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Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approach

Soumen Atta¹, Goutam Sen²

¹Indian Institute of Information Technology (IIIT) Vadodara, Gandhinagar Campus, Sector 28, Gandhinagar, Gujarat-382028, India
E-mail: soumen_atta@iiitvadodara.ac.in (Corresponding author)
²Industrial and Systems Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur-721302, West Bengal, India
E-mail: gsen@iem.iitkgp.ernet.in

The instances for the multiple allocation p-hub location problem (MApHLP) mentioned in this page are generated from the real world movie data set known as MovieLens 100K data set (Harper and Konstan, 2016) available at https://grouplens.org/datasets/movielens/100k/. Twenty-eight different size instances are generated for MApHLP. All the instances are given in the table shown below where n, f, p and α denote the number of users, number of files, number of hubs and discount factor per unit inter-hub flow respectively. Each instance consists of the query matrix (Q) and the distance matrix (D). In the table shown below, the column "Solution" denotes the locations of the hubs. For example, solution vector {3, 9, 7, 10} denotes that data segments s₁, s₂, s₃ and s₄ are located at user locations of i₃, i₉, i₇ and i₁₀ respectively. Each of the instances can be separately downloaded by clicking on the respective serial number (Sl. No.) of the instance.

Cite this article as follows:
Atta, S., Sen, G. Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approach. Appl Intell 50, 1573–1589 (2020). https://doi.org/10.1007/s10489-019-01609-y.

Click here to download all the instances as a single zip file.

*Sl. No.*	n	f	p	α	Solution	*Cost*
1	10	100	5	0.2	1 4 5 8 6	534821.739
2	10	100	5	0.4	1 4 5 9 6	886465.946
3	10	100	5	0.6	1 4 5 9 6	1225689.184
4	10	100	5	0.8	1 3 5 9 6	1545829.321
5	20	100	5	0.2	20 10 1 13 19	1174751.690
6	20	100	5	0.4	20 10 1 13 19	1742999.258
7	20	100	5	0.6	20 14 1 5 19	2270990.698
8	20	100	5	0.8	20 14 1 5 16	2767781.887
9	30	100	5	0.2	16 1 29 18 23	1650403.231
10	30	100	5	0.4	20 1 14 18 23	2398318.466
11	30	100	5	0.6	20 1 14 18 23	3116460.984
12	30	100	5	0.8	20 1 4 18 23	3764140.155
13	40	100	5	0.2	29 32 23 1 18	2098047.205
14	40	100	5	0.4	40 32 23 1 18	3004050.520
15	40	100	5	0.6	40 20 23 1 18	3843431.304
16	40	100	5	0.8	4 20 23 39 1	4550001.077
17	50	100	5	0.2	20 1 23 18 45	2511837.826
18	50	100	5	0.4	20 1 23 18 40	3466266.515
19	50	100	5	0.6	42 1 23 18 40	4328109.677
20	50	100	5	0.8	42 1 23 18 4	5069979.230
21	100	100	5	0.2	42 100 37 59 78	5718242.293^☆
22	100	100	5	0.4	90 3 100 86 54	7897318.306^☆
23	100	100	5	0.6	71 44 100 62 68	9353107.990^☆
24	100	100	5	0.8	71 65 75 100 80	10576393.705^☆
25	150	100	5	0.2	65 100 104 47 41	8589123.426^☆
26	150	100	5	0.4	63 64 18 32 137	11350864.093^☆
27	150	100	5	0.6	53 73 54 95 105	13410026.515^☆
28	150	100	5	0.8	42 150 62 97 114	15128130.805^☆

^☆ denotes the best obtained solution.

Reference:
Harper FM, Konstan JA (2016) The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5(4):19

This page was created on April 23, 2019.