CLSC under Uncertainty Data Instances

Uster, H. and Hwang, S., “Closed-loop Supply Chain Network Design under Demand and Return Uncertainty,” Transportation Science, Vol. 51, No. 4, pp. 1063-1085, 2017.

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CLSC Design under Uncertainty Data Instances – Model4.zip

Format:

1. The data instances for computational study in Section 5.1 are named B_1.txt to B_120.txt and for analysis of recovery location in Section 5.4 is R_1.txt

2. For Section 5.1, distances are randomly generated using Unif[1,20] between pairs of points. In section 5.4 using US data, distances are calculated using Haversine formula between the pairs of cities whose coordinates are here.

3. There are 12 different classes for computational study in section 4.1 and each class includes 10 instances.

4. The Table below gives the number of scenarios, SFs, CTRs, and RTs in each class.

Class	Scenarios	SFs	CTRs	RTs
C1	250	10	30	60
C2	250	10	30	90
C3	250	10	30	120
C4	500	10	30	60
C5	500	10	30	90
C6	500	10	30	120
C7	250	20	60	60
C8	250	20	60	90
C9	250	20	60	120
C10	500	20	60	60
C11	500	20	60	90
C12	500	20	60	120

5. The Legend for the input file format is given below:

Symbol	Details
Omega	Scenario
SF	SF
CTR	CTR
RT	Customer
D[omega][k]	Demand at customer k under scenario omega
S[omega][k]	Return demand at customer k under scenario omega
Ff[i]	Fixed cost of opening a SF at location i
Fr[i]	Fixed cost of selecting SF i as remanufacturing at location i
Fc[j]	Fixed cost of opening CTR at location j
Kappa_F[i]	Manufacturing cost at SF location i
Kappa_R[i]	Remanufacturing cost at SF location i
Eta_D[j]	Distribution processing cost at CTR location j
Eta_C[j]	Collection processing cost at CTR location j
Psi_F[i]	Forward capacity expansion cost at SF location i
Psi_R[i]	Reverse capacity expansion cost at SF location i
Rho_F[j]	Distribution capacity expansion cost at CTR location j
Rho_R[j]	Collection capacity expansion cost at CTR location j
b_F[i]	Base forward capacity at SF location i
b_R[i]	Base reverse capacity at SF location i
l_F[j]	Base distribution capacity at CTR location j
l_R[j]	Base collection capacity at CTR location j
p_F[i]	Allowed forward capacity expansion at SF location i
p_R[i]	Allowed reverse capacity expansion at SF location i
q_F[j]	Allowed distribution capacity expansion at CTR location j
q_R[j]	Allowed collection capacity expansion at CTR location j
Lambda[i]	Recovery fraction at SF location i
H[omega]	Probability of scenario omega
G_1	Forward transportation cost (per unit per mile) between SF and CTR
G_2	Forward transportation cost (per unit per mile) between CTR and RT
G_3	Reverse transportation cost (per unit per mile) between RT and CTR
G_4	Reverse transportation cost (per unit per mile) between CTR and SF

6. All the text files follow the following sample format. (Scenarios=2, SFs=2, CTRs=3, RTs=3)

Omega

CTR

D[omega][k]

[ [1165, 1405, 658], [3128, 2513, 3217] ]

S[omega][k]

[ [583, 703, 329], [1564, 1257, 1609] ]

Ff[i]

[ 920929, 900569]

Fr[i]

[ 407789, 393113]

Fc[j]

[ 161485, 160760, 163795]

Kappa_F[i]

[ 112, 123]

Kappa_R[i]

[ 65, 44]

Eta_D[j]

[ 25, 18, 18]

Eta_C[j]

[ 22, 18, 18]

Psi_F[i]

[ 101, 98]

Psi_R[i]

[ 115, 85]

Rho_F[i]

[ 90, 94, 93]

Rho_R[i]

[ 78, 101, 71]

b_F[i]

[ 1063, 886]

b_R[i]

[ 709, 665]

l_F[j]

[ 1506, 1684, 1684]

l_R[j]

[ 621, 621, 443]

p_F[i]

[ 149, 125]

p_R[i]

[ 107, 120]

q_F[i]

[ 241, 253, 320]

q_R[i]

[ 87, 106, 85]

Lambda[i]

[ 0.67, 0.7]

H[omega]

[ 0.5, 0.5]

0.02