Simulation Optimization of Discrete Logistics Processes: A Case Study on Logistics of an E-Commerce Enterprise in Shanghai

Table 1

Raw data.

Name	Latitude	Longitude	Number of orders	Product category	Delivery Time
s1	31.1431986	121.4210407	100	1	100
s2	31.15728268	121.4120836	50	1	150
s3	31.15546308	121.4371571	10	2	0
s4	31.16333719	121.4147434	90	1	200
s5	31.1343072	121.4481408	30	1	250
s6	31.16970695	121.4252906	20	2	0
s7	31.16707584	121.4416896	30	2	0
s8	31.18410185	121.4266127	40	2	0
s9	31.18935785	121.4276737	40	2	0
s10	31.19520877	121.4184094	100	1	300
s11	31.1988346	121.4461166	30	2	0
s12	31.18874702	121.371578	20	2	0
s13	31.20861033	121.4197887	50	1	350
s14	31.2231975	121.3954163	30	2	0
s15	31.27327301	121.4338336	40	2	0
s16	31.26069097	121.4462738	30	2	0
s17	31.26820805	121.4564257	100	1	400
s18	31.28828157	121.4429477	20	2	0
s19	31.29730166	121.4110734	40	2	0
s20	31.24911288	121.4890998	60	1	450
s21	31.27418872	121.4633177	40	2	0
s22	31.27321697	121.4688453	30	2	0
s23	31.2796735	121.4802926	80	1	500
s24	31.30568695	121.4669172	20	2	0
s25	31.23087848	121.4437099	40	2	0
s26	31.28454415	121.5018165	90	1	550
s27	31.26859412	121.5399282	20	2	0
s28	31.28451296	121.5115345	30	2	0
s29	31.29881095	121.499463	100	1	600
s30	31.30787625	121.4993919	90	1	650
s31	31.29473544	121.5409558	30	2	0
s32	31.2911163	121.5489509	80	1	700
s33	31.33387484	121.5288115	40	2	0
s34	31.31553869	121.3699688	30	2	0
s35	31.31371912	121.3505742	20	2	0
s36	31.35066483	121.4538375	40	2	0
s37	31.1259537	121.3866134	20	2	0
s38	31.18216029	121.4056214	40	2	0
s39	31.22972111	121.4052614	100	1	750
s40	31.22416167	121.3773722	30	2	0
s41	31.23514491	121.4078012	40	2	0
s42	31.23383979	121.4318426	30	2	0
s43	31.24339301	121.418824	20	2	0
s44	31.24539919	121.4137998	30	2	0
s45	31.23813096	121.355908	40	2	0
s46	31.25328122	121.3872342	30	2	0
s47	31.26148927	121.4184197	20	2	0
s48	31.25603512	121.3732177	30	2	0
s49	31.26155358	121.3545372	30	2	0
s50	31.28576633	121.3412785	40	2	0
s51	31.19722065	121.4698344	40	2	0