An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Abstract

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing Problems

“…There are also ABC algorithms for the Vehicle Routing Problem (VRP) [48] and its variations [17,18]. However, there is only one ABC algorithm for the CARP and even that is for just a variation of CARP, the undirected CARP with profits [19].…”

“…As the next step, the algorithm calculates the winning probability values p i for the solutions S i (lines [16][17][18][19]. The probability values p i are calculated with the same function as in the basic ABC algorithm (Equation ( 13)).…”

“…From the resulted solutions, the best one is chosen with the selectBestSolution function as the new S i,j (line 16). If the new S i,j is better than the currently known best solution in the neighborhood of S i,j (i.e., S * i,j ), then it is set as the new best solution S * i,j (lines [17][18][19]. Otherwise, the age of S * i,j (i.e., α * i,j ) is increased by one (lines 20-21).…”

“…The Artificial Bee Colony (ABC) algorithm is a swarm intelligence-based algorithm for optimization problems [13]. It was successfully applied on multiple combinatorial optimization problems that are similar to the CARP [14][15][16][17][18][19] and was shown that the ABC algorithm provides better performance than most of the evolutionary computation-based optimization algorithms [16]. However, the ABC algorithm was never applied before, neither on the CARP nor on the DCARP.…”

Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

“…In addition to forming mathematical models, previous studies have applied or developed an efficient algorithm to search for solutions quickly. To find optimal solutions, algorithms include the following: an estimation of distribution, heuristic, artificial bee colony, local search, response surface method, biased random-key genetic, exact branch-price-andcut algorithm, tabu search, and simulated annealing (Qi and Hu, 2020;Kantawong and Pravesjit, 2020;Londoño et al, 2020;Aggarwal and Kumar, 2019;Pérez-Rodríguez and Hernández-Aguirre, 2019;Tasar et al, 2019;Setamanit, 2019Zhu andHu, 2019;Ruiz et al, 2019;Zhang et al, 2017;Huang et al, 2017;Afifi et al, 2016;Spliet and Desaulniers, 2015).…”