Oil product transportation daytime scheduling planning method and device

Abstract

This invention discloses a method and apparatus for daytime scheduling planning of oil transportation. The method includes: acquiring input parameter data; inputting the input parameter data into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation; the mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function; the objective function of the mixed integer programming model includes a penalty function; both the constraints and the objective function of the mixed integer programming model include slack variables, which can improve the scientificity, accuracy, efficiency, and flexibility of daytime scheduling planning for oil transportation, reduce the cost of daytime scheduling planning for oil transportation, and avoid problems such as uneven inventory distribution, congestion at stations, and oil shortages in some areas caused by unreasonable daytime scheduling planning for oil transportation.

Description

Technical Field

[0001] This invention relates to the field of railway logistics scheduling technology for refined oil products, and in particular to a method and apparatus for daytime scheduling planning of oil product transportation. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] In the oil industry, the timeliness and continuity of transportation are crucial to ensuring the stability of oil supply. Due to the complexity of oil demand and supply chain processes, delays in transportation often lead to the risk of oil shortages, which can have a serious negative impact on the operations of oil companies.

[0004] Furthermore, rail transport and dispatch of oil products are crucial links in the modern logistics industry. With economic development and increasing energy demand, the scale and complexity of oil logistics are constantly increasing.

[0005] For monthly railway scheduling of various types of oil products, traditional methods often rely on manual experience and simple rules, lacking systematicity and scientific rigor. In past practice, dispatchers typically formulated transportation plans based on limited information and subjective judgment. This approach has many problems, such as difficulty in fully considering the characteristics of various oil products, changes in demand, constraints on transportation resources, and special requirements of different regions. Moreover, manual planning is inefficient, prone to errors and omissions, resulting in low feasibility and optimization levels of the plans.

[0006] Furthermore, in existing technologies, some researchers have attempted to propose optimization strategies and solutions through general analyses of supply chain management and logistics distribution operations. However, most of these studies remain at the level of qualitative analysis or macro-level network layout, lacking in-depth exploration of practical daytime scheduling problems and the establishment of specific mathematical models. Due to the lack of effective mathematical models and analytical tools, as well as the absence of real-time and accurate in-transit data and oil depot / refinery inventory data, it is impossible to accurately predict oil demand and supply, or to rationally arrange transportation routes and volumes, resulting in increased transportation costs and wasted resources. Simultaneously, traditional scheduling methods struggle to cope with unforeseen circumstances and uncertainties, such as weather changes and transportation disruptions, leading to significant difficulties in adjusting plans and a lack of flexibility.

[0007] In summary, existing methods for scheduling oil rail transport, namely the breakdown of oil transport plans, primarily rely on the personal experience of demanders and managers. This not only suffers from numerous drawbacks, including unscientific and inaccurate planning, low efficiency, high costs, and poor flexibility, but also easily leads to uneven inventory distribution, congestion at stations, and oil shortages in some areas, failing to meet the ever-increasing demand for oil logistics and the complex market environment. Therefore, these problems have become critical pain points that urgently need to be addressed. Summary of the Invention

[0008] This invention provides a method for daytime scheduling planning of oil transportation, which improves the scientific nature, accuracy, efficiency, and flexibility of daytime scheduling planning for oil transportation, reduces the cost of daytime scheduling planning for oil transportation, and avoids problems such as uneven inventory distribution, truck congestion at stations, and oil shortages in some areas caused by unreasonable daytime scheduling planning for oil transportation. The method includes:

[0009] Obtain input parameter data; the input parameter data includes one or any combination of the following: monthly oil transportation plan data, oil depot inventory data at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil.

[0010] Input parameter data is input into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation; the mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function; the objective function of the mixed integer programming model includes a penalty function; both the constraints and the objective function of the mixed integer programming model include slack variables.

[0011] This invention also provides a daytime oil transportation scheduling planning device to improve the scientific nature, accuracy, efficiency, and flexibility of daytime oil transportation scheduling planning, reduce the cost of daytime oil transportation scheduling planning, and avoid problems such as uneven inventory distribution, truck congestion at stations, and oil shortages in some areas caused by unreasonable daytime oil transportation scheduling planning. The device includes:

[0012] The acquisition module is used to acquire input parameter data. The input parameter data includes one or any combination of the following: monthly plan data for oil transportation, inventory data of oil depots at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil.

[0013] The daytime scheduling planning module is used to input input parameter data into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation. The mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function. The objective function of the mixed integer programming model includes a penalty function. Both the constraints and the objective function of the mixed integer programming model include slack variables.

[0014] Compared with existing oil railway transportation scheduling methods, this invention improves the scientific rigor, accuracy, efficiency, and flexibility of daily oil transportation scheduling, reduces the cost of daily oil transportation scheduling, and avoids problems such as uneven inventory distribution, station congestion, and oil shortages caused by unreasonable daily oil transportation scheduling. The input parameter data includes one or any combination of monthly oil transportation plan data, oil depot inventory data at key nodes, oil input data, oil output data, and expected market demand data for oil. This data is then input into a pre-established mixed-integer programming model to determine the daily scheduling plan data for oil transportation. The mixed-integer programming model is established with the constraints of completing the monthly plan and ensuring inventory data remains within a preset range, and with stable operation as the objective function. The objective function of the mixed-integer programming model includes a penalty function. Both the constraints and the objective function of the mixed-integer programming model include slack variables, which improves the scientific rigor, accuracy, efficiency, and flexibility of daily oil transportation scheduling, reduces the cost of daily oil transportation scheduling, and avoids problems such as uneven inventory distribution, congestion at stations, and oil shortages in some areas caused by unreasonable daily oil transportation scheduling. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0016] Figure 1 This is a flowchart of a daytime scheduling and planning method for oil transportation provided in an embodiment of the present invention;

[0017] Figure 2 A flowchart illustrating a specific example of a daytime scheduling and planning method for oil transportation provided in this embodiment of the invention;

[0018] Figure 3 This is a schematic diagram of a daytime scheduling and planning device for oil transportation provided in an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of a specific example of a daytime scheduling and planning device for oil transportation provided in an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0022] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0023] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0024] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0025] To address the problems in the background technology, this invention provides an auxiliary decision-making method for intelligent logistics optimization. This method requires in-depth analysis of the influencing factors of oil product transportation demand, and proposes a mixed-integer programming approach. By quantitatively analyzing factors such as monthly plans, inventory dynamics, and sales forecasts, it generates the optimal daily scheduling plan. Furthermore, considering practical issues such as railway unit quantity constraints and time constraints, a general mixed-integer programming model is established by combining inventory and sales data, and specific solution methods are provided to achieve scientific distribution.

[0026] The core objective of this invention is to develop an efficient, reliable, and adaptable daytime dispatching model for refined oil products, overcoming the limitations of existing technologies and significantly improving the overall efficiency and reliability of refined oil product transportation. Specifically, the objectives of this invention can be refined into the following aspects:

[0027] (1) Construct a reasonable mathematical programming model for the breakdown of logistics railway plans.

[0028] During model construction, constraints such as oil depot or refinery inventory, pipeline injection or distribution plans, monthly plans, and railway tank car capacity are fully considered. A mixed-integer programming model is constructed that comprehensively considers imperfectly accurate data such as monthly plans, inventory dynamics, and sales forecasts. Through comprehensive analysis and integration of these factors, the decomposed daily transportation plans can conform to actual logistics operations, avoiding problems such as insufficient or excessive inventory and incoordination between pipeline and railway transportation.

[0029] (2) Improve the robustness of the model and solve the case where there is no solution.

[0030] In actual logistics scheduling, various uncertainties and unforeseen circumstances are often encountered, such as sudden changes in fuel demand and transportation equipment failures. Traditional models may fail to provide solutions to these complex situations, leading to a stalemate in scheduling. This invention introduces a penalty function into the objective function to enhance the model's adaptability to uncertainties and abnormal situations, ensuring that feasible dispatching solutions can be provided under various complex circumstances.

[0031] (3) Improve the efficiency of model solving and improve the executability of results.

[0032] Because oil logistics scheduling involves a large amount of data and complex calculations, the efficiency of model solving directly affects the speed of dispatch plan formulation. This invention employs efficient calculation methods and optimization techniques to shorten model solving time, enabling dispatchers to obtain the latest dispatch plans in a timely manner, thereby improving the response speed and operational efficiency of the entire logistics system.

[0033] Figure 1 This is a flowchart of a daytime scheduling and planning method for oil transportation provided in an embodiment of the present invention, such as... Figure 1 As shown, the method may include:

[0034] Step 101: Obtain input parameter data; the input parameter data includes one or any combination of the following: monthly oil transportation plan data, oil depot inventory data at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil.

[0035] Step 102: Input the input parameter data into the pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation; the mixed integer programming model is established with the constraints of completing the monthly plan and keeping the inventory data within a preset range, and with stable operation as the objective function; the objective function of the mixed integer programming model includes a penalty function; both the constraints and the objective function of the mixed integer programming model include slack variables.

[0036] This invention applies IoT technology and mathematical programming to achieve a daily logistics scheduling plan breakdown technique. For long-cycle refined oil transportation plans, it efficiently and accurately breaks them down into multi-cycle plans and implements them in actual transportation. This improves the scientific rigor, accuracy, efficiency, and flexibility of daily oil transportation scheduling planning, reduces planning costs, and avoids problems such as uneven inventory distribution, congestion at stations, and oil shortages in some areas caused by unreasonable daily oil transportation scheduling planning.

[0037] Figure 2 A flowchart illustrating a specific example of a daytime scheduling and planning method for oil transportation provided in this embodiment of the invention is shown below. Figure 2 As shown, in one embodiment, before obtaining the input parameter data, the method may further include: step 201, establishing a mixed integer programming model.

[0038] In one embodiment, the overall process for formulating a logistics plan is as follows: The refined oil logistics plan is based on the overall monthly, refinery-specific, and province-specific procurement and allocation plans. First, a monthly procurement and sales plan for each refinery and province is formulated. Based on the procurement and sales plan, a logistics plan from multiple refineries to multiple provinces is further decomposed. Taking into account the sales volume and inventory dynamics in each province, a transportation plan for transporting oil products from refineries to designated oil depots by road, rail, pipeline, and waterway is refined. Based on the transportation plan, considering the fluctuations in the daily delivery and receipt batches of rail, pipeline, road, and waterway transportation, an optimal scheduling plan is comprehensively formed and implemented in the daily scheduling and execution of road, rail, pipeline, and waterway transportation.

[0039] The specific process of the daytime scheduling and planning method for oil transportation provided in this embodiment of the invention can be as follows:

[0040] Step 1: Establish a mixed integer programming model, with penalty function and stable operation as objective functions, and establish a daytime scheduling model. The objective function involves ensuring that inventory does not exceed the upper and lower limits, the plan achieves the long-term plan target, market demand meets the input requirements, and the pipeline input and output meet the requirements of the previous model.

[0041] Step 2: Obtain input parameter data. Input parameter data may include: oil transportation plan (including start date, end date, departure station, arrival station, oil type, planned total quantity), beginning inventory, safety stock upper and lower limits, ending inventory target range, shipment volume, harvest volume, market demand, maximum batch size, etc.

[0042] Step 3: Input the input parameter data into the mixed integer programming model established in Step 1 to determine the daily breakdown data of oil transportation; and use simulation methods to verify the simulation. The mixed integer programming model is established with constraints such as the completion of the monthly plan, oil depot inventory, oil depot shipment and receipt volume, batch quantity, batch number and unit quantity, etc., and with stable operation as the objective function. It outputs the daily shipment plan and warehousing plan.

[0043] In one embodiment, the input parameter data further includes: batch quantity data, number of batches data, shipment quantity data, receipt quantity data, and carrying oil quantity data;

[0044] Batch volume data includes: the maximum and minimum batch volume of a single batch of oil products transported from one node to another using a transportation method within a time period;

[0045] The batch number data includes the maximum and minimum number of batches of oil products transported from one node to another using a single mode of transport;

[0046] The shipment volume data includes: the shipment volume of oil products transported from a node using a transportation method, the minimum and maximum values ​​of the total shipment volume of oil products transported from a node using a transportation method, the shipment volume of oil products transported from a node using a transportation method within a preset period, and the minimum and maximum values ​​of the shipment volume of oil products transported from a node using a transportation method within a preset period.

[0047] The receiving volume data includes: the volume of oil products transported by a certain mode of transport received by a node, the minimum and maximum values ​​of the total volume of oil products received by a node in a certain mode of transport, the volume of oil products transported by a certain mode of transport received by a node within a preset period, and the minimum and maximum values ​​of the volume of oil products received by a node in a certain mode of transport within a preset period.

[0048] The oil carrying capacity data refers to the unit quantity of oil transported from one node to another using a transportation method; the unit quantity is the minimum oil carrying capacity of the smallest unit of the transportation vehicle used in that transportation method.

[0049] Monthly plan data includes the monthly planned volume of oil products transported from one node to another using a single mode of transport.

[0050] The inventory data includes one or any combination of the following: the preset upper limit of oil depot inventory, the preset lower limit of oil depot inventory, the upper limit and lower limit of the ending inventory of oil products at a certain point within a preset period, the ending inventory data of oil products at a certain point within a preset period, and the beginning inventory data of oil products at a certain point within a preset period.

[0051] The oil input data is the increase in oil volume at a given point within a preset period;

[0052] The oil output data represents the amount of oil reduced at a given point within a preset period.

[0053] The expected market demand data for oil products is the expected sales volume of oil products at a certain point within a preset period.

[0054] In this embodiment, the decision variables of the mixed integer programming model include: the amount of oil transported from one node to another in a transportation mode within a time period after a departure time, the order delivery status of oil transported from one node to another in a transportation mode within a time period after a departure time, the oil inventory in the oil depot of one node at a time, the number of transportation vehicles transporting oil from one node to another in a transportation mode within a time period after a departure time, and the upper limit of the number of transportation vehicles used for transporting oil per day, or any combination thereof.

[0055] Slack variables include: positive and negative deviations of the monthly planned volume of oil transported from one node to another using a transportation method, positive and negative deviations of the oil inventory in the oil depot at one node at a given time, and one or any combination of the remaining quantity; wherein, the remaining quantity is the remainder obtained by dividing the volume of oil transported from one node to another using a transportation method within a time period after a departure time by the minimum carrying capacity of the smallest unit of the transportation vehicle used by that transportation method.

[0056] In step two, the mixed-integer programming model requires data as a foundation, and the input parameter data needs to be obtained. The input parameters are shown in Table 1 below. Based on the monthly execution plan, the plan is broken down to establish a mixed-integer programming model for solution.

[0057] Because real-world data may contain conflicting constraints, direct calculation using the model may result in no solution. This invention incorporates a penalty function and slack variables into the objective function, making the model more general and capable of providing an optimal feasible solution for any data. This generality of the model provides valuable guidance for developing daytime scheduling and operation plans on-site.

[0058] In one embodiment, the variables and input parameters of the mixed-integer programming model are shown in Table 1 below:

[0059] Table 1

[0060]

[0061]

[0062]

[0063]

[0064] To address the potential issues with actual data, such as missing numbers or errors, which can lead to unsolvable problems when directly using the model for calculation, this invention employs a mixed-integer programming model to solve the daytime scheduling problem of refined oil products. This model combines actual data with incompletely accurate sales data, introducing slack variables and residual variables to establish a general mixed-integer programming model, and provides a solution method. This method not only theoretically possesses the potential to find a globally optimal solution but also demonstrates good feasibility and guidance in practical applications.

[0065] However, in real-world data scenarios, various factors often prevent the model constraints from being fully satisfied, making it impossible to directly obtain a feasible solution. To address this issue, this invention employs a soft constraint method and introduces relaxation and residual amounts to ensure that the model can still find a feasible solution even when given unreasonable parameters or inaccurate data.

[0066] Actual data often does not meet the constraints, making it impossible for the model to obtain a feasible solution. This is mainly due to situations where the model has no solution caused by inventory levels or monthly planned quantities.

[0067] (1) When the initial outbound inventory is 0, the inventory data of some oil depots cannot be obtained for various reasons. The system default value is 0. The initial inventory is less than the safety stock, which exceeds the lower limit of inventory, making the model infeasible.

[0068] (2) When the initial inventory is greater than the tank capacity, some oil depots have a smaller tank capacity due to data synchronization issues, resulting in an initial inventory that exceeds the tank capacity and exceeds the upper limit of inventory, making the model infeasible.

[0069] (3) The railway transport volume has a unit quantity. For example, the minimum number of cars must transport 55 units. The monthly planned volume is usually an integer multiple of the minimum unit quantity. Directly writing the constraint formula will not provide a solution. Therefore, a relaxation quantity is added.

[0070] In the above three cases, by adding relaxation and residual amounts, the model has a feasible solution. At the same time, it is necessary to ensure that the relaxation amount is as small as possible so that the impact of relaxation and residual amounts on other constraints and objective function is minimized. Therefore, it is necessary to add an objective function to minimize the weighted sum of all relaxation amounts.

[0071] The model proposed in this embodiment of the invention requires sales forecast data as a foundation. Therefore, before modeling, sales forecasting is required, and the input data is organized. Oil depots have daily sales data. For versatility, the sales data is designed with a start date and an end date; that is, the sales data for this time period is considered to be for a specific day if the start and end times are the same.

[0072] Since sales data in actual business is often affected by a variety of factors, the results of predictions made directly using historical sales data are usually difficult to match the actual situation perfectly. Therefore, a prediction range can be given, or historical values ​​of multiple factors and corresponding historical sales data can be obtained, and artificial intelligence or machine learning algorithms can be used to predict sales data to better reflect the actual business situation.

[0073] The objective function of the mixed-integer programming model is described below:

[0074] In one embodiment, the objective function includes: minimizing the sum of positive and negative deviations of the monthly planned amount of oil transported from one node to another in a transportation mode within a time period after a departure time; minimizing the sum of positive and negative deviations of the oil inventory in the oil depot of one node at a given time; minimizing the remaining amount; and minimizing the upper limit of the daily number of transportation vehicles used for transporting oil.

[0075] In one embodiment, the objective function may specifically include:

[0076]

[0077] Minγ;

[0078]

[0079] minxr i,j,p,k,h,q ;

[0080] Where i represents the i-th node in the oil rail transportation process; j represents the j-th node in the oil rail transportation process; p represents the oil category; k represents the transportation mode; h represents the time period; q represents the departure time; w i,j,p,k,h,q γ represents the number of vehicles used to transport oil p from node i to node j using transport mode k within a time period h after departure time q; γ is the daily upper limit for the number of vehicles used to transport oil p. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transportation mode k; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; xr represents the negative deviation of the inventory of oil product p in the oil depot at node i at time q; i,j,p,k,h,q The remainder is the amount of oil p transported from node i to node j using transport mode k within a time period h after departure time q, divided by the minimum oil capacity of the smallest unit of the transport vehicle used by that transport mode.

[0081] For the daily planning breakdown of a logistics operation, considering constraints such as monthly planning constraints, inventory constraints, oil depot delivery and receipt quantity constraints, batch quantity, number of batches, and unit quantity, a mixed-integer programming model with the objective of stable operation is first established. Let the number of units be w. i,j,p,k,h,q Less than a certain threshold γ, and w i,j,p,k,h,q Compression to one side to maintain relative stability is an optimization strategy aimed at reducing transportation fluctuations and improving scheduling efficiency. The optimal scheduling scheme is then obtained through solution.

[0082]

[0083] The following is a detailed introduction to each objective function:

[0084] Objective function 1 (Objective function for the upper limit of the number of departures per day):

[0085] Minγ

[0086] To minimize γ, w i,j,p,k,h,q Compress to one side to maintain its relative stability.

[0087] Objective function 2 (Objective function for positive and negative deviations of monthly plans):

[0088]

[0089] Because real-world data may contain various issues, such as missing data or errors, direct calculation using the model may result in no solution. This invention incorporates slack variables and residual variables, making the model more universal and capable of providing an optimal feasible solution for any data. The model's versatility provides guidance for developing daytime scheduling and operation plans on-site.

[0090] Breaking down the monthly plan may result in incomplete breakdown and negative bias. On the one hand, there's the issue of slack in the monthly plan; on the other hand, there might be excessive splitting, resulting in a positive deviation. This refers to the excess amount exceeding the monthly plan. For the monthly plan to be fully executed, the smaller the sum of positive and negative deviations, the better. Therefore, an objective function should be established for the target.

[0091] Objective function 3 (Objective function for positive and negative inventory deviation):

[0092]

[0093] For each oil depot, although inventory constraints are considered hard constraints, in practice, the upper and lower limits of inventory are not precise and a certain degree of deviation is permissible. This indicates a negative inventory deviation. Let this represent the positive deviation of inventory, and the goal is to minimize the sum of the positive and negative deviations of inventory.

[0094] Objective function 4 (objective function for the residual):

[0095] minxr i,j,p,k,h,q ;

[0096] In theory, the residual should be 0, that is, the smaller the residual, the better. However, in reality, the residual cannot completely satisfy the conditions. Therefore, the objective function is established with the minimum residual.

[0097] In one embodiment, the constraints specifically include:

[0098] The difference between the negative and positive deviations of the monthly planned volume of oil transported from one node to another using one mode of transport, and the sum of the transport volume of oil transported from one node to another using one mode of transport, equals the monthly planned volume of oil transported from one node to another using one mode of transport.

[0099] The difference between the negative and positive deviations of the oil inventory in the oil depot at a given moment and the sum of the oil inventory at a given moment and the oil inventory in the oil depot at a given moment and the given oil depot inventory are within the range of the preset upper limit and lower limit of the oil depot inventory.

[0100] The difference between the negative and positive deviations of the oil inventory in the oil depot at a node within a preset period, and the sum of the oil inventory in the oil depot at a node within a preset period, are within the upper limit and lower limit of the ending inventory of the oil products in the oil depot at a node within the preset period.

[0101] The sum of the initial inventory data of oil products at a certain point within a preset period and the increase in oil products at a certain point within a preset period, minus the sum of the decrease in oil products at a certain point within a preset period and the expected sales volume of oil products at a certain point within a preset period, equals the ending inventory data of oil products at a certain point within a preset period.

[0102] In one embodiment, the constraints specifically include:

[0103]

[0104] Where, x i,j,p,k,h,qLet p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transport mode k; mp i,j,p,k This represents the monthly planned volume of oil product p transported from node i to node j using transport method k. hr i,p Let p be the preset lower limit of oil depot inventory for the i-th node; z is the preset upper limit of oil storage capacity for oil product p at node i; i,p,q Let p be the inventory of oil product p in the oil depot at node i at time q; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; Let p be the negative deviation of the inventory of oil product p in the oil depot at node i at time q. hm i,p Let p be the lower limit of the ending inventory of oil product p at node i within a preset period; Let t be the upper limit of the ending inventory of oil product p at node i within a preset period; max This indicates that the data is the end-of-period data for a preset period t; z i,p,t-1 This represents the initial inventory data of oil product p at node i within a preset period t-1; owi i,p,t Let p be the increase in oil product p at node i within a preset period t; owo i,p,t pf represents the decrease in oil product p at node i within a preset period t. i,p,t Let be the expected sales volume of oil product p at node i within a preset period t.

[0105] The constraints of the mixed-integer programming model are described below:

[0106] 1. Monthly plan completion constraints:

[0107] To ensure the complete breakdown of the monthly plan, making the total transportation volume and its bias the same as the monthly plan volume, the completion constraint for the monthly plan is: Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transport mode k; mp i,j,p,k Let p be the monthly planned volume of oil products transported from node i to node j using transport method k.

[0108] 2. Inventory upper and lower limit constraints:

[0109] If the inventory does not exceed the lower limit and upper limit of the safety stock, then the upper and lower limit constraints of the inventory are: in, hr i,p Let p be the preset lower limit of oil depot inventory for the i-th node; z is the preset upper limit of oil inventory for oil product p at node i; i,p,q Let p be the inventory of oil product p in the oil depot at node i at time q; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; Let be the negative deviation of the inventory of oil product p in the oil depot at node i at time q.

[0110] 3. Upper and lower limits of ending inventory:

[0111] For some oil depots, it is desirable to reach a certain level of ending inventory. The upper and lower limits of ending inventory differ from those during normal periods, therefore, the upper and lower limit constraints of ending inventory must also be met. The upper and lower limit constraints of ending inventory are as follows: Among them, z i,p,q Let p be the inventory of oil product p in the oil depot at node i at time q; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; Let p be the negative deviation of the inventory of oil product p in the oil depot at node i at time q. hm i,p Let p be the lower limit of the ending inventory of oil product p at node i within a preset period; Let t be the upper limit of the ending inventory of oil product p at node i within a preset period; max This indicates that the data is the end-of-period data for a preset period t.

[0112] 4. Inventory balance constraints:

[0113] Beginning inventory + In-transit inputs (increase in oil product volume at each node) - In-transit outputs (decrease in oil product volume at each node) - Wholesale and retail = Ending inventory. Therefore, the inventory balance constraint is: Among them, z i,p,t Let z be the inventory of oil product p in the oil depot at node i at time q; i,p,t-1 This represents the initial inventory data of oil product p at node i within a preset period; owi i,p,t Let p be the increase in oil product p at node i within a preset period t; owo i,p,t pf represents the decrease in oil product p at node i within a preset period t. i,p,t Let be the expected sales volume of oil product p at node i within a preset period t.

[0114] In one embodiment, the constraint may further include:

[0115] The volume of oil transported from a node using a single mode of transport is within the minimum and maximum range of the total shipment volume of oil transported from a node using a single mode of transport.

[0116] The volume of oil transported from a node in a certain mode of transport within a preset period, within the range of the minimum and maximum values ​​of the volume of oil shipped from a node in a certain mode of transport within that period.

[0117] The amount of oil transported by a node in a certain mode of transport is within the minimum and maximum range of the total amount of oil received by a node in a certain mode of transport.

[0118] The amount of oil transported by a node in a certain mode of transport within a preset period, within the minimum and maximum range of the amount of oil received by a node in a certain mode of transport within the preset period.

[0119] The amount of oil transported from one node to another using a transportation method within a time period after a departure time is within a preset range; the preset range is the range between the order shipment status of oil transported from one node to another using a transportation method within a time period after a departure time and the product of the maximum and minimum batch quantities of a single batch of oil transported from one node to another using a transportation method within a time period.

[0120] The sum of orders for transporting oil products from one node to another using a single mode of transport is within the range of the minimum number of batches of oil products transported from one node to another using a single mode of transport, and the maximum number of batches of oil products transported from one node to another using a single mode of transport, multiplied by the product of the order for transporting oil products from one node to another using a single mode of transport within a time period after a departure time.

[0121] The product of the unit quantity of oil transported from one node to another by one mode of transport and the number of vehicles transporting oil from one node to another by one mode of transport within a time period after the departure time, plus the remaining quantity, equals the amount of oil transported from one node to another by one mode of transport within a time period after the departure time.

[0122] In one embodiment, the constraints specifically include:

[0123]

[0124] x i,j,p,k,h,q =w i,j,p,k,h,q ×dwli,j,p,k +xr i,j,p,k,h,q ;

[0125] Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. fz i,p,k Let this be the minimum total shipment of oil product p transported from node i using transport method k; This represents the maximum total shipment volume of oil product p transported from node i using transportation method k. fr i,p,k,t Let $t$ be the minimum amount of oil product $p$ shipped from node $i$ using transport method $k$ within period $t$. This represents the maximum amount of oil product p shipped from node i using transport method k within period t. sz i,p,k Let be the minimum total amount of oil p transported by mode k received at node i; This represents the maximum value of the total amount of oil product p transported by mode k received at node i. sr j,p,k,t Let $t$ be the minimum amount of oil product $p$ received at node $j$ within period $t$, transported by mode $k$. y represents the maximum amount of oil product p transported by mode k received at node j within period t; i,j,p,k,h,q This describes the order shipment status for transporting oil product p from node i to node j using transportation method k within a time period h after departure time q. The order shipment status is defined by the shipment time y. i,j,p,k,h,q The value is 1, indicating the order status is "not shipped" (y is the case when the order is 1). i,j,p,k,h,q =0; pcl i,j,p,k,h,q Let f be the minimum batch size of a single shipment of oil p transported from node i to node j using transport mode k within a time period h after departure time q. This represents the maximum batch size of a single shipment of oil product p transported from node i to node j using transport mode k within a time period h after departure time q. pcs i,j,p,k This represents the minimum number of batches of oil product p transported from node i to node j using transport method k. w represents the maximum number of batches of oil product p transported from node i to node j using transport method k; i,j,p,k,h,,q Let dwl be the number of transport vehicles that transport oil p from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k xr represents the unit quantity of oil p transported from node i to node j using transport method k; i,j,p,k,h,qLet p be the remaining amount of oil transported from node i to node j using transport method k within the time period h after departure time q.

[0126] In one embodiment, the constraints of the mixed-integer programming model may further include:

[0127] 5. Total shipment limit:

[0128] The shipping volume needs to meet the requirement of not exceeding the maximum value of the total shipment volume and not being less than the minimum value of the total shipment volume. Therefore, the total shipment volume constraint is: Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. fz i,p,k Let this be the minimum total shipment of oil product p transported from node i using transport method k; This represents the maximum value of the total shipment volume of oil product p transported from node i using transportation method k.

[0129] 6. Daily shipment volume constraints:

[0130] If the total transportation volume does not exceed the maximum daily shipment volume and is not less than the minimum daily shipment volume, then the daily shipment volume constraint is: Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. fr i,p,k,t Let $t$ be the minimum amount of oil product $p$ shipped from node $i$ using transport method $k$ within period $t$. This represents the maximum amount of oil product p shipped from node i using transport method k within period t.

[0131] 7. Total quantity of goods received:

[0132] Similar to the total shipment constraint, the total receipt constraint is: Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. sz i,p,k Let be the minimum total amount of oil p transported by mode k received at node i; This represents the maximum value of the total amount of oil p transported by mode k received at node i.

[0133] 8. Daily delivery volume constraints:

[0134] Similar to the shipping day quantity constraint, the receiving day quantity constraint is: Where, x i,j,p,k,h,qLet p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. sr j,p,k,t Let $t$ be the minimum amount of oil product $p$ received at node $j$ within period $t$, transported by mode $k$. This represents the maximum amount of oil product p transported by mode k received at node j within period t.

[0135] 9. Batch quantity constraints:

[0136] The shipment volume cannot exceed the maximum batch size for a single batch, nor can it be less than the minimum batch size for a single batch. Therefore, the batch size constraint is: Where, x i,j,p,k,h,q Let y be the amount of oil p transported from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k,h,q This describes the order shipment status for transporting oil product p from node i to node j using transportation method k within a time period h after departure time q. The order shipment status is defined by the shipment time y. i,j,p,k,h,q The value is 1, indicating the order status is "not shipped" (y is the case when the order is 1). i,j,p,k,h,q =0; pcl i,j,p,k,h,q Let f be the minimum batch size of a single shipment of oil p transported from node i to node j using transport mode k within a time period h after departure time q. This represents the maximum batch size of a single shipment of oil product p transported from node i to node j using transport mode k within a time period h after departure time q.

[0137] 10. Batch number constraint:

[0138] If the total number of orders shipped does not exceed the maximum batch number and is not less than the minimum batch number, then the constraint formula for the batch number is expressed as follows: Among them, y i,j,p,k,h,q This describes the order shipment status for transporting oil product p from node i to node j using transportation method k within a time period h after departure time q. The order shipment status is defined by the shipment time y. i,j,p,k,h,q The value is 1, indicating the order status is "not shipped" (y is the case when the order is 1). i,j,p,k,h,q =0; pcs i,j,p,k This represents the minimum number of batches of oil product p transported from node i to node j using transport method k. This represents the maximum number of batches of oil product p transported from node i to node j using transport method k.

[0139] 11. Unit Constraint:

[0140] x i,j,p,k,h,qLet w be the amount of oil p transported from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k,h,q Let dwl be the number of transport vehicles that transport oil p from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k xr represents the unit quantity of oil p transported from node i to node j using transport method k; i,j,p,k,h,q For the remaining amount of oil p transported from node i to node j using transport method k within the time interval h after departure time q, theoretically, x i,j,p,k,h,q It's dwl i,j,p,k If w is an integer multiple of , then i,j,p,k,h,q It is an integer, that is: x i,j,p,k,h,q =w i,j,p,k,h,q ×dw i,j,p,k .

[0141] In reality, floating-point numbers cannot perfectly satisfy this condition, therefore xr is used. i,j,p,k,h,q To represent the remaining quantity, the unit quantity constraint is: x i,j,p,k,h,q =w i,j,p,k,h,q ×dwl i,j,p,k +xr i,j,p,k,h,q .

[0142] In one embodiment, inputting input parameter data into a pre-established mixed-integer programming model to determine the daytime scheduling planning data for oil transportation may include: inputting input parameter data into a pre-established mixed-integer programming model and using an exact algorithm and / or a heuristic algorithm to determine the daytime scheduling planning data for oil transportation. An exact algorithm is an algorithm capable of finding the optimal solution to a problem. For difficult combinatorial optimization problems, when the problem size or the amount of input parameter data is small, an exact algorithm can be used to find the optimal solution within an acceptable time. When the problem size or the amount of input parameter data is large, a combination of exact and heuristic algorithms can be used. The exact algorithm can provide a feasible solution to the problem and an initial solution for the heuristic algorithm to search for a better solution. Exact algorithms mainly include branch and bound, cutting plane, etc. The branch and bound method can theoretically find the global optimal solution of mixed-integer programming. By continuously decomposing integer variables into constraints, solving the linear programming model, and pruning, the optimal solution is finally obtained. The model calls a solver to solve the problem; other algorithms will not be described in detail here. Heuristic algorithms are methods for solving problems by inductive reasoning based on past experience and experimental analysis. In other words, they rely on intuitive judgment or trial and error to find a suboptimal solution or the optimal solution with a certain probability.

[0143] This invention also proposes a daytime scheduling and planning device for oil transportation, the principle of which is similar to that of the daytime scheduling and planning method for oil transportation, and will not be described in detail here.

[0144] Figure 3 This is a schematic diagram of a daytime scheduling and planning device for oil transportation provided in an embodiment of the present invention, such as... Figure 3 As shown, the daytime scheduling and planning device for oil transportation may include:

[0145] The acquisition module 301 is used to acquire input parameter data; the input parameter data includes one or any combination of the following: monthly plan data for oil transportation, inventory data of oil depots at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil.

[0146] The daytime scheduling planning module 302 is used to input input parameter data into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation. The mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function. The objective function of the mixed integer programming model includes a penalty function. Both the constraints and the objective function of the mixed integer programming model include slack variables.

[0147] In one embodiment, the input parameter data further includes: batch quantity data, number of batches data, shipment quantity data, receipt quantity data, and carrying oil quantity data;

[0148] Batch volume data includes: the maximum and minimum batch volume of a single batch of oil products transported from one node to another using a transportation method within a time period;

[0149] The batch number data includes the maximum and minimum number of batches of oil products transported from one node to another using a single mode of transport;

[0150] The shipment volume data includes: the shipment volume of oil products transported from a node using a transportation method, the minimum and maximum values ​​of the total shipment volume of oil products transported from a node using a transportation method, the shipment volume of oil products transported from a node using a transportation method within a preset period, and the minimum and maximum values ​​of the shipment volume of oil products transported from a node using a transportation method within a preset period.

[0151] The receiving volume data includes: the volume of oil products transported by a certain mode of transport received by a node, the minimum and maximum values ​​of the total volume of oil products received by a node in a certain mode of transport, the volume of oil products transported by a certain mode of transport received by a node within a preset period, and the minimum and maximum values ​​of the volume of oil products received by a node in a certain mode of transport within a preset period.

[0152] The oil carrying capacity data refers to the unit quantity of oil transported from one node to another using a transportation method; the unit quantity is the minimum oil carrying capacity of the smallest unit of the transportation vehicle used in that transportation method.

[0153] Monthly plan data includes the monthly planned volume of oil products transported from one node to another using a single mode of transport.

[0154] The inventory data includes one or any combination of the following: the preset upper limit of oil depot inventory, the preset lower limit of oil depot inventory, the upper limit and lower limit of the ending inventory of oil products at a certain point within a preset period, the ending inventory data of oil products at a certain point within a preset period, and the beginning inventory data of oil products at a certain point within a preset period.

[0155] The oil input data is the increase in oil volume at a given point within a preset period;

[0156] The oil output data represents the amount of oil reduced at a given point within a preset period.

[0157] The expected market demand data for oil products is the expected sales volume of oil products at a certain point within a preset period.

[0158] In one embodiment, the decision variables of the mixed integer programming model include: the amount of oil transported from one node to another in a transportation mode within a time period after a departure time, the order delivery status of oil transported from one node to another in a transportation mode within a time period after a departure time, the oil inventory in the oil depot of one node at a time, the number of transportation vehicles transporting oil from one node to another in a transportation mode within a time period after a departure time, and the upper limit of the number of transportation vehicles used for transporting oil per day, or any combination thereof.

[0159] Slack variables include: positive and negative deviations of the monthly planned volume of oil transported from one node to another using a transportation method, positive and negative deviations of the oil inventory in the oil depot at one node at a given time, and one or any combination of the remaining quantity; wherein, the remaining quantity is the remainder obtained by dividing the volume of oil transported from one node to another using a transportation method within a time period after a departure time by the minimum carrying capacity of the smallest unit of the transportation vehicle used by that transportation method.

[0160] In one embodiment, the objective function includes: minimizing the sum of positive and negative deviations of the monthly planned amount of oil transported from one node to another in a transportation mode within a time period after a departure time; minimizing the sum of positive and negative deviations of the oil inventory in the oil depot of one node at a given time; minimizing the remaining amount; and minimizing the upper limit of the daily number of transportation vehicles used for transporting oil.

[0161] In one embodiment, the objective function includes:

[0162]

[0163] Minγ;

[0164]

[0165] minxr i,j,p,k,h,q ;

[0166] Where i represents the i-th node in the oil rail transportation process; j represents the j-th node in the oil rail transportation process; p represents the oil category; k represents the transportation mode; h represents the time period; q represents the departure time; w i,j,p,k,h,q γ represents the number of vehicles used to transport oil p from node i to node j using transport mode k within a time period h after departure time q; γ is the daily upper limit for the number of vehicles used to transport oil p. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transportation mode k; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; xr represents the negative deviation of the inventory of oil product p in the oil depot at node i at time q; i,j,p,k,h,q The remainder is the amount of oil p transported from node i to node j using transport mode k within a time period h after departure time q, divided by the minimum oil capacity of the smallest unit of the transport vehicle used by that transport mode.

[0167] In one embodiment, the constraints specifically include:

[0168] The difference between the negative and positive deviations of the monthly planned volume of oil transported from one node to another using one mode of transport, and the sum of the transport volume of oil transported from one node to another using one mode of transport, equals the monthly planned volume of oil transported from one node to another using one mode of transport.

[0169] The difference between the negative and positive deviations of the oil inventory in the oil depot at a given moment and the sum of the oil inventory at a given moment and the oil inventory in the oil depot at a given moment and the given oil depot inventory are within the range of the preset upper limit and lower limit of the oil depot inventory.

[0170] The difference between the negative and positive deviations of the oil inventory in the oil depot at a node within a preset period, and the sum of the oil inventory in the oil depot at a node within a preset period, are within the upper limit and lower limit of the ending inventory of the oil products in the oil depot at a node within the preset period.

[0171] The sum of the initial inventory data of oil products at a certain point within a preset period and the increase in oil products at a certain point within a preset period, minus the sum of the decrease in oil products at a certain point within a preset period and the expected sales volume of oil products at a certain point within a preset period, equals the ending inventory data of oil products at a certain point within a preset period.

[0172] In one embodiment, the constraints specifically include:

[0173]

[0174] Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transport mode k; mp i,j,p,k This represents the monthly planned volume of oil product p transported from node i to node j using transport method k. hr i,p Let p be the preset lower limit of oil depot inventory for the i-th node; z is the preset upper limit of oil storage capacity for oil product p at node i; i,p,q Let p be the inventory of oil product p in the oil depot at node i at time q; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; Let p be the negative deviation of the inventory of oil product p in the oil depot at node i at time q. hm i,p Let p be the lower limit of the ending inventory of oil product p at node i within a preset period; Let t be the upper limit of the ending inventory of oil product p at node i within a preset period; max This indicates that the data is the end-of-period data for a preset period t; z i,p,t-1 This represents the initial inventory data of oil product p at node i within a preset period t-1; owi i,p,t Let p be the increase in oil product p at node i within a preset period t; owo i,p,t pf represents the decrease in oil product p at node i within a preset period t. i,p,t Let be the expected sales volume of oil product p at node i within a preset period t.

[0175] In one embodiment, the constraints also include:

[0176] The volume of oil transported from a node using a single mode of transport is within the minimum and maximum range of the total shipment volume of oil transported from a node using a single mode of transport.

[0177] The volume of oil transported from a node in a certain mode of transport within a preset period, within the range of the minimum and maximum values ​​of the volume of oil shipped from a node in a certain mode of transport within that period.

[0178] The amount of oil transported by a node in a certain mode of transport is within the minimum and maximum range of the total amount of oil received by a node in a certain mode of transport.

[0179] The amount of oil transported by a node in a certain mode of transport within a preset period, within the minimum and maximum range of the amount of oil received by a node in a certain mode of transport within the preset period.

[0180] The amount of oil transported from one node to another using a transportation method within a time period after a departure time is within a preset range; the preset range is the range between the order shipment status of oil transported from one node to another using a transportation method within a time period after a departure time and the product of the maximum and minimum batch quantities of a single batch of oil transported from one node to another using a transportation method within a time period.

[0181] The sum of orders for transporting oil products from one node to another using a single mode of transport is within the range of the minimum number of batches of oil products transported from one node to another using a single mode of transport, and the maximum number of batches of oil products transported from one node to another using a single mode of transport, multiplied by the product of the order for transporting oil products from one node to another using a single mode of transport within a time period after a departure time.

[0182] The product of the unit quantity of oil transported from one node to another by one mode of transport and the number of vehicles transporting oil from one node to another by one mode of transport within a time period after the departure time, plus the remaining quantity, equals the amount of oil transported from one node to another by one mode of transport within a time period after the departure time.

[0183] In one embodiment, the constraints specifically include:

[0184]

[0185] x i,j,p,k,h,q =w i,j,p,k,h,q ×dwl i,j,p,k +xr i,j,p,k,h,,q ;

[0186] Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. fz i,p,kLet this be the minimum total shipment of oil product p transported from node i using transport method k; This represents the maximum total shipment volume of oil product p transported from node i using transportation method k. fr i,p,k,t Let $t$ be the minimum amount of oil product $p$ shipped from node $i$ using transport method $k$ within period $t$. This represents the maximum amount of oil product p shipped from node i using transport method k within period t. sz i,p,k Let be the minimum total amount of oil p transported by mode k received at node i; This represents the maximum value of the total amount of oil product p transported by mode k received at node i. sr j,p,k,t Let $t$ be the minimum amount of oil product $p$ received at node $j$ within period $t$, transported by mode $k$. y represents the maximum amount of oil product p transported by mode k received at node j within period t; i,j,p,k,h,,q This describes the order shipment status for transporting oil product p from node i to node j using transportation method k within a time period h after departure time q. The order shipment status is defined by the shipment time y. i,j,p,k,h,q The value is 1, indicating the order status is "not shipped" (y is the case when the order is 1). i,j,p,k,h,q =0; pcl i,j,p,k,h,q Let f be the minimum batch size of a single shipment of oil p transported from node i to node j using transport mode k within a time period h after departure time q. This represents the maximum batch size of a single shipment of oil product p transported from node i to node j using transport mode k within a time period h after departure time q. pcs i,j,p,k This represents the minimum number of batches of oil product p transported from node i to node j using transport method k. w represents the maximum number of batches of oil product p transported from node i to node j using transport method k; i,j,p,k,h,q Let dwl be the number of transport vehicles that transport oil p from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k xr represents the unit quantity of oil p transported from node i to node j using transport method k; i,j,p,k,h,q Let p be the remaining amount of oil transported from node i to node j using transport method k within the time period h after departure time q.

[0187] Figure 4 This is a schematic diagram of a specific example of a daytime scheduling and planning device for oil transportation provided in an embodiment of the present invention, such as... Figure 4As shown, in one embodiment, the oil transportation daytime scheduling planning device may further include: a model building module 401, used to: build a mixed integer programming model.

[0188] In one embodiment, the daytime scheduling planning module 302 is specifically used for:

[0189] Input parameter data is fed into a pre-established mixed integer programming model, and exact algorithms and / or heuristic algorithms are used to determine the daytime scheduling planning data for oil transportation.

[0190] Compared with existing oil railway transportation scheduling methods, this invention improves the scientific rigor, accuracy, efficiency, and flexibility of daily oil transportation scheduling, reduces the cost of daily oil transportation scheduling, and avoids problems such as uneven inventory distribution, station congestion, and oil shortages caused by unreasonable daily oil transportation scheduling. The input parameter data includes one or any combination of monthly oil transportation plan data, oil depot inventory data at key nodes, oil input data, oil output data, and expected market demand data for oil. This data is then input into a pre-established mixed-integer programming model to determine the daily scheduling plan data for oil transportation. The mixed-integer programming model is established with the constraints of completing the monthly plan and ensuring inventory data remains within a preset range, and with stable operation as the objective function. The objective function of the mixed-integer programming model includes a penalty function. Both the constraints and the objective function of the mixed-integer programming model include slack variables, which improves the scientific rigor, accuracy, efficiency, and flexibility of daily oil transportation scheduling, reduces the cost of daily oil transportation scheduling, and avoids problems such as uneven inventory distribution, congestion at stations, and oil shortages in some areas caused by unreasonable daily oil transportation scheduling.

[0191] This invention improves the efficiency and accuracy of refined oil transportation scheduling by constructing and optimizing a mixed-integer programming model, combining key data such as actual monthly delivery plans, shipments in transit, receipts, inventory, and sales volume, and comprehensively considering multiple factors. This invention falls within the application scope of logistics optimization and scheduling algorithms, aiming to solve the complex daytime scheduling problem in refined oil transportation, and achieves the following significant technical effects:

[0192] (1) Comprehensive constraints: The mixed integer programming model established in this embodiment of the invention considers monthly plan constraints, inventory constraints, oil depot delivery and receipt quantity constraints, batch quantity, batch number and unit quantity constraints, with the goal of stable operation.

[0193] (2) Adapting to actual needs: In view of the complexity of actual problems, the untimely and inaccurate data collection, and the difficulty in predicting the outbound volume, this invention adopts upper and lower limits for all constraints, so that the obtained scheduling scheme is closer to the actual operation scheme.

[0194] (3) Universality and robustness: Since real-world data may contain missing or erroneous information, this invention incorporates slack variables and residual variables into the model, making it more universal and capable of providing the optimal feasible solution for any data. This feature makes the model highly instructive for developing daytime scheduling and operation plans on-site.

[0195] This invention also provides a computer device. Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention. The computer device 500 includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, it implements the above-mentioned daytime scheduling and planning method for oil transportation.

[0196] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described daytime scheduling and planning method for oil transportation.

[0197] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described oil transportation daytime scheduling planning method.

[0198] Those skilled in the art will understand that 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 completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0199] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0200] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0201] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0202] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for daytime scheduling and planning of oil transportation, characterized in that, include: Obtain input parameter data; Input parameter data includes one or any combination of the following: monthly oil transportation plan data, oil depot inventory data at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil. Input parameter data is input into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation; the mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function; the objective function of the mixed integer programming model includes a penalty function; both the constraints and the objective function of the mixed integer programming model include slack variables.

2. The method as described in claim 1, characterized in that, The input parameter data also includes: batch quantity data, number of batches data, shipment quantity data, receipt quantity data, and oil carrying capacity data; The batch quantity data includes: the maximum and minimum batch quantities of a single batch of oil products transported from one node to another in a certain transportation mode within a time period; The batch number data includes the maximum and minimum number of batches of oil products transported from one node to another using a transportation method; The shipment data includes: the shipment volume of oil products transported from a node in a transportation mode, the minimum and maximum values ​​of the total shipment volume of oil products transported from a node in a transportation mode, the shipment volume of oil products transported from a node in a transportation mode within a preset period, and the minimum and maximum values ​​of the shipment volume of oil products transported from a node in a transportation mode within a preset period. The received volume data includes: the volume of oil products transported by a certain mode of transport received by a node, the minimum and maximum values ​​of the total volume of oil products received by a node in a certain mode of transport, the volume of oil products transported by a certain mode of transport received by a node within a preset period, and the minimum and maximum values ​​of the volume of oil products received by a node in a certain mode of transport within a preset period. The oil carrying capacity data refers to the unit quantity of oil transported from one node to another using a transportation method; the unit quantity refers to the minimum oil carrying capacity of the smallest unit of the transportation vehicle used in this transportation method. The monthly plan data includes the monthly planned volume of oil products transported from one node to another using a transportation method. The inventory data includes one or any combination of the following: a preset upper limit for oil depot inventory, a preset lower limit for oil depot inventory, the upper limit and lower limit for the ending inventory of oil products at a node within a preset period, the ending inventory data of oil products at a node within a preset period, and the beginning inventory data of oil products at a node within a preset period. The oil input data is the increase in oil volume at a certain node within a preset period; The oil output data is the amount of oil reduction at a certain node within a preset period; The expected market demand data for oil products refers to the expected sales volume of oil products at a certain point within a preset period.

3. The method as described in claim 2, characterized in that, The decision variables of the mixed integer programming model include: the amount of oil transported from one node to another in a transportation mode within a time period after a departure time; the order delivery status of oil transported from one node to another in a transportation mode within a time period after a departure time; the oil inventory in the oil depot of one node at a given time; the number of transportation vehicles used to transport oil from one node to another in a transportation mode within a time period after a departure time; and the upper limit of the number of transportation vehicles used for transporting oil per day, or any combination thereof. The slack variables include: positive and negative deviations of the monthly planned volume of oil transported from one node to another using a transportation method, positive and negative deviations of the oil inventory in the oil depot of one node at a given moment, and one or any combination of the remaining quantity; wherein, the remaining quantity is the remainder obtained by dividing the volume of oil transported from one node to another using a transportation method within a time period after a departure moment by the minimum carrying capacity of the smallest unit of the transportation vehicle used by that transportation method.

4. The method as described in claim 3, characterized in that, The objective function includes: minimizing the sum of positive and negative deviations of the monthly planned amount of oil transported from one node to another in a certain time period after a departure time; minimizing the sum of positive and negative deviations of the oil inventory in the oil depot at one node at a certain time; minimizing the remaining amount; and minimizing the upper limit of the daily number of transport vehicles used for transporting oil.

5. The method as described in claim 4, characterized in that, The objective function includes: Minγ; min xr i,j,p,k,h,q ; Where i represents the i-th node in the oil rail transportation process; j represents the j-th node in the oil rail transportation process; p represents the oil category; k represents the transportation mode; h represents the time period; q represents the departure time; w i,j,p,k,h,q γ represents the number of vehicles used to transport oil p from node i to node j using transport mode k within a time period h after departure time q; γ is the daily upper limit for the number of vehicles used to transport oil p. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transportation mode k; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; xr represents the negative deviation of the inventory of oil product p in the oil depot at node i at time q; i,j,p,k,h,q The remainder is the amount of oil p transported from node i to node j using transport mode k within a time period h after departure time q, divided by the minimum oil capacity of the smallest unit of the transport vehicle used by that transport mode.

6. The method as described in claim 3, characterized in that, The constraints specifically include: The difference between the negative and positive deviations of the monthly planned volume of oil transported from one node to another using one mode of transport, and the sum of the transport volume of oil transported from one node to another using one mode of transport, equals the monthly planned volume of oil transported from one node to another using one mode of transport. The difference between the negative and positive deviations of the oil inventory in the oil depot at a given moment and the sum of the oil inventory at a given moment and the oil inventory in the oil depot at a given moment and the given oil depot inventory are within the range of the preset upper limit and lower limit of the oil depot inventory. The difference between the negative and positive deviations of the oil inventory in the oil depot at a node within a preset period, and the sum of the oil inventory in the oil depot at a node within a preset period, are within the upper limit and lower limit of the ending inventory of the oil products in the oil depot at a node within the preset period. The sum of the initial inventory data of oil products at a certain point within a preset period and the increase in oil products at a certain point within a preset period, minus the sum of the decrease in oil products at a certain point within a preset period and the expected sales volume of oil products at a certain point within a preset period, equals the ending inventory data of oil products at a certain point within a preset period.

7. The method as described in claim 6, characterized in that, The constraints specifically include: Where, x i,j,q,k,h，,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. The positive deviation is the monthly planned volume of oil product p transported from node i to node j using transportation mode k. The negative deviation of the monthly planned volume of oil product p transported from node i to node j using transport mode k; mp i,j,p,k This represents the monthly planned volume of oil product p transported from node i to node j using transport method k. hr i ,p Let p be the preset lower limit of oil depot inventory for the i-th node; z is the preset upper limit of oil storage capacity for oil product p at node i; i,p,q Let p be the inventory of oil product p in the oil depot at node i at time q; Let p be the positive deviation of the inventory of oil product p in the oil depot at node i at time q; Let p be the negative deviation of the inventory of oil product p in the oil depot at node i at time q. hm i ,p Let p be the lower limit of the ending inventory of oil product p at node i within a preset period; Let t be the upper limit of the ending inventory of oil product p at node i within a preset period; max This indicates that the data is the end-of-period data for a preset period t; z i,p,t-1 This represents the initial inventory data of oil product p at node i within a preset period t-1; owi i,p,t Let p be the increase in oil product p at node i within a preset period t; owo i,p,t pf represents the decrease in oil product p at node i within a preset period t. i,p,t Let be the expected sales volume of oil product p at node i within a preset period t.

8. The method as described in claim 3, characterized in that, The constraints also include: The volume of oil transported from a node using a single mode of transport is within the minimum and maximum range of the total shipment volume of oil transported from a node using a single mode of transport. The volume of oil transported from a node in a certain mode of transport within a preset period, within the range of the minimum and maximum values ​​of the volume of oil shipped from a node in a certain mode of transport within that period. The amount of oil transported by a node in a certain mode of transport is within the minimum and maximum range of the total amount of oil received by a node in a certain mode of transport. The amount of oil transported by a node in a certain mode of transport within a preset period, within the minimum and maximum range of the amount of oil received by a node in a certain mode of transport within the preset period. The amount of oil transported from one node to another using a transportation method within a time period after a departure time is within a preset range; the preset range is the range between the order shipment status of oil transported from one node to another using a transportation method within a time period after a departure time and the product of the maximum and minimum batch quantities of a single batch of oil transported from one node to another using a transportation method within a time period. The sum of orders for transporting oil products from one node to another using a single mode of transport is within the range of the minimum number of batches of oil products transported from one node to another using a single mode of transport, and the maximum number of batches of oil products transported from one node to another using a single mode of transport, multiplied by the product of the order for transporting oil products from one node to another using a single mode of transport within a time period after a departure time. The product of the unit quantity of oil transported from one node to another by one mode of transport and the number of vehicles transporting oil from one node to another by one mode of transport within a time period after the departure time, plus the remaining quantity, equals the amount of oil transported from one node to another by one mode of transport within a time period after the departure time.

9. The method as described in claim 8, characterized in that, The constraints specifically include: x i,j,p,k,h,q =w i,j,p,k,h,,q ×dwl i,j,p,k +xr i,j,p,k,h,q ; Where, x i,j,p,k,h,q Let p be the amount of oil transported from node i to node j using transport mode k within a time period h after departure time q. fz i ,p,k Let this be the minimum total shipment of oil product p transported from node i using transport method k; This represents the maximum total shipment volume of oil product p transported from node i using transportation method k. fr i ,p,k,t Let $t$ be the minimum amount of oil product $p$ shipped from node $i$ using transport method $k$ within period $t$. This represents the maximum amount of oil product p shipped from node i using transport method k within period t. sz i,p,k Let be the minimum total amount of oil p transported by mode k received at node i; This represents the maximum value of the total amount of oil product p transported by mode k received at node i. sr j,p,k,t Let $t$ be the minimum amount of oil product $p$ received at node $j$ within period $t$, transported by mode $k$. y represents the maximum amount of oil product p transported by mode k received at node j within period t; i,j,p,k,h,,q This describes the order shipment status for transporting oil product p from node i to node j using transportation method k within a time period h after departure time q. The order shipment status is defined by the shipment time y. i,j,p,k,k,q The value is 1, indicating the order status is "not shipped" (y is the case when the order is 1). i,j,p,k,h,q =0; pcl i,j,p,k,h,q Let f be the minimum batch size of a single shipment of oil p transported from node i to node j using transport mode k within a time period h after departure time q. This represents the maximum batch size of a single shipment of oil product p transported from node i to node j using transport mode k within a time period h after departure time q. pcs i,j,p,k This represents the minimum number of batches of oil product p transported from node i to node j using transport method k. w represents the maximum number of batches of oil product p transported from node i to node j using transport method k; i,j,p,k,h,q Let dwl be the number of transport vehicles that transport oil p from node i to node j using transport mode k within a time period h after departure time q; i,j,p,k xr represents the unit quantity of oil p transported from node i to node j using transport method k; i,j,p,k,h,q Let p be the remaining amount of oil transported from node i to node j using transport method k within the time period h after departure time q.

10. The method as described in claim 1, characterized in that, Input parameter data is fed into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation, including: Input parameter data is fed into a pre-established mixed integer programming model, and exact algorithms and / or heuristic algorithms are used to determine the daytime scheduling planning data for oil transportation.

11. A daytime scheduling and planning device for oil transportation, characterized in that, include: The acquisition module is used to acquire input parameter data; Input parameter data includes one or any combination of the following: monthly oil transportation plan data, oil depot inventory data at oil transportation nodes, oil input data, oil output data, and expected market demand data for oil. The daytime scheduling planning module is used to input input parameter data into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation. The mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function. The objective function of the mixed integer programming model includes a penalty function. Both the constraints and the objective function of the mixed integer programming model include slack variables.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 10.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 10.

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