Cigarette logistics network optimization method, system, equipment and medium

Abstract

The invention belongs to the technical field of logistics, and discloses a cigarette logistics network optimization method, system and device and a medium, and the method comprises the steps: obtaining the position of each cigarette factory, the position of each front cabin, the position of a municipal logistics center, the model of a vehicle participating in transportation, and the demand of each municipal logistics center; constructing a target function taking the position selection of the front cabin, the model selection of vehicles participating in transportation, and the transfer amount of the front cabin and the direct delivery amount of the cigarette factory as optimization targets; based on a preset algorithm, solving the target function to obtain an optimal distribution scheme, the optimal distribution scheme comprising the optimal number and the optimal position of the selected preposed bins, the optimal model of vehicles participating in transportation, the optimal transfer amount of cigarettes delivered to a municipal logistics center from each selected front warehouse and the optimal direct delivery amount of cigarettes delivered to the municipal logistics center from each cigarette factory are determined. According to the invention, logistics network optimization of direct delivery and front warehouse cooperation in the cigarette industry is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics, and in particular relates to a cigarette logistics network optimization method, system, equipment and medium. Background Art

[0002] The direct delivery model involves tobacco companies using high-capacity trucks to transport cigarettes directly to the logistics centers of tobacco commercial companies. This model is an effective way to reduce logistics costs in networks where production facilities are dispersed and located far from demand locations. However, with the deepening market-oriented reforms in cigarette marketing, the trend toward decentralized, small-batch, and frequent demand for cigarette brands has posed challenges to the traditional direct delivery model of tobacco companies. The model has also become increasingly vulnerable to shortcomings, such as slow response times, reduced vehicle load rates, and excessively high inventory costs.

[0003] To address this conflict, some tobacco industry and commercial companies have begun exploring the introduction of a forward warehouse model alongside the traditional direct delivery model. This involves pre-positioning cigarette inventory closer to demand, creating a collaborative cigarette logistics network that combines direct delivery with forward warehouses. For municipal logistics centers with large order volumes and sufficient truckload capacity, cigarette factories continue to utilize direct delivery to leverage economies of scale. For municipal logistics centers with smaller, more dispersed orders, cigarette factories aggregate multiple small orders and ship them to forward warehouses. Then, through optimized loading strategies, they utilize smaller truckloads from the forward warehouses to various municipal logistics centers. This collaborative model improves loading rates while shortening response times. Therefore, the forward warehouse model has become a popular logistics solution for tobacco industry enterprises serving customers with smaller, more dispersed orders. Currently, the synergy between the forward warehouse model's logistics network and the existing direct cigarette delivery logistics network has become a key issue for tobacco industry enterprises in optimizing their logistics systems.

[0004] Current research on cigarette logistics system optimization focuses on cigarette industry-industry collaboration, regional reconstruction of cigarette logistics networks, and optimization of cigarette distribution routes. Regarding cigarette industry-industry collaboration, from the perspective of the tobacco industry's overall industrial and commercial supply, it is proposed that improving cigarette logistics efficiency requires collaboration between industrial and commercial enterprises. For example, Li Qing and Pang Dangqing (2021), through a comprehensive study of the upstream and downstream processes of cigarette warehousing, proposed a management approach of pre-positioning industrial and commercial warehouses and precise collaboration; Lao Min and Wu Yuzhong et al. (2023) explored a synchronized and precise coordination mechanism between nodes throughout the entire supply chain. Regarding the optimization of the regional layout of cigarette logistics networks, Zhong Yu et al. (2024) used GIS technology, spatial geographic information, and internet datasets to construct a regional logistics location model for cigarettes; Xu Xiakai et al. (2023) used a top-down approach to gradually optimize large and small regions and distribution routes, constructing a cigarette logistics network layout, improving logistics distribution efficiency, and reducing operating costs. In the research on cigarette distribution route optimization, Zhang Chao et al. (2023) proposed an adaptive scheduling algorithm for intelligent cigarette logistics distribution routes based on deep neural networks, and constructed a logistics scheduling mathematical model with the shortest total distribution mileage as the goal; Ma Nan and Wang Longfei (2024) explored the application of ant colony algorithm in cigarette logistics distribution route optimization; Zeng Yuqing, Zhang Shuangwu et al. (2024) established a cigarette finished product distribution model with the goal of minimizing distribution costs under the premise of meeting market demand.

[0005] In summary, existing research mainly focuses on establishing regional logistics centers under the collaboration of cigarette industry and commerce to achieve logistics system optimization. However, in practice, there is great resistance to the transformation from the existing logistics model to the regional logistics model. After the concept of cigarette forward warehouse was proposed, its advantages of industrial and commercial collaboration were recognized by the industry, but there is still a lack of research on optimizing the existing cigarette logistics system from the perspective of forward warehouse. Research on the location selection of forward warehouses is also mainly concentrated in non-cigarette industries such as fresh food e-commerce and pharmaceuticals, and there is less research in the tobacco industry. Liu Dongrong, Lu Qingquan et al. (2023) discussed the operation mode of forward warehouses shared by industry and commerce. Wang Quan et al. (2023) established a location selection model for the location decision of shared warehouses for industrial and commercial companies, which takes into account the factors of non-full load shipments and empty truck returns during cigarette transportation, aiming to minimize the total cost of cigarette transportation.

[0006] However, these studies only explored a single forward warehouse logistics model and were unable to achieve coordination between the forward warehouse model logistics network and the existing direct cigarette delivery logistics network. Summary of the Invention

[0007] The purpose of the present invention is to provide a cigarette logistics network optimization method, system, equipment and medium to solve the problem that the existing technology cannot achieve coordination between the forward warehouse model logistics network and the existing cigarette direct delivery logistics network.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for optimizing a cigarette logistics network, the method comprising:

[0010] Obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center;

[0011] Based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as the optimization objectives;

[0012] The objective function is solved based on a preset algorithm to obtain the optimal distribution plan, which includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed from each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed from each cigarette factory to the municipal logistics center.

[0013] Preferably, based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as optimization targets, including:

[0014] Taking the selected forward warehouse as the target forward warehouse, based on the location of the target forward warehouse, the locations of each cigarette factory, and the location of the municipal logistics center, calculate the distance between each cigarette factory and the target forward warehouse, the distance between each cigarette factory and each municipal logistics center, and the distance between each municipal logistics center and the target forward warehouse;

[0015] The selected vehicle model is used as the target transport vehicle to obtain the transport volume of the target transport vehicle;

[0016] Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and the target forward warehouse, the first function is constructed;

[0017] Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and each municipal logistics center, the second function is constructed;

[0018] Based on the transport volume of the target transport vehicle and the distance between each city-level logistics center and the target forward warehouse, a third function is constructed;

[0019] Construct the fourth function based on the inventory volume of the forward warehouse and the unit inventory holding cost of the commodity;

[0020] Constructing an objective function based on the first function, the second function, the third function and the fourth function;

[0021] The constraints of the objective function are constructed based on the demand of each city-level logistics center, the transportation volume of the target transport vehicle, and the capacity of the target forward warehouse.

[0022] Preferably, the objective function is expressed as:

[0023] minf=F1+F2+F3+F4+ΔD;

[0024] Where minf is the objective function, F1 is the first function, F2 is the second function, F3 is the third function, F4 is the fourth function, and ΔD is the offset.

[0025] Preferably, the expression of the first function F1 is:

[0026]

[0027] Where I is the set of cigarette factories, K is the set of forward warehouses, V1 is the set of vehicle models participating in the delivery task of the cigarette factory, and a v is the unit transportation rate of vehicle type v, L ik is the distance between cigarette factory i and forward warehouse k, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to forward warehouse k, F v is the unit fixed cost of vehicle type v as the target transport vehicle, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0;

[0028] The expression of the second function F2 is:

[0029]

[0030] Where J is the set of city-level logistics centers, V2 is the set of vehicle models participating in the forward warehouse delivery task, and L kj is the distance between the forward warehouse k and the municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from the forward warehouse k to the municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from forward warehouse k to municipal logistics center j;

[0031] The expression of the third function F3 is:

[0032]

[0033] Where, L ij is the distance between cigarette factory i and municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from cigarette factory i to municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to municipal logistics center j;

[0034] The expression of the fourth function F4 is:

[0035]

[0036] Where h is the unit product inventory holding cost of the forward warehouse, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, The inventory level of the forward warehouse.

[0037] Preferably, the calculation expression of the offset ΔD is:

[0038]

[0039] Where, F k is the operating cost of the forward warehouse k, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0.

[0040] Preferably, the constraints include at least: constraints on the total cargo capacity of each vehicle type, constraints on the sum of the quantity transported directly by the cigarette factory to the municipal logistics center and the quantity delivered by the cigarette factory to the municipal logistics center through the forward warehouse, constraints between flow variables, constraints on the quantity delivered by the cigarette factory to the forward warehouse, constraints on the total number of forward warehouses and constraints on non-negative variables.

[0041] Preferably, the preset algorithm is a CPLEX algorithm, a LINGO algorithm or a GUROBI algorithm.

[0042] In a second aspect, the present invention provides a cigarette logistics network optimization system for implementing the above-mentioned cigarette logistics network optimization method, the system comprising:

[0043] The data acquisition module is used to obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center;

[0044] A function construction module is used to construct an objective function based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, with the location of the forward warehouse, the type of vehicle involved in transportation, and the transfer volume of the forward warehouse and the direct delivery volume of the cigarette factory as the optimization objectives;

[0045] The function solving module is used to solve the objective function based on a preset algorithm to obtain the optimal distribution plan. The optimal distribution plan includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed by each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed by each cigarette factory to the municipal logistics center.

[0046] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cigarette logistics network optimization method when executing the computer program.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned cigarette logistics network optimization method when executed by a processor.

[0048] Beneficial effects:

[0049] The present invention constructs an objective function with the location selection of the forward warehouse, the model selection of vehicles involved in transportation, and the transit volume of the forward warehouse and the direct delivery volume of the cigarette factory as optimization targets, so as to realize the optimization of the logistics network of direct delivery and forward warehouse coordination in the cigarette industry. It can optimize the transportation efficiency and economy of the existing cigarette logistics network, provide a modeling and program planning method for the layout and operation optimization of the forward warehouse in the cigarette industry, and provide a scientific basis for enterprises to optimize resource allocation and reduce operating costs. It has certain reference significance for industrial and commercial enterprises in the tobacco industry to optimize the logistics system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a schematic diagram of a cigarette logistics network scenario provided by one embodiment of the present invention;

[0052] Figure 2 This is a flow chart of a cigarette logistics network optimization method provided by one embodiment of the present invention;

[0053] Figure 3 is a block diagram of a cigarette logistics network optimization system provided by one embodiment of the present invention;

[0054] Figure 4 This is a location distribution map of cigarette logistics nodes in a certain province provided by one embodiment of the present invention;

[0055] Figure 5 This is a diagram of a collaborative logistics network optimization for direct cigarette delivery and forward warehouses provided by one embodiment of the present invention;

[0056] Figure 6 is a schematic diagram of the optimization result of the direct delivery mode provided by one embodiment of the present invention;

[0057] Figure 7 It is a schematic diagram of the site selection and allocation results of the cigarette forward warehouse logistics network provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0059] Example 1

[0060] Figure 1 This is a schematic diagram of a cigarette logistics network provided by an embodiment of the present invention, such as Figure 1 As shown in the figure, the cigarette logistics network includes: cigarette factories, forward warehouses and municipal logistics centers; among them, the forward warehouses are deployed in two ways: one is a forward warehouse deployed based on the idle storage capacity of the municipal logistics center, and the other is an independent forward warehouse. There are two distribution modes in this logistics network:

[0061] 1. Direct delivery mode: Directly delivered from the cigarette factory to the municipal logistics center, such as Figure 1 As shown, it includes cigarette factories I = i0, i1…i4, municipal logistics centers J = j0, j1…j 49 Cigarette factory I directly transports cigarettes to each municipal logistics center J based on the order status of each municipal logistics center J. The advantage of this model is that it has fewer transportation links and high loading and unloading efficiency, but it is only suitable for scenarios with large order volumes and stable demand.

[0062] 2. Forward warehouse model: When the single order volume of each city-level logistics center is small, the loading rate of large-capacity trucks drops significantly, resulting in a waste of transportation resources. The low loading rate directly increases the unit transportation cost, affecting the overall economic efficiency of the entire system. To solve this problem, the cigarette industry is considering introducing the forward warehouse model based on the traditional direct delivery model. Figure 1 This is a cigarette logistics network diagram that uses the direct delivery mode and the forward warehouse mode to coordinate. The forward warehouse K is set up in the network, including the newly built forward warehouse K N =K N0 ...K N2 And relying on the existing municipal logistics center J=j0, j1…j 20 Forward warehouse K set up with idle storage capacity C =K C0 …K C20 , where K = K N ∪K C According to the order situation of each municipal logistics center J, when the order volume is large, if the requirements for full vehicle transportation are met, it can be directly transported from cigarette factory I to municipal logistics center J. When the order is small and the scale economy requirements for full vehicle transportation are not met, it is necessary to aggregate the order demands of each municipal logistics center J and deliver them to the forward warehouse K by large-capacity trucks. A joint inventory pool across cigarette specifications is formed at the forward warehouse. Then, according to the order demands of each municipal logistics center, multiple cigarette specifications are combined and delivered to the municipal logistics center by economical-capacity trucks. This model is suitable for areas with small and scattered order volumes. Through the coordinated optimization of trunk transportation and branch distribution of carpooling, logistics efficiency and economy are significantly improved. This paper intends to construct a transportation solution optimization model for the collaborative direct delivery and forward warehouse modes, design a distribution plan for each order of the municipal logistics center, and based on the location and capacity of the given optional forward warehouse K, comprehensively consider factors such as transportation cost, transportation distance, vehicle loading rate, available storage capacity, forward warehouse construction and operating costs, to construct a cigarette logistics network optimization model for the collaborative direct delivery and forward warehouse modes, and plan a distribution plan for each order to minimize the total cost of the system.

[0063] Therefore, the problems that need to be solved in the collaborative logistics network optimization model of direct cigarette delivery and forward warehouses include: 1. How to distribute the logistics volume in the two networks, how much is directly delivered by the cigarette factory to each municipal logistics center, and how much is borne by the forward warehouse network; 2. Selecting and determining the location and number of forward warehouses; 3. Determining which forward warehouse the cigarette factory delivers to and the corresponding transportation volume; 4. Determining which forward warehouse the municipal logistics center will be delivered to and the corresponding transportation volume.

[0064] In order to solve the above-mentioned coordination problem, this embodiment provides a cigarette logistics network optimization method, such as Figure 2 As shown, the method includes:

[0065] Step S10: Obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center;

[0066] Step S20: Based on the locations of each cigarette factory, each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as optimization targets;

[0067] Step S30: Solve the objective function based on a preset algorithm to obtain an optimal distribution plan, which includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed by each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed by each cigarette factory to the municipal logistics center; wherein the preset algorithm is CPLEX algorithm, LINGO algorithm or GUROBI algorithm; CPLEX, LINGO and GUROBI are the three major mainstream solvers in the field of mathematical optimization, developed by IBM, LINDO Systems and GurobiOptimization respectively; CPLEX algorithm excels at linear programming (LP), mixed integer programming (MIP), quadratic programming (QP), and supports specific problems such as network flow optimization. Its combination of branch and bound method and interior point method performs well in sparse problems; the LINGO algorithm uses an improved branch and bound algorithm and heuristic search, and supports parallel computing (multi-core / distributed); the GUROBI algorithm has a built-in LPL-like language, supports nonlinear programming, 0-1 integer programming and mixed integer programming, and can call meta-heuristic algorithms such as genetic algorithms and simulated annealing.

[0068] As a further optimization of this embodiment, based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as optimization targets, including:

[0069] Step a10: Take the selected forward warehouse as the target forward warehouse, and calculate the distance between each cigarette factory and the target forward warehouse, the distance between each cigarette factory and each municipal logistics center, and the distance between each municipal logistics center and the target forward warehouse based on the location of the target forward warehouse, the location of each cigarette factory, and the location of the municipal logistics center.

[0070] Step a20: Using the selected vehicle model as the target transport vehicle, and obtaining the transport volume of the target transport vehicle.

[0071] Step a30: Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and the target forward warehouse, a first function F1 is constructed. The expression of the first function F1 is:

[0072]

[0073] Where I is the set of cigarette factories, K is the set of forward warehouses, V1 is the set of vehicle models participating in the delivery task of the cigarette factory, and a v is the unit transportation rate of vehicle type v, L ik is the distance between cigarette factory i and forward warehouse k, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to forward warehouse k, F v is the unit fixed cost of vehicle type v as the target transport vehicle, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0.

[0074] Step a40: Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and each municipal logistics center, a second function F2 is constructed; the expression of the second function F2 is:

[0075]

[0076] Where J is the set of city-level logistics centers, V2 is the set of vehicle models participating in the forward warehouse delivery task, and L kj is the distance between the forward warehouse k and the municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from the forward warehouse k to the municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from forward warehouse k to municipal logistics center j.

[0077] Step a50: Based on the transport volume of the target transport vehicle and the distance between each city-level logistics center and the target forward warehouse, a third function F3 is constructed; the expression of the third function F3 is:

[0078]

[0079] Where, L ij is the distance between cigarette factory i and municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from cigarette factory i to municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to municipal logistics center j.

[0080] Step a60: Based on the inventory of the forward warehouse, construct a fourth function, and the expression of the fourth function F4 is:

[0081]

[0082] Where h is the unit product inventory holding cost of the forward warehouse, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, The inventory level of the forward warehouse.

[0083] Step a70: construct an objective function based on the first function, the second function, the third function and the fourth function; the expression of the objective function is:

[0084] minf=F1+F2+F3+F4+ΔD (5);

[0085] Where minf is the objective function, F1 is the first function, F2 is the second function, F3 is the third function, F4 is the fourth function, and ΔD is the offset.

[0086] The calculation expression of the offset ΔD is:

[0087]

[0088] Where, F k is the operating cost of the forward warehouse k, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0.

[0089] Step a80: Construct the constraint conditions of the objective function based on the demand of each city-level logistics center, the transportation volume of the target transport vehicle, and the capacity of the target forward warehouse.

[0090] In this embodiment, the constraints include, but are not limited to: the total cargo capacity of each vehicle type, the sum of the volume of cigarettes shipped directly from the cigarette factory to the municipal logistics center and the volume of cigarettes shipped to the municipal logistics center via the forward warehouse, constraints between flow variables, the volume of cigarettes delivered from the cigarette factory to the forward warehouse, the total number of forward warehouses, and non-negative variables. Therefore, the constraints of the constructed objective function have the following relationship:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] ∑ k∈K x k =n(14);

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Where H v is the vehicle load of vehicle type v, d ij is the demand for cigarettes from city-level logistics center j to cigarette factory i, Q k is the inventory capacity of the forward warehouse k, n is the number of forward warehouses to be built, M is a large positive number, q ikj is the transportation volume from cigarette factory i to forward warehouse k and finally to municipal logistics center j.

[0113] Among them, equations (7) to (9) indicate that the total cargo capacity of various vehicle models must meet the transportation volume demand, that is, the constraint of the total cargo capacity of each vehicle model.

[0114] Formula (10) indicates that for any cigarette factory and any municipal logistics center, the volume of cigarettes directly transported from the cigarette factory to the municipal logistics center and the volume of cigarettes delivered to the municipal logistics center through the forward warehouse must meet the demand of the municipal logistics center for the cigarette factory, that is, the sum of the volume of cigarettes directly transported from the cigarette factory to the municipal logistics center and the volume of cigarettes delivered to the municipal logistics center through the forward warehouse.

[0115] Among them, equations (11) and (12) are the constraints between flow variables.

[0116] Among them, formula (13) means that for any forward warehouse, the quantity delivered to the forward warehouse by all cigarette factories must not exceed the inventory capacity of the forward warehouse, that is, the constraint on the quantity delivered by the cigarette factories to the forward warehouse.

[0117] Among them, formula (14) indicates that the total number of selected forward warehouses must be n.

[0118] Among them, formulas (15)-(19) indicate that when the candidate forward warehouse is selected as the forward warehouse, traffic and vehicles will occur at this point.

[0119] Among them, equations (20)-(22) are non-negative integer constraints for vehicles; equations (23)-(26) are non-negative constraints for flow; and equation (27) is a 0-1 variable constraint.

[0120] Therefore, the present invention constructs an objective function with the location selection of the forward warehouse, the model selection of vehicles involved in transportation, and the transit volume of the forward warehouse and the direct delivery volume of the cigarette factory as optimization targets, so as to realize the optimization of the logistics network of direct delivery and forward warehouse coordination in the cigarette industry. It can optimize the transportation efficiency and economy of the existing cigarette logistics network, provide a modeling and program planning method for the layout and operation optimization of forward warehouses in the cigarette industry, and provide a scientific basis for enterprises to optimize resource allocation and reduce operating costs. It has certain reference significance for industrial and commercial enterprises in the tobacco industry to optimize the logistics system.

[0121] Example 2

[0122] Figure 3 FIG. 1 is a block diagram of a cigarette logistics network optimization system provided by an embodiment of the present invention. Figure 3 As shown, this embodiment provides a cigarette logistics network optimization system for implementing the cigarette logistics network optimization method in Example 1. The system includes:

[0123] The data acquisition module is used to obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center;

[0124] A function construction module is used to construct an objective function based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, with the location of the forward warehouse, the type of vehicle involved in transportation, and the transfer volume of the forward warehouse and the direct delivery volume of the cigarette factory as the optimization objectives;

[0125] The function solving module is used to solve the objective function based on a preset algorithm to obtain the optimal distribution plan. The optimal distribution plan includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed by each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed by each cigarette factory to the municipal logistics center.

[0126] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the cigarette logistics network optimization method in the first embodiment is implemented.

[0127] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the cigarette logistics network optimization method in the first embodiment is implemented.

[0128] Therefore, the present invention constructs an objective function with the location selection of the forward warehouse, the model selection of vehicles involved in transportation, and the transit volume of the forward warehouse and the direct delivery volume of the cigarette factory as optimization targets, so as to realize the optimization of the logistics network of direct delivery and forward warehouse coordination in the cigarette industry. It can optimize the transportation efficiency and economy of the existing cigarette logistics network, provide a modeling and program planning method for the layout and operation optimization of forward warehouses in the cigarette industry, and provide a scientific basis for enterprises to optimize resource allocation and reduce operating costs. It has certain reference significance for industrial and commercial enterprises in the tobacco industry to optimize the logistics system.

[0129] Example 3

[0130] This example uses the actual data of a tobacco commercial enterprise supply network in a certain province as an example. It considers optimizing a collaborative logistics network of cigarette direct delivery and forward warehouse mode consisting of 5 cigarette factories (i = 0, 1, ..., 4), 50 municipal logistics centers (j = 0, 1, ..., 49), and 24 alternative forward warehouses (k = 0, 1, ..., 23). The facility location distribution is as follows: Figure 3Among them, 24 candidate forward warehouses were determined based on the jurisdiction and statistics of idle storage capacity resources of commercial enterprises, including 21 forward warehouses using municipal logistics centers (j = 0, 1, ..., 20), idle storage capacity (k = 0, 1, ..., 20) and 3 independent forward warehouses (k = 21, 22, 23). The location distribution map of cigarette logistics nodes is shown in the figure below. Figure 4 shown.

[0131] The demand for each municipal logistics center is known and relatively stable. The data is derived from monthly historical demand data from enterprise statistical reports. Data on demand and available idle storage capacity are shown in Table 1. For forward warehouses established using idle resources, the operating cost is set at 10,000 yuan; for newly established forward warehouses, the construction cost is set at 20,000 yuan. Distance data between cigarette factories, forward warehouses, and municipal logistics centers are shown in Tables 2-4. Considering three common vehicle types in the cigarette industry: large and medium-sized trucks (17.5m and 9.6m) are used for transporting cigarettes from cigarette factories to municipal logistics centers or forward warehouses, and small trucks (4.2m) are used for transporting cigarettes from forward warehouses to municipal logistics centers. Specific parameters for these three types of trucks are shown in Table 5. The vehicle load capacity, unit transport rate, and unit fixed cost for each vehicle type in this table are derived from industry statistics and the distance between locations.

[0132] Table 1 Monthly demand of each city-level logistics center d ij and idle storage capacity Q k

[0133]

[0134]

[0135]

[0136] Note: - indicates that inventory redundancy resources for this facility are not available due to commercial enterprise jurisdiction.

[0137] Table 2 Distance L between cigarette factory and alternative forward warehouse ik (km)

[0138]

[0139] Table 3 Distance L between each city-level logistics center and the alternative forward warehouse kj (km)

[0140]

[0141] Table 4 Distance L between each city-level logistics center and cigarette factory ij (km)

[0142]

[0143] Table 5 Related parameters of three transport models

[0144]

[0145] The objective function was constructed and coded based on the Gurobi 11.0.1 optimization solver. The experimental platform used an Intel(R) Core(TM) i5-8300H CPU @ 2.30 GHz processor, 8 GB of memory, and a Windows 10 operating system. The results are shown in Table 6 below.

[0146] Table 6 Optimization results of direct cigarette delivery and forward warehouse collaborative logistics network

[0147]

[0148] The optimization results of the direct delivery of cigarettes and the collaborative logistics network of the forward warehouse show that the total cost of opening two forward warehouses is 1,195,841.35 yuan. The network layout optimization results are as follows: Figure 5 Among them, 38 municipal logistics centers were directly delivered by cigarette factories, with a direct delivery volume of 144,845.99 pieces, accounting for 77.93% of the total transportation volume; 49 municipal logistics centers were delivered by forward warehouses, with a forward warehouse transit volume of 41,013.55 pieces, accounting for 22.07% of the total transportation volume.

[0149] Figure 6 This is the optimized result of the direct delivery model in the logistics network. The specific direct delivery plans for each cigarette factory to municipal logistics centers are shown in Table 7. Cigarette factory i0 directly delivers to 37 municipal logistics centers, i1 directly delivers to 10 municipal logistics centers, i2 directly delivers to 4 municipal logistics centers, i3 directly delivers to 10 municipal logistics centers, and i4 directly delivers to 11 municipal logistics centers. The transportation volume and vehicle usage under this model are shown in Table 8. 92 17.5m vehicles were used, with an average vehicle load factor of 90.99%. The transport volume was 125,572.82 pieces, accounting for 86.69% of the total transport volume under the direct delivery model. This indicates that high-capacity vehicles dominate the direct delivery model and are primarily used to meet the needs of municipal logistics centers with large volumes. Twenty-seven 9.6m vehicles were used, with an average vehicle load factor of 81.30%. The transport volume was 19,273.17 pieces, accounting for 13.31% of the total transport volume, and they were primarily used by municipal logistics centers with medium demand. In the direct delivery mode, the high utilization rate and high loading rate of the 17.5m vehicle model verify the economic advantages of large-scale transportation. The supplementary role of the 9.6m vehicle model shows that multi-vehicle scheduling can effectively balance demand and cost and reduce total cost.

[0150] Table 7 Flow distribution results under direct delivery mode

[0151]

[0152]

[0153] Table 8 Transportation volume and vehicle usage under direct delivery mode

[0154]

[0155] Figure 7 This is the location selection and allocation result of the cigarette forward warehouse logistics network. As can be seen from the figure, k1 and k5 are enabled among the 22 candidate forward warehouses, among which k1 forward warehouse serves a total of 20 municipal logistics centers including itself; k5 forward warehouse serves a total of 32 municipal logistics centers including itself. From the perspective of spatial distribution, k1 and k5 may be located at the intersection of areas with high demand density. The two are spatially asymmetric (such as k1 to the north and k5 to the south), which can effectively avoid overlapping service areas and achieve balanced coverage of regional demand. Some municipal logistics centers such as j 29 and j 33 Delivery is carried out by two forward warehouses because, during the distribution process, the forward warehouses need to combine and distribute multiple cigarette specifications (such as A, B, and C cigarettes) from different cigarette factories to increase vehicle loading rates. If a forward warehouse has insufficient inventory of a specific specification, it must be transferred from other forward warehouses, resulting in coordinated delivery between the two forward warehouses. Overall, k1 and k5 play an extremely important role in the final delivery of cigarettes within the forward warehouse network within the cigarette distribution range. Table 9 shows the flow distribution results of the cigarette forward warehouse logistics network, including the transit volume of each municipal logistics center through forward warehouses k1 and k5 and the municipal logistics centers they serve.

[0156] Table 9 Flow distribution results of cigarette forward warehouse logistics network

[0157]

[0158]

[0159] Under the forward warehouse model, the cigarette factory consolidates the demands of multiple municipal logistics centers into full truckloads, transporting them to the forward warehouse for distribution. Table 10 shows the transportation volume and vehicle usage under this model. Twenty-eight 17.5m trucks were used to deliver to the forward warehouse, with an average vehicle load factor of 97.65%. The forward warehouse used 101 4.2m trucks for transportation to the various municipal logistics centers, with an average vehicle load factor of 81.18%. Through the forward warehouse's cargo consolidation function, the demands of multiple municipal logistics centers were consolidated into full truckloads, fully utilizing the loading capacity of large trucks and demonstrating the economies of scale of carpooling. Despite the challenges of dispersed demand and relatively small demand at each point, end-to-end delivery still achieved a load factor of 81.18% for the 4.2m trucks, demonstrating that the multi-order consolidation strategy is conducive to improving resource utilization.

[0160] Table 10: Transportation volume and vehicle usage in the forward warehouse model

[0161]

[0162] The impact of the number of forward warehouses on the total cost of the cigarette logistics network is shown in Table 11. It can be seen that under the traditional direct delivery mode, that is, when the number of forward warehouses is 0, the total cost is 1,384,939.75 yuan; when one forward warehouse is allowed, the enabled forward warehouse is k5, and the total cost is 1,196,693.11 yuan; when two forward warehouses are allowed, the enabled forward warehouses are k1 and k5, and the total cost is 1,195,841.35 yuan; when three forward warehouses are allowed, the enabled forward warehouses are k1, k6, and k 13 , with a total cost of 1,210,204.51 yuan. When the forward warehouse was introduced, the total cost dropped significantly compared to the traditional direct delivery model. This result verifies the effectiveness of the forward warehouse model in meeting the "small batch, high frequency" demand. By shortening the terminal delivery distance and optimizing the carpooling transportation strategy, it significantly improves logistics efficiency and economy. In summary, the total cost is lowest when two forward warehouses are activated, which is the appropriate number of forward warehouses. Its core advantage lies in balancing economies of scale and coverage. It provides companies with a clear basis for planning the layout of forward warehouse facilities and verifies the applicability of the forward warehouse model in the tobacco industry.

[0163] Table 11 The impact of the number of forward warehouses on transportation volume and total cost

[0164]

[0165] Therefore, the present invention constructs a logistics network optimization model based on the collaboration of direct delivery and forward warehouses in the cigarette industry, aiming to optimize the transportation efficiency and economy of the existing cigarette logistics network, and provides a modeling and program planning method for the layout and operation optimization of forward warehouses in the cigarette industry. The cigarette logistics network of a certain province was selected to verify the model, determine the optimal location and coverage of the forward warehouse, and verify the efficiency of the collaborative distribution model. In the direct delivery model, large-capacity trucks are directly delivered to municipal logistics centers with large demand, with a high loading rate, giving full play to the advantages of economies of scale; in the forward warehouse model, through the order aggregation and distribution function, the demands of multiple municipal logistics centers are assembled into whole vehicles and transported to the forward warehouse, and then small-capacity trucks are used for branch line distribution, which effectively solves the distribution problem of small batch demand, significantly reduces the cost of trunk transportation and branch line distribution, and improves the utilization rate of vehicle resources.

[0166] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0167] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0168] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A cigarette logistics network optimization method, characterized in that: The method comprises: Obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center; Based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as the optimization objectives; The objective function is solved based on a preset algorithm to obtain the optimal distribution plan, which includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed from each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed from each cigarette factory to the municipal logistics center.

2. The cigarette logistics network optimization method according to claim 1, characterized in that: Based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, an objective function is constructed with the location selection of the forward warehouse, the type of vehicles involved in transportation, the transfer volume of the forward warehouse, and the direct delivery volume of the cigarette factory as the optimization goals, including: Taking the selected forward warehouse as the target forward warehouse, based on the location of the target forward warehouse, the locations of each cigarette factory, and the location of the municipal logistics center, calculate the distance between each cigarette factory and the target forward warehouse, the distance between each cigarette factory and each municipal logistics center, and the distance between each municipal logistics center and the target forward warehouse; The selected vehicle model is used as the target transport vehicle to obtain the transport volume of the target transport vehicle; Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and the target forward warehouse, the first function is constructed; Based on the transport volume of the target transport vehicle and the distance between each cigarette factory and each municipal logistics center, the second function is constructed; Based on the transport volume of the target transport vehicle and the distance between each city-level logistics center and the target forward warehouse, a third function is constructed; Based on the inventory of the forward warehouse, construct the fourth function; Constructing an objective function based on the first function, the second function, the third function and the fourth function; The constraints of the objective function are constructed based on the demand of each city-level logistics center, the transportation volume of the target transport vehicle, and the capacity of the target forward warehouse.

3. The cigarette logistics network optimization method according to claim 2, characterized in that: The expression of the objective function is: minf=F1+F2+F3+F4+ΔD; Where minf is the objective function, F1 is the first function, F2 is the second function, F3 is the third function, F4 is the fourth function, and ΔD is the offset.

4. The cigarette logistics network optimization method according to claim 3, characterized in that: The expression of the first function F1 is: Where I is the set of cigarette factories, K is the set of forward warehouses, V1 is the set of vehicle models participating in the delivery task of the cigarette factory, and a v is the unit transportation rate of vehicle type v, L ik is the distance between cigarette factory i and forward warehouse k, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to forward warehouse k, F v is the unit fixed cost of vehicle type v as the target transport vehicle, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0; The expression of the second function F2 is: Where J is the set of city-level logistics centers, V2 is the set of vehicle models participating in the forward warehouse delivery task, and L kj is the distance between the forward warehouse k and the municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from the forward warehouse k to the municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from forward warehouse k to municipal logistics center j; The expression of the third function F3 is: Where, L ij is the distance between cigarette factory i and municipal logistics center j, The transport capacity of the target transport vehicle using vehicle type v as the transport vehicle from cigarette factory i to municipal logistics center j is: is the total number of target transport vehicles using vehicle type v as the delivery vehicle from cigarette factory i to municipal logistics center j; The expression of the fourth function F4 is: Where h is the unit product inventory holding cost of the forward warehouse, To use vehicle type v as the target transport vehicle for delivery from cigarette factory i to forward warehouse k, The inventory level of the forward warehouse.

5. The cigarette logistics network optimization method according to claim 4, characterized in that: The calculation expression of the offset ΔD is: Where, F k is the operating cost of the forward warehouse k, x k is a variable. When the kth forward warehouse is selected as the target forward warehouse, x k The value is 1; when the kth forward warehouse is not selected as the target forward warehouse, x k The value is 0.

6. The cigarette logistics network optimization method according to claim 3, characterized in that: The constraints include at least: constraints on the total cargo capacity of each vehicle type, constraints on the sum of the quantity directly transported by the cigarette factory to the municipal logistics center and the quantity delivered by the cigarette factory to the municipal logistics center through the forward warehouse, constraints between flow variables, constraints on the quantity delivered by the cigarette factory to the forward warehouse, constraints on the total number of forward warehouses and constraints on non-negative variables.

7. The cigarette logistics network optimization method according to any one of claims 1 to 6, characterized in that: The preset algorithm is CPLEX algorithm, LINGO algorithm or GUROBI algorithm.

8. A cigarette logistics network optimization system, used to implement the cigarette logistics network optimization method according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center; A function construction module is used to construct an objective function based on the location of each cigarette factory, the location of each forward warehouse, the location of the municipal logistics center, the types of vehicles involved in transportation, and the demand of each municipal logistics center, with the location of the forward warehouse, the type of vehicle involved in transportation, and the transfer volume of the forward warehouse and the direct delivery volume of the cigarette factory as the optimization objectives; The function solving module is used to solve the objective function based on a preset algorithm to obtain the optimal distribution plan. The optimal distribution plan includes: the optimal number and optimal location of forward warehouses selected, the optimal model of vehicles involved in transportation, the optimal transit volume of cigarettes distributed by each selected forward warehouse to the municipal logistics center, and the optimal direct delivery volume of cigarettes distributed by each cigarette factory to the municipal logistics center.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the cigarette logistics network optimization method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the cigarette logistics network optimization method according to any one of claims 1 to 7 is implemented.

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