Cigarette warehouse logistics system AGV number configuration method based on simulation modeling

Abstract

The invention provides a cigarette warehouse logistics system AGV number configuration method based on simulation modeling. The method comprises the following steps that S1, a simulation model of a cigarette warehouse logistics system is constructed; s2, constructing a corresponding mathematical model according to the simulation model; and S3, the optimal number of AGVs is configured through a whale algorithm. According to the method, the optimal AGV number can be found out, and the problems that the storage efficiency and the cost are affected due to the improper number of the AGVs are effectively solved.

Description

Technical Field

[0001] The invention relates to a cigarette storage and logistics system, in particular to a method for configuring the number of AGV trolleys of the cigarette storage and logistics system based on simulation modeling. Background Art

[0002] Traditional tobacco warehousing logistics mainly rely on manual material handling and storage, which is limited by the accelerated aging process and rising labor costs. Therefore, with the development of technology, many tobacco companies have introduced automated guided vehicles (AGVs) to replace manual warehousing operations. Although AGVs are more efficient and accurate than traditional labor, if the number of AGVs configured during the entire warehousing operation is too small, the timeliness of the task cannot be guaranteed; on the contrary, too many AGVs will lead to cost and energy waste, and will also cause various problems such as road congestion during operations. Summary of the invention

[0003] In order to solve at least one technical problem in the prior art, the embodiment of the present invention provides a method for configuring the number of AGV trolleys in a cigarette storage logistics system based on simulation modeling, which can find the optimal number of AGV trolleys and effectively solve the problems of affecting storage efficiency and cost due to inappropriate number of trolleys. In order to achieve the above technical objectives, the technical solution adopted by the embodiment of the present invention is:

[0004] The embodiment of the present invention provides a method for configuring the number of AGVs in a cigarette storage and logistics system based on simulation modeling, comprising the following steps:

[0005] Step S1, constructing a simulation model of a cigarette storage and logistics system;

[0006] Step S2, constructing a corresponding mathematical model according to the simulation model;

[0007] Step S3, configure the optimal number of AGVs through the White Whale algorithm.

[0008] Furthermore, constructing a corresponding mathematical model according to the simulation model specifically includes:

[0009] Step S201, assuming that the shelf number is m, the type of cigarette goods is n, and the transportation time is T 总 ; Construct a no-return path matrix, as shown in formula (1);

[0010]

[0011] Among them, D (n,m) Represents the non-return distance for each AGV to store goods;

[0012] Step S202, updating the remaining quantity of goods at the import port, as shown in formula (2);

[0013] R n =RA n (2)

[0014] Among them, R n represents the remaining quantity of goods of type n, R represents the quantity of goods to be processed, A n Represents the number of n types of goods that can be transported by all AGVs without return trips;

[0015] Step S203, when the current shelf is full of goods, retrieve the next empty shelf, transport the goods to the next empty shelf, and update the no-return distance of the AGV to the next empty shelf based on the no-return path matrix;

[0016] Step S204, calculating the time for the AGV to transport goods without returning, as shown in formula (3);

[0017] T 1 =D (n,m) ÷v (3)

[0018] Among them, T 1 represents the time required for an AGV to store goods from the cargo entrance to the cigarette storage area, and v represents the running speed of the AGV;

[0019] Step S205, calculating the time required for an AGV to perform a transport operation and the time required for an AGV to perform a one-way transport operation;

[0020] T 返 =2T 1 +T 2 +T Load (4)

[0021] T 单 =T 1 +T 2 +T Load (5)

[0022] Among them, T 2 It represents the time required for each AGV to go from the AGV parking area to the cargo entrance to pick up the cargo; T Load It represents the total time required for each AGV to unload and load once; T 返 It represents the time required for an AGV to transport the vehicle, T 单 Indicates that an AGV is only allowed to perform the time required for a one-way transport operation;

[0023] Step S206, calculating the total number of transport operations that an AGV can complete within a specified time, as shown in formula (6);

[0024] T 总 ÷T 返 =c…T 余 (6)

[0025] Among them, T 总 is the specified time for transportation, c is the total number of completed transportation operations, T 余 is the time remainder;

[0026] Step S207, calculate the number of n types of goods that all AGVs can transport A n ,include:

[0027] When T 余 = 0, A n =ci 2 ; Where i represents the number of AGV vehicles;

[0028] When T 余 ≠0, if T 余 >T 单 , A n =(c+1)i 2 If T 余 <T 单 , A n =ci 2 ;

[0029] Step S208, accumulating the quantity of each type of goods transported, and calculating the total quantity of goods that can be transported within the specified time, as shown in formula (7);

[0030]

[0031] Among them, Q 总 Indicates the total quantity of goods that can be transported within a specified time.

[0032] Furthermore, configuring the optimal number of AGVs through the White Whale algorithm specifically includes:

[0033] Step S301, constructing a fitness function, as shown in formula (8);

[0034]

[0035] Among them, F is the fitness function;

[0036] Step S302, set the number of white whales to N 2 , the maximum number of iterations is Tmax, the convergence factor is a, and the spiral parameter is b; initialize the whale position P and set the integer in the range [1, N1] to represent the number of AGVs required, initialize the fitness value F, and the optimal fitness value F best and the best individual position Pbest is negative infinity;

[0037] Step S303, calculate the fitness value F of each beluga whale and select the best individual position P best Its fitness value F best As the initial position of the white whale;

[0038] Step S304, calculate convergence factor a, coefficient A, coefficient C, balance factor B f ; When B f ≥0.5, the algorithm performs a global search. f <0.5, the algorithm performs a local search;

[0039] Balance Factor B f As shown in formula (9);

[0040]

[0041] Where t is the current iteration number, T max is the maximum number of iterations, B 0 is a random number between [0,1];

[0042] The convergence factor a is shown in formula (10);

[0043]

[0044] The coefficient A and coefficient C are shown in formula (11);

[0045]

[0046] Among them, r1 and r2 are random numbers (0,1);

[0047] When performing global search, the individual positions of the beluga whales are updated using formula (12);

[0048]

[0049] Among them, P N represents the individual position of the white whale, that is, the number of AGVs required, P best represents the best individual position in the current iteration, that is, the best number of AGVs; P r represents the current number of random cars, and r represents a random number in [1, N2];

[0050] When performing local search, the individual positions of the beluga whales are updated using formula (13);

[0051]

[0052] Where L represents the distance between the beluga individual and the prey, R represents a random number between (0,1); l is a random number for updating the spiral parameters; b is the spiral parameter;

[0053] Step S305: after the position is updated, the best individual position in the current iteration is compared with the best individual position in the previous iteration. If it is better than the best individual position in the previous iteration, it is replaced with the best individual position P. best ; Otherwise, keep;

[0054] Step S306, determine whether the maximum number of iterations has been reached, if yes, execute step S307, if no, return to step S304;

[0055] Step S307, output the final optimal number of AGVs N car and the best fitness value

[0056] The beneficial effect of the technical solution provided by the embodiment of the present invention is: the present application uses the White Whale algorithm to find the optimal number of AGV carts after modeling, which can improve the utilization rate of AGV carts, improve warehousing efficiency and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a flow chart of the method for configuring the number of AGV vehicles in an embodiment of the present invention.

[0058] Figure 2 It is a schematic diagram of the structure of the simulation model of the cigarette storage logistics system in the embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] In the description of the embodiments of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0061] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] The embodiment of the present invention proposes a method for configuring the number of AGV vehicles in a cigarette storage and logistics system based on simulation modeling, such as Figure 1 As shown, the following steps are included:

[0064] Step S1, constructing a simulation model of a cigarette storage and logistics system;

[0065] like Figure 2 As shown, the embodiment of the present invention uses Anylogic simulation modeling software to construct Figure 2 The simulation model of the cigarette warehouse logistics system shown;

[0066] The simulation model of the cigarette storage and logistics system includes the whole tobacco temporary storage area, sorting and packaging area, inlet, AGV parking area, cigarette carton storage area, cigarette carton outbound area, packaging area, conveyor belt and delivery point; the whole tobacco carton is first transported to the whole tobacco temporary storage area, and after being unpacked in the sorting and packaging area, the cigarette carton is transported by the AGV car at the inlet and sent to the shelf in the cigarette carton temporary storage area for temporary storage; this application focuses on the warehousing and transportation operation of cigarette carton;

[0067] Step S2, constructing a corresponding mathematical model according to the simulation model; specifically comprising:

[0068] The AGV car picks up a piece of cargo from the AGV parking area to the cargo entrance, transports it to the corresponding shelf in the cigarette storage area according to the cargo type, and then returns to the cargo entrance, which is a transportation operation;

[0069] Step S201, assuming that the shelf number is m, the type of cigarette goods is n, and the transportation time is T 总 ; Construct a no-return path matrix, as shown in formula (1);

[0070]

[0071] Among them, D (n,m) Represents the non-return distance for each AGV to store goods;

[0072] Step S202, updating the remaining quantity of goods at the import port, as shown in formula (2);

[0073] R n =RA n (2)

[0074] Among them, R n represents the remaining quantity of goods of type n, R represents the quantity of goods to be processed, A n Represents the number of n types of goods that can be transported by all AGVs without return trips;

[0075] Step S203, when the current shelf is full of goods, retrieve the next empty shelf, transport the goods to the next empty shelf, and update the no-return distance of the AGV to the next empty shelf based on the no-return path matrix;

[0076] Step S204, calculating the time for the AGV to transport goods without returning, as shown in formula (3);

[0077] T 1 =D (n,m) ÷v (3)

[0078] Among them, T 1 represents the time required for an AGV to store goods from the cargo entrance to the cigarette storage area, and v represents the running speed of the AGV;

[0079] Step S205, calculating the time required for an AGV to perform a transport operation and the time required for an AGV to perform a one-way transport operation;

[0080] T 返 =2T 1 +T 2 +T Load (4)

[0081] T 单 =T 1 +T 2 +T Load (5)

[0082] Among them, T 2 It represents the time required for each AGV to go from the AGV parking area to the cargo entrance to pick up the cargo; T Load It represents the total time required for each AGV to unload and load once; T 返 It represents the time required for an AGV to transport the vehicle, T 单 Indicates that an AGV is only allowed to perform the time required for a one-way transport operation;

[0083] One-way transport operation refers to the situation where the AGV does not return to the cargo entrance;

[0084] Step S206, calculating the total number of transport operations that an AGV can complete within a specified time, as shown in formula (6);

[0085] T 总 ÷T 返 =c…T 余 (6)

[0086] Among them, T 总 is the specified time for transportation, c is the total number of completed transportation operations, T 余 is the time remainder;

[0087] Step S207, calculate the number of n types of goods that all AGVs can transport A n ,include:

[0088] When T 余 = 0, A n =ci 2 ; Where i represents the number of AGV vehicles;

[0089] When T 余 ≠0, if T 余 >T 单 , A n =(c+1)i 2 If T 余 <T 单 , A n =ci 2 ;

[0090] Step S208, accumulating the quantity of each type of goods transported, and calculating the total quantity of goods that can be transported within the specified time, as shown in formula (7);

[0091]

[0092] Among them, Q 总 Indicates the total quantity of goods that can be transported within a specified time;

[0093] Step S3, configuring the optimal number of AGVs through the White Whale algorithm; specifically including:

[0094] Step S301, constructing a fitness function, as shown in formula (8);

[0095]

[0096] Among them, F is the fitness function;

[0097] Step S302, set the number of white whales to N 2 , the maximum number of iterations is T max, convergence factor a, spiral parameter b; initialize the whale position P and set the integer in the range [1, N1] to represent the number of AGVs required, initialize the fitness value F, the optimal fitness value F best and the best individual position P best is negative infinity;

[0098] Step S303, calculate the fitness value F of each beluga whale and select the best individual position P best Its fitness value F best As the initial position of the white whale;

[0099] Step S304, calculate convergence factor a, coefficient A, coefficient C, balance factor B f ; When B f ≥0.5, the algorithm performs a global search. f <0.5, the algorithm performs a local search;

[0100] Balance Factor B f As shown in formula (9);

[0101]

[0102] Where t is the current iteration number, T max is the maximum number of iterations, B 0 is a random number between [0,1];

[0103] The convergence factor a is shown in formula (10);

[0104]

[0105] The coefficient A and coefficient C are shown in formula (11);

[0106]

[0107] Among them, r 1 With r 2 is a random number from (0,1);

[0108] When performing global search, the individual positions of the beluga whales are updated using formula (12);

[0109]

[0110] Among them, P N represents the individual position of the white whale, that is, the number of AGVs required, P best represents the best individual position in the current iteration, that is, the best number of AGVs; P r represents the current number of random cars, and r represents a random number in [1, N2];

[0111] In this application, the individual positions of the white whales are the number of AGV carts required;

[0112] When performing local search, the individual positions of the beluga whales are updated using formula (13);

[0113]

[0114] Where L represents the distance between the beluga individual and the prey, R represents a random number between (0,1); l is a random number for updating the spiral parameters; b is the spiral parameter;

[0115] Step S305: after the position is updated, the best individual position in the current iteration is compared with the best individual position in the previous iteration. If it is better than the best individual position in the previous iteration, it is replaced with the best individual position P. best ; Otherwise, keep;

[0116] Step S306, determine whether the maximum number of iterations has been reached, if yes, execute step S307, if no, return to step S304;

[0117] Step S307, output the final optimal number of AGVs N car and the best fitness value

[0118] In a specific embodiment, the shelf layout of the cigarette storage area is a linear shelf layout; there are 5 types of cigarette goods, and the number of shelves for each type of goods is 5;

[0119] The number of beluga whales is N 2 =10, the maximum number of iterations is T max = 2000; Initialize the whale position P and set the integer in the range [1,25] to represent the number of AGVs required; Get the optimal number of AGVs N through the White Whale algorithm car After that, N car Return to AnyLogic simulation modeling software to perform online simulation verification; get

[0120] The verification results are shown in Table 1;

[0121]

[0122] Table 1

[0123] It can be seen from the simulation results that the present application can improve the utilization rate of AGV carts, improve warehousing efficiency and reduce costs.

[0124] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for configuring the number of AGVs in a cigarette storage and logistics system based on simulation modeling, characterized in that: The following steps are involved: Step S1, constructing a simulation model of a cigarette storage and logistics system; Step S2, constructing a corresponding mathematical model according to the simulation model; Step S3, configure the optimal number of AGVs through the White Whale algorithm.

2. The method for configuring the number of AGVs in a cigarette storage and logistics system based on simulation modeling according to claim 1, wherein the corresponding mathematical model constructed according to the simulation model specifically comprises: Step S201, assuming that the shelf number is m, the type of cigarette goods is n, and the transportation time is T 总 ; Construct a no-return path matrix, as shown in formula (1); Among them, D (n,m) Represents the non-return distance for each AGV to store goods; Step S202, updating the remaining quantity of goods at the import port, as shown in formula (2); R n =R-A n (2) Among them, R n represents the remaining quantity of goods of type n, R represents the quantity of goods to be processed, A n Represents the number of n types of goods that can be transported by all AGVs without return trips; Step S203, when the current shelf is full of goods, retrieve the next empty shelf, transport the goods to the next empty shelf, and update the no-return distance of the AGV to the next empty shelf based on the no-return path matrix; Step S204, calculating the time for the AGV to transport goods without returning, as shown in formula (3); T1=D (n,m) ÷v (3) Among them, T1 represents the time required for an AGV to store goods from the cargo entrance to the cigarette storage area, and v represents the running speed of the AGV; Step S205, calculating the time required for an AGV to perform a transport operation and the time required for an AGV to perform a one-way transport operation; T 返 =2T1+T2+T Load (4) T 单 =T1+T2+T Load (5) Among them, T2 represents the time required for each AGV to go from the AGV parking area to the cargo entrance to pick up the goods; T Load It represents the total time required for each AGV to unload and load once; T 返 It represents the time required for an AGV to transport the vehicle, T 单 Indicates that an AGV is only allowed to perform the time required for a one-way transport operation; Step S206, calculating the total number of transport operations that an AGV can complete within a specified time, as shown in formula (6); T 总 ÷T 返 =c…T 余 (6) Among them, T 总 is the specified time for transportation, c is the total number of completed transportation operations, T 余 is the time remainder; Step S207, calculate the number of n types of goods that all AGVs can transport A n ,include: When T 余 = 0, A n =ci 2 ; Where i represents the number of AGV vehicles; When T 余 ≠ 0, if T 余 > T 单 , A n = (c + 1)i 2 ; if T 余 < T 单 , A n = ci 2 ; Step S208, accumulating the quantity of each type of goods transported, and calculating the total quantity of goods that can be transported within the specified time, as shown in formula (7); Among them, Q 总 Indicates the total quantity of goods that can be transported within a specified time.

3. The method for configuring the number of AGVs in a cigarette storage and logistics system based on simulation modeling according to claim 2, wherein configuring the optimal number of AGVs by using the White Whale algorithm specifically comprises: Step S301, constructing a fitness function, as shown in formula (8); Among them, F is the fitness function; Step S302, set the number of white whales to N2 and the maximum number of iterations to T max , convergence factor a, spiral parameter b; initialize the whale position P and set the integer in the range [1, N1] to represent the number of AGVs required, initialize the fitness value F, the optimal fitness value F best and the best individual position P best is negative infinity; Step S303, calculate the fitness value F of each beluga whale and select the best individual position P best Its fitness value F best As the initial position of the white whale; Step S304, calculate convergence factor a, coefficient A, coefficient C, balance factor B f ; When B f ≥0.5, the algorithm performs a global search. f <0.5, the algorithm performs a local search; Balance Factor B f As shown in formula (9); Where t is the current iteration number, T max is the maximum number of iterations, B0 is a random number between [0,1]; The convergence factor a is shown in formula (10); The coefficient A and coefficient C are shown in formula (11); Among them, r1 and r2 are random numbers (0,1); When performing global search, the individual positions of the beluga whales are updated using formula (12); Among them, P N represents the individual position of the white whale, that is, the number of AGVs required, P best represents the best individual position in the current iteration, that is, the best number of AGVs; P r represents the current number of random cars, and r represents a random number in [1, N2]; When performing local search, the individual positions of the beluga whales are updated using formula (13); Where L represents the distance between the beluga individual and the prey, R represents a random number between (0,1); l is a random number for updating the spiral parameters; b is the spiral parameter; Step S305: after the position is updated, the best individual position in the current iteration is compared with the best individual position in the previous iteration. If it is better than the best individual position in the previous iteration, it is replaced with the best individual position P. best ; Otherwise, keep; Step S306, determine whether the maximum number of iterations has been reached, if yes, execute step S307, if no, return to step S304; Step S307, output the final optimal number of AGVs N car and the best fitness value

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