays in it, and no work on weekends) and no work on weekends) Work schedule: Work schedule:
RR11 and and RR22: 2 shifts, each is 8 hour long: 2 shifts, each is 8 hour long RR33: 3 shifts: 3 shifts
Homogenous machines: Homogenous machines: 1 for 1 for RR11 2 for 2 for RR22 1 for 1 for RR33
Maintenance time: only for Maintenance time: only for RR33: 5 hrs/week: 5 hrs/weekPerformance rate: Performance rate:
90% for 90% for RR11 and and RR33 80% for 80% for RR22
Solution (bSolution (bii) )
RRii = = N ∙ sN ∙ snn ∙ s ∙ shh ∙ m ∙ mnn ∙ 60 ∙ 60 ∙ ∙ N=(number of weeks) N=(number of weeks) ∙ ∙ (working days per week)(working days per week)
RR11 = 4 = 4 weeksweeks ∙ ∙ 5 5 working daysworking days ∙ ∙ 2 2 shiftsshifts ∙ ∙ 8 8 hours per shifthours per shift ∙ ∙ 60 60 minutes per hourminutes per hour ∙ ∙ 1 1 homogenous machinehomogenous machine ∙ ∙ 0,9 0,9 performance performance = = = 4 = 4 ∙ ∙ 5 5 ∙ ∙ 2 2 ∙ ∙ 8 8 ∙ ∙ 60 60 ∙ ∙ 1 1 ∙ ∙ 0,9 = 17 280 0,9 = 17 280 minutes per minutes per planning periodplanning period
RR22 = 4 = 4 ∙ ∙ 5 5 ∙ ∙ 2 2 ∙ ∙ 8 8 ∙ ∙ 60 60 ∙ ∙ 2 2 ∙ ∙ 0,8 = 38 720 0,8 = 38 720 minsmins
RR33 = (4 = (4 ∙ ∙ 5 5 ∙ ∙ 3 3 ∙ ∙ 8 8 ∙ ∙ 60 60 ∙ ∙ 1 1 ∙ ∙ 0,9) – (5 0,9) – (5 hrs per weekhrs per week maintenancemaintenance ∙ ∙ 60 60 minutes per hourminutes per hour ∙ ∙ 4 4 weeksweeks) = 25 920 – ) = 25 920 – 1200 = 24 720 1200 = 24 720 minsmins
SolutionSolution (RP matrix) (RP matrix)
PP11 PP22 PP33 PP44 b (b (mins/ymins/y))
RR11 1010 17 28017 280
RR22 2020 3030 30 72030 720
RR33 1010 24 72024 720
ExerciseExercise 1.2 1.2 Complete the corporate system matrix with the following Complete the corporate system matrix with the following
marketing data:marketing data: There are long term contract to produce at least:There are long term contract to produce at least:
50 50 PP11 100 100 PP22 120 120 PP33 50 50 PP44
ForForeecasts says the upper limit of the market is:casts says the upper limit of the market is: 10 000 units for 10 000 units for PP11 1 500 for 1 500 for PP22 1 000 for 1 000 for PP33 3 000 for 3 000 for PP44
Unit prices: Unit prices: pp1=100, 1=100, pp2=200, 2=200, pp3=33=3330, 0, pp4=1004=100 Variable costs: Variable costs: RR1=5/min, 1=5/min, RR2=8/min, 2=8/min, RR3=11/min3=11/min
SolutionSolution (CS matrix) (CS matrix)
PP11 PP22 PP33 PP44 b (mins/y)b (mins/y)
RR11 1010 17 28017 280
RR22 2020 3030 30 72030 720
RR33 1010 24 72024 720
MIN (MIN (pcs/ypcs/y)) 5050 100100 120120 5050
MAX (pcs/y)MAX (pcs/y)
10 10 000000 1 5001 500 1 0001 000 3 0003 000
priceprice 100100 200200 330330 100100
CMCM 5050 4040 9090 -10-10
What is the optimal product What is the optimal product mix to maximize revenues?mix to maximize revenues?
PP11= = 17 280 / 10 = 1728 < 10 00017 280 / 10 = 1728 < 10 000
PP22: : 200/20=10200/20=10
PP33: : 330/30=11330/30=11
PP44=24 720/10=2472<3000=24 720/10=2472<3000
PP22= 100= 100 PP33= = (30 720-100∙20-120∙30)/30= (30 720-100∙20-120∙30)/30=
837<MAX837<MAX
What if we want to What if we want to maximize profit?maximize profit?
The only difference is in The only difference is in the case of the case of PP44 because of its negative because of its negative contribution margin.contribution margin.
PP44=50=50
ExerciseExercise 2 2
PP11 PP22 PP33 PP44 PP55 PP66 b (hrs/y)b (hrs/y)
RR11 66 2 0002 000
RR22 33 22 3 0003 000
RR33 44 1 0001 000
RR44 66 33 6 0006 000
RR55 11 44 5 0005 000
MIN (pcs/y)MIN (pcs/y) 00 200200 100100 250250 400400 100100
MAX (pcs/y)MAX (pcs/y) 2000020000 500500 400400 10001000 20002000 200200
p (HUF/pcs)p (HUF/pcs) 200200 100100 400400 100100 5050 100100
CM (HUF/pcs) CM (HUF/pcs) 5050 8080 4040 3030 2020 -10-10
SolutionSolution
Revenue max.Revenue max. PP11=333=333
PP22=500=500
PP33=400=400
PP44=250=250
PP55=900=900
PP66=200=200
Profit max.Profit max. PP11=333=333
PP22=500=500
PP33=400=400
PP44=250=250
PP55=966=966
PP66=100=100