An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Table 1

Testing results for Solomons’ instances.

Instance	Optimal solution (cost) [45]	Proposed algorithm solution (cost)	From the optimal (%)	Number of vehicles optimal [45]	Number of vehicles (proposed algorithm)	Comments
c101	828.94	828.94	0	10	10	All constraints met
c201	591.56	591.56	0	3	3	All constraints met
r101	1650.8	1657.26	0.0646	19	18	All constraints met, cost slightly greater but one vehicle less used.
r201	1252.37	1225.14	−0.2723	4	5	All constraints met, but a greater number of vehicles used.
rc101	1696.95	1637.62	−0.5933	15	17	All constraints met, but a greater number of vehicles used.
rc201	1406.94	1375.93	−0.3101	4	5	All constraints met, but a greater number of vehicles used.
c104	824.78	842.61	2.162	10	10	All constraints met and an identical number of vehicles used.
r103	1292.68	1229.76	−4.87	13	15	All constraints met, but a greater number of vehicles used.
rc102	1554.75	1554.92	−0.63	12	13	All constraints met, but a greater number of vehicles used.
rc207	1061.14	1013.35	−4.5	3	5	All constraints met, but a greater number of vehicles used.