***********************************************************************************************************************************************************************************
**************** This description is for the small-sized test instances for building the logistic regression models for the computational tests of the paper titled ****************
*************** "Machine Learning-empowered Benders Decomposition for Flow Hub Location in E-Commerce"    **************************************************************
************************************************************************************************************************************************************************************

This data set contains 900 test instances. The parameter settings of all 900 test instances are given as below. Besides the below presented ones, all other parameters, including (C_o, C_d), C_e, C_w, C_a, C_f, and C_t, are set to the default values.

___________________________
ID	|O|	|D|	|K|	|H|	C_c
___________________________
1	15	10	25	15	0.7
2	15	10	25	15	0.7
3	15	10	25	15	0.7
4	15	10	25	15	0.7
5	15	10	25	15	0.7
6	15	10	25	15	0.7
7	15	10	25	15	0.7
8	15	10	25	15	0.7
9	15	10	25	15	0.7
10	15	10	25	15	0.7
11	15	10	25	15	0.7
12	15	10	25	15	0.7
13	15	10	25	15	0.7
14	15	10	25	15	0.7
15	15	10	25	15	0.7
16	15	10	25	15	0.7
17	15	10	25	15	0.7
18	15	10	25	15	0.7
19	15	10	25	15	0.7
20	15	10	25	15	0.7
21	15	10	25	15	0.7
22	15	10	25	15	0.7
23	15	10	25	15	0.7
24	15	10	25	15	0.7
25	15	10	25	15	0.7
26	15	10	25	15	0.7
27	15	10	25	15	0.7
28	15	10	25	15	0.7
29	15	10	25	15	0.7
30	15	10	25	15	0.7
31	15	10	25	15	0.7
32	15	10	25	15	0.7
33	15	10	25	15	0.7
34	15	10	25	15	0.7
35	15	10	25	15	0.7
36	15	10	25	15	0.7
37	15	10	25	15	0.7
38	15	10	25	15	0.7
39	15	10	25	15	0.7
40	15	10	25	15	0.7
41	15	10	25	15	0.7
42	15	10	25	15	0.7
43	15	10	25	15	0.7
44	15	10	25	15	0.7
45	15	10	25	15	0.7
46	15	10	25	15	0.7
47	15	10	25	15	0.7
48	15	10	25	15	0.7
49	15	10	25	15	0.7
50	15	10	25	15	0.7
51	15	10	25	15	0.7
52	15	10	25	15	0.7
53	15	10	25	15	0.7
54	15	10	25	15	0.7
55	15	10	25	15	0.7
56	15	10	25	15	0.7
57	15	10	25	15	0.7
58	15	10	25	15	0.7
59	15	10	25	15	0.7
60	15	10	25	15	0.7
61	15	10	25	15	0.7
62	15	10	25	15	0.7
63	15	10	25	15	0.7
64	15	10	25	15	0.7
65	15	10	25	15	0.7
66	15	10	25	15	0.7
67	15	10	25	15	0.7
68	15	10	25	15	0.7
69	15	10	25	15	0.7
70	15	10	25	15	0.7
71	15	10	25	15	0.7
72	15	10	25	15	0.7
73	15	10	25	15	0.7
74	15	10	25	15	0.7
75	15	10	25	15	0.7
76	15	10	25	15	0.7
77	15	10	25	15	0.7
78	15	10	25	15	0.7
79	15	10	25	15	0.7
80	15	10	25	15	0.7
81	15	10	25	15	0.7
82	15	10	25	15	0.7
83	15	10	25	15	0.7
84	15	10	25	15	0.7
85	15	10	25	15	0.7
86	15	10	25	15	0.7
87	15	10	25	15	0.7
88	15	10	25	15	0.7
89	15	10	25	15	0.7
90	15	10	25	15	0.7
91	15	10	25	15	0.7
92	15	10	25	15	0.7
93	15	10	25	15	0.7
94	15	10	25	15	0.7
95	15	10	25	15	0.7
96	15	10	25	15	0.7
97	15	10	25	15	0.7
98	15	10	25	15	0.7
99	15	10	25	15	0.7
100	15	10	25	15	0.7
101	15	10	25	15	1
102	15	10	25	15	1
103	15	10	25	15	1
104	15	10	25	15	1
105	15	10	25	15	1
106	15	10	25	15	1
107	15	10	25	15	1
108	15	10	25	15	1
109	15	10	25	15	1
110	15	10	25	15	1
111	15	10	25	15	1
112	15	10	25	15	1
113	15	10	25	15	1
114	15	10	25	15	1
115	15	10	25	15	1
116	15	10	25	15	1
117	15	10	25	15	1
118	15	10	25	15	1
119	15	10	25	15	1
120	15	10	25	15	1
121	15	10	25	15	1
122	15	10	25	15	1
123	15	10	25	15	1
124	15	10	25	15	1
125	15	10	25	15	1
126	15	10	25	15	1
127	15	10	25	15	1
128	15	10	25	15	1
129	15	10	25	15	1
130	15	10	25	15	1
131	15	10	25	15	1
132	15	10	25	15	1
133	15	10	25	15	1
134	15	10	25	15	1
135	15	10	25	15	1
136	15	10	25	15	1
137	15	10	25	15	1
138	15	10	25	15	1
139	15	10	25	15	1
140	15	10	25	15	1
141	15	10	25	15	1
142	15	10	25	15	1
143	15	10	25	15	1
144	15	10	25	15	1
145	15	10	25	15	1
146	15	10	25	15	1
147	15	10	25	15	1
148	15	10	25	15	1
149	15	10	25	15	1
150	15	10	25	15	1
151	15	10	25	15	1
152	15	10	25	15	1
153	15	10	25	15	1
154	15	10	25	15	1
155	15	10	25	15	1
156	15	10	25	15	1
157	15	10	25	15	1
158	15	10	25	15	1
159	15	10	25	15	1
160	15	10	25	15	1
161	15	10	25	15	1
162	15	10	25	15	1
163	15	10	25	15	1
164	15	10	25	15	1
165	15	10	25	15	1
166	15	10	25	15	1
167	15	10	25	15	1
168	15	10	25	15	1
169	15	10	25	15	1
170	15	10	25	15	1
171	15	10	25	15	1
172	15	10	25	15	1
173	15	10	25	15	1
174	15	10	25	15	1
175	15	10	25	15	1
176	15	10	25	15	1
177	15	10	25	15	1
178	15	10	25	15	1
179	15	10	25	15	1
180	15	10	25	15	1
181	15	10	25	15	1
182	15	10	25	15	1
183	15	10	25	15	1
184	15	10	25	15	1
185	15	10	25	15	1
186	15	10	25	15	1
187	15	10	25	15	1
188	15	10	25	15	1
189	15	10	25	15	1
190	15	10	25	15	1
191	15	10	25	15	1
192	15	10	25	15	1
193	15	10	25	15	1
194	15	10	25	15	1
195	15	10	25	15	1
196	15	10	25	15	1
197	15	10	25	15	1
198	15	10	25	15	1
199	15	10	25	15	1
200	15	10	25	15	1
201	15	10	25	15	1.1
202	15	10	25	15	1.1
203	15	10	25	15	1.1
204	15	10	25	15	1.1
205	15	10	25	15	1.1
206	15	10	25	15	1.1
207	15	10	25	15	1.1
208	15	10	25	15	1.1
209	15	10	25	15	1.1
210	15	10	25	15	1.1
211	15	10	25	15	1.1
212	15	10	25	15	1.1
213	15	10	25	15	1.1
214	15	10	25	15	1.1
215	15	10	25	15	1.1
216	15	10	25	15	1.1
217	15	10	25	15	1.1
218	15	10	25	15	1.1
219	15	10	25	15	1.1
220	15	10	25	15	1.1
221	15	10	25	15	1.1
222	15	10	25	15	1.1
223	15	10	25	15	1.1
224	15	10	25	15	1.1
225	15	10	25	15	1.1
226	15	10	25	15	1.1
227	15	10	25	15	1.1
228	15	10	25	15	1.1
229	15	10	25	15	1.1
230	15	10	25	15	1.1
231	15	10	25	15	1.1
232	15	10	25	15	1.1
233	15	10	25	15	1.1
234	15	10	25	15	1.1
235	15	10	25	15	1.1
236	15	10	25	15	1.1
237	15	10	25	15	1.1
238	15	10	25	15	1.1
239	15	10	25	15	1.1
240	15	10	25	15	1.1
241	15	10	25	15	1.1
242	15	10	25	15	1.1
243	15	10	25	15	1.1
244	15	10	25	15	1.1
245	15	10	25	15	1.1
246	15	10	25	15	1.1
247	15	10	25	15	1.1
248	15	10	25	15	1.1
249	15	10	25	15	1.1
250	15	10	25	15	1.1
251	15	10	25	15	1.1
252	15	10	25	15	1.1
253	15	10	25	15	1.1
254	15	10	25	15	1.1
255	15	10	25	15	1.1
256	15	10	25	15	1.1
257	15	10	25	15	1.1
258	15	10	25	15	1.1
259	15	10	25	15	1.1
260	15	10	25	15	1.1
261	15	10	25	15	1.1
262	15	10	25	15	1.1
263	15	10	25	15	1.1
264	15	10	25	15	1.1
265	15	10	25	15	1.1
266	15	10	25	15	1.1
267	15	10	25	15	1.1
268	15	10	25	15	1.1
269	15	10	25	15	1.1
270	15	10	25	15	1.1
271	15	10	25	15	1.1
272	15	10	25	15	1.1
273	15	10	25	15	1.1
274	15	10	25	15	1.1
275	15	10	25	15	1.1
276	15	10	25	15	1.1
277	15	10	25	15	1.1
278	15	10	25	15	1.1
279	15	10	25	15	1.1
280	15	10	25	15	1.1
281	15	10	25	15	1.1
282	15	10	25	15	1.1
283	15	10	25	15	1.1
284	15	10	25	15	1.1
285	15	10	25	15	1.1
286	15	10	25	15	1.1
287	15	10	25	15	1.1
288	15	10	25	15	1.1
289	15	10	25	15	1.1
290	15	10	25	15	1.1
291	15	10	25	15	1.1
292	15	10	25	15	1.1
293	15	10	25	15	1.1
294	15	10	25	15	1.1
295	15	10	25	15	1.1
296	15	10	25	15	1.1
297	15	10	25	15	1.1
298	15	10	25	15	1.1
299	15	10	25	15	1.1
300	15	10	25	15	1.1
301	20	20	20	20	0.7
302	20	20	20	20	0.7
303	20	20	20	20	0.7
304	20	20	20	20	0.7
305	20	20	20	20	0.7
306	20	20	20	20	0.7
307	20	20	20	20	0.7
308	20	20	20	20	0.7
309	20	20	20	20	0.7
310	20	20	20	20	0.7
311	20	20	20	20	0.7
312	20	20	20	20	0.7
313	20	20	20	20	0.7
314	20	20	20	20	0.7
315	20	20	20	20	0.7
316	20	20	20	20	0.7
317	20	20	20	20	0.7
318	20	20	20	20	0.7
319	20	20	20	20	0.7
320	20	20	20	20	0.7
321	20	20	20	20	0.7
322	20	20	20	20	0.7
323	20	20	20	20	0.7
324	20	20	20	20	0.7
325	20	20	20	20	0.7
326	20	20	20	20	0.7
327	20	20	20	20	0.7
328	20	20	20	20	0.7
329	20	20	20	20	0.7
330	20	20	20	20	0.7
331	20	20	20	20	0.7
332	20	20	20	20	0.7
333	20	20	20	20	0.7
334	20	20	20	20	0.7
335	20	20	20	20	0.7
336	20	20	20	20	0.7
337	20	20	20	20	0.7
338	20	20	20	20	0.7
339	20	20	20	20	0.7
340	20	20	20	20	0.7
341	20	20	20	20	0.7
342	20	20	20	20	0.7
343	20	20	20	20	0.7
344	20	20	20	20	0.7
345	20	20	20	20	0.7
346	20	20	20	20	0.7
347	20	20	20	20	0.7
348	20	20	20	20	0.7
349	20	20	20	20	0.7
350	20	20	20	20	0.7
351	20	20	20	20	0.7
352	20	20	20	20	0.7
353	20	20	20	20	0.7
354	20	20	20	20	0.7
355	20	20	20	20	0.7
356	20	20	20	20	0.7
357	20	20	20	20	0.7
358	20	20	20	20	0.7
359	20	20	20	20	0.7
360	20	20	20	20	0.7
361	20	20	20	20	0.7
362	20	20	20	20	0.7
363	20	20	20	20	0.7
364	20	20	20	20	0.7
365	20	20	20	20	0.7
366	20	20	20	20	0.7
367	20	20	20	20	0.7
368	20	20	20	20	0.7
369	20	20	20	20	0.7
370	20	20	20	20	0.7
371	20	20	20	20	0.7
372	20	20	20	20	0.7
373	20	20	20	20	0.7
374	20	20	20	20	0.7
375	20	20	20	20	0.7
376	20	20	20	20	0.7
377	20	20	20	20	0.7
378	20	20	20	20	0.7
379	20	20	20	20	0.7
380	20	20	20	20	0.7
381	20	20	20	20	0.7
382	20	20	20	20	0.7
383	20	20	20	20	0.7
384	20	20	20	20	0.7
385	20	20	20	20	0.7
386	20	20	20	20	0.7
387	20	20	20	20	0.7
388	20	20	20	20	0.7
389	20	20	20	20	0.7
390	20	20	20	20	0.7
391	20	20	20	20	0.7
392	20	20	20	20	0.7
393	20	20	20	20	0.7
394	20	20	20	20	0.7
395	20	20	20	20	0.7
396	20	20	20	20	0.7
397	20	20	20	20	0.7
398	20	20	20	20	0.7
399	20	20	20	20	0.7
400	20	20	20	20	0.7
401	20	20	20	20	1
402	20	20	20	20	1
403	20	20	20	20	1
404	20	20	20	20	1
405	20	20	20	20	1
406	20	20	20	20	1
407	20	20	20	20	1
408	20	20	20	20	1
409	20	20	20	20	1
410	20	20	20	20	1
411	20	20	20	20	1
412	20	20	20	20	1
413	20	20	20	20	1
414	20	20	20	20	1
415	20	20	20	20	1
416	20	20	20	20	1
417	20	20	20	20	1
418	20	20	20	20	1
419	20	20	20	20	1
420	20	20	20	20	1
421	20	20	20	20	1
422	20	20	20	20	1
423	20	20	20	20	1
424	20	20	20	20	1
425	20	20	20	20	1
426	20	20	20	20	1
427	20	20	20	20	1
428	20	20	20	20	1
429	20	20	20	20	1
430	20	20	20	20	1
431	20	20	20	20	1
432	20	20	20	20	1
433	20	20	20	20	1
434	20	20	20	20	1
435	20	20	20	20	1
436	20	20	20	20	1
437	20	20	20	20	1
438	20	20	20	20	1
439	20	20	20	20	1
440	20	20	20	20	1
441	20	20	20	20	1
442	20	20	20	20	1
443	20	20	20	20	1
444	20	20	20	20	1
445	20	20	20	20	1
446	20	20	20	20	1
447	20	20	20	20	1
448	20	20	20	20	1
449	20	20	20	20	1
450	20	20	20	20	1
451	20	20	20	20	1
452	20	20	20	20	1
453	20	20	20	20	1
454	20	20	20	20	1
455	20	20	20	20	1
456	20	20	20	20	1
457	20	20	20	20	1
458	20	20	20	20	1
459	20	20	20	20	1
460	20	20	20	20	1
461	20	20	20	20	1
462	20	20	20	20	1
463	20	20	20	20	1
464	20	20	20	20	1
465	20	20	20	20	1
466	20	20	20	20	1
467	20	20	20	20	1
468	20	20	20	20	1
469	20	20	20	20	1
470	20	20	20	20	1
471	20	20	20	20	1
472	20	20	20	20	1
473	20	20	20	20	1
474	20	20	20	20	1
475	20	20	20	20	1
476	20	20	20	20	1
477	20	20	20	20	1
478	20	20	20	20	1
479	20	20	20	20	1
480	20	20	20	20	1
481	20	20	20	20	1
482	20	20	20	20	1
483	20	20	20	20	1
484	20	20	20	20	1
485	20	20	20	20	1
486	20	20	20	20	1
487	20	20	20	20	1
488	20	20	20	20	1
489	20	20	20	20	1
490	20	20	20	20	1
491	20	20	20	20	1
492	20	20	20	20	1
493	20	20	20	20	1
494	20	20	20	20	1
495	20	20	20	20	1
496	20	20	20	20	1
497	20	20	20	20	1
498	20	20	20	20	1
499	20	20	20	20	1
500	20	20	20	20	1
501	20	20	20	20	1.1
502	20	20	20	20	1.1
503	20	20	20	20	1.1
504	20	20	20	20	1.1
505	20	20	20	20	1.1
506	20	20	20	20	1.1
507	20	20	20	20	1.1
508	20	20	20	20	1.1
509	20	20	20	20	1.1
510	20	20	20	20	1.1
511	20	20	20	20	1.1
512	20	20	20	20	1.1
513	20	20	20	20	1.1
514	20	20	20	20	1.1
515	20	20	20	20	1.1
516	20	20	20	20	1.1
517	20	20	20	20	1.1
518	20	20	20	20	1.1
519	20	20	20	20	1.1
520	20	20	20	20	1.1
521	20	20	20	20	1.1
522	20	20	20	20	1.1
523	20	20	20	20	1.1
524	20	20	20	20	1.1
525	20	20	20	20	1.1
526	20	20	20	20	1.1
527	20	20	20	20	1.1
528	20	20	20	20	1.1
529	20	20	20	20	1.1
530	20	20	20	20	1.1
531	20	20	20	20	1.1
532	20	20	20	20	1.1
533	20	20	20	20	1.1
534	20	20	20	20	1.1
535	20	20	20	20	1.1
536	20	20	20	20	1.1
537	20	20	20	20	1.1
538	20	20	20	20	1.1
539	20	20	20	20	1.1
540	20	20	20	20	1.1
541	20	20	20	20	1.1
542	20	20	20	20	1.1
543	20	20	20	20	1.1
544	20	20	20	20	1.1
545	20	20	20	20	1.1
546	20	20	20	20	1.1
547	20	20	20	20	1.1
548	20	20	20	20	1.1
549	20	20	20	20	1.1
550	20	20	20	20	1.1
551	20	20	20	20	1.1
552	20	20	20	20	1.1
553	20	20	20	20	1.1
554	20	20	20	20	1.1
555	20	20	20	20	1.1
556	20	20	20	20	1.1
557	20	20	20	20	1.1
558	20	20	20	20	1.1
559	20	20	20	20	1.1
560	20	20	20	20	1.1
561	20	20	20	20	1.1
562	20	20	20	20	1.1
563	20	20	20	20	1.1
564	20	20	20	20	1.1
565	20	20	20	20	1.1
566	20	20	20	20	1.1
567	20	20	20	20	1.1
568	20	20	20	20	1.1
569	20	20	20	20	1.1
570	20	20	20	20	1.1
571	20	20	20	20	1.1
572	20	20	20	20	1.1
573	20	20	20	20	1.1
574	20	20	20	20	1.1
575	20	20	20	20	1.1
576	20	20	20	20	1.1
577	20	20	20	20	1.1
578	20	20	20	20	1.1
579	20	20	20	20	1.1
580	20	20	20	20	1.1
581	20	20	20	20	1.1
582	20	20	20	20	1.1
583	20	20	20	20	1.1
584	20	20	20	20	1.1
585	20	20	20	20	1.1
586	20	20	20	20	1.1
587	20	20	20	20	1.1
588	20	20	20	20	1.1
589	20	20	20	20	1.1
590	20	20	20	20	1.1
591	20	20	20	20	1.1
592	20	20	20	20	1.1
593	20	20	20	20	1.1
594	20	20	20	20	1.1
595	20	20	20	20	1.1
596	20	20	20	20	1.1
597	20	20	20	20	1.1
598	20	20	20	20	1.1
599	20	20	20	20	1.1
600	20	20	20	20	1.1
601	25	25	30	25	0.7
602	25	25	30	25	0.7
603	25	25	30	25	0.7
604	25	25	30	25	0.7
605	25	25	30	25	0.7
606	25	25	30	25	0.7
607	25	25	30	25	0.7
608	25	25	30	25	0.7
609	25	25	30	25	0.7
610	25	25	30	25	0.7
611	25	25	30	25	0.7
612	25	25	30	25	0.7
613	25	25	30	25	0.7
614	25	25	30	25	0.7
615	25	25	30	25	0.7
616	25	25	30	25	0.7
617	25	25	30	25	0.7
618	25	25	30	25	0.7
619	25	25	30	25	0.7
620	25	25	30	25	0.7
621	25	25	30	25	0.7
622	25	25	30	25	0.7
623	25	25	30	25	0.7
624	25	25	30	25	0.7
625	25	25	30	25	0.7
626	25	25	30	25	0.7
627	25	25	30	25	0.7
628	25	25	30	25	0.7
629	25	25	30	25	0.7
630	25	25	30	25	0.7
631	25	25	30	25	0.7
632	25	25	30	25	0.7
633	25	25	30	25	0.7
634	25	25	30	25	0.7
635	25	25	30	25	0.7
636	25	25	30	25	0.7
637	25	25	30	25	0.7
638	25	25	30	25	0.7
639	25	25	30	25	0.7
640	25	25	30	25	0.7
641	25	25	30	25	0.7
642	25	25	30	25	0.7
643	25	25	30	25	0.7
644	25	25	30	25	0.7
645	25	25	30	25	0.7
646	25	25	30	25	0.7
647	25	25	30	25	0.7
648	25	25	30	25	0.7
649	25	25	30	25	0.7
650	25	25	30	25	0.7
651	25	25	30	25	0.7
652	25	25	30	25	0.7
653	25	25	30	25	0.7
654	25	25	30	25	0.7
655	25	25	30	25	0.7
656	25	25	30	25	0.7
657	25	25	30	25	0.7
658	25	25	30	25	0.7
659	25	25	30	25	0.7
660	25	25	30	25	0.7
661	25	25	30	25	0.7
662	25	25	30	25	0.7
663	25	25	30	25	0.7
664	25	25	30	25	0.7
665	25	25	30	25	0.7
666	25	25	30	25	0.7
667	25	25	30	25	0.7
668	25	25	30	25	0.7
669	25	25	30	25	0.7
670	25	25	30	25	0.7
671	25	25	30	25	0.7
672	25	25	30	25	0.7
673	25	25	30	25	0.7
674	25	25	30	25	0.7
675	25	25	30	25	0.7
676	25	25	30	25	0.7
677	25	25	30	25	0.7
678	25	25	30	25	0.7
679	25	25	30	25	0.7
680	25	25	30	25	0.7
681	25	25	30	25	0.7
682	25	25	30	25	0.7
683	25	25	30	25	0.7
684	25	25	30	25	0.7
685	25	25	30	25	0.7
686	25	25	30	25	0.7
687	25	25	30	25	0.7
688	25	25	30	25	0.7
689	25	25	30	25	0.7
690	25	25	30	25	0.7
691	25	25	30	25	0.7
692	25	25	30	25	0.7
693	25	25	30	25	0.7
694	25	25	30	25	0.7
695	25	25	30	25	0.7
696	25	25	30	25	0.7
697	25	25	30	25	0.7
698	25	25	30	25	0.7
699	25	25	30	25	0.7
700	25	25	30	25	0.7
701	25	25	30	25	1
702	25	25	30	25	1
703	25	25	30	25	1
704	25	25	30	25	1
705	25	25	30	25	1
706	25	25	30	25	1
707	25	25	30	25	1
708	25	25	30	25	1
709	25	25	30	25	1
710	25	25	30	25	1
711	25	25	30	25	1
712	25	25	30	25	1
713	25	25	30	25	1
714	25	25	30	25	1
715	25	25	30	25	1
716	25	25	30	25	1
717	25	25	30	25	1
718	25	25	30	25	1
719	25	25	30	25	1
720	25	25	30	25	1
721	25	25	30	25	1
722	25	25	30	25	1
723	25	25	30	25	1
724	25	25	30	25	1
725	25	25	30	25	1
726	25	25	30	25	1
727	25	25	30	25	1
728	25	25	30	25	1
729	25	25	30	25	1
730	25	25	30	25	1
731	25	25	30	25	1
732	25	25	30	25	1
733	25	25	30	25	1
734	25	25	30	25	1
735	25	25	30	25	1
736	25	25	30	25	1
737	25	25	30	25	1
738	25	25	30	25	1
739	25	25	30	25	1
740	25	25	30	25	1
741	25	25	30	25	1
742	25	25	30	25	1
743	25	25	30	25	1
744	25	25	30	25	1
745	25	25	30	25	1
746	25	25	30	25	1
747	25	25	30	25	1
748	25	25	30	25	1
749	25	25	30	25	1
750	25	25	30	25	1
751	25	25	30	25	1
752	25	25	30	25	1
753	25	25	30	25	1
754	25	25	30	25	1
755	25	25	30	25	1
756	25	25	30	25	1
757	25	25	30	25	1
758	25	25	30	25	1
759	25	25	30	25	1
760	25	25	30	25	1
761	25	25	30	25	1
762	25	25	30	25	1
763	25	25	30	25	1
764	25	25	30	25	1
765	25	25	30	25	1
766	25	25	30	25	1
767	25	25	30	25	1
768	25	25	30	25	1
769	25	25	30	25	1
770	25	25	30	25	1
771	25	25	30	25	1
772	25	25	30	25	1
773	25	25	30	25	1
774	25	25	30	25	1
775	25	25	30	25	1
776	25	25	30	25	1
777	25	25	30	25	1
778	25	25	30	25	1
779	25	25	30	25	1
780	25	25	30	25	1
781	25	25	30	25	1
782	25	25	30	25	1
783	25	25	30	25	1
784	25	25	30	25	1
785	25	25	30	25	1
786	25	25	30	25	1
787	25	25	30	25	1
788	25	25	30	25	1
789	25	25	30	25	1
790	25	25	30	25	1
791	25	25	30	25	1
792	25	25	30	25	1
793	25	25	30	25	1
794	25	25	30	25	1
795	25	25	30	25	1
796	25	25	30	25	1
797	25	25	30	25	1
798	25	25	30	25	1
799	25	25	30	25	1
800	25	25	30	25	1
801	25	25	30	25	1.1
802	25	25	30	25	1.1
803	25	25	30	25	1.1
804	25	25	30	25	1.1
805	25	25	30	25	1.1
806	25	25	30	25	1.1
807	25	25	30	25	1.1
808	25	25	30	25	1.1
809	25	25	30	25	1.1
810	25	25	30	25	1.1
811	25	25	30	25	1.1
812	25	25	30	25	1.1
813	25	25	30	25	1.1
814	25	25	30	25	1.1
815	25	25	30	25	1.1
816	25	25	30	25	1.1
817	25	25	30	25	1.1
818	25	25	30	25	1.1
819	25	25	30	25	1.1
820	25	25	30	25	1.1
821	25	25	30	25	1.1
822	25	25	30	25	1.1
823	25	25	30	25	1.1
824	25	25	30	25	1.1
825	25	25	30	25	1.1
826	25	25	30	25	1.1
827	25	25	30	25	1.1
828	25	25	30	25	1.1
829	25	25	30	25	1.1
830	25	25	30	25	1.1
831	25	25	30	25	1.1
832	25	25	30	25	1.1
833	25	25	30	25	1.1
834	25	25	30	25	1.1
835	25	25	30	25	1.1
836	25	25	30	25	1.1
837	25	25	30	25	1.1
838	25	25	30	25	1.1
839	25	25	30	25	1.1
840	25	25	30	25	1.1
841	25	25	30	25	1.1
842	25	25	30	25	1.1
843	25	25	30	25	1.1
844	25	25	30	25	1.1
845	25	25	30	25	1.1
846	25	25	30	25	1.1
847	25	25	30	25	1.1
848	25	25	30	25	1.1
849	25	25	30	25	1.1
850	25	25	30	25	1.1
851	25	25	30	25	1.1
852	25	25	30	25	1.1
853	25	25	30	25	1.1
854	25	25	30	25	1.1
855	25	25	30	25	1.1
856	25	25	30	25	1.1
857	25	25	30	25	1.1
858	25	25	30	25	1.1
859	25	25	30	25	1.1
860	25	25	30	25	1.1
861	25	25	30	25	1.1
862	25	25	30	25	1.1
863	25	25	30	25	1.1
864	25	25	30	25	1.1
865	25	25	30	25	1.1
866	25	25	30	25	1.1
867	25	25	30	25	1.1
868	25	25	30	25	1.1
869	25	25	30	25	1.1
870	25	25	30	25	1.1
871	25	25	30	25	1.1
872	25	25	30	25	1.1
873	25	25	30	25	1.1
874	25	25	30	25	1.1
875	25	25	30	25	1.1
876	25	25	30	25	1.1
877	25	25	30	25	1.1
878	25	25	30	25	1.1
879	25	25	30	25	1.1
880	25	25	30	25	1.1
881	25	25	30	25	1.1
882	25	25	30	25	1.1
883	25	25	30	25	1.1
884	25	25	30	25	1.1
885	25	25	30	25	1.1
886	25	25	30	25	1.1
887	25	25	30	25	1.1
888	25	25	30	25	1.1
889	25	25	30	25	1.1
890	25	25	30	25	1.1
891	25	25	30	25	1.1
892	25	25	30	25	1.1
893	25	25	30	25	1.1
894	25	25	30	25	1.1
895	25	25	30	25	1.1
896	25	25	30	25	1.1
897	25	25	30	25	1.1
898	25	25	30	25	1.1
899	25	25	30	25	1.1
900	25	25	30	25	1.1
__________________________

In each data file, the parameter values are given in the below sequence:

1. distance(origins, hubs) - The distance between origins and hubs." 
2. distance(hubs1, hubs2) - The distance between different hubs.
3. distance(hubs, destinations) - The distance between hubs and destinations.
4. Origin Coordinate(origins, X-or-Y) - The origin coordinates 1=X, 2=Y.
5. Hub Coordinate(hubs, X-or-Y) - The hub coordinates 1=X, 2=Y.
6. Destination Coordinate(destinations, X-or-Y) - The destination coordinates 1=X, 2=Y.
7. values(products) - Transportation weights for products.
8. demands(products) - Product demand.
9. Mapping between products and their origin nodes (products, origins).
10. Mapping between products and their destination nodes (products, destinations).
11. ocCost(origins, products) - Origin assignment costs by origins and products.
12. dcCost(destinations, products) - Destination assignment costs by destinations and products.
13. HubCost(hubs) - Hub costs by hubs.
14. CAcap(origins, products) - Origin capacity by origins and products.
15. CPcap(destinations, products) - Destination capacity by destinations and products.

For each parameter, indices are given at first, and parameter values are given subsequently.

************************************************************************************************************************************************************ 
************************************************************ END of DESCRIPTION ****************************************************************************
************************************************************************************************************************************************************ 


 
