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Math-based Decision Making in Logistics and Operations

Math-based Decision Making in Logistics and Operations

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Page 1: Math-based Decision Making in Logistics and Operations

Math-based Decision Making in Logistics and Operations

Page 2: Math-based Decision Making in Logistics and Operations

Where we are?The group Introduction Description Results Conclusion

UPM

Page 3: Math-based Decision Making in Logistics and Operations

Math-based Decision Making in Logistics and Operations

MbD

MLO

Grupo Inv INGOR

Grupo Inv OS

UD

Org

aniza

ción

de

la P

rodu

cció

n

UD

Adm

inist

raci

ón E

mpr

esas

UD

Eco

nom

ía

UD

Pro

yect

os

UD

Est

adísti

ca

Dep. Ingeniería de Organización, Adm.

de Empresas y Estadística

ETSII UPM

Lab IOL

Math-based Decision Making in Logistic and Operations

UPM

The group Introduction Description Results Conclusion

Page 4: Math-based Decision Making in Logistics and Operations

The team

• Miguel Ortega Mier• Álvaro García Sánchez• Javier Diego• Daniel Herrero• Natalia Ibáñez• Raúl Pulido • Jing Shao• Tamara Borreguero

• Others.

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The group Introduction Description Results Conclusion

Page 5: Math-based Decision Making in Logistics and Operations

Techniques and technologies

Optimization

Modeling

Solver

Simulation Other

Programming and other

specific software

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The group Introduction Description Results Conclusion

Page 6: Math-based Decision Making in Logistics and Operations

6

The inventory routing problem for the Mixed Car Model

Assembly Line

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The group Introduction Description Results Conclusion

Page 7: Math-based Decision Making in Logistics and Operations

7

Relevance

• A high inventory level in the assembly line is a big cost contributor. Some of the car manufacturer’s objectives are keeping low stock levels, performing the replenishment of the production line, and providing the required components at the right time (Monden, 1983).

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The group Introduction Description Results Conclusion

Page 8: Math-based Decision Making in Logistics and Operations

8

Abstract

• A car assembly line usually produces hundreds of cars every day; each workstation in the assembly line needs car components to perform their task.

• The replenishment of the components is a critical issue for the proper operation of the assembly line. In a multi-model assembly line, this task becomes more complicated than in a single model assembly line. A lack of inventory could cause some problems in the production line; excess inventory could also create it.

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The group Introduction Description Results Conclusion

Page 9: Math-based Decision Making in Logistics and Operations

9

Problem description

• In this problem, the assembly line already exists and the production plan, for the planning period, is already known.

• Each model has a set of characteristics, such as types of wheels and tires, radio, sunroof, car seat, and so on. In every workstation, a kit of components is installed; these components can have different trim levels.

• It is assumed that each component needed for the planning horizon is available in a single warehouse from where all the routes depart.

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The group Introduction Description Results Conclusion

Page 10: Math-based Decision Making in Logistics and Operations

10

Problem description

• The early arrival of the component causes space problems with the buffers of the production lines.

• The late arrival causes several problems in the production line. • Dispatch only with a just-in-time policy increases the

transportation cost and the green impact of the production line. It is necessary to select the route and the amount of required components to get the lower cost.

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The group Introduction Description Results Conclusion

Page 11: Math-based Decision Making in Logistics and Operations

11

Problem description

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The group Introduction Description Results Conclusion

Page 12: Math-based Decision Making in Logistics and Operations

12

MIP

• A MIP has been developed.– Transportation Cost– Transportation Vehicle Used Cost– Holding Cos– Violation Cost

• There is a conflict of interest among the objectives.• Right now it is being presented in the CIO 2013 by one of the

authors.

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The group Introduction Description Results Conclusion

min.

Page 13: Math-based Decision Making in Logistics and Operations

13

Two types of Ants• Total Production Rule

Violations• Sequence(i)• Class (i)

• Visited Stations• Total Cost• Vehicle (k)

• Tour (n)• Tour Length• Time Visited (n)

Sequencing

Routing

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The group Introduction Description Results Conclusion

Initial Data• Number of Cars• Number of Car

Classes• Number of Stations• Capacity of Stations• Requirements per

Station by each class• Distance• Number of Vehicles

Page 14: Math-based Decision Making in Logistics and Operations

14

Pheromones & Heuristic info structure

• Pheromone 1 (number of vehicles, number of vehicles)– MAX-MIN approach. – The best ant of the cycle deposit pheromone at the end.– Represent the learnt of desirability of scheduling Cj after Ci.

• Pheromone 2 (number of classes, number of classes)– Initialize in the lower bound– Each time no more cars can be scheduled without violation some

pheromones are added.– Represent the difficult to sequence this class without violating rules.

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The group Introduction Description Results Conclusion

Page 15: Math-based Decision Making in Logistics and Operations

15

Pheromones & Heuristic info structure 2

• Pheromone 3 (number of stations, number of stations)– The best ant of the cycle deposit pheromone at the end.– Represents the learnt of desirability of visiting Sj after Si.

• Dynamic Heuristic info(number of stations, number of stations)– Different options using the stock at determinate time, Traveling time,

distance, and so on.

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The group Introduction Description Results Conclusion

Page 16: Math-based Decision Making in Logistics and Operations

16

Simplified Algorithm

• Pheromones trails are initialized.• For the maximum iteration allowed to do – The sequence ants construct a sequence. – Calculate the demand over the time. – Each route ant constructs a full route. – Each route ant construct a full route using one less

vehicle.– Evaluate the solution using the global function. – Update the pheromone trails with their respective

pheromone and rules.

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The group Introduction Description Results Conclusion

Page 17: Math-based Decision Making in Logistics and Operations

17

Probability

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The group Introduction Description Results Conclusion

Page 18: Math-based Decision Making in Logistics and Operations

18

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Consumed S1 S2 S3 S4 S5

0/0 0/0 0/0 0/0 0/0

Sj

Si

Sj

Si

Page 19: Math-based Decision Making in Logistics and Operations

19

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

RANDOM

Consumed S1 S2 S3 S4 S5

0/1 0/0 0/0 0/0 0/0

Page 20: Math-based Decision Making in Logistics and Operations

20

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

0/1 0/0 0/0 0/0 0/0

Page 21: Math-based Decision Making in Logistics and Operations

21

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

0/2 1/0 0/0 0/0 0/0

Page 22: Math-based Decision Making in Logistics and Operations

22

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

1/2 1/1 0/1 0/0 0/0

Page 23: Math-based Decision Making in Logistics and Operations

23

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

1/3 1/2 1/1 1/0 0/0

Page 24: Math-based Decision Making in Logistics and Operations

24

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .2 .2 .2 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

1/3 1/2 1/1 1/0 0/0

Page 25: Math-based Decision Making in Logistics and Operations

25

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

1/3 1/2 1/1 1/0 0/0

Page 26: Math-based Decision Making in Logistics and Operations

26

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

2/3 1/3 2/1 1/1 0/1

Page 27: Math-based Decision Making in Logistics and Operations

27

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .2 .2

T1

Stock S1 S2 S3 S4 S5

4 4 4 4 4

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

2/4 1/4 3/1 1/3 1/1

Page 28: Math-based Decision Making in Logistics and Operations

28

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

3/4 2/4 4/1 2/3 1/2

Page 29: Math-based Decision Making in Logistics and Operations

29

C 1 2 3 4 5 6 7 81 .8 .8 .8 .8 .8 .8 .82 .8 .8 .8 .8 .8 .8 .83 .8 .8 .8 .8 .8 .8 .84 .8 .8 .8 .8 .8 .8 .85 .8 .8 .8 .8 .8 .8 .86 .8 .8 .8 .8 .8 .8 .87 .8 .8 .8 .8 .8 .8 .88 .8 .8 .8 .8 .8 .8 .8

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

3/4 2/4 4/1 2/3 1/2

Page 30: Math-based Decision Making in Logistics and Operations

30

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .7 .72 .7 .7 .7 .7 .7 .7 .73 .7 .7 .7 .7 .7 .7 .74 .7 .7 .7 .7 .7 .7 .75 .7 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .7 .7 .77 .7 .7 .7 .7 .7 .7 .78 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .4 .2 .3 .2

Y .2 .3 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .3 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Other Ant

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31

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .7 .72 .7 .7 .7 .7 .7 .7 .73 .7 .7 .7 .7 .7 .7 .74 .7 .7 .7 .7 .7 .7 .75 .7 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .7 .7 .77 .7 .7 .7 .7 .7 .7 .78 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .3 .2 .3 .2

Y .2 .3 .2 .2

Bk .3 .3 .3 .2

Bl .2 .2 .3 .3

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

We keep doing for the total number of ants

Page 32: Math-based Decision Making in Logistics and Operations

32

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .7 .72 .7 .7 .7 .7 .7 .7 .73 .7 .7 .7 .7 .7 .7 .74 .7 .7 .7 .7 .7 .7 .75 .7 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .7 .7 .77 .7 .7 .7 .7 .7 .7 .78 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

For the best antEvaporate T1

Page 33: Math-based Decision Making in Logistics and Operations

33

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Best Ant

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

Page 34: Math-based Decision Making in Logistics and Operations

34

Iterate

Page 35: Math-based Decision Making in Logistics and Operations

35

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH

WH

WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

Page 36: Math-based Decision Making in Logistics and Operations

36

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3 S4S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2

WH

WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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37

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2

WH S4

WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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38

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WH

S3S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2

WH S4

WH S1

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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39

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WH

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2 S3

WH S4

WH S1

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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40

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WH

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2 S3

WH S4 S5

WH S1

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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41

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2 S3 WH

WH S4 S5 WH

WH S1 WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Routing

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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42

Iterate

Page 43: Math-based Decision Making in Logistics and Operations

43

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH

WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

One vehicle less

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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44

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2 S3 S5 WH

WH S4 S1 WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

One vehicle less

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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45

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WH

S 0 1 2 3 4 5

0 .5 .5 .5 .5 .5 .5

1 .5 .5 .5 .5 .5 .5

2 .5 .5 .5 .5 .5 .5

3 .5 .5 .5 .5 .5 .5

4 .5 .5 .5 .5 .5 .5

5 .5 .5 .5 .5 .5 .5

T3

T2

WH S2 S3 S5 S1 S4

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

One vehicle less

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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46

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .3 .3 .3 .3 .3 .3

1 .3 .3 .3 .3 .3 .3

2 .3 .3 .3 .3 .3 .3

3 .3 .3 .3 .3 .3 .3

4 .3 .3 .3 .3 .3 .3

5 .3 .3 .3 .3 .3 .3

T3

T2

Cl R Y Bk Bl

R .2 .2 .2 .2

Y .2 .2 .2 .2

Bk .5 .3 .3 .2

Bl .2 .2 .2 .2

WH S2 S3 S5 WH

WH S4 S1 WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Update the best ant

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47

C 1 2 3 4 5 6 7 81 .7 .7 .7 .7 .7 .9 .72 .7 .7 .9 .7 .7 .7 .73 .7 .7 .7 .7 .9 .7 .74 .7 .7 .9 .7 .7 .7 .75 .9 .7 .7 .7 .7 .7 .76 .7 .7 .7 .7 .9 .7 .77 .7 .7 .7 .7 .7 .7 .98 .7 .7 .7 .7 .7 .7 .7

WHS1S3

S2S4

S5

S 0 1 2 3 4 5

0 .3 .3 .5 .3 .5 .3

1 .5 .3 .3 .3 .3 .3

2 .3 .3 .3 .5 .3 .3

3 .3 .3 .3 .3 .3 .5

4 .3 .5 .3 .3 .3 .3

5 .5 .3 .3 .3 .3 .3

T3

T2

WH S2 S3 S5 WH

WH S4 S1 WH

T1

Sj

Si

Sj

Si

Consumed S1 S2 S3 S4 S5

4/4 2/5 4/2 2/3 1/2

Update the best ant

Cl R Y Bk Bl

R .2 .2 .3 .2

Y .2 .2 .2 .2

Bk .3 .3 .4 .2

Bl .2 .2 .2 .2

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48

Iterate

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49

Repeat the entire process

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50

We keep working

• Which heuristic information is better for the problem. Time to be out stock, time to arrive, distance, and so on.

• Type of filling, series or parallel

• Other updates of pheromones

UPM

The group Introduction Description Results Conclusion

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51

Results of MIP

WH S1 S2 S3 S4 S5

Vehicle 1 11 8 5

Vehicle 2 14 4 7 10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

M3 M4 M5 M5 M4 M5 M5 M1 M5 M5 M3 M4 M2 M5 M5

M2 M5 M4 M3 M5 M5 M4 M5 M4 M5 M4 M5 M5 M4 M4

The group Introduction Description Results Conclusion

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52

Results of ACO

• The ACO was tuned using the instance small instance where an exact solution can be found by the MIP algorithm.

• The ACO algorithm finds solutions with the gap of 5% in less than 1 minute, when the MIP algorithm takes more than one hour.

• As bigger instance was not possible to solve by exact algorithm and no public instance for the entire problem was founded to compare with the optimal.

• We use the car sequencing instances reported by [Regin, 1997], then we create the replenishment data, such as capacity, vehicle number, and so on.

The group Introduction Description Results Conclusion

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53

Practical conclusions• In this work, the car sequencing, the inventory and the routing problem

have been solved jointly. The routing model should consider more factors than just the transportation cost. The main factor in the delivery of material should not only be the decrease of the transportation costs but also the decrease of the holding cost of the components.

• The cost of the space is an amplifier of the savings of the model. The replenishment is made before the inventory level reaches the safety stock. Following the Lean idea, it is possible to decrease the safety stock until it reaches zero safety stock, always keeping in mind the risk of any delay with the consequence of the stoppage of the assembly line.

UPM

The group Introduction Description Results Conclusion

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54

Thank you!

UPM

The group Introduction Description Results Conclusion