Combination optimization dispatching method for integrating primary and secondary logistics of product oil

Abstract

The invention relates to the field of primary and secondary allocation processes of product oil, in particular to a combined optimization allocation method for integrating primary and secondary logistics of product oil, which comprises the following steps of: constructing a basic combined allocation model of the primary and secondary logistics of the product oil; and according to the basic combination dispatching model, a corresponding combination optimization dispatching result is obtained based on an iterative greedy algorithm, a primary and secondary logistics combination optimization model is constructed on the basis of the whole-link dispatching process of the refined oil product from a refinery plant to a gas station, and a resource dispatching scheme is solved by using the improved iterative greedy algorithm. According to refinery resource supply data and gas station demand data, the method can simulate, measure, calculate and optimize a whole-link resource allocation scheme of product oil primary and secondary logistics before and after optimization, and quantitatively evaluate indexes such as whole-link logistics operation cost, a primary sub-transportation mode proportion and a secondary radiation range of an oil depot. And a foundation is laid for reasonably optimizing a primary and secondary resource allocation scheme from a global perspective in the future.

Description

Technical Field

[0001] The present invention relates to the field of the primary and secondary transportation processes of refined oil, and particularly relates to a combined optimization transportation method for integrating the primary and secondary logistics of refined oil. Background Art

[0002] At present, the refined oil logistics industry is facing new situations, new tasks and new requirements. Relying on the support of informatization, the direction of integrated and specialized refined oil logistics has taken shape. On the premise of ensuring the smoothness of the refinery's backhaul and sufficient market supply, improving operation efficiency, reducing logistics costs, and optimizing the transportation pattern have become the top priorities for optimizing the operation of the refined oil logistics system.

[0003] In the refined oil logistics system, the primary logistics is an important channel connecting the production of refined oil and the storage and transportation in oil depots, and the secondary logistics is a necessary step to directly deliver refined oil from oil depots to gas stations. Promoting the construction of intelligent logistics and optimizing the primary and secondary logistics optimization transportation have become one of the important tasks for the transformation and upgrading of refined oil sales enterprises.

[0004] In the existing solutions, the primary and secondary logistics of refined oil are coupled with each other. The primary and secondary transportation processes are connected through oil depots. Optimizing the primary logistics or secondary logistics alone cannot make the entire refined oil transportation process optimal; the primary and secondary logistics networks of refined oil are complex. With the increase in the number of refineries, oil depots and gas stations, the complexity of the transportation network increases exponentially. When using precise methods, such as linear programming methods, for solving, the solving time is long and the efficiency is low. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a combined optimization transportation method for integrating the primary and secondary logistics of refined oil. By constructing a refined oil transportation model for the combined optimization of primary and secondary logistics; at the same time, introducing a weight coefficient into the iterative greedy algorithm and using the improved iterative greedy algorithm for solving. This model can simulate and calculate the overall link resource transportation plan of the primary and secondary logistics of refined oil under the current oil depot conditions according to the refinery resource supply data and gas station demand data, and quantitatively evaluate indicators such as the overall link logistics operation cost, the proportion of the primary transportation mode, and the secondary radiation range of the oil depot, laying a foundation for reasonably optimizing the primary and secondary resource transportation plans from a global perspective in the future.

[0006] To achieve the above object, the present invention provides a combined optimization transportation method for integrating the primary and secondary logistics of refined oil, including:

[0007] S1. Construct a basic combined transportation model for the primary and secondary logistics of refined oil;

[0008] S2. Obtain the corresponding combined optimization transportation result based on the iterative greedy algorithm according to the basic combined transportation model.

[0009] Preferably, the basic combined transportation model for constructing the primary and secondary logistics of refined oil products includes:

[0010] Dividing the transportation network between the refined oil refinery and the refined oil depot into a primary transportation logistics plan;

[0011] Dividing the transportation network between the refined oil depot and the gas station into a secondary transportation logistics plan;

[0012] Establishing a basic combined transportation model using the primary transportation logistics plan and the secondary transportation logistics plan.

[0013] Furthermore, obtaining the corresponding combined optimization transportation result based on the iterative greedy algorithm according to the basic combined transportation model includes:

[0014] S2-1. Establishing the comprehensive optimization conditions of the basic combined transportation model according to the basic combined transportation model;

[0015] S2-2. Obtaining the corresponding combined optimization transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions.

[0016] Furthermore, establishing the comprehensive optimization conditions of the basic combined transportation model according to the basic combined transportation model includes:

[0017] Establishing the calculation formula with the transportation cost of the corresponding primary transportation logistics plan and the transportation cost of the secondary transportation logistics plan as the optimization objective according to the basic combined transportation model as follows:

[0018]

[0019] Among them, is the refined oil tons matrix of the primary transportation logistics, cx ijmn is the refined oil tons cost of transporting oil product j from refinery i to depot j by transportation mode m, is the refined oil quality matrix of the primary transportation logistics, x ijmn is the refined oil quality of transporting oil product n from refinery i to depot j by transportation mode m; I n×1 is an n-row and 1-column unit matrix, is the refined oil tons matrix of the secondary transportation logistics, cy jsmn is the refined oil tons cost of transporting oil product n from depot j to gas station s by transportation mode m, is the refined oil quality matrix of the secondary transportation logistics, y jsmn is the refined oil quality of transporting oil product n from depot j to gas station s by transportation mode m;

[0020] According to the above basic combined transportation model, the calculation formula for the monthly demand of refined oil of a specific product number corresponding to a gas station as a constraint condition at the gas station end is as follows:

[0021] I 1×j ·Y sn ·I m×1 ≤CapDemand sn

[0022] Wherein, is the secondary transportation logistics plan matrix of refined oil of product n at gas station s;

[0023] According to the above basic combined transportation model, the calculation formula for the material balance of the oil depot corresponding to the refined oil depot as a constraint condition at the refined oil depot end is as follows:

[0024] I 1×i ·X jn ·I m×1 -I 1×s ·Y jn ·I m×1 ≤G jn +D jn

[0025] Wherein, is the primary transportation logistics plan matrix for refined oil depot j to receive refined oil of product n, is the secondary transportation logistics plan matrix for refined oil depot j to send out refined oil of product n, G jn is the target inventory of refined oil of product n in refined oil depot j, D jn is the allowable inventory fluctuation of refined oil of product n in refined oil depot j;

[0026] According to the above basic combined transportation model, the calculation formula for the monthly output of refined oil of a specific product number of the refined oil refinery as a constraint condition at the refined oil refinery end is as follows:

[0027] I 1×j ·X in ·I m×1 ≤CapSupply in

[0028] Wherein, is the primary transportation logistics plan matrix for refined oil refinery i to deliver refined oil of product n, CapSupply in is the upper limit of the production volume of refined oil of product n produced by refined oil refinery i;

[0029] Using the above optimization objective, gas station end constraint conditions, refined oil depot end constraint conditions and refined oil refinery end constraint conditions as the comprehensive optimization conditions of the basic combined transportation model.

[0030] Further, obtaining the corresponding combined optimization transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions includes:

[0031] S2-2-1. Establish the calculation formula for the weight coefficient of the comprehensive optimization conditions according to the comprehensive optimization conditions as follows:

[0032]

[0033] Wherein, and Λ n =[λ1 λ2…λ n , α ijmn , β ijmn , δ ijmn , γ ijmn and λ n are all coefficients;

[0034] S2-2-2. Obtain the corresponding combined optimization transportation result based on the iterative greedy algorithm according to the comprehensive optimization conditions using the weight coefficient.

[0035] Further, obtaining the corresponding combined optimization transportation result based on the iterative greedy algorithm according to the comprehensive optimization conditions using the weight coefficient includes:

[0036] S2-2-2-1. Perform initialization processing based on the iterative greedy algorithm according to the comprehensive optimization conditions using the weight coefficient to obtain the initialization processing result;

[0037] S2-2-2-2. Perform destruction-reconstruction processing based on the iterative greedy algorithm using the initialization processing result to obtain the destruction-reconstruction processing result;

[0038] S2-2-2-3. Perform neighborhood search processing based on the iterative greedy algorithm using the destruction-reconstruction processing result to obtain the neighborhood search processing result;

[0039] S2-2-2-4. Perform precise evaluation processing based on the iterative greedy algorithm using the neighborhood search processing result to obtain the precise evaluation processing result;

[0040] S2-2-2-5. Judge whether the freight corresponding to the current precise evaluation processing result is less than the initial freight. If so, retain the current precise evaluation processing result as the combined optimization transportation result; otherwise, abandon the processing.

[0041] Further, performing initialization processing based on the iterative greedy algorithm according to the comprehensive optimization conditions using the weight coefficient to obtain the initialization processing result includes:

[0042] S2-2-2-1-1. Establish the calculation formula for the initial solution of the comprehensive optimization condition according to the route distance and logistics time of the primary transportation logistics plan and the secondary transportation logistics plan corresponding to the comprehensive optimization condition as follows:

[0043] (X origin ,Y origin ), and (X * ,Y * ) = (X origin ,Y origin )

[0044] Among them, the X origin is the initial solution of the primary transportation logistics plan, which is a set of , is the refined oil quality of transporting refined oil n by transportation mode m from refinery i to oil depot j, and Y origin is the initial solution of the secondary transportation logistics plan, which is a set of , is the refined oil quality of transporting refined oil n by transportation mode m from oil depot j to gas station s, X * is the optimal solution of the current primary transportation logistics plan, and Y * is the optimal solution of the current secondary transportation logistics plan;

[0045] S2-2-2-1-2. Randomly obtain the first random X element x ijmn and the second random X element x ij+1mn , the first random Y element y ijmn and the second random Y element y ij+1mn respectively by using the initial solution;

[0046] S2-2-2-1-3. Perform position swapping processing on the first random X element x ijmn and the second random X element x ij+1mn to establish an updated X element sequence X 0 = {x ij+1mn ,x ijmn};

[0047] S2-2-2-1-4. Perform position swapping processing on the first random Y element y ijmn and the second random Y element y ij+1mn to establish an updated Y element sequence Y 0 = {y ij+1mn ,y ijmn};

[0048] S2-2-2-1-5. When OBJ(X 0 ,Y 0 ) < OBJ(X * ,Y *) When it is determined whether the current moment meets the output condition, if so, output the currently updated X element sequence X 0 ={x ij+1mn , x ijmn} and the updated Y element sequence Y 0 ={y ij+1mn , y ijmn} as the initialization processing result; otherwise, use l = l + 1, and (X * , Y * )=(X 0 , Y 0 ), and return to S2-2-2-1-2;

[0049] Among them, the output condition is ||OBJ(X 0 , Y 0 ) - OBJ(X * , Y * )|| / OBJ(X * , Y * ) ≤ ε, and the ε is a constant and infinitely approaches 0.

[0050] Furthermore, using the initialization processing result, perform destruction - reconstruction processing based on the iterative greedy algorithm to obtain the destruction - reconstruction processing result, including:

[0051] S2-2-2-2-1. Randomly obtain different elements from the initialization processing result to establish an updated sequence (X remove , Y remove );

[0052] S2-2-2-2-2. Use the updated sequence (X remove , Y remove ) to obtain the remaining initialization processing result according to the initialization processing result;

[0053] S2-2-2-2-3. Perform random insertion processing on the remaining initialization processing result using the updated sequence (X remove , Y remove ) to obtain an updated transportation plan (X new , Y new );

[0054] S2-2-2-2-4. Use the updated transportation plan (X new , Y new ) to obtain the corresponding weight coefficient P(X new , Y new );

[0055] S2-2-2-2-5. When l = l + 1, determine whether l is less than N station, if so, return S2-2-2-2-3, otherwise, output (X * ,Y * ) = {X,Y|P(X,Y) = Min P(X new ,Y new )} as the destruction-reconstruction processing result.

[0056] Furthermore, using the destruction-reconstruction processing result to perform neighborhood search processing based on the iterative greedy algorithm to obtain the neighborhood search processing result includes:

[0057] S2-2-2-3-1. Randomly obtain positions a and b, where a < b;

[0058] S2-2-2-3-2. Use positions a and b to respectively point to and obtain the first neighborhood search element x ij+amn and the second neighborhood search element x ij+bmn ;

[0059] S2-2-2-3-3. Use the first neighborhood search element x ij+amn and the second neighborhood search element x ij+bmn to perform position exchange processing to establish an updated neighborhood search sequence X new ;

[0060] S2-2-2-3-4. Use positions a and b to respectively point to and obtain the third neighborhood search element y ij+amn and the fourth neighborhood search element y ij+bmn ;

[0061] S2-2-2-3-5. Use the third neighborhood search element y ij+amn and the fourth neighborhood search element y ij+bmn to perform position exchange processing to establish an updated neighborhood search sequence Y new ;

[0062] S2-2-2-3-6. When l = l + 1, judge whether l is less than N station , if so, return S2-2-2-3-1, otherwise, output the updated neighborhood search sequence X new and the updated neighborhood search sequence Y new as the neighborhood search processing result.

[0063] Furthermore, using the neighborhood search processing result to perform precise evaluation processing based on the iterative greedy algorithm to obtain the precise evaluation processing result includes:

[0064] Using the neighborhood search processing result to perform descending order arrangement processing according to the corresponding weight coefficient of the destruction-reconstruction processing result to obtain the updated neighborhood search processing result;

[0065] Obtain the top n transportation plans in the obtained updated neighborhood search processing results, and sequentially determine whether the top n transportation plans simultaneously meet the comprehensive optimization conditions corresponding to the gas station end constraint conditions, the refined oil depot end constraint conditions, and the refined oil refinery end constraint conditions. If so, retain the current plan; otherwise, discard the current plan;

[0066] wherein, n = N station × 25%.

[0067] Compared with the closest prior art, the beneficial effects of the present invention are:

[0068] Based on the entire process of transporting refined oil from the refinery to the gas station, a primary and secondary logistics combined optimization model is constructed, and an improved iterative greedy algorithm is used to solve the resource transportation plan. This method can simulate and calculate the entire process resource transportation plans of refined oil primary and secondary logistics before and after optimization according to the refinery resource supply data and gas station demand data, and quantitatively evaluate indicators such as the operating cost of the entire logistics process, the proportion of the primary transportation mode, and the secondary radiation range of the oil depot, laying a foundation for reasonably optimizing the primary and secondary resource transportation plans from a global perspective in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flowchart of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0070] Figure 2 is a schematic diagram of the entire process distribution network of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0071] Figure 3 is a flowchart of an improved iterative greedy algorithm of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0072] Figure 4 is a schematic diagram of the optimized flow direction of the actual application of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0073] Figure 5 is a schematic diagram of the entire process cost of the actual application of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0074] Figure 6 is a schematic diagram of the optimized design range of the actual application of a combined optimization transportation method for integrating refined oil primary and secondary logistics provided by the present invention;

[0075] Figure 7It is a schematic diagram of the actual application scope of a combined optimization transportation method for integrating the primary and secondary logistics of refined oil provided by the present invention;

[0076] Figure 8 It is a schematic diagram of the optimized scope of the actual application of a combined optimization transportation method for integrating the primary and secondary logistics of refined oil provided by the present invention. Specific embodiments

[0077] The following further elaborates on the specific embodiments of the present invention with reference to the accompanying drawings.

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0079] Embodiment 1:

[0080] The present invention provides a combined optimization transportation method for integrating the primary and secondary logistics of refined oil, as Figure 1 shown, including:

[0081] S1. Construct a basic combined transportation model for the primary and secondary logistics of refined oil;

[0082] S2. Obtain the corresponding combined optimization transportation result based on the iterative greedy algorithm according to the basic combined transportation model.

[0083] S1 specifically includes:

[0084] S1-1. Divide the transportation network between the refined oil refinery and the refined oil depot into a primary transportation logistics plan;

[0085] S1-2. Divide the transportation network between the refined oil depot and the gas station into a secondary transportation logistics plan;

[0086] S1-3. Establish a basic combined transportation model using the primary transportation logistics plan and the secondary transportation logistics plan.

[0087] S2 specifically includes:

[0088] S2-1. Establish the comprehensive optimization conditions of the basic combined transportation model according to the basic combined transportation model;

[0089] S2-2. Obtain the corresponding combined optimization transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions.

[0090] S2-1 specifically includes:

[0091] S2-1-1. Establish the calculation formula with the transportation cost of the corresponding primary transportation logistics plan and the transportation cost of the secondary transportation logistics plan as the optimization objectives based on the above basic combined transportation model as follows:

[0092]

[0093] Among them, is the matrix of tons of refined oil per unit for the primary transportation logistics, \(c_x\) ijmn is the cost of tons of refined oil for transporting refined oil \(j\) from refinery \(i\) to oil depot \(j\) by transportation mode \(m\), is the matrix of the quality of refined oil for the primary transportation logistics, \(x\) ijmn is the quality of refined oil \(n\) transported from refinery \(i\) to oil depot \(j\) by transportation mode \(m\); \(I\) n×1 is an \(n\times1\) identity matrix, is the matrix of tons of refined oil per unit for the secondary transportation logistics, \(c_y\) jsmn is the cost of tons of refined oil for transporting refined oil \(n\) from refinery \(j\) to oil depot \(s\) by transportation mode \(m\), is the matrix of the quality of refined oil for the secondary transportation logistics, \(y\) jsmn is the quality of refined oil \(n\) transported from oil depot \(j\) to gas station \(s\) by transportation mode \(m\);

[0094] S2-1-2. Establish the calculation formula with the monthly demand for specific-grade refined oil at the corresponding gas station as the constraint condition at the gas station end based on the above basic combined transportation model as follows:

[0095] \(I\) 1×j · \(Y\) sn · \(I\) m×1 ≤ CapDemand sn

[0096] Among them, is the matrix of the secondary transportation logistics plan for refined oil \(n\) at gas station \(s\);

[0097] S2-1-3. Establish the calculation formula with the material balance of the oil depot at the corresponding refined oil depot as the constraint condition at the refined oil depot end based on the above basic combined transportation model as follows:

[0098] \(I\) 1×i · \(X\) jn · \(I\) m×1 - \(I\) 1×s · \(Y\) jn · \(I\) m×1 ≤ \(G\) jn + \(D\) jn

[0099] Among them, is the matrix of the primary transportation logistics plan for refined oil depot \(j\) to receive refined oil \(n\), It is the secondary transportation logistics plan matrix G for the refined oil j dispatched from the refined oil depot jn It is the target inventory of the refined oil n in the refined oil depot j, D jn It is the allowable fluctuation amount of the inventory of the refined oil n in the refined oil depot j;

[0100] S2-1-4. According to the above basic combined transportation model, the calculation formula for the monthly output of the refined oil of a specific product number of the refined oil refinery as the constraint condition at the refined oil refinery end is as follows:

[0101] I 1×j ·X in ·I m×1 ≤CapSupply in

[0102] Among them, It is the primary transportation logistics plan matrix of the refined oil n dispatched from the refined oil refinery i, CapSupply in It is the upper limit of the production volume of the refined oil n produced by the refined oil refinery i;

[0103] S2-1-5. Use the above optimization objective, gas station end constraint conditions, refined oil depot end constraint conditions and refined oil refinery end constraint conditions as the comprehensive optimization conditions of the basic combined transportation model.

[0104] In this embodiment, a combined optimization transportation method for integrating the primary and secondary logistics of refined oil is provided. The optimization objective is defined as that the transportation cost of the whole link is an important part of the operating cost of the refined oil sales enterprise and directly affects the enterprise benefit;

[0105] As Figure 2 shown, the gas station end constraint conditions are defined as that the existing consumption of gas stations generally shows regular changes, and each department will also make unified arrangements for the demand of gas stations in each region according to the regional distribution. Therefore, the monthly demand of the refined oil of a specific product number of gas stations needs to be used as a limiting condition to ensure that it does not exceed its demand upper limit;

[0106] The refined oil depot end constraint conditions are defined as that the unloading volume, outbound volume and inventory volume of the oil depot are all restricted by the existing conditions of the oil depot, including the types and capacities of logistics channels, equipment configuration, storage capacity design values, etc. Therefore, relevant elements should be included in the limiting conditions as the material balance constraint of the oil depot;

[0107] The refined oil refinery end constraint conditions are defined as that the actual production capacity of the existing devices in the refinery is generally difficult to adjust in a short time, and each department will also make arrangements for the comprehensive production capacity of each refinery in accordance with the overall national plan. Therefore, the monthly output of the refined oil of a specific product number of the refinery needs to be used as a limiting condition to ensure that it does not exceed its maximum production capacity.

[0108] S2-2 specifically includes:

[0109] S2-2-1. The calculation formula for the weight coefficient of the comprehensive optimization conditions is established according to the said comprehensive optimization conditions as follows:

[0110]

[0111] Wherein, and Λ n = [λ1 λ2 … λ n , α ijmn , β ijmn , δ ijmn , γ ijmn and λ n are all coefficients;

[0112] S2-2-2. Using the said comprehensive optimization conditions, based on the weight coefficient, the corresponding combined optimization transportation result is obtained by means of the iterative greedy algorithm.

[0113] In this embodiment, a combined optimization transportation method for integrating the primary and secondary logistics of refined oil is as Figure 3 shown. The iterative greedy (IG) algorithm is a simple and efficient intelligent optimization algorithm. Due to its advantages such as simple structure and strong embeddability, it has received extensive attention from researchers in the logistics field. Some obvious advantages of the iterative greedy (IG) algorithm are:

[0114] (1) This algorithm has a simple structure and few parameters, and can integrate constructive heuristic and meta-heuristic algorithms into its framework;

[0115] (2) Different from the existing swarm intelligence algorithms, the IG algorithm generates only one solution in each iteration, so it can focus on the in-depth exploration of the solution;

[0116] (3) The IG algorithm is an intelligent optimization algorithm with strong local search ability, so it can combine strategies to improve diversity, thereby more effectively reducing the computational amount of a single iteration;

[0117] It takes a relatively long computing time to accurately evaluate whether each new transportation plan is feasible using the existing complex model. In order to save computing time and improve computing efficiency, on the basis of comprehensively considering the logistics costs and constraint conditions of the whole process, the weight coefficient P(X, Y) is introduced to preliminarily evaluate the performance of the solution space;

[0118] The more the logistics costs of the whole process are, the smaller the weight coefficient P(X, Y) is. The more it exceeds the upper limit of transport capacity / loading and unloading capacity, the smaller the weight coefficient P(X, Y) is. The performance of the solution (X, Y) is preliminarily evaluated by measuring the weight coefficient P(X, Y) of the solution (X, Y).

[0119] S2-2-2 specifically includes:

[0120] S2-2-2-1. Perform initialization processing based on the iterative greedy algorithm according to the weight coefficient using the comprehensive optimization conditions to obtain an initialization processing result;

[0121] S2-2-2-2. Perform destruction-reconstruction processing based on the iterative greedy algorithm using the initialization processing result to obtain a destruction-reconstruction processing result;

[0122] S2-2-2-3. Perform neighborhood search processing based on the iterative greedy algorithm using the destruction-reconstruction processing result to obtain a neighborhood search processing result;

[0123] S2-2-2-4. Perform precise evaluation processing based on the iterative greedy algorithm using the neighborhood search processing result to obtain a precise evaluation processing result;

[0124] S2-2-2-5. Determine whether the transportation and miscellaneous expenses corresponding to the current precise evaluation processing result are less than the initial transportation and miscellaneous expenses. If so, retain the current precise evaluation processing result as the combined optimization transportation result; otherwise, abandon the processing.

[0125] S2-2-2-1 specifically includes:

[0126] S2-2-2-1-1. Establish a calculation formula for the initial solution of the comprehensive optimization conditions according to the route distance and logistics time of the first transportation logistics plan and the second transportation logistics plan corresponding to the comprehensive optimization conditions and the weight coefficient as follows:

[0127] (X origin ,Y origin ), and (X * ,Y * ) = (X origin ,Y origin )

[0128] Among them, the X origin is the initial solution of the first transportation logistics plan, which is a set of , is the refined oil quality of transporting oil product n from refinery i to oil depot j by transportation mode m, Y origin is the initial solution of the second transportation logistics plan, which is a set of , is the refined oil quality of transporting oil product n from oil depot j to gas station s by transportation mode m, X * is the optimal solution of the current first transportation logistics plan, and Y * is the optimal solution of the current second transportation logistics plan;

[0129] S2-2-2-1-2. Randomly obtain the first random X element x respectively using the initial solutionijmn with the second random X element x ij+1mn , the first random Y element y ijmn and the second random Y element y ij+1mn ;

[0130] S2-2-2-1-3. Use the first random X element x ijmn and the second random X element x ij+1mn to perform a position swapping process to establish an updated X element sequence X 0 ={x ij+1mn , x ijmn};

[0131] S2-2-2-1-4. Use the first random Y element y ijmn and the second random Y element y ij+1mn to perform a position swapping process to establish an updated Y element sequence Y 0 ={y ij+1mn , y ijmn};

[0132] S2-2-2-1-5. When OBJ(X 0 , Y 0 ) < OBJ(X * , Y * ), determine whether the current moment meets the output condition. If so, output the current updated X element sequence X 0 ={x ij+1mn , x ijmn} and the updated Y element sequence Y 0 ={y ij+1mn , y ijmn} as the initialization processing result. Otherwise, use l = l + 1, and (X * , Y * ) = (X 0 , Y 0 ), and return to S2-2-2-1-2;

[0133] where the output condition is ||OBJ(X 0 , Y 0 ) - OBJ(X * , Y * )|| / OBJ(X * , Y * ) ≤ ε, and the ε is a constant and approaches 0 infinitely.

[0134] In this embodiment, a combined optimization dispatching method for integrating the primary and secondary logistics of refined oil is provided. The full-link logistics transportation plan may result in an increase in freight due to different dispatching arrangements. To ensure oil supply and minimize freight as much as possible, it is very important to adopt an effective initialization strategy. The NEH heuristic algorithm is often used in metaheuristic algorithms to generate initial solutions and has been proven to be a very effective method. Therefore, the NEH initialization strategy is used to generate an initial scheduling sequence.

[0135] S2-2-2-2 specifically includes:

[0136] S2-2-2-2-1. Randomly obtain different elements using the initialization processing result to establish an update sequence (X remove , Y remove );

[0137] S2-2-2-2-2. Use the update sequence (X remove , Y remove ) to obtain the remaining initialization processing results according to the initialization processing result;

[0138] S2-2-2-2-3. Use the update sequence (X remove , Y remove ) to perform random insertion processing on the remaining initialization processing results to obtain an updated dispatching plan (X new , Y new );

[0139] S2-2-2-2-4. Use the updated dispatching plan (X new , Y new ) to obtain the corresponding weight coefficient P(X new , Y new );

[0140] S2-2-2-2-5. When l = l + 1, determine whether l is less than N station , if so, return to S2-2-2-2-3, otherwise, output (X * , Y * ) = {X, Y | P(X, Y) = Min P(X new , Y new )} as the destruction-reconstruction processing result.

[0141] In this embodiment, a combined optimization dispatching method for integrating the primary and secondary logistics of refined oil is provided. Due to the complex combined dispatching network of the primary and secondary logistics cooperation, the difference in transportation and miscellaneous expenses under different combinations is relatively large. During the solution search process, to avoid falling into local optimal search earlier, a destruction and reconstruction strategy is introduced. Through a more jumping global search method, the solution search space is expanded, and the performance of the solution is improved.

[0142] S2-2-2-3 specifically includes:

[0143] S2-2-2-3-1. Randomly obtain positions a and b, where a < b;

[0144] S2-2-2-3-2. Use positions a and b to respectively point to and obtain the first neighborhood search element x according to the destruction-reconstruction processing result ij+amn and the second neighborhood search element x ij+bmn ;

[0145] S2-2-2-3-3. Use the first neighborhood search element x ij+amn and the second neighborhood search element x ij+bmn to perform a position exchange process to establish an updated neighborhood search sequence X new ;

[0146] S2-2-2-3-4. Use positions a and b to respectively point to and obtain the third neighborhood search element y ij+amn and the fourth neighborhood search element y ij+bmn ;

[0147] S2-2-2-3-5. Use the third neighborhood search element y ij+amn and the fourth neighborhood search element y ij+bmn to perform a position exchange process to establish an updated neighborhood search sequence Y new ;

[0148] S2-2-2-3-6. When l = l + 1, determine whether l is less than N station . If so, return to S2-2-2-3-1; otherwise, output the updated neighborhood search sequence X new and the updated neighborhood search sequence Y new as the neighborhood search processing result.

[0149] In this embodiment, for a combined optimization dispatching method for the primary and secondary logistics of refined oil, small-scale adjustment of some sequences can effectively reduce transportation and miscellaneous expenses and improve the overall distribution efficiency. This operation needs to be completed using a strategy with strong local search ability. The neighborhood search strategy obtains a better solution by adjusting the relative positions of a small number of distribution sequences and has strong local search ability. Therefore, the neighborhood search strategy is introduced to change the dispatching order of some sequences to achieve the purpose of improving the solution performance.

[0150] S2-2-2-4 specifically includes:

[0151] S2-2-2-4-1. Use the neighborhood search processing result to perform a descending order arrangement process according to the corresponding weight coefficient of the destruction-reconstruction processing result to obtain the updated neighborhood search processing result;

[0152] S2-2-2-4-2. Obtain the top n transportation plans in the updated neighborhood search processing results, and sequentially determine whether the top n transportation plans simultaneously meet the comprehensive optimization conditions corresponding to the gas station end constraints, refined oil depot end constraints, and refined oil refinery end constraints. If so, retain the current plan; otherwise, discard the current plan;

[0153] where n = N station ×25%.

[0154] Embodiment 2:

[0155] The present invention provides a specific implementation scheme of a combined optimization transportation method for integrating the primary and secondary logistics of refined oil. To effectively verify the applicability of the model constructed by this scheme in the whole-link resource optimization work, taking a provincial oil depot as an example, the resource plans of 7 relevant refineries, the demand plans of more than 2,000 gas stations, and the basic data of more than 30,000 channel information between refineries and oil depots and between oil depots and gas stations are integrated, as shown in Table 1 below, to construct a whole-link resource optimization model for a certain province;

[0156] Table 1

[0157]

[0158] Based on this, use the whole-link resource optimization model to comprehensively calculate indicators such as the whole-link logistics operation cost, the proportion of each primary transportation mode, and the radiation range of the oil depot under the primary and secondary optimization flow directions, and evaluate the current logistics operation status.

[0159] To comprehensively evaluate the performance of the whole-link resource optimization model, three concepts of design value, actual value, and optimized value are defined;

[0160] Among them, the design value refers to the primary and secondary flow directions that occur when strictly implementing the company's rules on the binding relationship between depots and stations (i.e., a certain gas station can only receive goods from a specific oil depot);

[0161] The actual value is the primary and secondary flow directions that actually occur when a certain province completes the logistics transportation task at the present stage. Its binding relationship with the depot-station is not strictly consistent, and the corresponding relationship between gas stations and oil depots is more flexible;

[0162] The optimized value is to use the model established in this article, with the goal of minimizing the total primary and secondary transportation costs, to reconstruct the corresponding relationship between oil depots and gas stations, and calculate the optimized primary and secondary flow directions;

[0163] Through calculation and evaluation, the current total whole-link transportation cost is higher than the total cost under the originally designed depot-station binding situation, and there is still a large room for further optimization in terms of the total transportation cost and radiation range compared with the optimal solution.

[0164] The optimized parts are as follows:

[0165] 1. Flow optimization: As Figure 4 shown, on the basis of the original plant and warehouse model, the transportation process between depots is integrated, and a primary and secondary optimization model is established to realize the re-optimization of the transportation flow. For example, according to the original depot binding relationship, refinery C supplies oil depot B, and the freight per ton of oil is 158 yuan / ton. Then, oil depot B supplies gas station S, and the freight per ton of oil is 69 yuan / ton. The total freight per ton of oil for the whole process is 227 yuan / ton. After the primary and secondary linkage optimization, refinery D supplies oil depot A, and the freight per ton of oil is 83 yuan / ton. Then, oil depot A supplies gas station S, and the freight per ton of oil is 83 yuan / ton. The total freight per ton of oil for the whole process is 166 yuan / ton. Calculated according to 1000 tons, the logistics cost can be saved by 61,000 yuan, realizing the re-optimization of the refinery shipping flow and the radiation range of the oil depot.

[0166] 2. Optimization of the total cost of the whole process: As Figure 5 shown, they are the total logistics freight (the sum of the primary and secondary logistics freight) of the whole process with a certain provincial oil depot as the transfer node under three types of situations: the designed flow (designed value) that strictly implements the depot binding relationship, the currently actual flow (actual value), and the resource allocation flow (actual value) obtained from the total process resource optimization model. It can be seen that compared with the optimized value (total freight of 39.68 million yuan) that comprehensively considers the primary and secondary logistics and ensures the lowest total freight, there is room for optimization in the first two types of situations. Among them, when strictly implementing the depot binding relationship, since all gas stations draw oil from the nearest oil depot, it is indeed possible to minimize the freight per ton of oil for the secondary logistics between depots, about 13.3 million yuan. However, this leads to some oil depots having to ship from refineries farther away to meet the surrounding demand, resulting in a relatively high primary logistics freight of 28.19 million yuan. In the actual flow, the primary logistics freight is reduced to 26.32 million yuan, but this causes a significant increase in the secondary cost of 16.94 million yuan. Thus, it can be seen that compared with the total freight of the whole process in the currently actual flow, the resource allocation plan obtained by using the total process resource optimization model can save 3.6 million yuan in freight per month and create an annual efficiency of about 43 million yuan.

[0167] 3. Primary logistics by transportation mode: As shown in Tables 2 and 3 below, primary logistics mainly includes four transportation modes: railway, waterway, highway, and pipeline transportation. This province is an inland province without waterway transportation. Among the remaining three transportation modes, the highway transportation mode has relatively low transportation costs per ton of oil, but due to its limitation in long-distance transportation, the total transportation volume is limited. Compared with the design value, the resource allocation plan obtained using the full-link resource optimization model optimizes the transportation distances of railway and pipeline transportation while keeping the total transportation volume basically unchanged, giving full play to the difference in transportation costs per ton of oil between pipeline and railway, thus reducing the total freight of primary logistics. Compared with the actual value, the resource allocation plan obtained using the full-link resource optimization model optimizes the transportation volume and distance of railway and pipeline transportation, making full use of the advantage of low transportation costs per ton of oil of pipeline transportation, and collaboratively optimizing to reduce the freight of primary logistics.

[0168] Table 2

[0169]

[0170]

[0171] Table 3

[0172]

[0173] 4. Radiation range of oil depots in secondary logistics:

[0174] During the secondary logistics process, there is a one-to-one correspondence between a certain grade of oil at a gas station and the delivery oil depot. The flow direction of the depot-station binding relationship requires that gas stations draw oil from the nearest oil depot, which also generates the radiation range of the gas stations served by a certain oil depot;

[0175] Appendix Figure 6 、 7 Figures 8 show the radiation ranges of diesel related to Oil Depot A, Oil Depot B, Oil Depot C, and Oil Depot D. The relevant parameters of the oil depots are shown in Table 4. Compared with the railway route, the current pipeline transportation mode has a larger transportation volume, more flexible transportation volume, and lower transportation price. Therefore, taking Oil Depot B as an example, as a pipeline transportation depot with a larger tank capacity, it has a stronger relative advantage. However, as Figure 6 shown, in the transportation mode with strict implementation of the depot-station binding relationship (the minimum transportation cost per ton of oil between the depot and the station), the advantage of Oil Depot B is not fully utilized. Under the actual depot-station distribution relationship, as Figure 7 shown, the radiation range of Oil Depot B has increased significantly, but the economic factor of transportation cost has not been fully considered, resulting in higher secondary logistics costs. Therefore, there is a large room for optimization in both of the above two situations.

[0176] As Figure 8As shown, in the optimization plan obtained from the operation results of the full-link resource optimization model, on the one hand, the freight of the full link is reduced, and on the other hand, the transportation and storage capacity advantages of the pipeline transportation depot are fully utilized, comprehensively optimizing the logistics flow direction, providing a reference for the subsequent implementation of resource allocation optimization.

[0177] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0179] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A combined optimization transportation method for integrating the primary and secondary logistics of refined oil, characterized in that Including: S1. Construct a basic combined transportation model for the primary and secondary logistics of refined oil; S2. Obtain the corresponding combined optimized transportation result based on the iterative greedy algorithm according to the basic combined transportation model.

2. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil as claimed in claim 1, wherein The construction of the basic combined transportation model for the primary and secondary logistics of refined oil includes: Dividing the transportation network between the refined oil refinery and the refined oil depot into a primary transportation logistics plan; Dividing the transportation network between the refined oil depot and the gas station into a secondary transportation logistics plan; Establishing a basic combined transportation model using the primary transportation logistics plan and the secondary transportation logistics plan.

3. The combined optimization dispatching method for integrating the primary and secondary logistics of finished oil according to claim 2, wherein, Obtaining the corresponding combined optimized transportation result based on the iterative greedy algorithm according to the basic combined transportation model includes: S2-1. Establish the comprehensive optimization conditions of the basic combined transportation model according to the basic combined transportation model; S2-2. Obtain the corresponding combined optimized transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions.

4. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil as claimed in claim 3, wherein Establishing the comprehensive optimization conditions of the basic combined transportation model according to the basic combined transportation model includes: Establishing the calculation formula with the transportation cost of the corresponding primary transportation logistics plan and the transportation cost of the secondary transportation logistics plan as the optimization objective according to the basic combined transportation model as follows: Among them, is the matrix of tons of refined oil per unit for the primary transportation logistics, cx ijmn is the cost of tons of refined oil for transporting refined oil j from refinery i to oil depot j by transportation mode m, is the matrix of the quality of refined oil for the primary transportation logistics, x ijmn is the quality of refined oil for transporting refined oil n from refinery i to oil depot j by transportation mode m; I n×1 is an n×1 identity matrix, is the matrix of tons of refined oil per unit for the secondary transportation logistics, cy jsmn is the cost of tons of refined oil for transporting refined oil n from refinery j to gas station s by transportation mode m, is the matrix of the quality of refined oil for the secondary transportation logistics, y jsmn is the quality of refined oil for transporting refined oil n from oil depot j to gas station s by transportation mode m; Establishing the calculation formula with the monthly demand of refined oil of a specific grade at the corresponding gas station as the gas station-side constraint condition according to the basic combined transportation model as follows: I 1× j·Ysn·Im ×1 ≤ CapDemandsn Among them, is the secondary transportation logistics plan matrix of oil product n at gas station s; Establishing the calculation formula with the material balance of the oil depot at the corresponding refined oil depot as the refined oil depot-side constraint condition according to the basic combined transportation model as follows: I 1× i·Xjn·Im ×1 -I 1× s·Yjn·Im ×1 ≤Gjn + Djn Among them, is the primary transportation logistics plan matrix for receiving oil product n by oil depot j, is the secondary transportation logistics plan matrix for sending out oil product n by oil depot j, G jn is the target inventory of oil product n in oil depot j, D jn is the allowable inventory fluctuation amount of oil product n in oil depot j; Establishing the calculation formula with the monthly output of refined oil of a specific grade at the refined oil refinery as the refined oil refinery-side constraint condition according to the basic combined transportation model as follows: I1×j·Xin·Im×1≤CapSupplyin Among them, is the primary transportation logistics plan matrix for the refined oil product n produced by the refined oil refinery i, and CapSupply in is the upper limit of the production volume of the refined oil product n produced by the refined oil refinery i; Using the optimization objective, the gas station-side constraint condition, the refined oil depot-side constraint condition, and the refined oil refinery-side constraint condition as the comprehensive optimization conditions of the basic combined transportation model.

5. A combined optimization dispatching method for integrating the primary and secondary logistics of refined oil as claimed in claim 4, characterized in that, Obtaining the corresponding combined optimized transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions includes: S2-2-1. Establish the calculation formula for the weight coefficient of the comprehensive optimization conditions according to the comprehensive optimization conditions as follows: Among them, and Λ n = [λ1 λ2 … λ n , α ijmn 、β ijmn 、δ ijmn 、γ ijmn and λ n are all coefficients; S2-2-2. Obtain the corresponding combined optimized transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions according to the weight coefficient.

6. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil according to claim 5, wherein, Obtaining the corresponding combined optimized transportation result based on the iterative greedy algorithm using the comprehensive optimization conditions according to the weight coefficient includes: S2-2-2-1. Performing initialization processing based on the iterative greedy algorithm using the comprehensive optimization conditions according to the weight coefficient to obtain the initialization processing result; S2-2-2-2. Performing destruction-reconstruction processing based on the iterative greedy algorithm using the initialization processing result to obtain the destruction-reconstruction processing result; S2-2-2-3. Performing neighborhood search processing based on the iterative greedy algorithm using the destruction-reconstruction processing result to obtain the neighborhood search processing result; S2-2-2-4. Performing precise evaluation processing based on the iterative greedy algorithm using the neighborhood search processing result to obtain the precise evaluation processing result; S2-2-2-5. Determine whether the freight corresponding to the current precise evaluation result is less than the initial freight. If so, retain the current precise evaluation result as the combined optimization transportation result; otherwise, abandon the processing.

7. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil according to claim 6, characterized in that Using the comprehensive optimization conditions, perform initialization processing based on the iterative greedy algorithm according to the weight coefficients. The initialization processing result includes: S2-2-2-1-1. Using the comprehensive optimization conditions, establish the calculation formula for the initial solution of the comprehensive optimization conditions according to the route distance and logistics time of the first transportation logistics plan and the second transportation logistics plan corresponding to the weight coefficients as follows: (Xorigin,Yorigin), and (X*,Y*) = (Xorigin,Yorigin) Among them, the said X origin is the initial solution of the primary transportation logistics plan, and is the set of the refined oil quality of transporting refined oil n by transportation mode m from refinery i to oil depot j. Y origin is the initial solution of the secondary transportation logistics plan, and is the set of the refined oil quality of transporting refined oil n by transportation mode m from oil depot j to gas station s. X * is the optimal solution of the current primary transportation logistics plan, and Y * is the optimal solution of the current secondary transportation logistics plan; S2-2-2-1-2. Randomly obtain the first random X element x and the second random X element x respectively using the initial solution ijmn and the first random Y element y ij+1mn and the second random Y element y ijmn ; ij+1mn ; S2-2-2-1-3. Use the first random X element x ijmn to perform a position swapping process with the second random X element x ij+1mn to establish an updated X element sequence X 0 = {x ij+1mn , x ijmn}; S2-2-2-1-4. Use the first random Y element y ijmn to perform a position swapping process with the second random Y element y ij+1mn to establish an updated Y element sequence Y 0 ={y ij+1mn , y ijmn}; S2-2-2-1-5. When OBJ(X 0 , Y 0 ) < OBJ(X * , Y * ), determine whether the current moment meets the output condition. If so, output the current updated X element sequence X 0 = {x ij+1mn , x ijmn} and the updated Y element sequence Y 0 = {y ij+1mn , y ijmn} as the initialization processing result. Otherwise, use l = l + 1, and (X * , Y * ) = (X 0 , Y 0 ), and return to S2-2-2-1-2; wherein, the output condition is ||OBJ(X 0 ,Y 0 ) - OBJ(X * ,Y * )|| / OBJ(X * ,Y * ) ≤ ε, and ε is a constant and infinitely approaches 0.

8. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil according to claim 7, characterized in that, Using the initialization processing result, perform destruction-reconstruction processing based on the iterative greedy algorithm. The destruction-reconstruction processing result includes: S2-2-2-2-1. Randomly obtain different elements using the initialization processing result to establish an update sequence (X remove , Y remove ); S2-2-2-2-2. Obtain the remaining initialization processing result according to the initialization processing result by using the update sequence (X remove , Y remove ); S2-2-2-2-3. Use the update sequence (X remove , Y remove ) to perform a random insertion process on the remaining initial processing results to obtain an updated transportation plan (X new , Y new ); S2-2-2-2-4. Use the updated transportation plan (X new , Y new ) to obtain the corresponding weight coefficient P(X new , Y new ); S2-2-2-2-5. When l = l + 1, determine whether l is less than N station , if so, return to S2-2-2-2-3, otherwise, output (X * , Y * ) = {X, Y | P(X, Y) = Min P(X new , Y new )} as the damage-reconstruction processing result.

9. The combined optimization dispatching method for integrating the primary and secondary logistics of refined oil as claimed in claim 8, wherein, Using the destruction-reconstruction processing result, perform neighborhood search processing based on the iterative greedy algorithm. The neighborhood search processing result includes: S2-2-2-3-1. Randomly obtain position a and position b, where a < b; S2-2-2-3-2, respectively point to obtain the first neighborhood search element x and the second neighborhood search element x according to the damage-reconstruction processing result by using the position a and the position b ij+amn and the second neighborhood search element x ij+bmn ; S2-2-2-3-3. Search for element x using the first neighborhood ij+amn and search for element x using the second neighborhood ij+bmn to perform a position swapping process to establish an updated neighborhood search sequence X new ; S2-2-2-3-4, respectively point to obtain the third neighborhood search element y and the fourth neighborhood search element y according to the destruction-reconstruction processing result by using the position a and the position b ij+amn and the fourth neighborhood search element y ij+bmn ; S2-2-2-3-5. Search for element y using the third neighborhood ij+amn and the fourth neighborhood to search for element y ij+bmn Perform a position swap process to establish an updated neighborhood search sequence Y new ; S2-2-2-3-6. When l = l + 1, determine whether l is less than N station , if yes, return to S2-2-2-3-1; otherwise, output the updated neighborhood search sequences X new and the updated neighborhood search sequence Y new as the neighborhood search processing result.

10. A combined optimization dispatching method for integrating the primary and secondary logistics of refined oil as described in claim 9, characterized in that, Using the neighborhood search processing result, perform precise evaluation processing based on the iterative greedy algorithm. The precise evaluation processing result includes: Using the neighborhood search processing result, perform descending order arrangement processing according to the weight coefficients corresponding to the destruction-reconstruction processing result to obtain the updated neighborhood search processing result; Obtain the first n transportation plans in the updated neighborhood search processing result, and sequentially determine whether the first n transportation plans simultaneously satisfy the gas station end constraint conditions, refined oil depot end constraint conditions, and refined oil refinery end constraint conditions corresponding to the comprehensive optimization conditions. If so, retain the current plan; otherwise, abandon the current plan; Among them, n = N station × 25%.

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