Presenting a Fuzzy Multiobjective Mathematical Model of the Reverse Logistics Supply Chain Network in the Automotive Industry to Reduce Time and Energy

Table 8

Computational results of solving the model with small and medium dimensions.

Numbers	(PSO)				Genetic algorithms
	Z1	Z2		Time	Z1	Z2		Time	Dev%
1	80213561.6	0.993394	0.016172	30.39	80012075.1	0.983841	0.102114	5.41	0.08085
2	80684332.1	1.020138	0.118273	48.58	80428613.8	1.030593	0.151098	59.61	−0.0278
3	80695703.2	1.074841	0.15742	14.89	80670872.4	1.079542	0.165343	65.84	−0.005
4	80695638.9	1.091239	0.15451	33.117	80688404.2	1.10473	0.15064	3.124	−0.0956
5	80757659.3	1.110258	0.33424	48.192	80826518.9	1.119485	0839751	14.216	−0.01512
6	81082510.5	1.113359	0.020759	27.361	82008390.7	1.119468	0.067699	19.402	−0.02261
7	82193529.8	1.065134	0.022115	64.480	83022644.4	1.072373	0.028505	25.487	−0.0289