Parameter tuning for a cooperative parallel implementation of process-network synthesis algorithms

Abstract

Process-network synthesis is the determination of the optimal network structure of a process system together with optimal configurations and capacities of the operating units incorporated into the system. The aim of developing more and more sophisticated solver algorithms is to find the optimum as fast as possible and increase the circle of practically solvable process synthesis problems. The P-graph framework can effectively reduce the number of structures to be examined and accelerate the computation searching for the optimum due to the exploitation of combinatorial characteristics of candidate solution structures. A cooperative parallel implementation of P-graph algorithms have been published recently to exploit the capabilities of multi-core and multiprocessor systems (Bartos and Bertok in De Gruyter Ser Logic Appl 1:303–313, 2015). The parallel implementation has increased performance significantly but this can be further improved by fine tuning the parameters of the parallel algorithm. Outcomes of experiments on parameter optimization are to be presented herein.

Appendix

Test base available at “p-graph.org”.

Test base properties:

Name	# of op.units	# of materials	Difficulty	Name	# of op.units	# of materials	Difficulty	Name	# of op.units	# of materials	Difficulty
0	9	14	6	50	18	27	7	100	50	81	7
1	10	20	4	51	18	31	0	101	50	95	14
2	10	25	6	52	18	30	12	102	50	85	8
3	10	18	4	53	24	48	9	103	50	80	9
4	10	15	5	54	24	37	1	104	50	82	0
5	10	14	6	55	29	46	0	105	50	86	12
6	11	14	4	56	29	52	0	106	50	101	0
7	11	21	3	57	30	57	9	107	50	85	0
8	11	17	1	58	30	55	5	108	50	80	8
9	11	18	7	59	30	49	9	109	50	90	0
10	12	23	7	60	30	50	10	110	50	86	0
11	12	20	0	61	30	53	11	111	50	68	5
12	13	16	5	62	30	53	4	112	54	74	25
13	13	22	0	63	30	48	16	113	55	97	3
14	13	26	2	64	30	48	3	114	68	102	5
15	13	20	6	65	30	49	8	115	70	101	30
16	14	22	7	66	30	54	19	116	70	109	24
17	14	25	0	67	30	53	12	117	70	117	0
18	14	28	7	68	30	53	10	118	70	120	13
19	14	14	6	69	30	58	13	119	70	111	2
20	15	27	3	70	30	50	0	120	70	108	15
21	15	28	10	71	30	51	0	121	70	115	11
22	15	26	10	72	30	40	0	122	70	115	14
23	15	24	7	73	30	44	13	123	70	123	0
24	15	31	3	74	30	41	0	124	70	104	19
25	15	23	8	75	30	48	0	125	70	113	18
26	15	24	6	76	30	50	0	126	70	112	30
27	15	27	8	77	35	56	3	127	70	108	20
28	15	31	5	78	35	57	3	128	70	117	18
29	15	31	4	79	38	65	17	129	70	107	19
30	15	22	2	80	38	55	15	130	70	113	6
31	15	26	6	81	40	55	14	131	70	115	4
32	15	22	5	82	43	74	7	132	70	109	4
33	15	33	5	83	46	67	9	133	70	115	14
34	15	29	5	84	46	68	0	134	70	101	26
35	15	29	7	85	47	77	20	135	70	102	7
36	15	24	7	86	50	78	0	136	90	140	5
37	15	30	3	87	50	84	8	137	90	158	13
38	15	27	0	88	50	93	15	138	90	143	3
39	15	26	0	89	50	81	2	139	90	160	26
40	15	25	0	90	50	84	15	140	90	133	4
41	15	29	0	91	50	80	7	141	90	148	3
42	15	22	0	92	50	79	9	142	90	140	1
43	15	23	0	93	50	90	8	143	90	155	41
44	15	26	0	94	50	81	10	144	90	142	23
45	15	25	0	95	50	87	21	145	90	149	28
46	15	23	0	96	50	78	5	146	90	136	4
47	15	33	6	97	50	83	19	147	90	143	2
48	15	21	8	98	50	87	13	148	90	149	3
49	17	28	1	99	50	73	12	149	90	143	8