Logistics distribution center site selection method based on logistics service and cost

Abstract

The invention relates to a logistics distribution center site selection method based on logistics service and cost, and belongs to the technical field of logistics distribution center site selection, and the method comprises the following steps: S1, quantifying a logistics service level from two dimensions of customer time satisfaction and inventory turnover capability, and quantifying the logistics service level through weighting, constructing a logistics service quantification function; s2, obtaining initial information of a logistics distribution center site selection problem, and constructing a logistics cost function based on the initial information; s3, taking the logistics cost function as an upper layer objective function and the logistics service quantification function as a lower layer objective function, and constructing a logistics distribution center double-layer planning site selection model; and S4, solving the logistics distribution center double-layer planning site selection model to obtain an optimal logistics distribution center site selection scheme. According to the method, the correlation and mutual restriction relationship between the logistics cost and the logistics service is considered, so that the comprehensive targets of the lowest logistics cost and the optimal logistics service level are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of site selection for logistics distribution centers, and in particular relates to a site selection method for logistics distribution centers based on logistics services and costs. Background Art

[0002] In recent years, with the continuous improvement of China's infrastructure and the rapid development of Internet of Things technology, the logistics industry has been able to take off economically. Especially under the guidance of the government, my country is making every effort to build a modern logistics system. The demand for the logistics industry is gradually recovering. While the logistics market is steadily improving, the proportion of my country's total social logistics costs to GDP is still high. In particular, high transportation costs have become a key factor in compressing corporate profits and increasing the burden on the national economy. It is urgent to improve through technological innovation and optimization of logistics network layout.

[0003] Logistics distribution centers are vital to enterprises. They are the core of supply chain management and the key to efficient logistics operations. A reasonable logistics distribution center can significantly reduce logistics costs, improve delivery speed and service quality, thereby enhancing customer satisfaction and corporate competitiveness. As global market competition intensifies, logistics distribution centers help companies respond quickly to market changes, optimize global supply chains, and ensure that products can reach consumers in a timely and accurate manner.

[0004] Traditional logistics center location selection methods, such as the invention patent application with application publication number CN112163808A, discloses a method for solving the logistics center location selection problem using an adaptive whale algorithm based on adversarial learning. It includes: S1. Establishing a mathematical model for the logistics center location selection problem; S2. Solving the problem using an adaptive whale algorithm based on adversarial learning; Initializing the population using the adversarial learning method to improve the global exploration capability of the algorithm; Using an adaptive convergence factor to improve the local search capability of the algorithm, avoiding the inability to perform fine search in the middle and late stages of the previous method; Using mutation operations to prevent the algorithm from falling into a local optimum due to the loss of population diversity; S3. Outputting the result; Repeating step S2 until the maximum number of iterations T is reached or the objective function does not change during the iteration process, and outputting the best logistics location center.

[0005] Although the optimization of logistics distribution centers is very important to enterprises, there are still many problems in the current site selection process. First, the traditional site selection method fails to fully analyze the logistics cost structure and cannot provide the most cost-effective site selection solution, which reduces the economic benefits of enterprises and affects the service quality. Secondly, the traditional site selection method does not take into account the logistics service. It usually only focuses on one dimension and lacks comprehensive consideration of multiple factors. The quality of logistics services, such as delivery time accuracy, service reliability, and inventory turnover capacity, directly affects the brand reputation and market share of enterprises. Logistics services that do not meet customer expectations may lead to customer dissatisfaction and loss, affecting the long-term development of enterprises. By considering logistics services in multiple dimensions, we can ensure that site selection decisions fully support the optimization of logistics services and help enterprises maintain their advantages in the fierce market competition. Summary of the invention

[0006] The present invention provides a method for site selection of a logistics distribution center based on logistics services and costs, so as to solve the problems that the traditional site selection method fails to fully analyze the logistics cost structure and does not take the logistics services into consideration.

[0007] In order to solve the above technical problems, the technical solution provided by the present invention is:

[0008] The present invention relates to a method for selecting a location for a logistics distribution center based on logistics services and costs, which comprises the following steps:

[0009] S1. Quantify the logistics service level from the two dimensions of customer time satisfaction and inventory turnover capacity, and quantify the logistics service level by weighting to construct a logistics service quantification function;

[0010] S2. Obtain the initial information of the logistics distribution center location problem and construct the logistics cost function based on the initial information;

[0011] S3. Taking the logistics cost function as the upper objective function and the logistics service quantification function as the lower objective function, a two-level planning and location selection model for logistics distribution centers is constructed;

[0012] S4. Solve the two-level planning site selection model of the logistics distribution center to obtain the optimal logistics distribution center site selection plan.

[0013] Preferably, the logistics service quantification function constructed in S1 is:

[0014]

[0015] Where μ is a constant coefficient, F is the logistics service function, max means taking the maximum value, m, s, and n represent the total number of production points, the total number of logistics distribution centers, and the total number of demand points, respectively. kj It is expressed as the delivery volume from the kth logistics distribution center to the jth demand point, S(t kj) is the customer time satisfaction of the goods delivered from the kth logistics distribution center to the jth demand point, H norm,k is the normalized inventory turnover number of the kth logistics distribution center, and TDV is the total distribution volume of the logistics distribution center.

[0016] Preferably, the construction of the logistics service quantification function in S1 includes the following steps:

[0017] S1.1. Construct the customer time satisfaction function. The expression of the customer time satisfaction function is:

[0018]

[0019] Among them, S(t kj ) is the customer time satisfaction function, t kj is the time it takes for goods to be delivered from the kth logistics distribution center to the jth demand point, L j The waiting time for customers, R j is the customer's tolerable time, σ represents the sensitivity coefficient of the customer's time satisfaction function;

[0020] S1.2. Perform weighted average on the customer time satisfaction function to obtain the customer time satisfaction function. The customer time satisfaction is expressed as:

[0021]

[0022] Among them, T represents customer time satisfaction, s is the set of logistics distribution centers, and n is the set of demand points;

[0023] S1.3. Construct the distribution volume function of each logistics distribution center. The distribution volume function of each logistics distribution center is expressed as:

[0024]

[0025] Among them, DV k The delivery volume for each logistics distribution center;

[0026] S1.4. According to the delivery volume of each logistics distribution center, the total delivery volume function of the logistics distribution center is constructed. The total delivery volume function of the logistics distribution center is expressed as:

[0027]

[0028] Among them, TDV is the total delivery volume of the logistics distribution center;

[0029] S1.5. Construct the inventory turnover number H of each logistics distribution center k , expressed as:

[0030]

[0031] Among them, U k represents the average inventory capacity of the kth logistics distribution center;

[0032] S1.6. Combine the delivery volume of each logistics distribution center, the total delivery volume of the logistics distribution center, and the inventory turnover times of each logistics distribution center to construct a weighted inventory turnover function of the logistics distribution center. The weighted inventory turnover function of the logistics distribution center is expressed as:

[0033]

[0034] or

[0035]

[0036] Among them, H total is the weighted inventory turnover number;

[0037] S1.7. The inventory turnover times are normalized to obtain a normalized inventory turnover times function of the logistics distribution center. The normalized inventory turnover times function of the logistics distribution center is expressed as:

[0038]

[0039] Among them, H norm,k is the normalized inventory turnover number of the k-th logistics distribution center. The Min-Max normalization method is used to normalize the inventory turnover number. The normalized inventory turnover number of the k-th logistics distribution center is expressed as:

[0040]

[0041] Among them, H k is the original inventory turnover number of the kth logistics distribution center, H min and H max They represent the minimum and maximum inventory turnover times of the logistics distribution center respectively;

[0042] S1.8. By weighting and quantifying the customer time satisfaction and the normalized inventory turnover function of the logistics distribution center, the logistics service quantification function is obtained.

[0043] Preferably, the sensitivity coefficient σ of the customer time satisfaction function in S1.1 is 1,

[0044] At this time, the expression of customer time satisfaction function is:

[0045]

[0046] Preferably, a logistics cost function is constructed in S2, and the logistics cost function is expressed as:

[0047]

[0048] Among them, C is the logistics cost, min means taking the minimum value, m, s, and n represent the total number of production points, logistics distribution centers, and demand points respectively, and a ik 、x ik They represent the unit transportation cost and transportation volume of goods from the i-th production point to the k-th logistics distribution center, and b kj ,y kj They represent the unit transportation cost and transportation volume of goods from the kth logistics distribution center to the jth demand point, respectively. k represents the fixed investment cost of the kth logistics distribution center, W k represents the storage cost of the kth logistics distribution center, q k represents the unit management cost of the k-th logistics distribution center, Z k is a variable between 0 and 1, Z k =1 means that the kth alternative is to establish a logistics distribution center, otherwise, Z k =0 means that the kth alternative does not establish a logistics distribution center.

[0049] Preferably, in S3, the upper layer objective function and the lower layer objective function further include the following constraints:

[0050] 1) The balance constraint of the inbound and outbound cargo volume of each logistics distribution center is expressed as:

[0051]

[0052] 2) Supply constraints at production points, expressed as:

[0053]

[0054] 3) The capacity constraint of the logistics distribution center is expressed as:

[0055]

[0056] 4) The number of locations for building logistics distribution centers is constrained, and its expression is:

[0057]

[0058] 5) The construction cost constraint of the logistics distribution center is expressed as:

[0059]

[0060] 6) Whether the alternative address is selected as the constraint of the logistics distribution center is expressed as:

[0061] Z k ∈{0,1} (8),

[0062] 7) The non-negative constraints on the transportation volume from the production point to the logistics distribution center and the distribution volume from the logistics distribution center to the demand point are expressed as:

[0063] x ik ,y kj ≥0 (9),

[0064] 8) The demand constraint of the demand point is expressed as:

[0065]

[0066] Where m, s, and n represent the total number of production sites, the total number of logistics distribution centers, and the total number of demand sites, respectively; i represents the i-th production site, j represents the j-th demand site, k represents the k-th logistics distribution center, and x represents the k-th logistics distribution center. ik represents the transportation volume of goods from the i-th production point to the k-th logistics distribution center, y kj represents the transportation volume of goods from the kth logistics distribution center to the jth demand point, D i represents the supply at the production point, A k It is represented as the maximum storage capacity of logistics distribution center k, Z k represents a variable between 0 and 1, p k represents the fixed investment cost of the k-th logistics distribution center, B represents the total fixed investment budget, Q j is the demand of the jth demand point.

[0067] Preferably, in S4, the specific steps of solving the two-level planning site selection model of the logistics distribution center to obtain the optimal logistics distribution center site selection plan are:

[0068] S4.1. Input multiple alternative logistics distribution centers into the two-level planning location selection model of logistics distribution centers, encode each alternative logistics distribution center and the distribution volume of each alternative logistics distribution center to the demand point, determine the population size based on the encoding length and initialize the population;

[0069] S4.2. The lower objective function is used as the fitness function, and the fitness is calculated to obtain the logistics service index of each pre-selected logistics distribution center in each alternative plan, and the best logistics distribution center and the worst logistics distribution center are obtained;

[0070] S4.3. Determine whether the number of iterations reaches the set total number of iterations. If the number of iterations does not reach the total number of iterations, perform iterations through genetic operations and return to S4.2 after iteration. If the number of iterations reaches the total number of iterations, output the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point.

[0071] S4.4. Bring the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point into the upper objective function to obtain the optimal transportation volume from the production point to the logistics distribution center, and then obtain the logistics distribution center location plan with the lowest logistics cost and the best logistics service.

[0072] Preferably, the specific method of the genetic operation in S4.3 is: determine the best logistics distribution center C in the current population best and the worst logistics distribution center C worst , using the best logistics distribution center and the best logistics distribution center C iterated so far best *Compare, if the current best logistics distribution center C best Better than the best logistics distribution center C so far best *, then use the current best logistics distribution center C best The best logistics distribution center C so far best *, and then use the best logistics distribution center C so far best *Replace the worst logistics distribution center C worst , and then realize the selection operation.

[0073] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0074] 1. The logistics distribution center site selection method based on logistics services and costs involved in the present invention constructs a two-level planning site selection model for the logistics distribution center. The model uses the logistics cost function as the upper objective function and the logistics service quantification function as the lower objective function. The two-level planning site selection model of the logistics distribution center is then solved to obtain the optimal logistics distribution center site selection plan. The method takes into account the interrelated and mutually restrictive relationship between logistics costs and logistics services to achieve the comprehensive goal of minimizing logistics costs and optimizing logistics service levels.

[0075] 2. The logistics distribution center site selection method based on logistics services and costs involved in the present invention constructs a logistics cost function based on transportation costs, fixed investment costs and warehousing costs, and more comprehensively analyzes the key cost factors in the logistics distribution center site selection process. Through a detailed evaluation of these cost factors, a cost basis for quantifying the logistics distribution center site selection decision is established, providing economic evaluation indicators under actual constraints for the site selection method.

[0076] 3. The logistics distribution center site selection method based on logistics service and cost involved in the present invention quantifies the logistics service level from the two dimensions of customer time satisfaction and inventory turnover capacity. Quantifying the logistics service level through the two key dimensions of customer time satisfaction and inventory turnover capacity has better practical value and availability. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flowchart of a method for selecting a logistics distribution center location based on logistics services and costs;

[0078] Figure 2 A coordinate distribution diagram drawn according to the coordinate information of the demand point and the candidate point in the present invention;

[0079] Figure 3 A distribution plan that directly delivers from the production point to the demand side when no logistics distribution center is established;

[0080] Figure 4 The distribution plan for selecting point C as the logistics center;

[0081] Figure 5 The distribution plan for selecting points C and D as logistics centers;

[0082] Figure 6 A distribution plan for selecting points A, C, and D as logistics centers;

[0083] Figure 7 A distribution plan for selecting points A, B, C, and D as logistics centers;

[0084] Figure 8 It is the total logistics cost convergence curve before and after the logistics service is considered in the present invention;

[0085] Fig. 9 It is the logistics service level convergence curve before and after the logistics service considered in the present invention. DETAILED DESCRIPTION

[0086] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with examples. The following examples are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0087] See attached Figure 1 As shown, the present invention relates to a method for selecting a location for a logistics distribution center based on logistics services and costs, comprising the following steps:

[0088] S1. Quantify the logistics service level from the two dimensions of customer time satisfaction and inventory turnover capacity, and quantify the logistics service level by weighting to construct a logistics service quantification function. The construction of the logistics service quantification function includes the following steps:

[0089] S1.1. Construct a customer time satisfaction function. For customer time satisfaction, this embodiment quantifies the delivery time satisfaction through a numerical range of 0 to 1, thereby reflecting the customer's satisfaction level with the delivery time efficiency; specifically, 0 indicates that the customer is completely dissatisfied with the delivery time, 1 indicates that the customer is completely satisfied with the delivery time, and a value between 0 and 1 indicates that the customer is partially satisfied. Therefore, the expression of the customer time satisfaction function is:

[0090]

[0091] Among them, S(t kj ) is the customer time satisfaction function, t kj is the time it takes for goods to be delivered from the kth logistics distribution center to the jth demand point, L j The waiting time for customers, R j is the tolerable time of the customer; σ represents the sensitivity coefficient of the customer time satisfaction function, σ>1 represents the current convex time satisfaction function, σ<1 represents the current concave time satisfaction function, σ=1 represents the current linear time satisfaction function. In order to facilitate the quantification of logistics services and the solution of subsequent models, the sensitivity coefficient σ is taken as 1 in this embodiment. At this time, the customer time satisfaction function can also be expressed as:

[0092]

[0093] S1.2. Perform weighted average on the customer time satisfaction function to obtain the customer time satisfaction function. The customer time satisfaction is expressed as:

[0094]

[0095] Among them, T represents customer time satisfaction, s is the set of logistics distribution centers, and n is the set of demand points;

[0096] S1.3. The scales of logistics distribution centers in different places are different, and the cargo handling capacity is also different. In order to better measure the inventory turnover capacity of the logistics distribution center in the logistics service, this embodiment further constructs the distribution volume function of each logistics distribution center. The distribution volume function of each logistics distribution center is expressed as:

[0097]

[0098] Among them, DV k The delivery volume for each logistics distribution center;

[0099] S1.4. According to the delivery volume of each logistics distribution center, the total delivery volume function of the logistics distribution center is constructed. The total delivery volume function of the logistics distribution center is expressed as:

[0100]

[0101] Among them, TDV is the total delivery volume of the logistics distribution center;

[0102] S1.5. Construct the inventory turnover number H of each logistics distribution center k , expressed as:

[0103]

[0104] Among them, U k represents the average inventory capacity of the kth logistics distribution center;

[0105] S1.6. Combine the delivery volume of each logistics distribution center, the total delivery volume of the logistics distribution center, and the inventory turnover times of each logistics distribution center to construct a weighted inventory turnover function of the logistics distribution center. The weighted inventory turnover function of the logistics distribution center is expressed as:

[0106]

[0107] According to the above definition of the inventory turnover capacity of the logistics distribution center, multiple logistics distribution centers provide services to multiple customers within their scope. The inventory turnover frequency of the logistics distribution center is H total The following formula can be used for calculation:

[0108]

[0109] Among them, H total is the weighted inventory turnover number;

[0110] S1.7. In order to better measure the logistics service and facilitate the calculation of the bi-level programming model in the following text, the inventory turnover times are normalized here, and the Min-Max method is used to map the results between [0,1]. The normalized inventory turnover times can be calculated as follows:

[0111]

[0112] Among them, H norm,k is the normalized inventory turnover number of the k-th logistics distribution center, H k is the original inventory turnover number of the kth logistics distribution center, H min and H max They represent the minimum and maximum inventory turnover times of the logistics distribution center respectively;

[0113] Therefore, the normalized inventory turnover function of the logistics distribution center is expressed as:

[0114]

[0115] S1.8. By weighting and quantifying the customer time satisfaction and the normalized logistics distribution center inventory turnover function, we get the logistics service quantification function, which is:

[0116]

[0117] Where μ is a constant coefficient, F is the logistics service function, max means taking the maximum value, m, s, and n represent the total number of production points, the total number of logistics distribution centers, and the total number of demand points, respectively. kj It is expressed as the delivery volume from the kth logistics distribution center to the jth demand point, S(t kj ) is the customer time satisfaction of the goods delivered from the kth logistics distribution center to the jth demand point, H norm,k is the normalized inventory turnover number of the kth logistics distribution center, and TDV is the total distribution volume of the logistics distribution center.

[0118] S2. Obtain the initial information of the logistics distribution center location problem, including the supply capacity, coordinate information and supply capacity of the production point; the coordinate information, fixed investment cost, storage cost and inventory capacity of the logistics distribution center; the coordinate information, demand, arrival frequency, average expected time per unit cycle and tolerable time of the demand point; the unit transportation cost and vehicle delivery speed from the production point to the logistics distribution center and from the logistics distribution center to the demand point. Based on the initial information, the logistics cost function is constructed. The logistics cost function is expressed as:

[0119]

[0120] Among them, C is the logistics cost, min means taking the minimum value, m, s, and n represent the total number of production points, logistics distribution centers, and demand points respectively, and a ik 、x ik They represent the unit transportation cost and transportation volume of goods from the i-th production point to the k-th logistics distribution center, and b kj ,y kj They represent the unit transportation cost and transportation volume of goods from the kth logistics distribution center to the jth demand point, respectively. k represents the fixed investment cost of the kth logistics distribution center, W k represents the storage cost of the kth logistics distribution center, q k represents the unit management cost of the k-th logistics distribution center, Z k is a variable between 0 and 1, Z k =1 means that the kth alternative is to establish a logistics distribution center, otherwise, Z k =0 means that the kth alternative does not establish a logistics distribution center.

[0121] S3. Construct a two-level planning and location selection model for logistics distribution centers. The model aims to minimize the total logistics cost. At the same time, in order to optimize logistics services and improve customer time satisfaction and inventory turnover, the model uses the logistics cost function as the upper objective function and the logistics service quantification function as the lower objective function. The upper and lower objective functions also include the following constraints:

[0122] 1) The balance constraint of the inbound and outbound cargo volume of each logistics distribution center is expressed as:

[0123]

[0124] 2) Supply constraints at production points, expressed as:

[0125]

[0126] 3) The capacity constraint of the logistics distribution center is expressed as:

[0127]

[0128] 4) The number of locations for building logistics distribution centers is constrained, and its expression is:

[0129]

[0130] 5) The construction cost constraint of the logistics distribution center is expressed as:

[0131]

[0132] 6) Whether the alternative address is selected as the constraint of the logistics distribution center is expressed as:

[0133] Z k ∈{0,1} (8),

[0134] 7) The non-negative constraints on the transportation volume from the production point to the logistics distribution center and the distribution volume from the logistics distribution center to the demand point are expressed as:

[0135] x ik ,y kj ≥0 (9),

[0136] 8) The demand constraint of the demand point is expressed as:

[0137]

[0138] Where m, s, and n represent the total number of production sites, the total number of logistics distribution centers, and the total number of demand sites, respectively; i represents the i-th production site, j represents the j-th demand site, k represents the k-th logistics distribution center, and x represents the k-th logistics distribution center. ikrepresents the transportation volume of goods from the i-th production point to the k-th logistics distribution center, y kj represents the transportation volume of goods from the kth logistics distribution center to the jth demand point, D i represents the supply at the production point, A k It is represented as the maximum storage capacity of logistics distribution center k, Z k represents a variable between 0 and 1, p k represents the fixed investment cost of the k-th logistics distribution center, B represents the total fixed investment budget, Q j is the demand of the jth demand point.

[0139] S4. Solve the two-level planning location selection model of the logistics distribution center to obtain the optimal logistics distribution center location selection plan. The specific steps are:

[0140] S4.1. Input multiple alternative logistics distribution centers into the two-level planning location selection model of logistics distribution centers, encode each alternative logistics distribution center and the distribution volume of each alternative logistics distribution center to the demand point, where the variable Z k Determine the transportation volume y from the logistics distribution center to the demand point kj The value of the variable Z k When the value is 1, the transportation volume y from the logistics distribution center to the demand point kj Therefore, in this embodiment, the variable Z k , the transportation volume y from the logistics distribution center to the demand point kj Perform real number encoding, the encoding table is shown in Table 1:

[0141] Table 1: Real number encoding table

[0142]

[0143] The performance of the genetic algorithm, including the efficiency of solving and the quality of the results, is significantly affected by the size of the initial population, by determining the population size based on the encoding length and initializing the population. Although a small population can speed up the execution of the algorithm, it may only be able to find the local optimal solution and fail to reach the global optimal solution; relatively speaking, a large population helps to improve the overall quality of the solution, but it also increases the computational burden. In order to balance efficiency and solution quality, this embodiment determines the population size to be 200 based on the encoding length, and creates 200 initial individuals in accordance with the constraints in S3 to form the starting population. In addition, in the present invention, Z k is a 0-1 variable, so there is no need to k Perform mutation operation. Because there are variables in the lower-level planning model, namely the transportation volume y kj , so when Z k =1, to achieve the transport volume y kj The mutation operation.

[0144] S4.2. The lower objective function is used as the fitness function, and the fitness is calculated to obtain the logistics service index of each pre-selected logistics distribution center in each alternative plan, and the best logistics distribution center and the worst logistics distribution center are obtained;

[0145] S4.3. Determine whether the number of iterations has reached the set total number of iterations. If the number of iterations has not reached the total number of iterations, perform iterations through genetic operations. Specifically, determine the best logistics distribution center C in the current population. best and the worst logistics distribution center C worst Then, use the best logistics distribution center and the best logistics distribution center C iterated so far best *Compare, if the current best logistics distribution center C best Better than the best logistics distribution center C so far best *, then use the current best logistics distribution center C best The best logistics distribution center C so far best *, and then use the best logistics distribution center C so far best *Replace the worst logistics distribution center C worst , and then realize the selection operation; after iteration, return to S4.2; if the number of iterations reaches the total number of iterations, output the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point;

[0146] S4.4. Bring the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point into the upper objective function to obtain the optimal transportation volume from the production point to the logistics distribution center, and then obtain the logistics distribution center location plan with the lowest logistics cost and the best logistics service.

[0147] Effect example

[0148] According to the mathematical model and algorithm analysis in the embodiment, a calculation example is set to test the feasibility and performance of the two-level planning model of the logistics distribution center based on logistics services. Faced with the increasing logistics costs and the increasing attention of the market to logistics services, a large group enterprise Q plans to build a new logistics distribution center in the M area. In order to reduce logistics costs and improve service efficiency, the enterprise has selected 5 alternative plans after field investigation and market research in the early stage. Now it is necessary to select at least 1 of the 5 alternative locations to establish a new logistics distribution center to better adapt to future development. It is known that there are 3 production points (manufacturing department, m = 3) and 20 demand points (prefecture-level city wholesalers, n = 20) in the region. The unit transportation cost from the manufacturing department to the logistics distribution center is 1 yuan / box·km, the unit transportation cost from the logistics distribution center to the prefecture-level city wholesaler is 1 yuan / box·km, the unit management cost of each logistics distribution center is 10 yuan / box, and the total fixed investment cost B of enterprise Q is 45 million yuan. The weight of customer time satisfaction in logistics service is set to 0.6, and the weight of inventory turnover capacity is set to 0.4. At the same time, considering the coordinates of the two points, the Haversine formula is used here to solve the distance between the two points. Considering the loading, unloading, and distribution of goods in the process, the average speed of distribution is set to 25km / h. The example is solved based on the above known conditions, and the total logistics cost is minimized and the logistics service is optimized under the constraints.

[0149] The data in this effect example comes from the annual demand for a certain specification product in 2023 in the integrated logistics management platform of a large group enterprise in Zhejiang Province, and 20 prefecture-level cities are selected as research objects. The data of the alternative points involved in the experiment are shown in Table 2, the data of prefecture-level city wholesalers (demand points) are shown in Table 3, and the relevant data of the production and manufacturing department (production point) are shown in Table 4.

[0150] Table 2: Coordinates of alternative points, fixed investment costs, storage costs and inventory capacity (boxes)

[0151]

[0152]

[0153] Table 3: Coordinates of wholesalers (demand points) in prefecture-level cities and annual demand (boxes)

[0154] Serial number coordinate Annual demand Serial number coordinate Annual demand 1 (119.52,26.65) 117024 11 (121.43,28.66) 274320 2 (117.65,24.51) 242784 12 (114.80,25.71) 244272 3 (119.00,25.44) 131472 13 (117.24,28.34) 49152 4 (118.17,26.65) 108720 14 (113.01,25.75) 170496 5 (117.01,25.12) 101520 15 (120.15,30.49) 134064 6 (116.00,29.70) 147360 16 (116.01,25.87) 90850 7 (117.97,28.45) 182800 17 (119.65,29.08) 242736 8 (113.85,27.63) 65664 18 (118.86,28.97) 92304 9 (114.38,27.81) 161620 19 (119.92,28.45) 100032 10 (114.97,27.12) 125520 20 (116.16,24.30) 160032

[0155] Table 4: Coordinates and supply capabilities of the manufacturing department (production point)

[0156] Production point number Production point coordinates Supply capacity (million boxes / year) 1 (118.75,32.01) 150 2 (112.11,27.99) 100 3 (121.52,31.26) 120

[0157] Furthermore, the distribution method for each demand point is periodic distribution, where the arrival frequency of the demand points, the average expected time per unit cycle, and the tolerable time data are shown in Tables 5 and 6.

[0158] Table 5: Frequency of arrival at demand points

[0159] Demand point number Arrival frequency Demand point number Arrival frequency 1 Three days a week 11 Friday 2 Friday 12 Friday 3 Tuesday 13 One week to 4 Three days a week 14 Three days a week 5 Three days a week 15 Three days a week 6 Three days a week 16 Tuesday 7 Friday 17 Friday 8 One week to 18 Tuesday 9 Three days a week 19 Three days a week 10 Three days a week 20 Three days a week

[0160] Table 6: Average expected duration and tolerable duration per unit cycle of demand points (hours)

[0161] Demand point sequence Expectation time Tolerable time Demand point number Expectation time Tolerable time 1 10.8 19.5 11 14.7 22.6 2 8.6 15.9 12 15.4 21.0 3 11.7 19.1 13 12.6 18.9 4 9.2 14.3 14 17.6 26.8 5 7.1 13.9 15 18.6 26.8 6 10.5 18.7 16 18.1 23.5 7 12.3 15.7 17 14.7 20.4 8 13.3 17.6 18 12.4 21.1 9 11.9 17.8 19 13.7 19.7 10 10.8 19.2 20 17.4 24.8

[0162] According to the experimental parameters and combined with the experimental data, the above-mentioned location selection model is solved using a genetic algorithm, and the coordinate distribution is drawn by the coordinates of the above-mentioned prefecture-level city wholesalers (demand points) and alternative points. See the attached Figure 2 The location selection results of different logistics distribution centers are shown in the attached Figures 4 to 7 When no logistics distribution center is established, the production site will directly deliver the goods to the demand side, as shown in the attached Figure 3 shown.

[0163] According to different logistics distribution center site selection schemes, the logistics cost and logistics service are calculated. In order to better measure the availability of the site selection results, the inventory turnover times in the logistics service level are also used as a comparison standard. The site selection results are shown in Table 7. Among them, when the number of logistics distribution centers is five, it exceeds the fixed investment budget of the enterprise, so the results are not compared.

[0164] Table 7: Site selection results comparison table

[0165] Number of sites Site selection plan Total logistics cost (ten thousand yuan) Logistics service level Inventory turnover times 0 From production point to demand point 6387.68 56.13％ 11.58 1 C 5874.56 73.26％ 24.66 2 C.D 5929.05 81.58％ 28.42 3 A, C, D 5792.87 90.57％ 35.71 4 A, B, C, D 6040.45 88.67％ 34.45

[0166] As shown in Table 7, when the number of sites is 3 (site selection scheme: A, C, D), it is the optimal scheme for comprehensive logistics cost and logistics service, and the total logistics cost is the lowest, which is 57.9287 million yuan. The logistics service level and inventory turnover times are the highest among all schemes, which are 90.57% and 35.71 respectively. When the logistics distribution center is not built, the goods are sent from the production point to the demand point, and the total logistics cost is 63.8768 million yuan, of which the logistics service is extremely low, which is 56.13%, and the inventory turnover times are only 11.58. Compared with the case where the logistics distribution center is not built, the total logistics cost of the best site selection scheme (site selection scheme: A, C, D) is reduced by 5.9481 million yuan, the saving ratio is 9.35%, the logistics service is increased by 34.44%, and the inventory turnover times are increased by 24.13.

[0167] At the same time, in order to better verify the usability of the present invention in the dimension of logistics service, the above optimal solution is taken, that is, when the number of sites is 3 (site selection schemes: A, C, D), the model before and after considering logistics service is solved respectively, and the solution results are shown in Table 8. The total logistics cost convergence curve and the logistics service level convergence curve are shown in Table 8. Figure 8 and Fig. 9 shown.

[0168] Table 8: Comparison of site selection results before and after logistics service consideration

[0169]

[0170]

[0171] As can be seen from Table 8, although the plan considering logistics services increased the total logistics costs by RMB 3.5432 million compared to the plan not considering logistics services, the logistics service level was significantly improved by 18.74%. In the long run, enterprises can significantly improve customer satisfaction and market competitiveness by adopting solutions based on logistics services.

[0172] The present invention is described in detail above in conjunction with the embodiments, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for selecting a logistics distribution center location based on logistics services and costs, characterized by: It includes the following steps: S1. Quantify the logistics service level from the two dimensions of customer time satisfaction and inventory turnover capacity, and quantify the logistics service level by weighting to construct a logistics service quantification function; S2. Obtain the initial information of the logistics distribution center location problem and construct the logistics cost function based on the initial information; S3. Taking the logistics cost function as the upper objective function and the logistics service quantification function as the lower objective function, a two-level planning and location selection model for logistics distribution centers is constructed; S4. Solve the two-level planning site selection model of the logistics distribution center to obtain the optimal logistics distribution center site selection plan.

2. The method for selecting a logistics distribution center based on logistics services and costs according to claim 1 is characterized by: The logistics service quantification function constructed in S1 is: Where μ is a constant coefficient, F is the logistics service function, max means taking the maximum value, m, s, and n represent the total number of production points, the total number of logistics distribution centers, and the total number of demand points, respectively. kj It is expressed as the delivery volume from the kth logistics distribution center to the jth demand point, S(t kj ) is the customer time satisfaction of the goods delivered from the kth logistics distribution center to the jth demand point, H norm,k is the normalized inventory turnover number of the kth logistics distribution center, and TDV is the total distribution volume of the logistics distribution center.

3. The method for selecting a logistics distribution center based on logistics services and costs according to claim 2 is characterized by: The construction of the logistics service quantification function in S1 includes the following steps: S1.

1. Construct the customer time satisfaction function. The expression of the customer time satisfaction function is: Among them, S(t kj ) is the customer time satisfaction function, t kj is the time it takes for goods to be delivered from the kth logistics distribution center to the jth demand point, L j The waiting time for customers, R j is the customer's tolerable time, σ represents the sensitivity coefficient of the customer's time satisfaction function; S1.

2. Perform weighted average on the customer time satisfaction function to obtain the customer time satisfaction function. The customer time satisfaction is expressed as: Among them, T represents customer time satisfaction, s is the set of logistics distribution centers, and n is the set of demand points; S1.

3. Construct the distribution volume function of each logistics distribution center. The distribution volume function of each logistics distribution center is expressed as: Among them, DV k The delivery volume for each logistics distribution center; S1.

4. According to the delivery volume of each logistics distribution center, the total delivery volume function of the logistics distribution center is constructed. The total delivery volume function of the logistics distribution center is expressed as: Among them, TDV is the total delivery volume of the logistics distribution center; S1.

5. Construct the inventory turnover number H of each logistics distribution center k , expressed as: Among them, U k represents the average inventory capacity of the kth logistics distribution center; S1.

6. Combine the delivery volume of each logistics distribution center, the total delivery volume of the logistics distribution center, and the inventory turnover times of each logistics distribution center to construct a weighted inventory turnover function of the logistics distribution center. The weighted inventory turnover function of the logistics distribution center is expressed as: or Among them, H total is the weighted inventory turnover number; S1.

7. The inventory turnover times are normalized to obtain a normalized inventory turnover times function of the logistics distribution center. The normalized inventory turnover times function of the logistics distribution center is expressed as: Among them, H norm,k is the normalized inventory turnover number of the k-th logistics distribution center. The Min-Max normalization method is used to normalize the inventory turnover number. The normalized inventory turnover number of the k-th logistics distribution center is expressed as: Among them, H k is the original inventory turnover number of the kth logistics distribution center, H min and H max They represent the minimum and maximum inventory turnover times of the logistics distribution center respectively; S1.

8. By weighting and quantifying the customer time satisfaction and the normalized inventory turnover function of the logistics distribution center, the logistics service quantification function is obtained.

4. The method for selecting a logistics distribution center based on logistics services and costs according to claim 3 is characterized by: The sensitivity coefficient σ of the customer time satisfaction function in S1.1 is 1. At this time, the expression of customer time satisfaction function is:

5. The method for selecting a logistics distribution center based on logistics services and costs according to claim 1 is characterized by: The logistics cost function is constructed in S2, and the logistics cost function is expressed as: Among them, C is the logistics cost, min means taking the minimum value, m, s, and n represent the total number of production points, logistics distribution centers, and demand points respectively, and a ik 、x ik They represent the unit transportation cost and transportation volume of goods from the i-th production point to the k-th logistics distribution center, and b kj ,y kj They represent the unit transportation cost and transportation volume of goods from the kth logistics distribution center to the jth demand point, respectively. k represents the fixed investment cost of the kth logistics distribution center, W k represents the storage cost of the kth logistics distribution center, q k represents the unit management cost of the k-th logistics distribution center, Z k is a variable between 0 and 1, Z k =1 means that the kth alternative is to establish a logistics distribution center, otherwise, Z k =0 means that the kth alternative does not establish a logistics distribution center.

6. The method for selecting a logistics distribution center based on logistics services and costs according to claim 1 is characterized by: In S3, the upper layer objective function and the lower layer objective function also include the following constraints: 1) The balance constraint of the inbound and outbound cargo volume of each logistics distribution center is expressed as: 2) Supply constraints at production points, expressed as: 3) The capacity constraint of the logistics distribution center is expressed as: 4) The number of locations for building logistics distribution centers is constrained, and its expression is: 5) The construction cost constraint of the logistics distribution center is expressed as: 6) Whether the alternative address is selected as the constraint of the logistics distribution center is expressed as: Z k ∈{0，1} (8)， 7) The non-negative constraints on the transportation volume from the production point to the logistics distribution center and the distribution volume from the logistics distribution center to the demand point are expressed as: xik,ykj≥0 (9), 8) The demand constraint of the demand point is expressed as: Where m, s, and n represent the total number of production sites, the total number of logistics distribution centers, and the total number of demand sites, respectively; i represents the i-th production site, j represents the j-th demand site, k represents the k-th logistics distribution center, and x represents the k-th logistics distribution center. ik represents the transportation volume of goods from the i-th production point to the k-th logistics distribution center, y kj represents the transportation volume of goods from the kth logistics distribution center to the jth demand point, D i represents the supply at the production point, A k It is represented as the maximum storage capacity of logistics distribution center k, Z k represents a variable between 0 and 1, p k represents the fixed investment cost of the k-th logistics distribution center, B represents the total fixed investment budget, Q j is the demand of the jth demand point.

7. The method for selecting a logistics distribution center based on logistics services and costs according to claim 1 is characterized by: In S4, the specific steps of solving the two-level planning location selection model of the logistics distribution center to obtain the optimal logistics distribution center location selection plan are: S4.

1. Input multiple alternative logistics distribution centers into the two-level planning location selection model of logistics distribution centers, encode each alternative logistics distribution center and the distribution volume of each alternative logistics distribution center to the demand point, determine the population size based on the encoding length and initialize the population; S4.

2. The lower objective function is used as the fitness function, and the fitness is calculated to obtain the logistics service index of each pre-selected logistics distribution center in each alternative plan, and the best logistics distribution center and the worst logistics distribution center are obtained; S4.

3. Determine whether the number of iterations reaches the set total number of iterations. If the number of iterations does not reach the total number of iterations, perform iterations through genetic operations and return to S4.2 after iteration. If the number of iterations reaches the total number of iterations, output the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point. S4.

4. Bring the logistics distribution center with the best logistics service and the distribution volume from the logistics distribution center to the demand point into the upper objective function to obtain the optimal transportation volume from the production point to the logistics distribution center, and then obtain the logistics distribution center location plan with the lowest logistics cost and the best logistics service.

8. The method for selecting a logistics distribution center based on logistics services and costs according to claim 7 is characterized by: The specific method of genetic operation in S4.3 is: determine the best logistics distribution center C in the current population best and the worst logistics distribution center C worst , using the best logistics distribution center and the best logistics distribution center C iterated so far best *Compare, if the current best logistics distribution center C best Better than the best logistics distribution center C so far best *, then use the current best logistics distribution center C best The best logistics distribution center C so far best *, and then use the best logistics distribution center C so far best *Replace the worst logistics distribution center C worst , and then realize the selection operation.

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