Evolución histórica de los Derechos Humanos

0 ji ii j j J i I L O Y     





: Defined for multi-stop routes (5-17)

This constraint is applied to only multi-stop routes connecting an open DC to a retailer and then through the first retailer serving other retailer(s). Retailers have no supply of products therefore open DCs and routes connecting DCs to served retailers are included in constraint 8 (5-17).

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five

102 Constraint 9: AHP-Integrated constraint

( ) , , and on , and m mn n m sj ji ii m M n N S w T B i j s V L O    

 

(5-18)

In order to involve DMs and their priorities in the model, a green constraint is defined. A multi-criteria decision making tool, AHP, is used to formulate this constraint. The DMs prioritise the available transportation options considering a number of criteria in order to select the best one. The process of decision-making using this AHP-integrated constraint is illustrated in Figure 5.3:

Figure 5.3 The process of developing a weight matrix in order to contract an AHP- integrated constraint

Three types of trucks are considered for transportation of products. The characteristics of the trucks are considered as followed:

1:

T Truck type 1 (Medium CO2 emission / Medium cost)

2:

T Truck type 2 (Low CO2 emission / High cost)

3:

T Truck type 3 (High CO2 emission / Low cost)

DMs have been asked to prioritise these three options of transportation based on two criteria, viz., CO2 emission and cost, thereby introducing flexibility in the decision- making process. The priorities of the DMs form weight matrix (

w

_mn). The matrices B_m

(i.e., right hand side of the third constraint 5-18) contribute to the parameters of the objective functions (5-1) and (5-4). Figure 5.4 depicts how the vehicles/trucks, attributes of the decision-making.

Chapter Five 103 Average of c_sj,c c_ji, _ii Truck types

:

mn

w

Pair-wise comparison matrix obtained from AHP 1

2

S S

Figure 5.4 The integration of AHP to the objective functions

In Ireland or the EU no limit has been defined for CO2 emissions therefore an average of the CO2 emission for transportation, viz. p_Vsj,p_Lji,p_Oii, is considered as the limit for this attribute. In the case of the limit for costs an average of the total costs of serving routes, viz. c c c_sj, _ji, _ii, is considered as the limit for this attribute.

…. Integer constraints: if DC is open if not 1, 0, j j J Y _{ }   (5-19) if path is operating out of processing

if not 1, 0, s sj k P s S V _{ }    (5-20) if path is operating out of processing

if not 1, 0, j ji k P j J L _{ }    (5-21) if path is operating out of retailer

if not 1, 0, i ii k P i I O _{ }    (5-22)

if truck is selected to transport the products if not 1, 0, n n n T T _{ }   (5-23)

if processing plant s is open if not 1, 0, s S X _{ }   (5-24) Non-negativity constraints:

Quantity shipped from precessing plant to DC

0

sj s S j J

Q



  (5-25)

Quantity shipped from precessing DC to retailer

0

ji j J i I

Q



  (5-26)

Quantity shipped from precessing retailer to retailer

0

ii i I i I

Q



  (5-27) Alternatives Attributes 1 T T₂ T₃ RHS Matrix CO2 emission 11 12 13 21 22 23 w w w w w w       1 2 B B       Costs Average of , , sj ji ii V L O p p p Average of , , sj ji ii V L O p p p

Chapter Five

104

In document Derechos de la niñez y la adolescencia, a la luz de los tratados internacionales y la aplicación de los Convenios sobre Adopción y Sustracción Internacional de Menores, en el nuevo Juzgado de Niñez y Adolescencia (página 33-37)