The NR-EGA for the EVRP Problem with the Electric Energy Consumption Model

Abstract

Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.

Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

“…GVRP is an NP-hard problem, and solved by many modern heuristic optimization algorithms, such as neighborhood search, particle swarm optimization, GA, etc. Among these algorithms, GA is proved to be an effective and powerful algorithm [23]. The object of GA is the population, and each individual in the population is called a chromosome, which consists of genes and represents a solution to the problem according to a certain coding.…”

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

“…6. Likewise, the optimal routes obtained by IABC on other three instances are {0, 18, 17, 4, 2, 1, 0, 3, 20, 19, 9, 8, 0, 15, 16, 0, 5, 6, 7, 0, 10, 14, 13, 11, 12, 0}, {0, 3, 19, 20, 4, 0, 6, 1, 0, 7, 8, 9, 5, 0, 10, 23, 18, 0, 15, 14, 11, 0, 2, 21, 0, 17, 13, 12, 0, 22, 16, 25, 24, 0}, and {0, 28, 30, 9, 10, 0, 23,26,18,0,11,29,25,4,0,27,19,17,5,3,7, 0, 2, 15, 14, 0, 24, 8, 21, 1, 20, 0, 16, 22, 6, 0, 12, 13, 0}, respectively. Figures 7−9 depict their routes.…”

“…CPLEX [13,14] , liner programming [15] , and branch-and-price algorithm [16] , as exact optimization techniques, can obtain satisfied but local optimal solution. With the development of intelligence optimization methods, variable neighborhood search [17] , genetic algorithm [18] , ant colony optimization [19] , and artificial bee colony algorithm (ABC) [20] have been employed to achieve the optimal routing scheme within acceptable computational time. Among them, artificial bee colony algorithm inspired by bee colony behavior has simpler structure with few parameters and thus has been widely applied in solving combinatorial optimization problems, such as traveling salesman problem [21] , arc routing problem [22] , and vehicle routing problem [23,24] .…”