With the rapid development of online shopping in recent years, logistics distribution has received much attention from enterprises and online consumers. Logistics distribution involves many factors and complex processes; conventional qualitative methods are unable to provide an effective analysis. Thus, this paper sets a framework to solve the above problem. A case study of an E-commerce enterprise in Shanghai on logistics distribution is proposed to discretize the whole process and minimize the total costs. Then the AnyLogic software is used to simulate and optimize the system from three aspects, including routes selection, warehouses quantity, and warehouses layout. Finally, this paper analyzes the simulation results, which would provide some valuable references for practical logistics.
In recent years, E-commerce industry has developed rapidly. Online shopping is almost necessary to everyone. Logistics distribution is the last link of online shopping whose importance is rising as society demand increases. Whether goods can be delivered to consumers in time affects the consumers satisfaction of this shopping directly. Especially in the annual “Double Eleven” shopping festival, parcel quantities in various regions have increased rapidly and many delivery points have exploded. As shown in Figure
Parcel quantities during the “Double Eleven”.
Meanwhile, the research on vehicle is not a theoretical problem in logistics distribution process. Lots of factors are contained including vehicles quantity, distribution terminal, delivery time, unloading time, and demand changes. Conventional qualitative methods are not insufficient to solve it. In recent years, computational technology including hardware and software has developed rapidly. This technology characterizes reflecting on complicated processes or behaviors to solve problems through simulation. Simulation is a new subject that has gradually formed with the development of computer technology. It was firstly proposed in the early 20th century and was mainly utilized in water conservancy research. Simulation is the process of experimental research on the system by establishing and using the real system model. Similar to the application of algorithm on theoretical issues, simulation has significant effects on practical problems, particularly the complex and practical problems like logistics distribution. By discretizing and dividing the whole process into different parts, an integrated model is established and analyzed for every part to obtain the system data. According to the obtained data, optimal results are calculated [
This paper studies the discrete logistics processes which include many stochastic variables and factors. The method of mathematical modeling is not suitable. Therefore, the simulation is used to optimize the logistics distribution system and get the practical results.
For the simulation research of logistics distribution problems, the GPSS language (The General Purpose Systems Simulator) was firstly presented by American Geoffrey Gordon in 1961, which is a solution to discrete events, particularly the queuing phenomenon [
This paper considers adopting a simulation tool, Anylogic software, which is developed early in this century to visualize modeling with a wide application scope. Complicated logistics distribution problems are discretized and simulated from the perspective of different processes. Moreover, one E-commerce enterprise in Shanghai is studied as a case. This paper starts with continuous changes of warehouse quantities and demands and optimizes the route selection, warehouses quantity, and warehouses layout. Ultimately, some optimization suggestions are raised based on simulation results of the software.
AnyLogic, a commercial simulation software released in 2000 by the AnyLogic Company, is a powerful simulation platform which can be applied in a wide range of fields, including logistics simulation, supply chain simulation, virus pervasion, road traffic, pedestrian evacuation, military simulation, and so on. This platform can also be used in discrete events modeling, agent-based modeling, and dynamics system modeling. This paper combines the AnyLogic technology with a case of an E-commerce enterprise in Shanghai on logistics distribution to propose the optimization suggestions.
The detailed introduction on the distribution case of an E-commerce enterprise in Shanghai is shown as follows.
E-commerce enterprises usually distribute goods to customers in two steps: (1) deliver goods from large-scale warehouses to distribution stations; (2) deliver goods from distribution station to customers with numerous manpower and material resources. Thus, two-part costs occur. The second costs are much more than the first one due to the large number of involving personnel. Moreover, higher risks of traffic accidents and loss of goods are generated. Hence, to save costs and reduce risks, the E-commerce company in Shanghai has established commodity self-raising points in various regions. The company only dispatches vehicles transferring goods to the distribution points and customers pick up the goods themselves so that the costs of the second part can be completely saved. At the same time, the injury of the delivery personnel and the loss of the goods are dramatically reduced.
According to statistics, 51 self-raising points have been established in the main urban area of Puxi by the E-commerce company. The distribution map is obtained through the AnyLogic platform as follows in Figure
Distribution of self-raising points in main urban area of Puxi.
Firstly, one large-scale warehouse is considered to construct in Northwest of Shanghai outside the main urban area, which is responsible for goods distribution to self-raising points in main urban area of Puxi. The location is shown in Figure
Single warehouse location.
The AnyLogic simulation is used to model and calculate the cost and the time requirement of completing 51 self-raising points on vehicles, which are salient criterion for assessing the solution.
Many practical factors need to be considered before starting the simulation. For instance, the time requirements for delivery beyond the limits are causing the compensation to the customer called tardiness cost which increases the total costs. Besides, the number of goods from every self-raising point affects the total delivery time and some time-sensitive delivery requirements for subsequent points.
Because of the different situations every day, the above two factors of every self-raising point can generate the orders quantity and the delivery time requirement through a random function as the fundamentals of the simulation. Thus, the following four items are included in every self-raising point: (1) name; (2) location; (3) goods quantity; (4) time limits.
While large-scale warehouses only involve delivery, only the following two items need to be considered: (1) name; (2) location.
Two solutions are considered in this paper for route planning: (1) the shortest route solution, which calculates the shortest route between two locations as the real path for vehicles travelling; (2) the time-limited precedence solution, which considers firstly to deliver the goods with time-sensitive requirements and then the shortest route solution is adopted. Solution one can reduce the vehicles travelling costs and increase the tardiness costs while solution two is completely the opposite. Ultimately, the simulation results are used to compare the two solutions on the total costs and the total time.
Setting up three agents for the simulation of this problem, they are as follows: (1) self-raising point agent; (2) warehouse agent, (3) distribution vehicle agent.
The distribution vehicle agent is the main activity target, including controlling the vehicle from the warehouse, searching for the closest self-raising point and unloading the goods, continuously searching for a new point, and returning to the warehouse until all goods are unloaded.
The time to accomplish the delivery of the self-raising points is calculated to compute the travelling costs. The time span consists of the vehicle travelling time from one point to another and the unloading time at the terminal.
The calculation formula of total costs is as follows:
where
Meanwhile, the total time of entire system is calculated to judge if the delivery solution meets the criterion.
The calculation formula is
where
The logical structure to implement the functions of the distribution vehicle agents is composed of four states, six transitions, and one selection structure, as shown in Figure
Logical structure of vehicle agent.
The following operation interface can be obtained through the above analysis and modeling, which is shown in Figure
Operation interface of simulation platform.
Simulation to the self-raising points of the E-commerce enterprise vehicle distribution can be performed by selecting the route planning mode and clicking the running button. Relevant data is obtained.
This paper considers the logistics distribution settings under single warehouse, double warehouses, and three warehouses. The raw data of these three settings are all in Table
Raw data.
| | | | | |
---|---|---|---|---|---|
s1 | 31.1431986 | 121.4210407 | 100 | 1 | 100 |
s2 | 31.15728268 | 121.4120836 | 50 | 1 | 150 |
s3 | 31.15546308 | 121.4371571 | 10 | 2 | 0 |
s4 | 31.16333719 | 121.4147434 | 90 | 1 | 200 |
s5 | 31.1343072 | 121.4481408 | 30 | 1 | 250 |
s6 | 31.16970695 | 121.4252906 | 20 | 2 | 0 |
s7 | 31.16707584 | 121.4416896 | 30 | 2 | 0 |
s8 | 31.18410185 | 121.4266127 | 40 | 2 | 0 |
s9 | 31.18935785 | 121.4276737 | 40 | 2 | 0 |
s10 | 31.19520877 | 121.4184094 | 100 | 1 | 300 |
s11 | 31.1988346 | 121.4461166 | 30 | 2 | 0 |
s12 | 31.18874702 | 121.371578 | 20 | 2 | 0 |
s13 | 31.20861033 | 121.4197887 | 50 | 1 | 350 |
s14 | 31.2231975 | 121.3954163 | 30 | 2 | 0 |
s15 | 31.27327301 | 121.4338336 | 40 | 2 | 0 |
s16 | 31.26069097 | 121.4462738 | 30 | 2 | 0 |
s17 | 31.26820805 | 121.4564257 | 100 | 1 | 400 |
s18 | 31.28828157 | 121.4429477 | 20 | 2 | 0 |
s19 | 31.29730166 | 121.4110734 | 40 | 2 | 0 |
s20 | 31.24911288 | 121.4890998 | 60 | 1 | 450 |
s21 | 31.27418872 | 121.4633177 | 40 | 2 | 0 |
s22 | 31.27321697 | 121.4688453 | 30 | 2 | 0 |
s23 | 31.2796735 | 121.4802926 | 80 | 1 | 500 |
s24 | 31.30568695 | 121.4669172 | 20 | 2 | 0 |
s25 | 31.23087848 | 121.4437099 | 40 | 2 | 0 |
s26 | 31.28454415 | 121.5018165 | 90 | 1 | 550 |
s27 | 31.26859412 | 121.5399282 | 20 | 2 | 0 |
s28 | 31.28451296 | 121.5115345 | 30 | 2 | 0 |
s29 | 31.29881095 | 121.499463 | 100 | 1 | 600 |
s30 | 31.30787625 | 121.4993919 | 90 | 1 | 650 |
s31 | 31.29473544 | 121.5409558 | 30 | 2 | 0 |
s32 | 31.2911163 | 121.5489509 | 80 | 1 | 700 |
s33 | 31.33387484 | 121.5288115 | 40 | 2 | 0 |
s34 | 31.31553869 | 121.3699688 | 30 | 2 | 0 |
s35 | 31.31371912 | 121.3505742 | 20 | 2 | 0 |
s36 | 31.35066483 | 121.4538375 | 40 | 2 | 0 |
s37 | 31.1259537 | 121.3866134 | 20 | 2 | 0 |
s38 | 31.18216029 | 121.4056214 | 40 | 2 | 0 |
s39 | 31.22972111 | 121.4052614 | 100 | 1 | 750 |
s40 | 31.22416167 | 121.3773722 | 30 | 2 | 0 |
s41 | 31.23514491 | 121.4078012 | 40 | 2 | 0 |
s42 | 31.23383979 | 121.4318426 | 30 | 2 | 0 |
s43 | 31.24339301 | 121.418824 | 20 | 2 | 0 |
s44 | 31.24539919 | 121.4137998 | 30 | 2 | 0 |
s45 | 31.23813096 | 121.355908 | 40 | 2 | 0 |
s46 | 31.25328122 | 121.3872342 | 30 | 2 | 0 |
s47 | 31.26148927 | 121.4184197 | 20 | 2 | 0 |
s48 | 31.25603512 | 121.3732177 | 30 | 2 | 0 |
s49 | 31.26155358 | 121.3545372 | 30 | 2 | 0 |
s50 | 31.28576633 | 121.3412785 | 40 | 2 | 0 |
s51 | 31.19722065 | 121.4698344 | 40 | 2 | 0 |
Only one delivery at all distribution points is accomplished in this mode. Assuming the shortest route solution is selected to the next point, the following results are obtained by running the AnyLogic:
TotalRunCost:41657.899;
TotalDelayCost:1074320.791;
TotalCost:1115978.69.
Meanwhile, some critical data on the sequence of every distribution point, time nodes, and time length during distribution processes are shown in Table
Simulation results in single warehouse mode (the shortest route solution).
| | | | | | | | |
---|---|---|---|---|---|---|---|---|
WareHouseA | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | WareHouseA |
s50 | 8.04 | 28.04 | 8.04 | 20.00 | 0.00 | 804.00 | 0.00 | WareHouseA |
s35 | 35.18 | 45.18 | 7.14 | 10.00 | 0.00 | 714.00 | 0.00 | WareHouseA |
s48 | 59.11 | 74.11 | 13.94 | 15.00 | 0.00 | 1394.00 | 0.00 | WareHouseA |
s46 | 76.46 | 91.46 | 2.34 | 15.00 | 0.00 | 234.00 | 0.00 | WareHouseA |
s49 | 97.33 | 112.33 | 5.87 | 15.00 | 0.00 | 587.00 | 0.00 | WareHouseA |
s45 | 119.04 | 139.04 | 6.71 | 20.00 | 0.00 | 671.00 | 0.00 | WareHouseA |
s40 | 143.81 | 158.81 | 4.77 | 15.00 | 0.00 | 477.00 | 0.00 | WareHouseA |
s14 | 163.05 | 178.05 | 4.24 | 15.00 | 0.00 | 424.00 | 0.00 | WareHouseA |
s41 | 182.12 | 202.12 | 4.07 | 20.00 | 0.00 | 407.00 | 0.00 | WareHouseA |
s39 | 204.91 | 254.91 | 2.79 | 50.00 | 0.00 | 279.00 | 0.00 | WareHouseA |
s43 | 259.37 | 269.37 | 4.46 | 10.00 | 0.00 | 446.00 | 0.00 | WareHouseA |
s44 | 271.22 | 286.22 | 1.85 | 15.00 | 0.00 | 185.00 | 0.00 | WareHouseA |
s42 | 292.76 | 307.76 | 6.55 | 15.00 | 0.00 | 655.00 | 0.00 | WareHouseA |
s25 | 311.13 | 331.13 | 3.36 | 20.00 | 0.00 | 336.00 | 0.00 | WareHouseA |
s13 | 337.75 | 362.75 | 6.62 | 25.00 | 0.00 | 662.00 | 0.00 | WareHouseA |
s10 | 367.06 | 417.06 | 4.31 | 50.00 | 67.06 | 431.00 | 13412. | WareHouseA |
s9 | 419.23 | 439.23 | 2.18 | 20.00 | 0.00 | 218.00 | 0.00 | WareHouseA |
s8 | 441.26 | 461.26 | 2.03 | 20.00 | 0.00 | 203.00 | 0.00 | WareHouseA |
s38 | 465.79 | 485.79 | 4.52 | 20.00 | 0.00 | 452.00 | 0.00 | WareHouseA |
s4 | 490.17 | 535.17 | 4.38 | 45.00 | 290.1 | 438.00 | 58032 | WareHouseA |
s2 | 536.73 | 561.73 | 1.57 | 25.00 | 386.7 | 157.00 | 77346 | WareHouseA |
s1 | 566.45 | 616.45 | 4.71 | 50.00 | 466.4 | 471.00 | 93290 | WareHouseA |
s3 | 621.10 | 626.10 | 4.65 | 5.00 | 0.00 | 465.00 | 0.00 | WareHouseA |
s6 | 633.17 | 643.17 | 7.07 | 10.00 | 0.00 | 707.00 | 0.00 | WareHouseA |
s7 | 648.96 | 663.96 | 5.79 | 15.00 | 0.00 | 579.00 | 0.00 | WareHouseA |
s11 | 671.47 | 686.47 | 7.52 | 15.00 | 0.00 | 752.00 | 0.00 | WareHouseA |
s51 | 691.79 | 711.79 | 5.32 | 20.00 | 0.00 | 532.00 | 0.00 | WareHouseA |
s20 | 724.95 | 754.95 | 13.16 | 30.00 | 274.9 | 1316.00 | 54990 | WareHouseA |
s23 | 762.95 | 802.95 | 8.00 | 40.00 | 262.9 | 800.00 | 52590 | WareHouseA |
s22 | 806.11 | 821.11 | 3.16 | 15.00 | 0.00 | 316.00 | 0.00 | WareHouseA |
s21 | 823.64 | 843.64 | 2.53 | 20.00 | 0.00 | 253.00 | 0.00 | WareHouseA |
s17 | 845.93 | 895.93 | 2.29 | 50.00 | 445.9 | 229.00 | 89186 | WareHouseA |
s16 | 898.68 | 913.68 | 2.75 | 15.00 | 0.00 | 275.00 | 0.00 | WareHouseA |
s15 | 917.43 | 937.43 | 3.75 | 20.00 | 0.00 | 375.00 | 0.00 | WareHouseA |
s18 | 942.59 | 952.59 | 5.16 | 10.00 | 0.00 | 516.00 | 0.00 | WareHouseA |
s19 | 959.43 | 979.43 | 6.84 | 20.00 | 0.00 | 684.00 | 0.00 | WareHouseA |
s47 | 989.60 | 999.60 | 10.17 | 10.00 | 0.00 | 1017.00 | 0.00 | WareHouseA |
s24 | 1014.17 | 1024.17 | 14.57 | 10.00 | 0.00 | 1457.00 | 0.00 | WareHouseA |
s30 | 1030.21 | 1075.21 | 6.03 | 45.00 | 380.2 | 603.00 | 76042 | WareHouseA |
s29 | 1077.77 | 1127.77 | 2.56 | 50.00 | 477.7 | 256.00 | 95554 | WareHouseA |
s26 | 1131.39 | 1176.39 | 3.62 | 45.00 | 581.3 | 362.00 | 116278 | WareHouseA |
s28 | 1178.76 | 1193.76 | 2.37 | 15.00 | 0.00 | 237.00 | 0.00 | WareHouseA |
s31 | 1200.60 | 1215.60 | 6.84 | 15.00 | 0.00 | 684.00 | 0.00 | WareHouseA |
s32 | 1217.66 | 1257.66 | 2.06 | 40.00 | 517.6 | 206.00 | 103532 | WareHouseA |
s27 | 1264.99 | 1274.99 | 7.33 | 10.00 | 0.00 | 733.00 | 0.00 | WareHouseA |
s33 | 1288.87 | 1308.87 | 13.88 | 20.00 | 0.00 | 1388.00 | 0.00 | WareHouseA |
s36 | 1325.59 | 1345.59 | 16.72 | 20.00 | 0.00 | 1672.00 | 0.00 | WareHouseA |
s34 | 1371.66 | 1386.66 | 26.07 | 15.00 | 0.00 | 2607.00 | 0.00 | WareHouseA |
s12 | 1419.80 | 1429.80 | 33.13 | 10.00 | 0.00 | 3313.00 | 0.00 | WareHouseA |
s37 | 1446.56 | 1456.56 | 16.76 | 10.00 | 0.00 | 1676.00 | 0.00 | WareHouseA |
s5 | 1470.34 | 1485.34 | 13.78 | 15.00 | 1220 | 1378.00 | 244068 | WareHouseA |
WareHouseA | 1541.58 | 1541.58 | 56.24 | 0.00 | 0.00 | 5624.00 | 0.00 | WareHouseA |
According to Table
As shown in Table
Through AnyLogic platform, the results of distribution costs in single warehouse (time-limited precedence solution) are as follows:
TotalRunCost:49796.137;
TotalDelayCost:59418.424;
TotalCost:109214.56.
Meanwhile, 1622.96 virtual times are required to accomplish the entire distribution procedure.
Comparisons of the results on the shortest route solution and the time-limited precedence solution in single warehouse mode are shown in Table
Comparison results of two solutions in single warehouse mode.
ShortestPath | LimitTime | Compare Result | |
---|---|---|---|
TotalRunCost | 41657.899 | 49796.137 | +19.54% |
TotalDelayCost | 1074320.791 | 59418.424 | -94.47% |
TotalCost | 1115978.69 | 109214.561 | -90.21% |
FinishTime | 1541.580 | 1622.960 | +5.28% |
As presented in Table
Moreover, the total time span to complete the distribution is long whatever the two solutions are. In practice, reducing the delivery time to the customers significantly is an important problem the E-commerce companies face. This paper considers adding one warehouse, that is, double warehouses mode to solve this problem.
The distribution in double warehouses mode is delivering goods to all the self-raising points simultaneously with two warehouses. According to the warehouse layout of the E-commerce enterprise, the distribution warehouse is added to the west of Shanghai interplaying with the original one. The distribution of double warehouses and self-raising points is shown in Figure
Distribution of double warehouses and self-raising points.
The simulation consequences of double warehouses in the shortest route solution by AnyLogic are shown as follows:
TotalRunCost:42067.155;
TotalDelayCost:377939.599;
TotalCost:420006.754.
It takes 848.60 virtual time to accomplish the entire distribution process.
Similarly, following consequences of double warehouses can be obtained in the time-limited precedence solution:
TotalRunCost:51287.608;
TotalDelayCost:45760.399;
TotalCost:97048.007.
It takes 890.88 virtual time to accomplish.
The consequences of two solutions are compared in Table
Comparison of two solutions in double warehouses mode.
ShortestPath | LimitTime | Compare Result | |
---|---|---|---|
TotalRunCost | 42067.155 | 51287.608 | +21.92% |
TotalDelayCost | 377939.599 | 45760.399 | -87.89% |
TotalCost | 420006.754 | 97048.007 | -76.89% |
FinishTime | 848.600 | 890.880 | +4.98% |
As shown in the Table
In order to comprehend the influence of the warehouse quantity on the distribution costs and the delivery time, above results are compared to gain Tables
Comparison results of two modes in shortest route solution.
SingleHouse | DoubleHouse | Compare Result | |
---|---|---|---|
TotalRunCost | 41657.899 | 42067.155 | +1% |
TotalDelayCost | 1074320.791 | 377939.599 | -64.82% |
TotalCost | 1115978.690 | 420006.754 | -62.36% |
FinishTime | 1541.580 | 848.600 | -44.95% |
Comparison results of two modes in time-limited precedence solution.
Single House | Double House | Compare Result | |
---|---|---|---|
TotalRunCost | 49796.137 | 51287.608 | +3% |
TotalDelayCost | 59418.424 | 45760.399 | -22.99% |
TotalCost | 109214.561 | 97048.007 | -11.14% |
FinishTime | 1622.960 | 890.880 | -45.11% |
By comparing Tables
Moreover, in special shopping festivals, for instance, 11.11, 618, and so on, goods quantities increase dramatically on every self-raising point of the E-commerce companies. Therefore, this article will enlarge ten times of the order number for each point to study this problem with AnyLogic.
Simulation results of double warehouses in the shortest route solution are shown (the order number is magnified by ten times):
TotalRunCost:42067.155;
TotalDelayCost:8148314.73;
TotalCost:8190381.884.
Meanwhile, the overall distribution time is 6428.60 virtual time.
Simulation time of double warehouses in the time-limited precedence solution is as follows (the order number is magnified by ten times):
TotalRunCost:51287.608;
TotalDelayCost:2758085.139;
TotalCost:2809372.747.
Meanwhile, the overall distribution time is 6470.88 virtual time.
Compared with the normal order number, two tables are obtained as in Tables
Comparison between normal order number and ten times of the shortest route solution in double warehouses mode.
NomalOrders | 10timesOrders | Compare Result | |
---|---|---|---|
TotalRunCost | 42067.155 | 42067.155 | +0% |
TotalDelayCost | 377939.599 | 8148314.73 | +2055.98% |
TotalCost | 420006.754 | 8190381.884 | +1850.06% |
FinishTime | 848.600 | 6428.600 | +657.55% |
Comparison between normal order number and ten times of the time-limited precedence solution in double warehouses mode.
NomalOrders | 10timesOrders | Compare Result | |
---|---|---|---|
TotalRunCost | 51287.608 | 51287.608 | +0% |
TotalDelayCost | 45760.399 | 2758085.139 | +5927.23% |
TotalCost | 97048.007 | 2809372.747 | +2794.83% |
FinishTime | 890.880 | 6870.880 | +671.25% |
As shown in Tables
Three warehouses are utilized to be distributed simultaneously in three-warehouse mode which is also the fundament for multiple warehouse distribution research. In this paper, the warehouse location of the E-commerce enterprise in Shanghai is taken as example. We expand the double one to three-warehouse model. The distribution of warehouses and self-raising points are shown in Figure
Distribution of three warehouses and stations.
The simulation consequences of three warehouses in the shortest route solution can be obtained by AnyLogic as follows:
TotalRunCost:45398.807;
TotalDelayCost:132487.919;
TotalCost:177886.726.
Meanwhile, the overall distribution time is 641.76 virtual time.
Similarly, the simulation consequences of three warehouses in the time-limited precedence solution are obtained as follows:
TotalRunCost:52468.394;
TotalDelayCost:15795.939;
TotalCost:68264.333.
Meanwhile, the overall distribution time is 676.48 virtual time.
Comparing the mentioned consequences with the two warehouses in two solutions, a significant improvement has occurred. However, by observing the three warehouses and distribution sites in Figure
Distribution of three warehouses (adjusted location) and stations.
As shown in Figure
With the AnyLogic, the simulation consequences of three warehouses (adjusted location) in the shortest route solution can be obtained:
TotalRunCost:42057.032;
TotalDelayCost:49689.094;
TotalCost:91746.126.
Meanwhile, the overall distribution time is 697.08 virtual time.
Similarly, the simulation consequences of three warehouses (adjusted location) in the time-limited precedence solution can be obtained:
TotalRunCost:49418.701;
TotalDelayCost:0;
TotalCost:49418.701.
Meanwhile, the overall distribution time is 747.00 virtual time.
Comparison of the simulation results in three warehouses mode without adjusting, Tables
Comparison between original location and adjusted location of three warehouses in the shortest route solution.
Original | Changed | Compare Result | |
---|---|---|---|
TotalRunCost | 45398.807 | 42057.032 | -7.36% |
TotalDelayCost | 132487.919 | 49689.094 | -62.5% |
TotalCost | 177886.726 | 91746.126 | -48.42% |
FinishTime | 641.760 | 697.080 | +8.62% |
Comparison between original location and adjusted location of three warehouses in the time-limited precedence solution.
Original | Changed | Compare Result | |
---|---|---|---|
TotalRunCost | 52468.394 | 49418.701 | -5.81% |
TotalDelayCost | 15795.939 | 0 | -100% |
TotalCost | 68264.333 | 49418.701 | -27.61% |
FinishTime | 676.480 | 747.000 | +10.42% |
As shown in Tables
This paper uses AnyLogic simulation software to model and simulate the vehicle distribution process. Then the results are analyzed and optimized on three factors including routes selection, warehouses quantity, and warehouses layout. As shown in the simulation consequences, the time-limited precedence solution can dramatically reduce the tardiness costs and the total costs; increasing the warehouses quantity can significantly lessen the overall delivery time; vehicle travelling costs, tardiness costs, and total costs can also be reduced by distribution of warehouse locations reasonably, which also have an influence on the overall delivery time. What can also be observed from the results is that the method studying logistics distribution by AnyLogic is feasible, which can visualize complicated problems and improve operability effectively. More optimal methods and algorithms like heuristic can be used in future research. The optimization module also can be contained.
All the data used to support the findings of this study are included in our manuscript and are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.