DVRPDomestic Violence Resource Project (Washington, DC)
DVRPDistance Vector Routing Protocol
DVRPDigital Video Recorder and Processor
DVRPDomestic Violence Recovery Program (California)
DVRPDisaster Vulnerability Reduction Project (World Bank)
References in periodicals archive ?
Most studies proposed for DVRP with reoptimization strategies focus on obtaining reduced computation times because the goal of dynamic VRP is obtaining information on routes as soon as possible, especially in real-world situations, for which it is necessary to transmit route adaptation information to a driver.
However, stochastic and dynamic VRPs (DVRPs) are not mutually exclusive.
For the DVRP, the goal of algorithm is not only to search optimum solution, but also to track the optimal solution over time by information of the previous search space.
In order to better meet dynamic environment, a great number of strategies are introduced to enhance ACO for resolving the DVRP. These can be summarized as (a) maintaining diversity by immigrant schemes [22], (b) memory-based methods [23], (c) multiple population approaches [24], and (d) clustering based algorithms [25].
In this paper, we design an enhanced ACO to solve different scale DVRP. A large number of actual instances show that ACO algorithms can efficiently solve optimization problems in different fields, including the Feature Subset Selection [26], Set Covering Problem [27], and Wireless Sensor Networks [28].
The first is that this paper solves DVRP by enhanced ACO which tries best to improve the degree of randomization and avoid falling into local search prematurely.
Therefore, dynamic vehicle routing problems (DVRPs) are more prominent in the network intensive vehicle scheduling problem.
First, according to the characteristic of DVRP, we gave the graph expression and formulated the mathematical model.
The DVRP problem is closely related to the actual production and life.
Due to the complexity of the problem, the current solving quality and efficiency for the DVRP are far from the practical requirements.
Larsen [19] defined DVRP with two aspects: not all information relevant to the planning of the routes is known by the planner when the routing process begins; information can change after the initial routes have been constructed.
By the nearest distance cluster algorithm, a whole DMDVRP is divided into several little DVRPs; meanwhile the whole service area is divided into a set of little regions.