Petroleum product distribution route determination method and device and storage medium

Abstract

The invention discloses a petroleum product distribution route determination method and device and a storage medium, and is applied to the technical field of petroleum product distribution, and the method comprises the steps: determining the sales prediction data of petroleum products in a target time period according to the historical sales data of the petroleum products of all terminal stations in a to-be-detected region; obtaining a library station distribution route set according to library station matching data and the sales volume prediction data of the petroleum products in the target time period; obtaining an initial refinery distribution route set according to refinery matching data and the sales prediction data of the petroleum products in the target time period; performing genetic operation on the initial refinery distribution route set, and determining the initial refinery distribution route set with the minimum cost as a target refinery distribution route set; and obtaining a target delivery route set based on the library station delivery route set and the target library refining delivery route set. According to the method, the occurrence of redundant distribution routes can be avoided, and the minimization of logistics cost is realized.

Description

Technical Field

[0001] This application relates to the technical field of petroleum product distribution, and particularly to a method, device, and storage medium for determining a petroleum product distribution route. Background Art

[0002] In the production, transportation, storage, and sales processes of petroleum products, each refinery, oil depot, and gas station makes independent decisions on its own business operations. As a result, there are situations where the resource allocation of petroleum products is uneven, leading to a large number of redundant logistics routes. This causes oil companies to bear more logistics costs.

[0003] In related technologies, oil companies usually plan the distribution routes between refineries and oil depots and between oil depots and gas stations based on the personal experience of relevant personnel. However, this method relies too much on personal experience and has poor accuracy, resulting in redundant distribution logistics routes and thus high logistics costs. Summary of the Invention

[0004] In view of this, this application provides a method, device, and storage medium for determining a petroleum product distribution route, which can avoid the appearance of redundant distribution routes and minimize logistics costs.

[0005] Specifically, the following technical solutions are included:

[0006] In a first aspect, an embodiment of this application provides a method for determining a petroleum product distribution route, and the method includes:

[0007] Determine the sales volume prediction data of petroleum products in a target time period according to the historical sales volume data of petroleum products at each terminal station in the area to be measured;

[0008] Obtain a set of depot-station distribution routes according to the depot-station matching data and the sales volume prediction data of petroleum products in the target time period;

[0009] Obtain an initial refinery-depot distribution route set according to the refinery-depot matching data and the sales volume prediction data of petroleum products in the target time period;

[0010] Perform genetic operations on the initial refinery-depot distribution route set, and determine the initial refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set;

[0011] Based on the set of depot-station distribution routes and the target refinery-depot distribution route set, obtain a target distribution route set.

[0012] In some embodiments, the determining the sales volume prediction data of petroleum products in a target time period according to the historical sales volume data of petroleum products at each terminal station in the area to be measured includes:

[0013] Determine the sales volume parameter based on the historical sales volume data of petroleum products at each terminal station within the area to be measured;

[0014] Perform a moving weighted average operation on the sales volume parameter to obtain the sales volume prediction data of petroleum products within the target time period.

[0015] In some embodiments, the obtaining of the initial refinery distribution route set according to the refinery - warehouse matching data and the sales volume prediction data of petroleum products within the target time period includes:

[0016] Determine the route generation parameter based on the refinery - warehouse matching data and the sales volume prediction data of petroleum products within the target time period;

[0017] Construct a multi - layer chromosome framework for the route generation parameter to obtain the initial refinery distribution route set.

[0018] In some embodiments, the route generation parameter includes:

[0019] The set of refineries \(L=\{1,2,\cdots,L\},l\in L\); the set of oil depots with demand \(M = \{1,2,\cdots,M\},m\in M\); the set of gas stations with demand \(N=\{1,2,\cdots,N\},n\in N\); the set of petroleum product types \(P=\{1,2,\cdots,P\},p\in P\); the planning period \(D\), specifically a certain month is represented by \(d\), \(D=\{1,2,\cdots,D\},d\in D\); the demand \(R\) of gas station \(n\) for petroleum product \(p\) in the \(d\) - th month npd ; the unit secondary transportation cost \(E\) of petroleum product \(p\) from oil depot \(m\) to gas station \(n\) mnp ; variables If the petroleum product \(p\) of gas station \(n\) can be delivered by oil depot \(m\), then the variable is 1, otherwise it is 0; the maximum delivery volume of petroleum product \(p\) from oil depot \(m\) the minimum delivery volume of petroleum product \(p\) from oil depot \(m\) the unit secondary delivery cost \(F\) of petroleum product \(p\) from oil depot \(m\) mp ; the delivery volume limit \(DF\) of petroleum product \(p\) from refinery \(l\) in the \(d\) - th month lpd ; the unit storage cost \(C\) of petroleum product \(p\) in oil depot \(m\) mp ; the unit primary delivery cost \(F\) of petroleum product \(p\) from refinery \(l\) lp ; the unit primary receiving cost \(S\) of petroleum product \(p\) in oil depot \(m\) mp ; the oil depot opening cost \(K\) of petroleum product \(p\) in oil depot \(m\) mp ; the wholesale volume \(PF\) of petroleum product \(p\) in oil depot \(m\) in the \(d\) - th month mpd ; the outbound volume \(V\) of petroleum product \(p\) in oil depot \(m\) in the future \(d\) - th month mpd ; the reasonable inventory level \(I\) of petroleum product \(p\) in oil depot \(m\) in the \(d\) - th month mpd; Initial inventory I of petroleum product p in oil depot m mp0 , i.e., the inventory at the beginning of this period; minimum ending inventory of petroleum product p in oil depot m Maximum ending inventory of petroleum product p in oil depot m Quantity Q of petroleum product p shipped from oil depot m to gas station n in the d-th month mnpd ; Quantity O of petroleum product p shipped from refinery l to gas station n in the d-th month lnpd ; Variable When petroleum product p is shipped from refinery l to oil depot m, variable is 1, otherwise 0; Replenishment quantity H of petroleum product p in oil depot m in the d-th month mpd , replenishment quantity of petroleum product p in oil depot m in the d-th month by transportation mode a

[0020] In some embodiments, the genetic operation on the initial refinery-depot distribution route set to determine the initial refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set includes:

[0021] Performing genetic operations on the initial refinery-depot distribution route set according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate refinery-depot distribution route sets;

[0022] Based on the cost minimization function and the cost minimization constraint conditions, determining the intermediate refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set.

[0023] In some embodiments, the cost minimization function is:

[0024]

[0025] Where L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, a specific month is represented by d, D = {1, 2,..., D}, d ∈ D, is 1 when petroleum product p at gas station n can be distributed by oil depot m, otherwise 0, E mnp , is the unit secondary transportation cost of petroleum product p from oil depot m to gas station n, F mp is the unit secondary shipping cost of petroleum product p in oil depot m, F lp is the unit primary shipping cost of petroleum product p in refinery l, S mp is the unit primary receiving cost of petroleum product p in oil depot m, I mpdis the reasonable inventory of petroleum product p in oil depot m in the d-th month, C mp is the unit storage cost of petroleum product p in oil depot m, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n in the d-th month, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n in the d-th month, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise 0, H mpd is the replenishment volume of petroleum product p in oil depot m in the d-th month.

[0026] In some embodiments, the cost minimization constraint is:

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[0036]

[0037] where L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, a specific month is represented by d, D = {1, 2,..., D}, d ∈ D, R npd is the demand for petroleum product p at gas station n in the d-th month, is the maximum fuel delivery volume of petroleum product p in oil depot m, is the minimum fuel delivery volume of petroleum product p in oil depot m, DF lp is the delivery volume limit of petroleum product p in refinery l, V mpd is the outbound volume of petroleum product p in oil depot m in the future d-th month, is the minimum end - of - period inventory of petroleum product p in oil depot m, is the maximum end - of - period inventory of petroleum product p in oil depot m, I mpd is the reasonable inventory of petroleum product p in oil depot m in the d - th month. a is the transportation mode. When a = 0, it represents rail transportation. When a = 1, it represents pipeline transportation, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n in the d - th month, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n in the d - th month, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise 0, is the replenishment volume of petroleum product p in oil depot m in the d - th month through transportation mode a, H mpd is the replenishment volume of petroleum product p in oil depot m in the d - th month, PF mpd is the wholesale volume of petroleum product p in oil depot m in the d - th month.

[0038] In some embodiments, after obtaining the target distribution route set based on the depot - station distribution route set and the target refinery - depot distribution route set, the method further includes:

[0039] Based on the target distribution route set, obtain the target distribution freight.

[0040] In a second aspect, an embodiment of the present application further provides a device for determining a petroleum product distribution route. The device includes:

[0041] A first determination module, configured to determine the sales forecast data of petroleum products in a target time period according to the historical sales data of petroleum products of each terminal station in a to - be - measured area;

[0042] A first obtaining module, configured to obtain a depot - station distribution route set according to the depot - station matching data and the sales forecast data of petroleum products in the target time period;

[0043] A second obtaining module, configured to obtain an initial refinery - depot distribution route set according to the refinery - depot matching data and the sales forecast data of petroleum products in the target time period;

[0044] A second determination module, configured to perform a genetic operation on the initial refinery - depot distribution route set to determine the initial refinery - depot distribution route set with the minimum cost as the target refinery - depot distribution route set;

[0045] A third obtaining module, configured to obtain a target distribution route set based on the depot - station distribution route set and the target refinery - depot distribution route set.

[0046] In a third aspect, an embodiment of the present application further provides a non-volatile readable storage medium, characterized in that at least one program is stored in the non-volatile readable storage medium, and the at least one program is loaded and executed by a processor to implement the method for determining an oil product distribution route as described in any one of the first aspects.

[0047] The beneficial effects of the technical solution provided by the embodiment of the present application at least include:

[0048] For the method for determining an oil product distribution route provided by the embodiment of the present application, first, the sales volume prediction data of oil products in the target time period is determined through the historical sales volume data of each terminal station in the area to be measured. Then, according to the depot-station matching data and the sales volume prediction data of oil products in the target time period, a depot-station distribution route set is obtained. Then, according to the refinery-depot matching data and the sales volume prediction data of oil products in the target time period, an initial refinery-depot distribution route set is obtained, and a genetic operation is performed on the initial refinery-depot distribution route set to determine that the route set with the minimum cost is the target refinery-depot distribution route set. Finally, the target distribution route set is obtained according to the depot-station distribution route set and the target refinery-depot distribution route set. This method obtains the distribution route set between the oil depot and the gas station and the distribution route set between the refinery and the oil depot through multiple processing and calculations of the existing data, replacing the method in the related art that relies on the personal experience of relevant personnel to plan the distribution routes between each refinery and each oil depot and between each oil depot and each gas station, avoiding the appearance of redundant distribution routes, and realizing the minimization of logistics costs. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a flowchart of a method for determining an oil product distribution route provided by an embodiment of the present application;

[0051] Figure 2 It is a flowchart of another method for determining an oil product distribution route provided by an embodiment of the present application;

[0052] Figure 3 It is a flowchart of a method for determining the sales volume prediction data of oil products in the target time period according to the historical sales volume data of oil products of each terminal station in the area to be measured in a method for determining an oil product distribution route provided by an embodiment of the present application;

[0053] Figure 4A flowchart of a method for obtaining an initial refinery distribution route set according to refinery matching data and sales volume prediction data of petroleum products within a target time period in a method for determining a petroleum product distribution route provided by an embodiment of the present application;

[0054] Figure 5 A multi-layer chromosome framework structure diagram provided by an embodiment of the present application;

[0055] Figure 6 A flowchart of a method for performing genetic operations on an initial refinery distribution route set to determine an initial refinery distribution route set with the minimum cost as the target refinery distribution route set in a method for determining a petroleum product distribution route provided by an embodiment of the present application;

[0056] Figure 7 A schematic diagram of a genetic operation process provided by an embodiment of the present application;

[0057] Figure 8 A schematic structural diagram of a device for determining a petroleum product distribution route provided by an embodiment of the present application.

[0058] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0059] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0060] Unless otherwise defined, all technical terms used in the embodiments of the present application have the same meaning as commonly understood by those of ordinary skill in the art.

[0061] To make the technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail in conjunction with the drawings.

[0062] In the production, transportation, storage, and sales processes of petroleum products, there are a large number of distribution and transportation tasks to be executed. Each refinery, oil depot, and gas station independently makes decisions on its own business operation conditions. Therefore, there will be a situation where the petroleum product resource allocation is uneven, resulting in a large number of redundant distribution routes. This makes the petroleum company bear more logistics costs. Therefore, how to reduce redundant distribution routes and lower the logistics costs borne by the petroleum company has become an urgent problem in the industry.

[0063] In the related art, oil companies usually plan the distribution routes between refineries and oil depots and between oil depots and gas stations based on the personal experience of relevant personnel. However, this method relies too much on personal experience, has poor accuracy, and there will be redundant distribution routes, resulting in too high logistics costs.

[0064] To solve the technical problems existing in the related art, the embodiments of the present application provide a method for determining the distribution route of petroleum products, which can avoid the appearance of redundant distribution routes and minimize the logistics cost.

[0065] Figure 1 It is a flowchart of a method for determining the distribution route of petroleum products provided by the embodiments of the present application. See Figure 1 This method is applied to a terminal installed with a system for determining the distribution route of petroleum products. The method includes the following steps:

[0066] Step 101: Determine the predicted sales volume data of petroleum products in the target time period according to the historical sales volume data of petroleum products at each terminal station in the area to be measured.

[0067] Step 102: Obtain a set of depot-station distribution routes according to the depot-station matching data and the predicted sales volume data of petroleum products in the target time period.

[0068] Step 103: Obtain an initial refinery-depot distribution route set according to the refinery-depot matching data and the predicted sales volume data of petroleum products in the target time period.

[0069] Step 104: Perform a genetic operation on the initial refinery-depot distribution route set to determine that the initial refinery-depot distribution route set with the minimum cost is the target refinery-depot distribution route set.

[0070] Step 105: Obtain a target distribution route set based on the set of depot-station distribution routes and the target refinery-depot distribution route set.

[0071] Therefore, in the method for determining the oil product distribution route provided by the embodiments of the present application, first, the sales volume prediction data of oil products within the target time period is determined through the historical sales volume data of each terminal station in the area to be measured. Then, according to the depot-station matching data and the sales volume prediction data of oil products within the target time period, a set of depot-station distribution routes is obtained. Next, according to the refinery-depot matching data and the sales volume prediction data of oil products within the target time period, an initial set of refinery-depot distribution routes is obtained, and a genetic operation is performed on the initial set of refinery-depot distribution routes to determine that the route set with the minimum cost is the target set of refinery-depot distribution routes. Finally, according to the set of depot-station distribution routes and the target set of refinery-depot distribution routes, a target distribution route set is obtained. This method obtains the set of distribution routes between the oil depot and the gas stations and the set of distribution routes between the refinery and the oil depot through multiple processing and calculations of the existing data, replacing the method in the related art that relies on the personal experience of relevant personnel to plan the distribution routes between each refinery and each oil depot and between each oil depot and each gas station, avoiding the emergence of redundant distribution routes, and realizing the minimization of logistics costs.

[0072] In some embodiments, determining the sales volume prediction data of oil products within the target time period according to the historical sales volume data of oil products of each terminal station in the area to be measured includes:

[0073] Based on the historical sales volume data of oil products of each terminal station in the area to be measured, determine the sales volume parameters;

[0074] Perform a moving weighted average operation on the sales volume parameters to obtain the sales volume prediction data of oil products within the target time period.

[0075] In some embodiments, obtaining the initial set of refinery-depot distribution routes according to the refinery-depot matching data and the sales volume prediction data of oil products within the target time period includes:

[0076] Based on the refinery-depot matching data and the sales volume prediction data of oil products within the target time period, determine the route generation parameters;

[0077] Construct a multi-layer chromosome framework for the route generation parameters to obtain the initial set of refinery-depot distribution routes.

[0078] In some embodiments, the route generation parameters include:

[0079] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of oil depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of oil product types P = {1, 2,..., P}, p ∈ P; the planning period D, specifically represented by d for a certain month, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for oil product p in the d-th month npd ; the unit secondary transportation cost E of oil product p from oil depot m to gas station nmnp ; Variable If the petroleum product p of gas station n can be distributed by oil depot m, then the variable is 1, otherwise 0; the maximum delivery volume of petroleum product p of oil depot m The minimum delivery volume of petroleum product p of oil depot m The unit secondary delivery cost F of petroleum product p of oil depot m mp ; The delivery volume limit DF of petroleum product p of refinery l in month d lpd ; The unit storage cost C of petroleum product p of oil depot m mp ; The unit primary delivery cost F of petroleum product p of refinery l lp ; The unit primary receiving cost S of petroleum product p of oil depot m mp ; The oil depot opening cost K of petroleum product p of oil depot m mp ; The wholesale volume PF of petroleum product p of oil depot m in month d mpd ; The outbound volume V of petroleum product p of oil depot m in the future month d mpd ; The reasonable inventory level I of petroleum product p of oil depot m in month d mpd ; The initial inventory level I of petroleum product p of oil depot m mp0 , that is, the inventory level at the beginning of this period; the minimum ending inventory of petroleum product p of oil depot m The maximum ending inventory of petroleum product p of oil depot m The delivery volume Q of petroleum product p from oil depot m to gas station n in month d mnpd ; The delivery volume O of petroleum product p from refinery l to gas station n in month d lnpd ; Variable When petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; the replenishment volume H of petroleum product p of oil depot m in month d mpd , the replenishment volume of petroleum product p of oil depot m through transportation mode a in month d

[0080] In some embodiments, genetic operations are performed on the initial refinery-depot distribution route set to determine that the initial refinery-depot distribution route set with the minimum cost is the target refinery-depot distribution route set, including:

[0081] According to the roulette wheel method, two-point crossover method, and binary mutation method, genetic operations are performed on the initial refinery-depot distribution route set to obtain multiple intermediate refinery-depot distribution route sets;

[0082] Based on the cost minimization function and cost minimization constraint conditions, determine that the intermediate refinery-depot distribution route set with the minimum cost is the target refinery-depot distribution route set.

[0083] In some embodiments, the cost minimization function is:

[0084]

[0085] Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\), \(M\) is the set of oil depots with demands, \(M=\{1, 2, \cdots, M\}\), \(m\in M\), \(N\) is the set of gas stations with demands, \(N = \{1, 2, \cdots, N\}\), \(n\in N\), \(P\) is the set of petroleum product types, \(P=\{1, 2, \cdots, P\}\), \(p\in P\), \(D\) is the planning period, and a specific month is represented by \(d\), \(D = \{1, 2, \cdots, D\}\), \(d\in D\). It is \(1\) when the petroleum product \(p\) at the gas station \(n\) can be distributed by the oil depot \(m\), otherwise it is \(0\), \(E\) mnp , is the unit secondary transportation cost of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\), \(F\) mp is the unit secondary shipping cost of the petroleum product \(p\) at the oil depot \(m\), \(F\) lp is the unit primary shipping cost of the petroleum product \(p\) at the refinery \(l\), \(S\) mp is the unit primary receiving cost of the petroleum product \(p\) at the oil depot \(m\), \(I\) mpd is the reasonable inventory of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month, \(C\) mp is the unit storage cost of the petroleum product \(p\) at the oil depot \(m\), \(Q\) mnpd is the shipping volume of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\) in the \(d\)-th month, \(O\) lnpd is the shipping volume of the petroleum product \(p\) from the refinery \(l\) to the gas station \(n\) in the \(d\)-th month. It is \(1\) when the petroleum product \(p\) is shipped from the refinery \(l\) to the oil depot \(m\), otherwise it is \(0\), \(H\) mpd is the replenishment volume of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month.

[0086] In some embodiments, the cost minimization constraint conditions are as follows:

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[0097] Among them, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demands, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demands, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, and a specific month is represented by d, D = {1, 2,..., D}, d ∈ D, R npd is the demand of gas station n for petroleum product p in the d-th month, is the maximum delivery volume of petroleum product p from oil depot m, is the minimum delivery volume of petroleum product p from oil depot m, DF lp is the delivery volume limit of petroleum product p from refinery l, V mpd is the outbound volume of petroleum product p from oil depot m in the future d-th month, is the minimum ending inventory of petroleum product p in oil depot m, is the maximum ending inventory of petroleum product p in oil depot m, I mpd is the reasonable inventory of petroleum product p in oil depot m in the d-th month. a is the transportation mode. When a = 0, it represents rail transportation. When a = 1, it represents pipeline transportation. Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n in the d-th month, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n in the d-th month, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, is the replenishment volume of petroleum product p from oil depot m in the d-th month through transportation mode a, H mpd is the replenishment volume of petroleum product p in oil depot m in the d-th month, PF mpd is the wholesale volume of petroleum product p in oil depot m in the d-th month.

[0098] In some embodiments, after obtaining the target distribution route set based on the depot-station distribution route set and the target refinery-depot distribution route set, the method further includes:

[0099] Based on the target distribution route set, obtain the target distribution freight.

[0100] Figure 2 is the flowchart of another method for determining the petroleum product distribution route provided by the embodiments of the present application. Refer to Figure 2 This method is applied to a terminal installed with a system for determining the petroleum product distribution route. The method includes the following steps:

[0101] Step 201: Determine the sales forecast data of petroleum products for the target time period based on the historical sales data of petroleum products at each terminal station within the area to be measured.

[0102] Determining the sales forecast data of petroleum products for the future target time period based on the historical sales data of petroleum products at each terminal station can provide a reference for determining the transportation plan in the subsequent steps.

[0103] In some embodiments, referring to Figure 3 , Step 201 includes the following sub-steps:

[0104] Step 2011: Determine the sales parameters based on the historical sales data of petroleum products at each terminal station within the area to be measured.

[0105] Step 2012: Perform a moving weighted average operation on the sales parameters to obtain the sales forecast data of petroleum products for the target time period.

[0106] It can be understood that the moving weighted average operation refers to using the moving weighted average method to predict the sales volume of petroleum products for the target time period. The formula used in this method is as follows:

[0107] F t = w1A t-1 + w2A t-2 + w3A t-3 +…+ w n A t-n

[0108] where A represents the historical sales data of petroleum products at each terminal station, w1 is the weight of the actual value in the (t - 1)th period; w2 is the weight of the actual value in the (t - 2)th period; w n is the weight of the actual value in the (t - n)th period; n is the number of prediction periods; w1 + w2 + w3 +…+ w n = 1, w1 > w2 >... > w n .

[0109] It can be understood that the influence of the variable values far from the target period, i.e., the tth period, is relatively low, so a lower weight is given.

[0110] Step 202: Obtain the set of depot-station distribution routes based on the depot-station matching data and the sales forecast data of petroleum products for the target time period.

[0111] Among them, the depot-station matching data includes basic information data of oil depots, depot-station matching relationship data, secondary transportation distance data, petroleum product density data, and petroleum product blending data. At the same time, for the convenience of calculation, the sales volume of gas stations in the sales forecast data is converted from tons to liters according to the density of different petroleum products.

[0112] In some embodiments, step 202 is implemented by the Vogel method.

[0113] It can be understood that the specific process of using the Vogel method includes screening and merging all oil depots that can match the demand for a certain petroleum product at a certain gas station; sorting the depots and stations in descending order of priority and ascending order of freight per ton of oil; matching the depots and stations with the optimal freight; and updating the oil delivery volumes of the oil depots and refineries.

[0114] When applying the Vogel method, considering that if the product from a certain origin cannot be supplied nearby at the minimum freight, it will be supplied at the next minimum freight. Therefore, a difference between the minimum freight and the next minimum freight is generated, and this difference is called the penalty number. The larger the penalty number, the more the freight increases when the minimum freight cannot be used for transportation. To minimize the logistics cost as much as possible, it is necessary to ensure that the minimum freight is used for transportation at the place with the largest penalty number first. The specific steps are as follows:

[0115] Step 1: Input the data into a table and calculate the difference between the second smallest unit freight rate and the smallest unit freight rate in each row and each column of the transportation table, which are called the row penalty number and the column penalty number respectively.

[0116] Step 2: Select the cell with the smallest unit freight rate in the row or column where the largest of these penalty numbers is located (if there are the same largest penalty numbers, choose any one of them), and allocate the largest possible transportation volume in the cell, then cross out the row / column.

[0117] Step 3: In the remaining rows or columns that have not been crossed out, repeat the above steps until the last cell is also allocated a transportation volume, obtaining the initial basic feasible solution of the transportation problem.

[0118] Step 4: Use the potential method to conduct an optimality test of the solution.

[0119] Specifically, add a potential column and a potential row to the table of the obtained initial basic feasible solution; since the inspection number of the basic variable is equal to 0, a potential equation system can be constructed for this group of basic variables:

[0120]

[0121] In the equation system, u i represents the potential of the i-th row (i = 1, 2...m), v j represents the potential of the j-th column (j = 1, 2...n), c ij represents the freight of the cell in the i-th row and the j-th column. Here, u i and v jIt can be positive, negative or zero. The number of non-zero variables in the feasible solution is (m + n - 1), so the potential equations contain (m + n - 1) equations. Since each row and each column in the transportation table contains basic variables, the equations constructed in this way contain (m + n) variables, which means the number of potentials is (m + n). It can be seen from this that the number of potentials is more than the number of equations. When solving, an arbitrary potential is often specified to be a smaller integer or zero, and this operation has no impact on the obtained test number table. Then calculate the test numbers to obtain the test number table. Finally, judge the optimality: for a minimization problem, if there is a σ ij < 0 in the test number table, it means that changing x ij to a basic variable will reduce the transportation cost, and the current solution has not reached the optimum, and the solution needs to be further adjusted; otherwise, the current solution reaches the optimum. After step 202 uses the Vogel method, the final output is the sum of the demands of gas station n for petroleum product p from which oil depots, the quantity of petroleum product p that oil depot m needs to ship daily, and the logistics cost.

[0122] Step 203, according to the refinery-depot matching data and the sales volume prediction data of petroleum products within the target time period, obtain the initial refinery-depot distribution route set.

[0123] That is to say, first obtain the initial refinery-depot distribution route set according to the refinery-depot matching data and the sales volume prediction data of petroleum products within the target time period, which is convenient for subsequent steps to further optimize the route set.

[0124] In some embodiments, referring to Figure 4 , step 203 includes the following sub-steps:

[0125] Step 2031, based on the refinery-depot matching data and the sales volume prediction data of petroleum products within the target time period, determine the route generation parameters.

[0126] That is to say, first obtain the route generation parameters according to the refinery-depot matching data and the sales volume prediction data of petroleum products within the target time period, which is convenient for subsequent steps to use the data as parameters for calculation.

[0127] In some embodiments, the route generation parameters include:

[0128] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of oil depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, a specific month is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R npd of gas station n for petroleum product p in the d-th month; the unit secondary transportation cost E of petroleum product p from oil depot m to gas station nmnp ; Variable If the petroleum product p of gas station n can be distributed by oil depot m, then the variable is 1, otherwise 0; The maximum delivery volume of petroleum product p of oil depot m The minimum delivery volume of petroleum product p of oil depot m The unit secondary delivery cost F of petroleum product p of oil depot m mp ; The delivery volume limit DF of petroleum product p of refinery l in month d lpd ; The unit storage cost C of petroleum product p of oil depot m mp ; The unit primary delivery cost F of petroleum product p of refinery l lp ; The unit primary receiving cost S of petroleum product p of oil depot m mp ; The oil depot opening cost K of petroleum product p of oil depot m mp ; The wholesale volume PF of petroleum product p of oil depot m in month d mpd ; The outbound volume V of petroleum product p of oil depot m in the future month d mpd ; The reasonable inventory I of petroleum product p of oil depot m in month d mpd ; The initial inventory I of petroleum product p of oil depot m mp0 , that is, the inventory at the beginning of this period; The minimum ending inventory of petroleum product p of oil depot m The maximum ending inventory of petroleum product p of oil depot m The delivery volume Q of petroleum product p from oil depot m to gas station n in month d mnpd ; The delivery volume O of petroleum product p from refinery l to gas station n in month d lnpd ; Variable When the petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; The replenishment volume H of petroleum product p of oil depot m in month d mpd , The replenishment volume of petroleum product p of oil depot m in month d by transportation method a

[0129] Step 2032, construct a multi-layer chromosome framework for the route generation parameters to obtain the initial refinery-depot distribution route set.

[0130] That is to say, before the genetic operation, it is necessary to first construct a multi-layer chromosome framework for the route generation parameters.

[0131] In some embodiments, the programmed function is the solution() function. The constructed multi-layer chromosome framework is as shown in Figure 5As shown, the first layer contains information such as refineries, oil depots, petroleum products, time, transportation methods, etc. The second to fourth layers respectively represent the replenishment volume of the oil depot, the estimated delivery date of petroleum products, the end-of-period inventory information of the oil depot, etc. Among them, the replenishment volume of the oil depot is randomly generated on the premise of meeting the restrictions of the oil depot's unloading capacity and the minimum and maximum end-of-period inventory restrictions. Among them, the length of the chromosome is equal to the number of oil depots that need to be replenished × the number of forecast months of the sales forecast data. In the process of constructing the multi-layer chromosome framework, first, according to the order of the oil depots that need to be replenished in the oil depot basic table, the basic information of the oil depot is filled into the chromosome, and then the end-of-period inventory is calculated. The end-of-period inventory is the beginning inventory minus the outgoing volume. If the end-of-period inventory is greater than 0, the oil depot does not need to be replenished. Otherwise, its replenishment volume is the minimum end-of-period inventory minus the end-of-period inventory; if the replenishment volume is greater than the available oil volume of the refinery, the replenishment volume is invalid; otherwise, the replenishment volume is filled into the chromosome.

[0132] Step 204, perform genetic operations on the initial refinery-depot distribution route set to determine that the initial refinery-depot distribution route set with the minimum cost is the target refinery-depot distribution route set.

[0133] Optimize the initial refinery-depot distribution route set through genetic operations to determine that the route set with the minimum logistics cost is the target refinery-depot distribution route set.

[0134] In some embodiments, set the population size to sizepop, randomly generate sizepop chromosomes that meet the constraint conditions according to the coding rules, use this as the initial population for genetic operations, and vertically merge the sizepop chromosomes.

[0135] In some embodiments, as Figure 6 shown, step 204 includes the following sub-steps:

[0136] Step 2041, according to the roulette method, two-point crossover method, and binary mutation method, perform genetic operations on the initial refinery-depot distribution route set to obtain multiple intermediate refinery-depot distribution route sets.

[0137] As Figure 7 shown, gen is the population evolution iteration number, gen maxLet [[ID=]] be the maximum number of iterations for population evolution. The selection operation uses the roulette wheel method, and the function defined by programming is the selecting() function. First, calculate the fitness of each individual. The probability of an individual in the population being selected is related to its fitness value, that is, the larger the fitness value, the higher the probability of being selected. Then, set the selection probability according to the different fitness values of the individuals. Finally, randomly rotate the roulette wheel to select two parent individuals. The crossover operation uses the two-point crossover method, and the function defined by programming is the jiaocha() function. For two individuals that need to be paired, first generate a random number between [0, 1]. If the value of the random number is less than the specified crossover probability, then randomly select a number of 0.01 times the chromosome length between the two chromosome individuals, take the same position of the two chromosome individuals as the crossover point, and exchange the genes of the two individuals within the crossover point. When the selected gene segments are exchanged, if the exchanged segments do not meet the fuel supply limit, then cancel the segment exchange, so as to ensure that the offspring individuals are all feasible solutions of the replenishment plan. The binary mutation method is to randomly select a chromosome, and the function defined by programming is the bianyi() function, which determines whether to mutate with the set mutation probability. Similar to the crossover operation, a random number between [0, 1] will be generated for each individual in the population. When the mutation probability is not less than this number, that is, select this individual for mutation. During the mutation process, randomly select a number of 0.01 times the chromosome length of a chromosome as the mutation point, generate the replenishment quantity of the mutation point according to the rules, and determine whether this point can successfully mutate according to the existing constraints. If not, then cancel the segment mutation, so as to ensure that the offspring individuals are feasible solutions of the replenishment plan. Finally, perform iterative optimization. By setting the maximum number of iterations as the termination condition of the genetic algorithm, when the number of iterations is less than the maximum number of iterations, repeat operations such as calculating the fitness values of population individuals, selection, crossover, and mutation; when the termination condition is met, the algorithm program stops running, and find the optimal chromosome through the findbest() function.

[0138] Step 2042, based on the cost minimization function and the cost minimization constraint conditions, determine the set of intermediate refinery distribution routes with the minimum cost as the set of target refinery distribution routes.

[0139] Fitness is the standard for evaluating the quality of chromosomes in the algorithm. The genetic algorithm usually uses its objective function as the fitness function to evaluate and select chromosomes. Therefore, this algorithm uses the cost minimization function as the fitness function.

[0140] In some embodiments, a decoding matrix (a matrix describing the characteristics of the population chromosomes) is used to decode the population chromosomes to obtain the population gene phenotype matrix.

[0141] In some embodiments, the cost minimization function is:

[0142]

[0143] Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demands, \(M=\{1, 2, \cdots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demands, \(N = \{1, 2, \cdots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P=\{1, 2, \cdots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific month is represented by \(d\), \(D=\{1, 2, \cdots, D\}\), \(d\in D\). It is \(1\) when the petroleum product \(p\) at the gas station \(n\) can be distributed by the oil depot \(m\), otherwise it is \(0\), \(E\) mnp , is the unit secondary transportation cost of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\), \(F\) mp is the unit secondary shipping cost of the petroleum product \(p\) at the oil depot \(m\), \(F\) lp is the unit primary shipping cost of the petroleum product \(p\) at the refinery \(l\), \(S\) mp is the unit primary receiving cost of the petroleum product \(p\) at the oil depot \(m\), \(I\) mpd is the reasonable inventory of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month, \(C\) mp is the unit storage cost of the petroleum product \(p\) at the oil depot \(m\), \(Q\) mnpd is the shipping volume of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\) in the \(d\)-th month, \(O\) lnpd is the shipping volume of the petroleum product \(p\) from the refinery \(l\) to the gas station \(n\) in the \(d\)-th month. It is \(1\) when the petroleum product \(p\) is shipped from the refinery \(l\) to the oil depot \(m\), otherwise it is \(0\), \(H\) mpd is the replenishment volume of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month.

[0144] In some embodiments, the cost minimization constraint conditions are:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demands, \(M = \{1, 2, \cdots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demands, \(N = \{1, 2, \cdots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P = \{1, 2, \cdots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific month is represented by \(d\), \(D = \{1, 2, \cdots, D\}\), \(d\in D\), \(R\) npd is the demand of gas station \(n\) for petroleum product \(p\) in the \(d\)-th month. is the maximum delivery volume of petroleum product \(p\) from oil depot \(m\). is the minimum delivery volume of petroleum product \(p\) from oil depot \(m\), \(DF\) lp is the delivery volume limit of petroleum product \(p\) from refinery \(l\), \(V\) mpd is the out-of-storage volume of petroleum product \(p\) from oil depot \(m\) in the future \(d\)-th month. is the minimum ending inventory of petroleum product \(p\) in oil depot \(m\). is the maximum ending inventory of petroleum product \(p\) in oil depot \(m\), \(I\) mpd is the reasonable inventory of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month. \(a\) is the transportation mode. When \(a = 0\), it represents railway transportation; when \(a = 1\), it represents pipeline transportation. \(Q\) mnpd is the shipment volume of petroleum product \(p\) from oil depot \(m\) to gas station \(n\) in the \(d\)-th month, \(O\) lnpd is the shipment volume of petroleum product \(p\) from refinery \(l\) to gas station \(n\) in the \(d\)-th month. is \(1\) when petroleum product \(p\) is shipped from refinery \(l\) to oil depot \(m\), otherwise it is \(0\). is the replenishment volume of petroleum product \(p\) from oil depot \(m\) through transportation mode \(a\) in the \(d\)-th month, \(H\) mpd is the replenishment volume of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month, \(PF\) mpd is the wholesale volume of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month.

[0156] Step 205: Based on the set of depot-station distribution routes and the set of target refinery-depot distribution routes, obtain the set of target distribution routes.

[0157] Combining the set of depot-station distribution routes and the set of target refinery-depot distribution routes gives the set of target distribution routes.

[0158] Step 206: Based on the set of target distribution routes, obtain the target distribution freight.

[0159] After obtaining the set of target distribution routes, the freight can be obtained according to the set of distribution routes. For example, when the data table obtained through Step 205 is as shown in Table 1 below, Step 206 can obtain the data table as shown in Table 2 below.

[0160] Table 1 Data Table of the Set of Target Distribution Routes

[0161] Oil depot code Month Oil product code Oil depot status Received oil volume Retail volume Wholesale volume Beginning inventory 602A 2023-10 300049 Start 11438.06 12000.00 9873.00 561.94 Number of refineries Freight per ton of oil for the first time Number of gas stations Freight per ton of oil for the second time Cost of receiving oil Cost of delivering oil Operating cost of oil depot Ending inventory 1 359729.04 31 44093.38 91504.48 119999.97 7284605.11 2031.70

[0162] Table 2 Data Table of Target Distribution Costs

[0163] Gas station code Month Oil product code Distribution volume Oil depot code Cost of secondary distribution Refinery number Cost of primary distribution LLZ05A 2023-10 300886 50.62 LLZ05A 658.06 7950 11722.42 642A 2023-10 300049 20.53 642A 119.21 7850 21801.76

[0164] As can be seen from Table 2, the gas station code, oil depot code, and refinery code are in one-to-one correspondence. The distribution route can be obtained according to the code, and the sum of the primary distribution cost and the secondary distribution cost in Table 2 is the target distribution freight.

[0165] Therefore, for the method for determining the petroleum product distribution route provided in the embodiment of the present application, first, the sales prediction data of petroleum products in the target time period is determined through the historical sales data of each terminal station in the area to be measured. Then, according to the depot-station matching data and the sales prediction data of petroleum products in the target time period, the depot-station distribution route set is obtained. Then, according to the refinery-depot matching data and the sales prediction data of petroleum products in the target time period, the initial refinery-depot distribution route set is obtained, and genetic operations are performed on the initial refinery-depot distribution route set to determine that the route set with the minimum cost is the target refinery-depot distribution route set. Then, the target distribution route set is obtained according to the depot-station distribution route set and the target refinery-depot distribution route set. This method obtains the distribution route set between the oil depot and the gas station and the distribution route set between the refinery and the oil depot through multiple processing and calculations of the existing data, replacing the method in the related art that relies on the personal experience of relevant personnel to plan the distribution routes between each refinery and each oil depot and between each oil depot and each gas station, avoiding the emergence of redundant distribution routes, and realizing the minimization of logistics costs.

[0166] Figure 8 It is a schematic structural diagram of a device for determining a petroleum product distribution route provided in an embodiment of the present application. Refer to Figure 8 , the device 800 includes:

[0167] The first determination module 801 is used to determine the sales prediction data of petroleum products in the target time period according to the historical sales data of petroleum products of each terminal station in the area to be measured;

[0168] The first obtaining module 802 is used to obtain the depot-station distribution route set according to the depot-station matching data and the sales prediction data of petroleum products in the target time period;

[0169] The second obtaining module 803 is used to obtain the initial refinery-depot distribution route set according to the refinery-depot matching data and the sales prediction data of petroleum products in the target time period;

[0170] The second determination module 804 is configured to perform a genetic operation on the initial refinery distribution route set, and determine the initial refinery distribution route set with the minimum cost as the target refinery distribution route set;

[0171] The third obtaining module 805 is configured to obtain a target distribution route set based on the depot-station distribution route set and the target refinery distribution route set.

[0172] In some embodiments, the first determination module 801 includes:

[0173] The first determination sub-module is configured to determine a sales volume parameter based on the historical sales volume data of petroleum products at each terminal station within the area to be measured;

[0174] The first obtaining sub-module is configured to perform a moving weighted average operation on the sales volume parameter to obtain the sales volume prediction data of petroleum products within the target time period.

[0175] In some embodiments, the second obtaining module 803 includes:

[0176] The second determination sub-module is configured to determine a route generation parameter based on the refinery matching data and the sales volume prediction data of petroleum products within the target time period;

[0177] The second obtaining sub-module is configured to construct a multi-layer chromosome framework for the route generation parameter to obtain an initial refinery distribution route set.

[0178] In some embodiments, the route generation parameter includes:

[0179] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, specifically a certain month is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for petroleum product p in the d-th month npd ; the unit secondary transportation cost E of petroleum product p from depot m to gas station n mnp ; the variable If the petroleum product p of gas station n can be delivered by depot m, then the variable is 1, otherwise it is 0; the maximum delivery volume of petroleum product p of depot m The minimum delivery volume of petroleum product p of depot m The unit secondary shipping cost F of petroleum product p of depot m mp ; the delivery volume limit DF of petroleum product p of refinery l in the d-th month lpd ; the unit storage cost C of petroleum product p of depot m mp; The unit one-time delivery cost F of the petroleum product p of refinery l lp ; The unit one-time receiving cost S of the petroleum product p of oil depot m mp ; The oil depot opening cost K of the petroleum product p of oil depot m mp ; The wholesale volume PF of the petroleum product p of oil depot m in the d-th month mpd ; The out-of-storage volume V of the petroleum product p of oil depot m in the future d-th month mpd ; The reasonable inventory level I of the petroleum product p of oil depot m in the d-th month mpd ; The initial inventory level I of the petroleum product p of oil depot m mp0 , that is, the inventory level at the beginning of this period; the minimum ending inventory of the petroleum product p of oil depot m The maximum ending inventory of the petroleum product p of oil depot m The delivery volume Q of the petroleum product p from oil depot m to gas station n in the d-th month mnpd ; The delivery volume O of the petroleum product p from refinery l to gas station n in the d-th month lnpd ; Variable When the petroleum product p is delivered from refinery l to oil depot m, the variable is 1, otherwise it is 0; the replenishment volume H of the petroleum product p of oil depot m in the d-th month mpd , the replenishment volume of the petroleum product p of oil depot m through transportation mode a in the d-th month

[0180] In some embodiments, the second determination module 804 includes:

[0181] A third obtaining sub-module, configured to perform genetic operations on the initial refinery-depot distribution route set according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate refinery-depot distribution route sets;

[0182] A third determination sub-module, configured to determine the intermediate refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set based on the cost minimization function and cost minimization constraint conditions.

[0183] In some embodiments, the cost minimization function is:

[0184]

[0185] where L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, specifically a certain month is represented by d, D = {1, 2,..., D}, d ∈ D, It is 1 when the petroleum product p at gas station n can be distributed by oil depot m, otherwise it is 0, E mnp , which is the unit secondary transportation cost of petroleum product p from oil depot m to gas station n, F mp which is the unit secondary shipping cost of petroleum product p at oil depot m, F lp which is the unit primary shipping cost of petroleum product p at refinery l, S mp which is the unit primary receiving cost of petroleum product p at oil depot m, I mpd which is the reasonable inventory of petroleum product p at oil depot m in the d-th month, C mp which is the unit storage cost of petroleum product p at oil depot m, Q mnpd which is the shipment volume of petroleum product p from oil depot m to gas station n in the d-th month, O lnpd which is the shipment volume of petroleum product p from refinery l to gas station n in the d-th month, It is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, H mpd which is the replenishment volume of petroleum product p at oil depot m in the d-th month.

[0186] In some embodiments, the cost minimization constraint is:

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] Among them, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, a specific month is represented by d, D = {1, 2,..., D}, d ∈ D, Rnpd The demand of gas station n for petroleum product p in the d-th month, The maximum delivery volume of petroleum product p in oil depot m, The minimum delivery volume of petroleum product p in oil depot m, DF lp The delivery volume limit of petroleum product p from refinery l, V mpd The outbound volume of petroleum product p in oil depot m in the future d-th month, The minimum ending inventory of petroleum product p in oil depot m, The maximum ending inventory of petroleum product p in oil depot m, I mpd The reasonable inventory of petroleum product p in oil depot m in the d-th month, a is the transportation mode. When a = 0, it represents railway transportation. When a = 1, it represents pipeline transportation, Q mnpd The shipment volume of petroleum product p from oil depot m to gas station n in the d-th month, O lnpd The shipment volume of petroleum product p from refinery l to gas station n in the d-th month, It is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, The replenishment volume of petroleum product p in oil depot m in the d-th month through transportation mode a, H mpd The replenishment volume of petroleum product p in oil depot m in the d-th month, PF mpd The wholesale volume of petroleum product p in oil depot m in the d-th month.

[0198] In some embodiments, the device further includes:

[0199] A fourth obtaining module, configured to obtain a target distribution freight based on the target distribution route set.

[0200] Therefore, for the device for determining the distribution route of petroleum products provided by the embodiments of the present application, first, the sales prediction data of petroleum products in the target time period is determined through the historical sales data of each terminal station in the area to be measured. Then, according to the depot-station matching data and the sales prediction data of petroleum products in the target time period, a depot-station distribution route set is obtained. Then, according to the refinery-depot matching data and the sales prediction data of petroleum products in the target time period, an initial refinery-depot distribution route set is obtained, and a genetic operation is performed on the initial refinery-depot distribution route set to determine that the route set with the minimum cost is the target refinery-depot distribution route set. Then, according to the depot-station distribution route set and the target refinery-depot distribution route set, a target distribution route set is obtained. This method obtains the distribution route set between the oil depot and the gas station and the distribution route set between the refinery and the oil depot through multiple processing and calculations of the existing data, replacing the method in the related art that relies on the personal experience of relevant personnel to plan the distribution routes between each refinery and each oil depot and between each oil depot and each gas station, avoiding the emergence of redundant distribution routes and realizing the minimization of logistics costs.

[0201] An embodiment of the present application also provides a non-volatile readable storage medium, in which at least one program is stored, and the at least one program is loaded and executed by a processor to implement the method for determining the oil product distribution route in any of the above embodiments.

[0202] In the present application, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance. The term "plurality" means two or more, unless otherwise clearly defined.

[0203] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the present application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary.

[0204] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for determining an oil product distribution route, characterized in that, The method includes: Determining sales forecast data of petroleum products for a target time period based on historical sales data of petroleum products at each terminal station within the area to be measured; Obtaining a set of depot-station distribution routes based on depot-station matching data and the sales forecast data of petroleum products for the target time period; Obtaining an initial refinery-depot distribution route set based on refinery-depot matching data and the sales forecast data of petroleum products for the target time period; Performing a genetic operation on the initial refinery-depot distribution route set to determine the initial refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set; Obtaining a target distribution route set based on the depot-station distribution route set and the target refinery-depot distribution route set.

2. The method for determining the distribution route of petroleum products according to claim 1, characterized in that The determining sales forecast data of petroleum products for a target time period based on historical sales data of petroleum products at each terminal station within the area to be measured includes: Determining sales parameters based on the historical sales data of petroleum products at each terminal station within the area to be measured; Performing a moving weighted average operation on the sales parameters to obtain the sales forecast data of petroleum products for the target time period.

3. The method for determining the oil product distribution route according to claim 1, wherein The obtaining an initial refinery-depot distribution route set based on refinery-depot matching data and the sales forecast data of petroleum products for the target time period includes: Determining route generation parameters based on the refinery-depot matching data and the sales forecast data of petroleum products for the target time period; Constructing a multi-layer chromosome framework for the route generation parameters to obtain the initial refinery-depot distribution route set.

4. The method for determining the distribution route of petroleum products according to claim 3, wherein The route generation parameters include: The set of refineries \(L = \{1, 2, \ldots, L\}\), \(l\in L\); the set of oil depots with demands \(M=\{1, 2, \ldots, M\}\), \(m\in M\); the set of gas stations with demands \(N = \{1, 2, \ldots, N\}\), \(n\in N\); the set of petroleum product types \(P=\{1, 2, \ldots, P\}\), \(p\in P\); the planning period \(D\), and a specific month is represented by \(d\), \(D = \{1, 2, \ldots, D\}\), \(d\in D\); the demand for petroleum product \(p\) at gas station \(n\) in the \(d\)-th month is \(R\). npd ; the unit quadratic transportation cost \(E\) of petroleum product \(p\) from oil depot \(m\) to gas station \(n\). mnp ; variables If the petroleum product \(p\) at gas station \(n\) can be delivered by oil depot \(m\), then the variable is 1, otherwise 0; the maximum delivery volume of petroleum product \(p\) at oil depot \(m\). The minimum delivery volume of petroleum product \(p\) at oil depot \(m\). The unit quadratic delivery cost \(F\) of petroleum product \(p\) at oil depot \(m\). mp ; the delivery volume limit \(DF\) of petroleum product \(p\) from refinery \(l\) in the \(d\)-th month. lpd ; the unit storage cost \(C\) of petroleum product \(p\) at oil depot \(m\). mp ; the unit primary delivery cost \(F\) of petroleum product \(p\) from refinery \(l\). lp ; the unit primary receiving cost \(S\) of petroleum product \(p\) at oil depot \(m\). mp ; the oil depot opening cost \(K\) of petroleum product \(p\) at oil depot \(m\). mp ; the wholesale volume \(PF\) of petroleum product \(p\) at oil depot \(m\) in the \(d\)-th month. mpd ; the out - bound volume \(V\) of petroleum product \(p\) at oil depot \(m\) in the future \(d\)-th month. mpd ; the reasonable inventory level \(I\) of petroleum product \(p\) at oil depot \(m\) in the \(d\)-th month. mpd ; the initial inventory level \(I\) of petroleum product \(p\) at oil depot \(m\). mp0 , that is, the inventory level at the beginning of this period; the minimum ending inventory of petroleum product \(p\) at oil depot \(m\). The maximum ending inventory of petroleum product \(p\) at oil depot \(m\). The delivery volume \(Q\) of petroleum product \(p\) from oil depot \(m\) to gas station \(n\) in the \(d\)-th month. mnpd ; the delivery volume \(O\) of petroleum product \(p\) from refinery \(l\) to gas station \(n\) in the \(d\)-th month. lnpd ; variables When petroleum product \(p\) is shipped from refinery \(l\) to oil depot \(m\), the variable is 1, otherwise 0; the replenishment volume \(H\) of petroleum product \(p\) at oil depot \(m\) in the \(d\)-th month. mpd , the replenishment volume of petroleum product \(p\) at oil depot \(m\) through transportation mode \(a\) in the \(d\)-th month.

5. The method for determining the distribution route of petroleum products according to claim 1, characterized in that, The performing a genetic operation on the initial refinery-depot distribution route set to determine the initial refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set includes: Performing a genetic operation on the initial refinery-depot distribution route set according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate refinery-depot distribution route sets; Determining the intermediate refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set based on the cost minimization function and cost minimization constraint conditions.

6. The method for determining the distribution route of petroleum products according to claim 5, characterized in that The cost minimization function is: Among them, \(L\) is the set of refineries, \(L = \{1, 2, \ldots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demands, \(M=\{1, 2, \ldots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demands, \(N = \{1, 2, \ldots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P=\{1, 2, \ldots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific month is represented by \(d\), \(D=\{1, 2, \ldots, D\}\), \(d\in D\). It is \(1\) when the petroleum product \(p\) at the gas station \(n\) can be delivered by the oil depot \(m\), otherwise it is \(0\), \(E\). mnp , is the unit secondary transportation cost of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\), \(F\). mp is the unit secondary shipping cost of the petroleum product \(p\) at the oil depot \(m\), \(F\). lp is the unit primary shipping cost of the petroleum product \(p\) at the refinery \(l\), \(S\). mp is the unit primary receiving cost of the petroleum product \(p\) at the oil depot \(m\), \(I\). mpd is the reasonable inventory of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month, \(C\). mp is the unit storage cost of the petroleum product \(p\) at the oil depot \(m\), \(Q\). mnpd is the shipment volume of the petroleum product \(p\) from the oil depot \(m\) to the gas station \(n\) in the \(d\)-th month, \(O\). lnpd is the shipment volume of the petroleum product \(p\) from the refinery \(l\) to the gas station \(n\) in the \(d\)-th month. It is \(1\) when the petroleum product \(p\) is shipped from the refinery \(l\) to the oil depot \(m\), otherwise it is \(0\), \(H\). mpd is the replenishment volume of the petroleum product \(p\) at the oil depot \(m\) in the \(d\)-th month.

7. The method for determining the distribution route of petroleum products according to claim 5, wherein The cost minimization constraint conditions are: or Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demand, \(M = \{1, 2, \cdots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demand, \(N = \{1, 2, \cdots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P = \{1, 2, \cdots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific month is represented by \(d\), \(D = \{1, 2, \cdots, D\}\), \(d\in D\), \(R\) npd is the demand of gas station \(n\) for petroleum product \(p\) in the \(d\)-th month, is the maximum delivery volume of petroleum product \(p\) from oil depot \(m\), is the minimum delivery volume of petroleum product \(p\) from oil depot \(m\), \(DF\) lp is the delivery volume limit of petroleum product \(p\) from refinery \(l\), \(V\) mpd is the out - warehouse volume of petroleum product \(p\) from oil depot \(m\) in the future \(d\)-th month, is the minimum ending inventory of petroleum product \(p\) in oil depot \(m\), is the maximum ending inventory of petroleum product \(p\) in oil depot \(m\), \(I\) mpd is the reasonable inventory of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month. \(a\) is the transportation mode. When \(a = 0\), it represents railway transportation; when \(a = 1\), it represents pipeline transportation, \(Q\) mnpd is the shipment volume of petroleum product \(p\) from oil depot \(m\) to gas station \(n\) in the \(d\)-th month, \(O\) lnpd is the shipment volume of petroleum product \(p\) from refinery \(l\) to gas station \(n\) in the \(d\)-th month, is \(1\) when petroleum product \(p\) is shipped from refinery \(l\) to oil depot \(m\), otherwise it is \(0\), is the replenishment volume of petroleum product \(p\) from oil depot \(m\) through transportation mode \(a\) in the \(d\)-th month, \(H\) mpd is the replenishment volume of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month, \(PF\) mpd is the wholesale volume of petroleum product \(p\) in oil depot \(m\) in the \(d\)-th month.

8. The method for determining the oil product distribution route according to claim 1, wherein After obtaining the target distribution route set based on the depot-station distribution route set and the target refinery-depot distribution route set, the method further includes: Obtaining a target distribution freight based on the target distribution route set.

9. An apparatus for determining an oil product distribution route, characterized in that, The device includes: A first determination module, configured to determine sales forecast data of petroleum products for a target time period based on historical sales data of petroleum products at each terminal station within the area to be measured; A first obtaining module, configured to obtain a set of depot-station distribution routes based on depot-station matching data and the sales forecast data of petroleum products for the target time period; A second obtaining module, configured to obtain an initial refinery-depot distribution route set based on refinery-depot matching data and the sales forecast data of petroleum products for the target time period; A second determination module, configured to perform a genetic operation on the initial refinery-depot distribution route set to determine the initial refinery-depot distribution route set with the minimum cost as the target refinery-depot distribution route set; A third obtaining module, configured to obtain a target distribution route set based on the depot-station distribution route set and the target refinery-depot distribution route set.

10. A non-volatile readable storage medium, characterized in that, At least one program is stored in the non-volatile readable storage medium, and the at least one program is loaded and executed by a processor to implement the method for determining the petroleum product distribution route according to any one of claims 1 to 8.

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