**************************************************************************************************************************************
**************** This description is for the SET1 data set generated for the computational tests of the paper titled ***************
*************** "Machine Learning-empowered Benders Decomposition for Flow Hub Location in E-Commerce"    ***************
***************************************************************************************************************************************

This data set contains 390 test instances. The parameter settings of all 390 test instances are given as below: 

_______________________________________________________________________
ID	|O|	|D|	|K|	|H|	(C_o, C_d)	C_e*0.0001	C_w	C_a	C_f	C_t	C_c
_______________________________________________________________________
101	60	60	50	50	4	0.00003	1	1	1	1	1.1
102	60	60	50	50	4	0.00003	1	1	1	1	1.1
103	60	60	50	50	4	0.00003	1	1	1	1	1.1
104	60	60	50	50	4	0.00003	1	1	1	1	1.1
105	60	60	50	50	4	0.00003	1	1	1	1	1.1
106	60	60	50	50	4	0.00003	1	1	1	1	1.1
107	60	60	50	50	4	0.00003	1	1	1	1	1.1
108	60	60	50	50	4	0.00003	1	1	1	1	1.1
109	60	60	50	50	4	0.00003	1	1	1	1	1.1
110	60	60	50	50	4	0.00003	1	1	1	1	1.1
116	60	60	50	50	4	0.00003	1	1	1	1	1
117	60	60	50	50	4	0.00003	1	1	1	1	1
118	60	60	50	50	4	0.00003	1	1	1	1	1
119	60	60	50	50	4	0.00003	1	1	1	1	1
120	60	60	50	50	4	0.00003	1	1	1	1	1
121	60	60	50	50	4	0.00003	1	1	1	1	1
122	60	60	50	50	4	0.00003	1	1	1	1	1
123	60	60	50	50	4	0.00003	1	1	1	1	1
124	60	60	50	50	4	0.00003	1	1	1	1	1
125	60	60	50	50	4	0.00003	1	1	1	1	1
131	60	60	50	50	4	0.00003	1	1	1	1	0.7
132	60	60	50	50	4	0.00003	1	1	1	1	0.7
133	60	60	50	50	4	0.00003	1	1	1	1	0.7
134	60	60	50	50	4	0.00003	1	1	1	1	0.7
135	60	60	50	50	4	0.00003	1	1	1	1	0.7
136	60	60	50	50	4	0.00003	1	1	1	1	0.7
137	60	60	50	50	4	0.00003	1	1	1	1	0.7
140	60	60	50	50	4	0.00003	1	1	1	1	0.7
141	60	60	50	50	4	0.00003	1	1	1	1	0.7
142	60	60	50	50	4	0.00003	1	1	1	1	0.7
146	60	60	50	50	4	0.00001	1	1	1	1	1.1
147	60	60	50	50	4	0.00001	1	1	1	1	1.1
148	60	60	50	50	4	0.00001	1	1	1	1	1.1
149	60	60	50	50	4	0.00001	1	1	1	1	1.1
150	60	60	50	50	4	0.00001	1	1	1	1	1.1
151	60	60	50	50	4	0.00001	1	1	1	1	1.1
152	60	60	50	50	4	0.00001	1	1	1	1	1.1
153	60	60	50	50	4	0.00001	1	1	1	1	1.1
154	60	60	50	50	4	0.00001	1	1	1	1	1.1
155	60	60	50	50	4	0.00001	1	1	1	1	1.1
161	60	60	50	50	4	0.00001	1	1	1	1	1
162	60	60	50	50	4	0.00001	1	1	1	1	1
163	60	60	50	50	4	0.00001	1	1	1	1	1
164	60	60	50	50	4	0.00001	1	1	1	1	1
165	60	60	50	50	4	0.00001	1	1	1	1	1
166	60	60	50	50	4	0.00001	1	1	1	1	1
167	60	60	50	50	4	0.00001	1	1	1	1	1
168	60	60	50	50	4	0.00001	1	1	1	1	1
169	60	60	50	50	4	0.00001	1	1	1	1	1
170	60	60	50	50	4	0.00001	1	1	1	1	1
176	60	60	50	50	4	0.00001	1	1	1	1	0.7
177	60	60	50	50	4	0.00001	1	1	1	1	0.7
178	60	60	50	50	4	0.00001	1	1	1	1	0.7
179	60	60	50	50	4	0.00001	1	1	1	1	0.7
180	60	60	50	50	4	0.00001	1	1	1	1	0.7
181	60	60	50	50	4	0.00001	1	1	1	1	0.7
182	60	60	50	50	4	0.00001	1	1	1	1	0.7
185	60	60	50	50	4	0.00001	1	1	1	1	0.7
186	60	60	50	50	4	0.00001	1	1	1	1	0.7
187	60	60	50	50	4	0.00001	1	1	1	1	0.7
191	60	60	50	50	4	0.00006	1	1	1	1	1.1
192	60	60	50	50	4	0.00006	1	1	1	1	1.1
193	60	60	50	50	4	0.00006	1	1	1	1	1.1
194	60	60	50	50	4	0.00006	1	1	1	1	1.1
195	60	60	50	50	4	0.00006	1	1	1	1	1.1
196	60	60	50	50	4	0.00006	1	1	1	1	1.1
197	60	60	50	50	4	0.00006	1	1	1	1	1.1
198	60	60	50	50	4	0.00006	1	1	1	1	1.1
199	60	60	50	50	4	0.00006	1	1	1	1	1.1
200	60	60	50	50	4	0.00006	1	1	1	1	1.1
206	60	60	50	50	4	0.00006	1	1	1	1	1
207	60	60	50	50	4	0.00006	1	1	1	1	1
208	60	60	50	50	4	0.00006	1	1	1	1	1
209	60	60	50	50	4	0.00006	1	1	1	1	1
210	60	60	50	50	4	0.00006	1	1	1	1	1
211	60	60	50	50	4	0.00006	1	1	1	1	1
212	60	60	50	50	4	0.00006	1	1	1	1	1
213	60	60	50	50	4	0.00006	1	1	1	1	1
214	60	60	50	50	4	0.00006	1	1	1	1	1
215	60	60	50	50	4	0.00006	1	1	1	1	1
221	60	60	50	50	4	0.00006	1	1	1	1	0.7
222	60	60	50	50	4	0.00006	1	1	1	1	0.7
223	60	60	50	50	4	0.00006	1	1	1	1	0.7
224	60	60	50	50	4	0.00006	1	1	1	1	0.7
225	60	60	50	50	4	0.00006	1	1	1	1	0.7
226	60	60	50	50	4	0.00006	1	1	1	1	0.7
227	60	60	50	50	4	0.00006	1	1	1	1	0.7
230	60	60	50	50	4	0.00006	1	1	1	1	0.7
231	60	60	50	50	4	0.00006	1	1	1	1	0.7
232	60	60	50	50	4	0.00006	1	1	1	1	0.7
236	60	60	50	50	3	0.00003	1	1	1	1	1.1
237	60	60	50	50	3	0.00003	1	1	1	1	1.1
238	60	60	50	50	3	0.00003	1	1	1	1	1.1
239	60	60	50	50	3	0.00003	1	1	1	1	1.1
240	60	60	50	50	3	0.00003	1	1	1	1	1.1
241	60	60	50	50	3	0.00003	1	1	1	1	1.1
242	60	60	50	50	3	0.00003	1	1	1	1	1.1
243	60	60	50	50	3	0.00003	1	1	1	1	1.1
244	60	60	50	50	3	0.00003	1	1	1	1	1.1
245	60	60	50	50	3	0.00003	1	1	1	1	1.1
251	60	60	50	50	3	0.00003	1	1	1	1	1
252	60	60	50	50	3	0.00003	1	1	1	1	1
253	60	60	50	50	3	0.00003	1	1	1	1	1
254	60	60	50	50	3	0.00003	1	1	1	1	1
255	60	60	50	50	3	0.00003	1	1	1	1	1
256	60	60	50	50	3	0.00003	1	1	1	1	1
257	60	60	50	50	3	0.00003	1	1	1	1	1
258	60	60	50	50	3	0.00003	1	1	1	1	1
259	60	60	50	50	3	0.00003	1	1	1	1	1
260	60	60	50	50	3	0.00003	1	1	1	1	1
266	60	60	50	50	3	0.00003	1	1	1	1	0.7
267	60	60	50	50	3	0.00003	1	1	1	1	0.7
268	60	60	50	50	3	0.00003	1	1	1	1	0.7
269	60	60	50	50	3	0.00003	1	1	1	1	0.7
270	60	60	50	50	3	0.00003	1	1	1	1	0.7
271	60	60	50	50	3	0.00003	1	1	1	1	0.7
272	60	60	50	50	3	0.00003	1	1	1	1	0.7
275	60	60	50	50	3	0.00003	1	1	1	1	0.7
276	60	60	50	50	3	0.00003	1	1	1	1	0.7
277	60	60	50	50	3	0.00003	1	1	1	1	0.7
281	60	60	50	50	6	0.00003	1	1	1	1	1.1
282	60	60	50	50	6	0.00003	1	1	1	1	1.1
283	60	60	50	50	6	0.00003	1	1	1	1	1.1
284	60	60	50	50	6	0.00003	1	1	1	1	1.1
285	60	60	50	50	6	0.00003	1	1	1	1	1.1
286	60	60	50	50	6	0.00003	1	1	1	1	1.1
287	60	60	50	50	6	0.00003	1	1	1	1	1.1
288	60	60	50	50	6	0.00003	1	1	1	1	1.1
289	60	60	50	50	6	0.00003	1	1	1	1	1.1
290	60	60	50	50	6	0.00003	1	1	1	1	1.1
296	60	60	50	50	6	0.00003	1	1	1	1	1
297	60	60	50	50	6	0.00003	1	1	1	1	1
298	60	60	50	50	6	0.00003	1	1	1	1	1
299	60	60	50	50	6	0.00003	1	1	1	1	1
300	60	60	50	50	6	0.00003	1	1	1	1	1
301	60	60	50	50	6	0.00003	1	1	1	1	1
302	60	60	50	50	6	0.00003	1	1	1	1	1
303	60	60	50	50	6	0.00003	1	1	1	1	1
304	60	60	50	50	6	0.00003	1	1	1	1	1
305	60	60	50	50	6	0.00003	1	1	1	1	1
311	60	60	50	50	6	0.00003	1	1	1	1	0.7
312	60	60	50	50	6	0.00003	1	1	1	1	0.7
313	60	60	50	50	6	0.00003	1	1	1	1	0.7
314	60	60	50	50	6	0.00003	1	1	1	1	0.7
315	60	60	50	50	6	0.00003	1	1	1	1	0.7
316	60	60	50	50	6	0.00003	1	1	1	1	0.7
317	60	60	50	50	6	0.00003	1	1	1	1	0.7
320	60	60	50	50	6	0.00003	1	1	1	1	0.7
321	60	60	50	50	6	0.00003	1	1	1	1	0.7
322	60	60	50	50	6	0.00003	1	1	1	1	0.7
326	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
327	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
328	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
329	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
330	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
331	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
332	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
333	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
334	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
335	60	60	50	50	4	0.00003	1	1	0.1	1	1.1
341	60	60	50	50	4	0.00003	1	1	0.1	1	1
342	60	60	50	50	4	0.00003	1	1	0.1	1	1
343	60	60	50	50	4	0.00003	1	1	0.1	1	1
344	60	60	50	50	4	0.00003	1	1	0.1	1	1
345	60	60	50	50	4	0.00003	1	1	0.1	1	1
346	60	60	50	50	4	0.00003	1	1	0.1	1	1
347	60	60	50	50	4	0.00003	1	1	0.1	1	1
348	60	60	50	50	4	0.00003	1	1	0.1	1	1
349	60	60	50	50	4	0.00003	1	1	0.1	1	1
350	60	60	50	50	4	0.00003	1	1	0.1	1	1
356	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
357	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
358	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
359	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
360	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
361	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
362	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
365	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
366	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
367	60	60	50	50	4	0.00003	1	1	0.1	1	0.7
371	60	60	50	50	4	0.00003	1	1	10	1	1.1
372	60	60	50	50	4	0.00003	1	1	10	1	1.1
373	60	60	50	50	4	0.00003	1	1	10	1	1.1
374	60	60	50	50	4	0.00003	1	1	10	1	1.1
375	60	60	50	50	4	0.00003	1	1	10	1	1.1
376	60	60	50	50	4	0.00003	1	1	10	1	1.1
377	60	60	50	50	4	0.00003	1	1	10	1	1.1
378	60	60	50	50	4	0.00003	1	1	10	1	1.1
379	60	60	50	50	4	0.00003	1	1	10	1	1.1
380	60	60	50	50	4	0.00003	1	1	10	1	1.1
386	60	60	50	50	4	0.00003	1	1	10	1	1
387	60	60	50	50	4	0.00003	1	1	10	1	1
388	60	60	50	50	4	0.00003	1	1	10	1	1
389	60	60	50	50	4	0.00003	1	1	10	1	1
390	60	60	50	50	4	0.00003	1	1	10	1	1
391	60	60	50	50	4	0.00003	1	1	10	1	1
392	60	60	50	50	4	0.00003	1	1	10	1	1
393	60	60	50	50	4	0.00003	1	1	10	1	1
394	60	60	50	50	4	0.00003	1	1	10	1	1
395	60	60	50	50	4	0.00003	1	1	10	1	1
401	60	60	50	50	4	0.00003	1	1	10	1	0.7
402	60	60	50	50	4	0.00003	1	1	10	1	0.7
403	60	60	50	50	4	0.00003	1	1	10	1	0.7
404	60	60	50	50	4	0.00003	1	1	10	1	0.7
405	60	60	50	50	4	0.00003	1	1	10	1	0.7
406	60	60	50	50	4	0.00003	1	1	10	1	0.7
407	60	60	50	50	4	0.00003	1	1	10	1	0.7
410	60	60	50	50	4	0.00003	1	1	10	1	0.7
411	60	60	50	50	4	0.00003	1	1	10	1	0.7
412	60	60	50	50	4	0.00003	1	1	10	1	0.7
416	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
417	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
418	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
419	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
420	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
421	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
422	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
423	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
424	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
425	60	60	50	50	4	0.00003	1	0.1	1	1	1.1
431	60	60	50	50	4	0.00003	1	0.1	1	1	1
432	60	60	50	50	4	0.00003	1	0.1	1	1	1
433	60	60	50	50	4	0.00003	1	0.1	1	1	1
434	60	60	50	50	4	0.00003	1	0.1	1	1	1
435	60	60	50	50	4	0.00003	1	0.1	1	1	1
436	60	60	50	50	4	0.00003	1	0.1	1	1	1
437	60	60	50	50	4	0.00003	1	0.1	1	1	1
438	60	60	50	50	4	0.00003	1	0.1	1	1	1
439	60	60	50	50	4	0.00003	1	0.1	1	1	1
440	60	60	50	50	4	0.00003	1	0.1	1	1	1
446	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
447	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
448	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
449	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
450	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
451	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
452	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
455	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
456	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
457	60	60	50	50	4	0.00003	1	0.1	1	1	0.7
461	60	60	50	50	4	0.00003	1	10	1	1	1.1
462	60	60	50	50	4	0.00003	1	10	1	1	1.1
463	60	60	50	50	4	0.00003	1	10	1	1	1.1
464	60	60	50	50	4	0.00003	1	10	1	1	1.1
465	60	60	50	50	4	0.00003	1	10	1	1	1.1
466	60	60	50	50	4	0.00003	1	10	1	1	1.1
467	60	60	50	50	4	0.00003	1	10	1	1	1.1
468	60	60	50	50	4	0.00003	1	10	1	1	1.1
469	60	60	50	50	4	0.00003	1	10	1	1	1.1
470	60	60	50	50	4	0.00003	1	10	1	1	1.1
476	60	60	50	50	4	0.00003	1	10	1	1	1
477	60	60	50	50	4	0.00003	1	10	1	1	1
478	60	60	50	50	4	0.00003	1	10	1	1	1
479	60	60	50	50	4	0.00003	1	10	1	1	1
480	60	60	50	50	4	0.00003	1	10	1	1	1
481	60	60	50	50	4	0.00003	1	10	1	1	1
482	60	60	50	50	4	0.00003	1	10	1	1	1
483	60	60	50	50	4	0.00003	1	10	1	1	1
484	60	60	50	50	4	0.00003	1	10	1	1	1
485	60	60	50	50	4	0.00003	1	10	1	1	1
491	60	60	50	50	4	0.00003	1	10	1	1	0.7
492	60	60	50	50	4	0.00003	1	10	1	1	0.7
493	60	60	50	50	4	0.00003	1	10	1	1	0.7
494	60	60	50	50	4	0.00003	1	10	1	1	0.7
495	60	60	50	50	4	0.00003	1	10	1	1	0.7
496	60	60	50	50	4	0.00003	1	10	1	1	0.7
497	60	60	50	50	4	0.00003	1	10	1	1	0.7
500	60	60	50	50	4	0.00003	1	10	1	1	0.7
501	60	60	50	50	4	0.00003	1	10	1	1	0.7
502	60	60	50	50	4	0.00003	1	10	1	1	0.7
506	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
507	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
508	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
509	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
510	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
511	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
512	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
513	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
514	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
515	60	60	50	50	4	0.00003	0.1	1	1	1	1.1
521	60	60	50	50	4	0.00003	0.1	1	1	1	1
522	60	60	50	50	4	0.00003	0.1	1	1	1	1
523	60	60	50	50	4	0.00003	0.1	1	1	1	1
524	60	60	50	50	4	0.00003	0.1	1	1	1	1
525	60	60	50	50	4	0.00003	0.1	1	1	1	1
526	60	60	50	50	4	0.00003	0.1	1	1	1	1
527	60	60	50	50	4	0.00003	0.1	1	1	1	1
528	60	60	50	50	4	0.00003	0.1	1	1	1	1
529	60	60	50	50	4	0.00003	0.1	1	1	1	1
530	60	60	50	50	4	0.00003	0.1	1	1	1	1
536	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
537	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
538	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
539	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
540	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
541	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
542	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
545	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
546	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
547	60	60	50	50	4	0.00003	0.1	1	1	1	0.7
551	60	60	50	50	4	0.00003	10	1	1	1	1.1
552	60	60	50	50	4	0.00003	10	1	1	1	1.1
553	60	60	50	50	4	0.00003	10	1	1	1	1.1
554	60	60	50	50	4	0.00003	10	1	1	1	1.1
555	60	60	50	50	4	0.00003	10	1	1	1	1.1
556	60	60	50	50	4	0.00003	10	1	1	1	1.1
557	60	60	50	50	4	0.00003	10	1	1	1	1.1
558	60	60	50	50	4	0.00003	10	1	1	1	1.1
559	60	60	50	50	4	0.00003	10	1	1	1	1.1
560	60	60	50	50	4	0.00003	10	1	1	1	1.1
566	60	60	50	50	4	0.00003	10	1	1	1	1
567	60	60	50	50	4	0.00003	10	1	1	1	1
568	60	60	50	50	4	0.00003	10	1	1	1	1
569	60	60	50	50	4	0.00003	10	1	1	1	1
570	60	60	50	50	4	0.00003	10	1	1	1	1
571	60	60	50	50	4	0.00003	10	1	1	1	1
572	60	60	50	50	4	0.00003	10	1	1	1	1
573	60	60	50	50	4	0.00003	10	1	1	1	1
574	60	60	50	50	4	0.00003	10	1	1	1	1
575	60	60	50	50	4	0.00003	10	1	1	1	1
581	60	60	50	50	4	0.00003	10	1	1	1	0.7
582	60	60	50	50	4	0.00003	10	1	1	1	0.7
583	60	60	50	50	4	0.00003	10	1	1	1	0.7
584	60	60	50	50	4	0.00003	10	1	1	1	0.7
585	60	60	50	50	4	0.00003	10	1	1	1	0.7
586	60	60	50	50	4	0.00003	10	1	1	1	0.7
587	60	60	50	50	4	0.00003	10	1	1	1	0.7
590	60	60	50	50	4	0.00003	10	1	1	1	0.7
591	60	60	50	50	4	0.00003	10	1	1	1	0.7
592	60	60	50	50	4	0.00003	10	1	1	1	0.7
596	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
597	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
598	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
599	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
600	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
601	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
602	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
603	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
604	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
605	60	60	50	50	4	0.00003	1	1	1	0.1	1.1
611	60	60	50	50	4	0.00003	1	1	1	0.1	1
612	60	60	50	50	4	0.00003	1	1	1	0.1	1
613	60	60	50	50	4	0.00003	1	1	1	0.1	1
614	60	60	50	50	4	0.00003	1	1	1	0.1	1
615	60	60	50	50	4	0.00003	1	1	1	0.1	1
616	60	60	50	50	4	0.00003	1	1	1	0.1	1
617	60	60	50	50	4	0.00003	1	1	1	0.1	1
618	60	60	50	50	4	0.00003	1	1	1	0.1	1
619	60	60	50	50	4	0.00003	1	1	1	0.1	1
620	60	60	50	50	4	0.00003	1	1	1	0.1	1
626	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
627	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
628	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
629	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
630	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
631	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
632	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
635	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
636	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
637	60	60	50	50	4	0.00003	1	1	1	0.1	0.7
641	60	60	50	50	4	0.00003	1	1	1	10	1.1
642	60	60	50	50	4	0.00003	1	1	1	10	1.1
643	60	60	50	50	4	0.00003	1	1	1	10	1.1
644	60	60	50	50	4	0.00003	1	1	1	10	1.1
645	60	60	50	50	4	0.00003	1	1	1	10	1.1
646	60	60	50	50	4	0.00003	1	1	1	10	1.1
647	60	60	50	50	4	0.00003	1	1	1	10	1.1
648	60	60	50	50	4	0.00003	1	1	1	10	1.1
649	60	60	50	50	4	0.00003	1	1	1	10	1.1
650	60	60	50	50	4	0.00003	1	1	1	10	1.1
656	60	60	50	50	4	0.00003	1	1	1	10	1
657	60	60	50	50	4	0.00003	1	1	1	10	1
658	60	60	50	50	4	0.00003	1	1	1	10	1
659	60	60	50	50	4	0.00003	1	1	1	10	1
660	60	60	50	50	4	0.00003	1	1	1	10	1
661	60	60	50	50	4	0.00003	1	1	1	10	1
662	60	60	50	50	4	0.00003	1	1	1	10	1
663	60	60	50	50	4	0.00003	1	1	1	10	1
664	60	60	50	50	4	0.00003	1	1	1	10	1
665	60	60	50	50	4	0.00003	1	1	1	10	1
671	60	60	50	50	4	0.00003	1	1	1	10	0.7
672	60	60	50	50	4	0.00003	1	1	1	10	0.7
673	60	60	50	50	4	0.00003	1	1	1	10	0.7
674	60	60	50	50	4	0.00003	1	1	1	10	0.7
675	60	60	50	50	4	0.00003	1	1	1	10	0.7
676	60	60	50	50	4	0.00003	1	1	1	10	0.7
677	60	60	50	50	4	0.00003	1	1	1	10	0.7
680	60	60	50	50	4	0.00003	1	1	1	10	0.7
681	60	60	50	50	4	0.00003	1	1	1	10	0.7
682	60	60	50	50	4	0.00003	1	1	1	10	0.7
_______________________________________________________________________


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.

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