时间窗约束下的车辆路径问题遗传算法外文翻译可编辑.docx

1、时间窗约束下的车辆路径问题遗传算法外文翻译可编辑时间窗约束下的车辆路径问题遗传算法外文翻译 外文翻译原文Genetic Algorithms for the Vehicle Routing Problem with Time WindowsMaterial Source: Special issue on Bioinformatics and Genetic AlgorithmsAuthor: Olli Br?ysy1 Introduction Vehicle Routing Problems VRP are all around us in the sense that many consu

2、mer products such as soft drinks, beer, bread, snack foods, gasoline and pharmaceuticals are delivered to retail outlets by a fleet of trucks whose operation fits the vehicle routing model. In practice, the VRP has been recognized as one of the great success stories of operations research and it has

3、 been studied widely since the late fifties. Public services can also take advantage of these systems in order to improve their logistics chain. Garbage collection, or town cleaning, takes an ever increasing part of the budget of local authorities A typical vehicle routing problem can be described a

4、s the problem of designing least cost routes from one depot to a set of geographically scattered points cities, stores, warehouses, schools, customers etc. The routes must be designed in such a way that each point is visited only once by exactly one vehicle, all routes start and end at the depot, an

5、d the total demands of all points on one route must not exceed the capacity of the vehicle The Vehicle Routing Problem with Time WindowsVRPTW is a generalization of the VRP involving the added complexity that every customer should be served within a given time window. Additional complexities encount

6、ered in the VRPTW are length of route constraint arising from depot time windows and cost of waiting time, which is incurred when a vehicle arrives too early at a customer location. Specific examples of problems with time windows include bank deliveries, postal deliveries, industrial refuse collecti

7、on, school-bus routing and situations where the customer must provide access, verification, or payment upon delivery of the product or serviceSolomon and Desrosiers, 1988. Besides being one of the most important problems of operations research in practical terms, the vehicle routing problem is also

8、one of the most difficult problems to solve. It is quite close to one of the most famous combinatorial optimization problems, the Traveling Salesperson ProblemTSP, where only one person has to visit all the customers. The TSP is an NP-hard problem. It is believed that one may never find a computatio

9、nal technique that will guarantee optimal solutions to larger instances for such problems. The vehicle routing problem is even more complicated. Even for small fleet sizes and a moderate number of transportation requests, the planning task is highly complex. Hence, it is not surprising that human pl

10、anners soon get overwhelmed, and must turn to simple, local rules for vehicle routing. Next we will describe basic principles of genetic algorithms and some applications for vehicle routing problem with time windows.2 General principles of genetic algorithms The Genetic Algorithm GA is an adaptive h

11、euristic search method based on population genetics. The basic concepts are developed by Holland, 1975, while the practicality of using the GA to solve complex problems is demonstrated in De Jong, 1975 and Goldberg, 1989. References and details about genetic algorithms can also be found for example

12、in Alander, 2000 and Mhlenbein, 1997 respectively. The creation of a new generation of individuals involves primarily four major steps or phases: representation, selection, recombination and mutation. The representation of the solution space consists of encoding significant features of a solution as

13、 a chromosome, defining an individual member of a population. Typically pictured by a bit string, a chromosome is made up of a sequence of genes, which capture the basic characteristics of a solution. The recombination or reproduction process makes use of genes of selected parents to produce offspri

14、ng that will form the next generation. It combines characteristics of chromosomes to potentially create offspring with better fitness. As for mutation, it consists of randomly modifying genes of a single individual at a time to further explore the solution space and ensure, or preserve, genetic dive

15、rsity. The occurrence of mutation is generally associated with low probability. A new generation is created by repeating the selection, reproduction and mutation processes until all chromosomes in the new population replace those from the old one. A proper balance between genetic quality and diversi

16、ty is therefore required within the population in order to support efficient search. Although theoretical results that characterize the behavior of the GA have been obtained for bit-string chromosomes, not all problems lend themselves easily to this representation. This is the case, in particular, for sequencing problems, like vehicle routing problem, where an integer representation is more often appropriate. We are aware of only one approach by Thangiah,