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Route Elimination Heuristic for Vehicle Routing Problem with Time Windows

Route Elimination Heuristic for Vehicle Routing Problem with Time Windows
Route Elimination Heuristic for Vehicle Routing Problem with Time Windows

Route Elimination Heuristic for Vehicle Routing Problem with Time Windows

Sándor Csiszár

Department of Microelectronics and Technology, Budapest Tech

Tavaszmez? u. 17, H-1084 Budapest, Hungary

csiszar.sandor@kvk.bmf.hu

Abstract: The paper deals with the design of a route elimination (RE) algorithm for the vehicle routing problem with time windows (VRPTW). The problem has two objectives, one of them is the minimal number of routes the other is the minimal cost. To cope with these objectives effectively two-phase solutions are often suggested in the relevant literature. In the first phase the main focus is the route elimination, in the second one it is the cost reduction. The algorithm described here is a part of a complete VRPWT study. The method was developed by studying the graph behaviour during the route elimination. For this purpose a model -called “Magic Bricks” was developed. The computation results on the Solomon problem set show that the developed algorithm is competitive with the best ones. Keywords: Vehicle routing problem, Time windows, Tabu search, Transportation

1 Introduction, Problem Definition

The Vehicle Routing Problem (VRP) has a rich literature, so in this paper only a short introduction of the topic is given. VRP is well known combinatorial NP hard problem having several industrial realizations: VRP with time windows, multi-depot, split delivery or other similar problems such as Travelling Salesman Problem, Bin packing, or Job-shop scheduling. Whether the subject of transportation is the raw-material supply to manufacturers or distribution of end products to vendors a vitally important question – next to the in time delivery and quality of the transportation – is the cost of the logistics service. To solve these problems effectively good logistics management is required including vehicle routing optimization. The lowest number of routes is primarily important because it determines the numbers of vehicles applied, consequently influences the investment and fix cost of the company. The second priority is the minima of the total travel distance. There are studies where the second objective is the minimum schedule time when quick and in time service is more important then the travel

distance. Exact mathematical formulation of VRP can be found in [1]. Problem characteristics are as follows:

?the number of customers, their demand, delivery time windows, service time, customer positions – coordinates – and vehicle capacities are given, ?distances between customers and depot are determined by the Euclidean distances,

?all vehicles start from and arrives at the depot,

?all customers can be visited only once,

?the capacity of vehicles is maximized and uniform and must not be violated, ?the service must be started within the given time window of the customer, ?vehicle travel time constraint is given by the depot time window.

As we know, the application of exact methods in the VRP problem solving is quite limited because of the combinatorial “explosion”. During the decades different successful metaheuristics have been developed, for instance Simulated Annealing, Evolutionary Algorithms, and Tabu Search (TS) etc. If we analyse the TS we must admit that despite its indisputable success it has problems in special cases when the route elimination goes together with considerable cost increment. Normally the object function of the TS is designed for finding cheaper solutions. Depending on the length of the tabu list the algorithm is able to reveal new regions. We can in the meantime change the object function and the length of the tabu-list but despite of these techniques it is difficult for the pure TS algorithm to get out from “deep valleys”, so the chance for eliminating a route is quite limited and the search is basically guided by the second priority objective. This topic is detailed in [2]. To leave such kind of deep valleys we have to find effective oscillation – sometimes it is called diversification – methods. In the route elimination respect – although it is the primary objective – the pure TS loses to other – lately developed – metaheuristics first of all hybrid metaheuristics [3]. The purpose of this part of the research and this article is to develop an effective route elimination phase.

The remaining part of the paper is structured as follows. Section 2 describes the “Magic Bricks” model and its consequences, Section 3 and 4 explains the developed route elimination procedure while Section 5 is about the computational results on route reduction and finally Section 6 is about experiments conclusions and future plans.

2 The “Magic Bricks” (MB) Model

If we want to study the features of the graph during the search we have to find an appropriate model. The graph itself is not suitable for that because all the

information about the graph is in the nodes and we know that it is impossible or at least not worthy– according to our present knowledge and computers – to reveal all the relationships within the nodes if the number of nodes is above 50. Suppose that we have an initial solution. Let the width of a brick the distance – cost – between two nodes on any route and the waiting time is the gap between the bricks. Similarly a single route can be considered a row of bricks in the wall and the whole number of routes would create a wall. Now the objective of VRP can be redrafted: rebuild the wall to get primarily smaller wall – with fewer routes – and secondly try to reduce the length of the brick-rows. We can easily recognize the unique behaviour of this wall, because if we swap any two bricks in the wall each of them changes its width and maybe the following gaps as well. Moreover, not only the two bricks but their predecessors in the rows change similarly, because their neighbours are changed. So swapping two bricks at least four brick widths will change and in bad situation many gaps are affected. From this respect this wall is exactly as complicated as the graph itself, but if we think of the route elimination we can identify its requirements more clearly. If we select a row for elimination:

a)the bricks have to be inserted into the gaps or make series of changes to find

an appropriate place for a certain brick,

b)if possible move certain bricks forward or backward (effective in case of

wide time windows).

2.1 Consequences of the Model

The point (a) can be satisfied easier if the bricks are narrower – consequently the gaps are wider – that means the chance for successful insertion from a lower cost graph state is better. It must be noted that in case of wide time windows the total waiting time is usually low and the mentioned effect is not significant. This recognition does not mean that from a local minimum it is easy to eliminate a route – the low cost is not a sufficient condition. It means only if we could wander many low cost solutions we could increase the chance of eliminating a route. Based on this idea a new route elimination (RE) procedure was developed where a continuous cost control is applied. It must be emphasized that owing to the continuous cost control the search is inclinable to clog, that is why a fundamental question is how to ensure the a continuous diversification and the cost control parallel. As far as point (b) is concerned, it is realistic in case of wide time windows. This seemingly increases the chance for the route elimination and it is true if the elimination can be achieved from many graph states (solutions), but at the same time the wide time windows are increasing the complexity of the search. The success depends on which of the above mentioned effects is stronger.

2.2 Checking the Model in Practice

Many computation trials were made to check this model on the Solomon problems, although trials were possible only on those problem instances where the initial number of routes were more then the ever found best one. Explanation below supports the concept. Figure 1 shows how the “flexibility” of the graph is changing during the route elimination and increasing after cost reduction. On the vertical axis for instance the numbers of possible insertions are indicated. These numbers can be obtained the following way: select two adjacent nodes and try all possible insertions excluded the selected one and the depot. Summarize these numbers for each possible pairs on the routes. The charts show a strong increment after the cost reduction especially in the insertion numbers.

Table 1 shows the formation of the route elimination data with and without cost reduction based on 25 cycles for each.

Figure 1

Average insertions and changes on R103 versus number of routes

25 search cycles were made in each case. From low cost solution also the successful ratio was higher and the number of necessary cycles was lower. The computation trials supported the idea to try this concept at the design of a new route elimination algorithm. At the application we must bear in mind two things. One of them is the extra time needs for the cost control the other one is the earlier mentioned clogging problem – or to avoid that a careful diversification is needed. Notations used in the following part of the article:

max r : Depot distance of the farthest customer,

ItNo : Actual number of iterations of the route elimination,

n : Number

of customers, v N : Number of vehicles (routes),

r N : Number of customers on the actual route,

C : Total cost,

r : Average customers distance,

i :

Customer identifier, serial number, i r :

Depot distance of the customer i , i w : Waiting time at customer i ,

Remark: Not explained equations and parameters in the following part of the article derived from computation run experiences.

Normal search

After cost reduction Ratio of success 72% 84% Necessary cycles

349 296 Table 1 Success ration of route elimination with and without cost reduction

3 Main Features of RE

The solution is based on depth-first search. Depth of the search tree depends on the average time constraint: ∑==n i i TC n TC 1/1. In this equation the time constraint factor of customer i is: ()()00/e l ei li i t t t t TC ??=, where ei t and li t are the earliest and latest times to start the service at customer i (depot is costumer 0). ? IF ()

45.0≤TC THEN depth=8 ELSE IF ()45.0>TC and ()6.0≤TC THEN depth=7

ELSE depth = 6

? Depth-first search is executed within given cost limits, otherwise the

expensive insertions and changes increase the total cost, damaging the first condition detailed in the MB model. The only exception is the last customer provided all the previous insertions were successful, in this case the cost limit is not considered.

? Until no unsuccessful insertion happens in case of failure a repair procedure is activated after a couple of cost reduction steps.

? After the route elimination, if a limited number of customers remain unrouted – and their time constraint factor and depot distance satisfy certain criteria – a post search is taking place.

?

The whole process is guided by Tabu Search for keeping the total cost down and controlling diversification.

? Successful insertions are registered. These data are used for three purposes:

-

route selection for elimination, -

insertion sequence for depth-first search, - diversification made by the TS,

? In case of unsuccessful route elimination, the route – if certain criteria are

satisfied – filled up in order to draw away customers from other routes.

4 Detailed Description of the RE Procedure

4.1 Route Selection for Elimination

Three types of root selections are used in the method. The first one selects according to the number of customers on the route (the shorter routes are preferred). The second one takes into account also the insertion frequency of the customers – that can not be used at the beginning of the search – and 65-35% weighting is applied by the following equation:

()[]

∑?+=ins N ItNo N n N N selCrit r v v r /135.0/65.0min (1) In equation (1) ∑ins is the total successful insertions of customers on the given route. It is important here to compare only relative quantities such as v r N N /. The third one selects by the route selection frequency. This latest one prefers those routes that are selected rarely. The route selection is controlled by the block management unit (Figure 4) and its purpose is to ensure the right balance between the diversification and the selection criteria.

4.2 Route Filling up

If the route elimination was not successful and only a few customers remained unrouted – less then ()v N n /3.02.0??? – then it seems to be rational to fill the route up as much as possible in order to draw off customers from the other routes and at the same time to increase diversification. The filling up is done by combining parameters in the insertion equation:

()()()[]

k b a ij ki ik i r w w r r r TC TC C λααω+??+?+?????=1 (2)

Equation (2) is a modified version of cost equation used at insertion heuristics that takes the time window constraint into account. Detailed description can be found in [4].

4.3 Depth-first Search

If a certain route is selected for elimination all the customers are tried to be inserted somewhere onto other routes. Depth-first search was applied because it effectively supports diversification – an important objective as it was stated earlier. The first task is to determine the insertion sequence for the customers on the selected route. At the beginning of the search the following method is used. If there is a customer whose time constraint factor is lower then 0.1 then this customer is selected otherwise a weighting is used considering the depot distance and the TC factor of the customers:

i i TC TC r r selCrit //max = (3) If enough insertion data are available customers with the least successful insertion are tried first. After a costumer has been selected for insertion it is tried first to insert to any possible place with a reasonable cost limit:()r 6.22???. The purpose of this limit is to avoid drastic cost increment that would hinder further insertions. If this insertion fails try 3-Opt insertions provided the time windows are wide enough. During the initial solution a “learning process is made” the successful intra route 3-Opt insertions are registered and if the success ratio reaches a certain percent the 3-Opt reordering is used – Figure 2 – otherwise not. On Figure 2 a continuous line shows the route before change while a dashed line after that.

Figure 2

Intra route 3-Opt exchanges If 3-Opt insertions fail try “Intelligent Reordering” suggested by O. Braysy (detailed description can be found in [5]). The main idea of Intelligent Reordering is to try the insertion of customers to an infeasible – previously unsuccessful but promising – place, the insertion cost calculation is made by:

()()()k b a ij ki ik r w w r r r C λαα+??+?+=1 (4) In Equation ,0=λ 5.0=α. Select the most promising insertion place using Equation (4) where the customer is inserted to. Then the window violated

customer to be looked for. Finally the window violation is resolved by either reordering customers before the window violation or moving customers from the preceding positions to a new position that follows the violated customer. The number of infeasible trials can be decided by the user. If none of the trials of the given customer were successful then compute all the possible swaps of the customer with the earlier mentioned cost limit and try the whole procedure with the replaced customer. The evolution of maybe circles must be blocked by storing all the already executed swaps on the suitable list. The depth of the search gives the number of consecutive swaps as it was described at the main features of the algorithm. If the insertion is unsuccessful and so far no other unsuccessful insertion has been made a repair algorithm is initiated.

4.4 Repair Algorithm

First the graph has to be modified by the TS algorithm in order to reduce the maybe increased cost and to diversify. (Diversification is detailed at the search management.) After that in a user given angle (+/- 40°) at both sides around the unsuccessfully inserted customer all the routes must be identified in two times 40° sector. Try to combine these routes every possible way according to Figure 3 and at each route combination try the depth-first search again.

Figure 3

Route combination for repair algorithm

4.5 Post Search

If a reasonable number of customers remained unrouted at the end of the depth-first search a post search is executed. A limited number of customers is allowed for the post search:)/1.0(2max v N n Trunc Cust +=. If this criterion is satisfied further investigation is made, because it would not make sense to spend time if there are customers among the remaining ones they have no or very few successful insertions (see data management). At the beginning of the search – no data available – a similar decision is made based on the TC factors and the depot distances of the remained customers. The total cycle number of the post search depends on the outlook for the success:

)]},5.0max(/)min ,3.0max([,40min{max r r c r TC B Round Cycle =

Here B c is the post search basic cycle time, minTC r is the minimum time constraint factor of the remained customers and r r is maximum relative depot distance of the

remained customers. Between the post-search cycles there are oscillations, those are identical to that one applied at depth-first search.

4.6 Search Data Management, Data Collection and Processing During the route elimination procedure a list is used to prevent evolution of circles in customer exchange, additionally the successful insertion frequency and the number of route elimination trials per route are registered and processed later. The insertion frequency is used for three purposes:

?route selection for elimination – already described,

?deciding insertion sequence – also discussed,

?diversification by TS, it is done by the search management.

At the initiation of each search block the customer move frequency data – used by the Tabu Search – are adjusted to 100 as a starting number. In RE procedure TS and the route elimination are sequentially running. If a successful insertion occurs at depth-first search also the customer move frequency of Tabu Search is modified in order to move those customers that are not successful at the insertion. This way the Tabu Search finds their move cheaper and prefers their move to reveal new regions for the depth-first search. As it is known the TS penalises frequently moving customers. This is the basic idea of this route elimination concept. This process must be controlled because after a while the graph would turn into an expensive state that would be disadvantageous for the search – according to the MB model. The Search management checks regularly the total cost and compares it to the initial cost. If the relative cost increment is higher then 1.1 – or the user defined value – then the customer move frequency data of TS are readjusted to 100. The 100 value of the adjusted move frequency must be in accordance with the block cycle to get reasonable cost and diversification ratio. See Figure (4).

5 Computation Results

The worked out RE algorithm is written on Delphi platform by dynamic memory programming and was tested on the Solomon Problem Set on 1.7 GHz computer.

A maximum search time of 30 minutes and fix configuration parameters were used. There are results in the literature with variable configuration parameters also, but at this research it was not applied. Fix parameters were used for the number of cycles in the block management (c1=3) and (c2=5). The cost limit used in the depth-first search and at the cost control cycle was()r6.2

2???.

In the literature slightly different comparisons are used. Usually the best result is selected from a given (5 - 18 runs). At this study an average value (10 runs) was applied. In this comparison the algorithm gave the ever found best results in the primary objective and proves to be the best one in the fix configuration parameter category. Similar result was achieved by J. Homberger and H. Gehring with variable parameters.

Figure 4

Block scheme of Route Elimination

In 96% of the total 560 runs the best number of routes was found. The computation time of the whole search process, the initial route construction and the second search phase were registered. The computation time was less then half of the lately developed best solutions. It must be noted that the computation time is the less comparable characteristic of the algorithm because it depends not only on the design of the algorithm itself but on the technical data of the computers (RAM, Processor etc.) and programming language, nevertheless the significant computation time difference can not be explained purely by differences in the

computers.At problem R207 the best result was found in 50% of the runs, at R211 in 40%. At problem R104 and R112 the best result was found in 20% of the runs. In Table 2-4 the average number of vehicles (MNV) and the best number of vehicles (Best NV) of 10 runs can be seen. The updated best results are available on the website: http://www.sintef.no/static/am/opti/projects/top/vrp/bknown.html Problem M N V Best M N V Problem M N V Best M N V

R101 R102 R103 R104 R105 R106 R107 R108 R109 R110 R111 R112 19

17

13

9.8

14

12

10

9

11

10

10

9.8

19

17

13

9

14

12

10

9

11

10

10

9

R201

R202

R203

R204

R205

R206

R207

R208

R209

R210

R211

-

4

3

3

2.1

3

3

2.5

2

3

3

2.6

-

4

3

3

2

3

3

2

2

3

3

2

-

Average 12.05 11.92 Average 2.84 2.73

Table 2

Computational results, R10X, R20X

Problem M N V Best M N V Problem M N V Best M N V

C101 C102 C103 C104 C105 C106 C107 C108 C109 10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

C201

C202

C203

C204

C205

C206

C207

C208

-

3

3

3

3

3

3

3

3

-

3

3

3

3

3

3

3

3

-

Table 3

Computational results, C10X, C20X

Problem M N V Best M N V Problem M N V Best M N V

RC101 RC102 RC103 RC104 RC105 RC106 RC107 RC108 14

12

11

10

13

11

11

10

14

12

11

10

13

11

11

10

RC201

RC202

RC203

RC204

RC205

RC206

RC207

RC208

4

3

3

3

4

3

3

3

4

3

3

3

4

3

3

3

Average 11.5 11.5 Average 3.25 3.25

Table 4

Computational results, RC10X, RC20X

Conclusion, Future Plans

The worked out method is a part of a research work devoted to VRPTW. The Route Elimination algorithm is based on the described MB model. One of the objectives of the study was to find out if the search can be guided by a general property – total cost – of the graph. The precondition of good realization was to wander the low cost solutions and find one where the depth-first search is effective from. For further development a possible way could be to use a simpler cost control algorithm to further reduce the computation time. For larger number of customers (above 200) more sophisticated intelligence is needed in order to decide on the application of deep search because of its time needs. Acknowledgement

The author wish to thank Prof. Dr. László Monostori (Budapest University of Technology and Economics and Deputy Director of Computer and Automation Research Institute of the Hungarian Academy of Sciences) and Dr. Tamás Kis (Computer and Automation Research Institute of the Hungarian Academy of Sciences) for their help and useful advices, Dr. Péter Turmezei (Head of Department of Microelectronics and Technology, Budapest Tech, Hungary) for his support.

References

[1] O. Br?ysy, W. Dullaert: A fast evolutionary metaheuristic for the vehicle

routing problem with time windows, Int. J. AI Tools 12 (2003), pp. 153-

172

[2] J. Homberger, H. Gehring: A two-phase metaheuristic for the vehicle

routing problem with time windows, European Journal of Operation Research 162 (2005), pp. 220-228

[3] O. Br?ysy and M. Gendreau: Tabu Search Heuristics for the Vehicle

Routing Problem with Time Windows. Sociedad de Estadística e Investigatión Operativa, Madrid, Spain (December, 2002)

[4] S. Csiszár: Initial route construction for Vehicle Routing Problem with

Time Windows, XXII nd International Conference “Science in Practice”, Schweinfurt (2005)

[5] O. Br?ysy: A reactive variable neighbourhood search for the Vehicle-

routing problem with time windows. Informs Journal on Computing 15 (2003), pp. 347-368

[6] A. Van Breedam: A parametric analysis of heuristics for the vehicle routing

problem with side-constraints, European Journal of Operation Reports 137 (2002), pp. 348-370

[7] G. Clarke, J. W. Wright: Scheduling of Vehicles from a Central Depot to a

number of delivery poing. Operation Research 12 (1964), pp. 568-581

[8] M. M. Solomon: Algorithm for the Vehicle Routing and Scheduling

Problem with Time Window Constraints, Operation Research 35 (1987), pp. 254-265

[9] N. Christofides, A. Mingozzi, P. Toth: The vehicle routing problem,

Combinatorial optimization, Chichester, Wiley (1979), pp. 315-338 [10] K. Altinkemer, B. Gavish: Parallel savings based heuristic for the delivery

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[12] E. Taillard: Parallel iterative search methods for vehicle routing problems,

Networks 23 (1993), pp. 661-672

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软件结构: BOOT ROM:主要功能是路由器加电后完成有关初始化工作,并向内存中加入操作系统代码。 COMWARE:华为路由器上运行的软件平台。 2.通过console口配置路由器 (1)搭建环境(类似配置交换机) (2)运行超级终端并设置通讯参数; (3)与路由器连接(按Enter键,将进入路由器视图)

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STC89C52单片机用户手册 [键入作者姓名] [选取日期]

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可由外部中断低电平触发中断方式唤醒 12.通用异步串行口(UART),还可用定时器软件实现多个UART 13.工作温度范围:-40~+85℃(工业级)/0~75℃(商业级) 14.PDIP封装 STC89C52RC单片机的工作模式 ●掉电模式:典型功耗<0.1μA,可由外部中断唤醒,中断返回后,继续执行原 程序 ●空闲模式:典型功耗2mA ●正常工作模式:典型功耗4Ma~7mA ●掉电模式可由外部中断唤醒,适用于水表、气表等电池供电系统及便携设备

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路由器的基本配置实验

路由器的基本配置实验 一、实验目的 掌握路由器上各种口令的配置 掌握路由器上接口的配置 掌握Cisco路由器的配置对话即SETUP模式的使用 二、实验仪器及环境 Cisco 2600系列的路由器的f0/0与HUB相连,控制口通过反接线,反接线的另一端连接RJ-45到DB9转换器,再与控制台的COM口相连,控制台上带超级终端仿真软件 三、实验内容 ?对路由器进行基本的配置 ?配置路由器的各种密码 ?配置路由器的各个接口,设置PC机,使其能远程登录到路由器 ?使用查看命令查看配置的变化 ?使用Setup对话框模式对路由器进行基本的配置。 四、配置的参数要求 ?路由器的主机名为:R_A ?路由器的登录提示信息为:welcome ?路由器的enable password为:net ?路由器的enable secret为:student ?路由器的控制终端密码、虚拟终端密码均为:network ?路由器中快速以太网F0/0端口(IP地址为192.168.1.1,子网掩码为255.255.255.0) ?路由器中S0/0端口(IP地址为192.168.3.1,子网掩码为255.255.255.0),并且S0/0口充当了DCE端 PC的IP地址为192.168.1.10 子网掩码为:255.255.255.0 缺省网关为:192.168.1.1 五、实验步骤 1:配置路由器的全局参数 Router> //用户模式提示符

Router>enable //进入特权模式 Router# //特权模式提示符 router#config terminal //进入全局配置模式 router(config) # //全局配置模式提示符 router(config) #hostnam e R_A //配置路由器的名称为R_A R_A (config) # banner motd #welcome# //配置路由器的登录提示信息为welcome 2:配置路由器的enable口令 R_A> //用户执行模式提示符 R_A>enable //进入特权模式 R_A# //特权模式提示符 R_A# configure terminal //进入全局配置模式 R_A(config)# //全局配置模式提示符 R_A(config)#enable password net //设置enable password 为net R_A(config)#enable secret student //设置enable secret 为student R_A(config)#exit //回到上一级模式 R_A # //特权模式提示符 R_A #show running-config //查看正在运行的配置文件,请注意“enable password”与“enable secret”在配置文件中的区别 R_A #copy running-config startup-config //把正在运行的配置文件备份到NVRAM中3:配置路由器的控制终端密码 ?控制终端密码是用户通过交换机的console端口访问交换机时需要输入的密码。其配置过程如下: ?R_A> //用户执行模式提示符 ?R_A>enable //进入特权模式 ?R_A# //特权模式提示符 ?R_A# configure terminal //进入全局配置模式 ?R_A(config)# //全局配置模式提示符 ?R_A(config)#line console 0 //进入line子模式 ?R_A(config-line)#login //设置登录 ?R_A(config-line)#password network //设置控制终端密码为network ?R_A(config-line)#ctrl+Z //返回到特权模式 ?R_A# //特权模式提示符 ?R_A#show running-config //查看正在运行的配置文件 ?R_A#copy running-config startup-config //把正在运行的配置文件备份到NVRAM中 ?4:配置路由器的虚拟终端(vty)密码 ?VTY密码是用户通过虚拟终端(telnet)访问交换机时需要输入的密码。其配置过程如下: ?R_A> //用户执行模式提示符 ?R_A>enable //进入特权模式 ?R_A# //特权模式提示符 ?R_A# configure terminal //进入全局配置模式 ?R_A(config)# //全局配置模式提示符 ?R_A(config)#line vty 0 5 //配置VTY0到VTY5的密码

STC89C52RC单片机特点

STC89C52RC 单片机介绍 STC89C52RC 单片机是宏晶科技推出的新一代高速/低功耗/超强抗干扰的单片机,指令代码完全兼容传统8051 单片机,12 时钟/机器周期和 6 时钟/机器周期可以任意选择。 主要特性如下: 1. 增强型8051 单片机,6 时钟/机器周期和12 时钟/机器周期可以任意选择,指令代码完全兼容传统 8051. 2. 工作电压:5.5V~ 3.3V<5V 单片机)/3.8V~2.0V<3V 单片机) 3. 工作频率范围:0~40MHz,相当于普通 8051 的 0~80MHz,实际工作频率可达 48MHz 4. 用户应用程序空间为 8K 字节 5. 片上集成 512 字节 RAM 6. 通用I/O 口<32 个)复位后为:,P1/P2/P3/P4 是准双向口/弱上拉,P0 口是漏极开路输出,作为总线扩展用时,不用加上拉电阻,作为I/O 口用时,需加上拉电阻。 7. ISP<在系统可编程)/IAP<在应用可编程),无需专用编程器,无需专用仿真器,可通过串口

实验五 路由器的基本设置

实验五路由器的基本设置 路由器的命令行界面配置 【实验目的】 掌握路由器命令行的各种操作,并能够进行模式区分和切换。 【实验设备】 一台思科1841 路由器。 【实验指导】 路由器的管理方式分为带内管理和带外管理两种。通过console 口管理路由器属于带外管理,不占用其网络接口。思科路由器的命令解释器使用了层次化结构,每个 层次都提供一些相关的命令,用于配置路由器相关参数及显示路由器的运行状态。思科的系统IOS 将会话分为用户模式、特权模式和配置模式。 (1)用户模式是路由器启动时的缺省模式,提供有限的路由器访问权限,允许 执行一些非破坏性的操作,如查看路由器的配置参数,测试路由器的连通性等,但不能对路由器的配置做出任何改动。该模式下的提示符为“>”,可使用show interface 命令查看路由器接口信息。 (2)特权模式可对路由器进行更多的操作,使用的命令集比用户模式多,可对路由器进行更高级的测试,如使用debug 命令,也称为使能模式。在用户模式下通过使能口令进入特权模式,提示符为“#”,show running-config 即为特权模式命令。 (3)配置模式是路由器的最高操作模式,可以设置路由器上运行的硬件和软件的相关参数,包括配置各接口、路由协议、广域网协议、设置用户和访问密码等。在特权模式“#”提示符下输入config 命令,可进入配置模式。 【实现功能】 掌握路由器的命令行界面的切换。 【实验拓扑】

路由器的命令行界面配置拓扑图如图1 所示。 1 拓扑图 【实验内容】 【步骤1】路由器启动时默认进入用户模式,如图 2 所示,在用户模式下输入命令 enable 进入特权模式,在特权模式下使用 exit 能够再次退回到用户模式。 图2 路由器特权模式 【步骤2】在特权模式下输入命令configure terminal 进入配置模式;在配置模式下输入exit 命令,退出到特权模式。 【步骤 3】在每个模式下均可输入“?”,了解此模式下所有的命令。

STC89C52单片机用户手册

STC89C52F单片机介绍 STC89C52F单片机是宏晶科技推出的新一代高速 /低功耗/超强抗干扰的单片机,指令代码完全兼容传统8051单片机,12时钟/机器周期和6时钟/机器周期可以任意选择。 主要特性如下: * 增强型8051单片机,6时钟/机器周期和12时钟/机器周期可以任意选择,指令代码完全兼容传统8051. * 工作电压:5.5V?3.3V (5V单片机)/3.8V?2.0V (3V单片机) * 工作频率范围:0?40MHz相当于普通8051的0?80MHz实际工作频率可达48MHz *用户应用程序空间为8K字节 * 片上集成512字节RAM * 通用I/O 口(32个),复位后为:P1/P2/P3/P4是准双向口 /弱上拉,P0 口是漏极开路输出,作为总线扩展用时,不用加上拉电阻,作为I/O 口 用时,需加上拉电阻。 * ISP (在系统可编程)/IAP (在应用可编程),无需专用编程器,无需专用仿真器,可通过串口( RxD/P3.0,TxD/P3.1 )直接下载用户程序,数秒 即可完成一片 * 具有 EEPROM能 *具有看门狗功能 * 共3个16位定时器/计数器。即定时器T0、T1、T2 * 外部中断4路,下降沿中断或低电平触发电路,Power Down模式可由外部中断低电平触发中断方式唤醒 * 通用异步串行口( UART,还可用定时器软件实现多个 UART * 工作温度范围:-40?+85C(工业级)/0?75C(商业级) * PDIP封装 STC89C52F单片机的工作模式 *掉电模式:典型功耗<0.1吩,可由外部中断唤醒,中断返回后,继续执行原程序

《计算机网络》实验四 路由器及其基本配置实验 实验报告

实验报告四 班级:07东方信息姓名:学号:实验时间:5-6 机房:9#205 组号:7 机号:A 一、实验题目 实验四路由器及其基本配置实验 二、实验设备 CISCO路由器,网线,专用电缆(RS232,V35),CONSOLE,PC 机。 三、实验内容 观察CISCO路由器,了解路由器基本知识; 学习电缆连接; 查看CISCO路由器的操作,了解路由器工作原理; 学习基本的路由器配置。 四、原理 路由器是工作在IP协议网络层实现子网之间转发数据的设备。路由器内部可以划分为控制平面和数据通道。在控制平面上,路由协议可以有不同的类型。路由器通过路由协议交换网络的拓扑结构信息,依照拓扑结构动态生成路由表。在数据通道上,转发引擎从输入线路接收IP包后,分析与修改包头,使用转发表查找输出端口,把数据交换到输出线路上。转发表是根据路由表生成的,其表项和路由表项有直接对应关系,但转发表的格式和路由表的格式不同,它更适合实现快速查找。转发的主要流程包括线路输入、包头分析、数据

存储、包头修改和线路输出。 路由协议根据网络拓扑结构动态生成路由表。IP协议把整个网络划分为管理区域,这些管理区域称为自治域,自治域区号实行全网统一管理。这样,路由协议就有域内协议和域间协议之分。域内路由协议,如OSPF、IS-IS,在路由器间交换管理域内代表网络拓扑结构的链路状态,根据链路状态推导出路由表。域间路由协议相邻节点交换数据,不能使用多播方式,只能采用指定的点到点连接。 五、实验步骤 配置相应的IP参数 打开计算机的“超级终端”程序…

此超级终端内输入的命令都是对路由器A的操作,超级终端窗口内所有输出都是路由器A的输出。 键入“?”列入命令提示。 7-A>? Exec commands: <1-99> Session number to resume access-enable Create a temporary Access-List entry access-profile Apply user-profile to interface clear Reset functions

实验二 路由基本配置(1)

实验二、路由器的基本配置(使用模拟器) 一、实验目的 1、掌握路由器模拟器的用法。 2、熟悉路由器软件的用户界面和路由器的配置方式,掌握一些基本的操作命令。 3、掌握路由器的基本配置方法。 4、理解网络互连的基本原理。 二、实验设备 1、一台安装了思科模拟器的个人计算机。 三、实验内容 1、使用思科模拟器Cisco Packet Tracer构造图3-1所示的互连网络拓扑图。 2、使用模拟器学习路由器的各种配置模式,熟悉各种模式的切换方式。 3、配置静态路由使图3-1中的主机实现互通。 图3-1 模拟两个局域网通过路由器实现远程互连的拓扑结构 四、实验步骤 (一)使用模拟软件Cisco Packet Tracer绘制网络拓扑图 1、启动Cisco Packet Tracer,打开该软件的主界面。如图3-2所示,主界面窗口分为四个 部分:上部是菜单栏和快捷工具栏,中间是绘图工作区,左下方是设备列表区,右下方是报文跟踪区。

图3-2 Cisco Packet Tracer的主界面 2、在工作区添加网络设备: 本实验需要2台路由器、2台PC机。用鼠标从设备列表区选出所需的设备添加至工作区。 【注意】添加设备时应先选设备类型,再选设备型号,如图3-3所示。建议选择2600以上系列路由器。用鼠标点击所需设备,然后在工作区点击鼠标,该设备将出现在点击位置,如图3-4所示。 图3-3a 路由器(Routers)列表 图3-3b 终端设备(End Devices)列表 菜单栏 快捷工具栏 拓 扑 图 工 作 区 拓扑图工具栏 设备列表区报文跟踪区 设备类别设备型号 绘图区

(完整版)实验4-路由器基本配置

宁德师范学院计算机系 实验报告 (—学年第学期) 课程名称计算机网络 实验名称实验4 路由器基本配置专业计算机科学与技术年级11级 学号姓名 指导教师 实验日期

实验目的与要求: 1、掌握路由器几种常用配置方法; 2、掌握采用Console线缆配置路由器的方法; 3、掌握采用telnet方式配置路由器的方法; 4、熟悉路由器不同的命令行操作模式以及各种模式之间的切换; 5、掌握路由器的基本配置命令; 实验设备(环境): Windows操作系统 Packet Tracer模拟器软件 实验内容: 1、新建实验拓扑图 2、熟悉路由器基本设置方式与常用命令 3、在路由器上配置IP地址 3、配置路由器远程密码 技术原理: 路由器的管理方式基本分为两种:带内管理和带外管理。通过路由器的Console口管理路由器属于带外管理,不占用路由器的网络接口,其特点是需要使用配置线缆,近距离配置。第一次配置时必须利用Console端口进行配置。 交叉线与直通线的用法:相同设备的连接要用交叉线,不同设备的连接要用直通线,此处的相同并不是以名称判断,而是以功能划分。如计算机和路由器也被认为是相同设备。 实验背景: 假设你是某公司新来的网络管理员,公司要求熟悉网络产品,首先要求你登录路由器,了解、掌握路由器的命令行操作。同时作为网管,你第一次在设备机房对路由器进行了初次配置后,希望以后再办公司或出差时也可以对设备进行远程管理,现在要在路由器上做适当的配置。 实验步骤: 路由器基本配置(一): 实验拓补图 1、用标准console线缆连接计算机的串口和路由器的console口,在计算机上启用超级终端,并配置

路由器的基本配置实验报告

实验4 路由器的基本配置 1. 按照环境的要求,建立实验的拓扑结构。 2. 配置六台主机的IP地址 ⑴配置198.8.15.0网络中的两台主机的地址分别是198.8.15.2和198.8.15.3。 ⑵配置193.10.18.0网络中的两台主机的地址分别是193.10.18.2和193.10.18.3。

⑶配置202.7.20.0网络中的两台主机的地址分别是202.7.20.2和202.7.20.3。 ⑷测试六台主机之间的连通性,并记录测试结果。

3. 配置路由器A的基本参数。 ⑴进入到路由器的全局配置模式。命令如下:Router0#config termainal ⑵配置路由器A的名称为Route_A。命令如下:

Router0#hostname Router_A 4. 配置路由器B的基本参数 ⑴进入到路由器的全局配置模式。 Router1#config termainal ⑵配置路由器B的名称为Route_B。Router1#hostname Router_B

5. 配置路由器A上各端口的地址 ⑴设置路由器A的f0/0端口的IP地址为193.10.18.1,子网掩码为255.255.255.0。命令如下: Router_A#interface f0/0 Router_A#ip address 193.10.18.1 255.255.255.0 Router_A#no shutdown

⑵设置路由器A的f0/1端口的IP地址为198.8.15.1,子网掩码为255.255.255.0 。 Router_A#interface f0/1 Router_A#ip address 198.8.15.1 255.255.255.0 Router_A#no shutdown

实验3 路由器的基本配置-实验报告模板

电子科技大学中山学院实验报告 2018-2019学年第1学期

报告内容 1、实验目的 (1)掌握路由器网络操作系统的基本操作 (2)掌握路由器登录的几种模式 (3)掌握路由器的几种基本配置模式 (4)掌握路由器接口IP地址的配置 2、实验环境 实验分组进行。每人一台装有Packet Tracer软件的PC,每组两台交换机、一台路由器及相关线缆。 实验拓扑图如下所示: 3、实验内容 (1)标注实验拓扑图中的PC和路由器接口的IP地址。 答: (2)记录在超级终端管理配置路由器的过程。(截图并说明) 答:enable (进入特权模式) conf t (进入全局配置模式) hostname R1 (R1为新设置的路由器名称)

exit exit进入全局配置模式 【在PC0超级终端配置路由f0/0接口】 int f0/0 (进入f0/0端口配置模式) ip address 192.168.1.11 255.255.255.0 (设置f0/0端口ip地址和掩码) no shutdown (激活端口) 【在Laptop1超级终端配置路由f0/1接口】 int f0/1 (进入f0/1端口配置模式) ip address 10.1.1.10 255.0.0.0 (设置f0/1端口ip地址和掩码) no shutdown (激活端口) (3)记录使用Ping命令来测试两个网段是否已经连通。(截图) 答: (4)记录主机telnet登录路由器的过程(截图)。 答: (5)总结实验中容易出现的错误。 答:容易忽略电脑跨局域网访问对方电脑时需要设置路由器网关才能连通

路由器和交换机基本配置-实验报告

路由器和交换机基本配置 一、实验目的 1.了解路由器和交换机; 2.掌握路由器和交换机的基本配置 二、实验设备 安装网络模拟软件YS-RouterSim的计算机。 三、实验内容 1.通过使用网络模拟软件YS-RouterSim来学习和掌握路由器命令; 2.配置IP地址、VLAN和路由协议。 四、实验步骤 1. 主要功能 网络模拟软件YS-RouterSim在功能上具有建立网络图、交换机和路由器的初始化、升级、备份、划分VLAN、静态路由、默认路由、动态路由的配置,练习写访问控制列表。支持子接口subif和接口的secondary ip,可以在线得到帮助,可以读入、保存网络结构图和配置信息。 图1 网络模拟软件的主窗体 图1是网络模拟软件的主操作窗体,在设计网络拓朴结构时,根据需要放置网络设备,

可以加入的设备包括:6个路由器、6个交换机、8个计算机1个防火墙,计算机模拟了的环境,具有设置和查看IP、网关的功能,可以执行ping、telnet、init 0等功能,路由器采用cisco 2621的命令集,交换机采用cisco 2900的命令集。 从图1可以看出系统的主要功能,当要加入一个设备时,是很方便的,对加入的设备能够实现移动和删除,建立一个你期望的网络结构图。 图2 构建基本网络结构图 网络结构设计方便、直观;在设备配置方面增加了设置的自由度,双击对应的设备进入Terminal状态。较好的帮助功能,支持“”得到参考命令。输入命令时,键入TAB键可以得到命令全称。系统提供了参考实验,和命令提示,显示配置信息帮你查看网络的IP、vlan、路由等。 2. 设备的配置方式 (1) 路由器的配置: 双击路由器进入终端方式: 图3 路由器配置界面

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