A method, system, terminal and medium for dispatching oil transportation operations at an oil terminal

Abstract

The present invention provides a method for scheduling oil transportation operations at an oil terminal, comprising: collecting information, the information at least including transshipment and / or unloading task information, and equipment information of the oil terminal; establishing a scheduling model based on the collected information, the scheduling model using a multi-objective genetic algorithm; wherein the establishment of the scheduling model at least includes: determining optimization goals and setting constraints; iterating the established scheduling model to obtain an optimal solution; and selecting an oil transportation operation scheduling plan according to the optimal solution. Accordingly, a system, a terminal, and a medium are also provided. The present invention can obtain a scheduling plan that is closer to the theoretical optimal solution, and further considers a mechanism for solving problems in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of oil transportation operations, and in particular to a method, system, terminal and medium for scheduling oil transportation operations at an oil terminal. Background Art

[0002] With the development of economy, the number of oil terminals has gradually increased, and the oil throughput has also been increasing. This has put forward higher requirements on the oil transportation operation scheduling of oil terminals. The previous manual and simple scheduling system has gradually failed to meet the requirements, which can easily cause oil transportation problems.

[0003] Due to the complexity and uncertainty of the oil terminal oil transportation scheduling problem, it has brought great difficulties to the design of the scheduling system. The existing scheduling systems and methods have the following limitations: First, the optimization process of oil transportation scheduling still relies on a lot of manual judgment, and the results of the scheduling plan are not good enough, which makes the terminal's transportation capacity unable to be fully utilized, causing huge waste for a long time; second, it is difficult to take into account the numerous and messy production information and the constraints contained therein, which makes it impossible to guarantee the rationality of scheduling and brings potential risks to terminal operations; finally, for disturbance events such as equipment failure and sudden weather, there is a lack of a mechanism to solve the problem in a timely manner, resulting in task congestion and a series of possible chain reactions.

[0004] After searching, it was found that there are many related technologies for oil terminal oil transportation operation scheduling in the prior art. For example, Chinese patent CN114881327A discloses a method for optimizing oil port resource scheduling, which uses a genetic algorithm and adopts the Euclidean distance from the ideal optimization target as the fitness function of the genetic algorithm. Compared with the traditional manual compilation method, the intelligence and rationality are significantly improved. However, this method cannot obtain a scheduling plan that is closer to the theoretical optimal solution, and does not consider the disturbance events in the actual operation process. There is a lack of a mechanism to solve the problem in a timely manner. Moreover, the specific algorithm adopts the Euclidean distance from the ideal optimization target, and the results obtained lack diversity. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a method, system, terminal and medium for scheduling oil transportation operations at an oil terminal, which can obtain a scheduling plan that is closer to the theoretical optimal solution and further consider a mechanism for solving problems in a timely manner.

[0006] According to one aspect of the present invention, a method for scheduling oil transportation operations at an oil terminal is provided, comprising:

[0007] Collecting information, the information at least including transshipment and / or unloading task information, and equipment information of the oil terminal;

[0008] A scheduling model is established based on the collected information, and the scheduling model adopts a multi-objective genetic algorithm; wherein the establishment of the scheduling model at least includes: determining an optimization target and setting constraints;

[0009] Iterating the established scheduling model to obtain an optimal solution;

[0010] An oil transportation operation scheduling plan is selected according to the optimal solution.

[0011] Optionally, the equipment information of the oil terminal at least includes:

[0012] -Basic properties of all devices waiting to be scheduled;

[0013] - Real-time availability information of equipment availability;

[0014] -The connection or assembly relationship of dock equipment.

[0015] Optionally, the optimization objectives include one or more of the following:

[0016] - Minimize the time cost of each tanker using the port;

[0017] - Minimize the cost of using oil storage tanks;

[0018] - Minimize tank switching costs;

[0019] - Minimize the cost of mixed oil loss in oil storage tanks;

[0020] - Minimize pipeline mixing loss costs;

[0021] -Minimize pipeline transportation costs.

[0022] Optionally, the constraint conditions include one or more of the following:

[0023] - Oil tank time constraint, that is, the start time of a fuel transfer operation is greater than the end time of the previous fuel transfer operation and must meet the residence time constraint, that is, the oil can only be discharged after a period of time after the filling is completed;

[0024] - Oil storage tank capacity limit, that is, the oil volume of the oil storage tank must be less than its maximum volume;

[0025] - Oil type restrictions in oil storage tanks, i.e. oil storage tanks can only store one type of oil;

[0026] - Tanker working time constraints, the next tanker needs to start unloading some time after the previous tanker finishes unloading;

[0027] -At the end, all oil unloading and oil transfer tasks should be completed.

[0028] Optionally, iterating the established scheduling model to obtain an optimal solution includes:

[0029] S1, based on the scheduling model, generate multiple populations of different sizes, initialize these populations with different parameters, and set the maximum number of iterations;

[0030] S2, for each of the populations, evaluating all individuals in the population;

[0031] S3, performing selection, crossover and mutation on each of the populations to generate the next generation population;

[0032] S4, repeat S2 and S3 several times to carry out inter-population migration;

[0033] S5, repeat S2 to S4 before the number of iterations reaches the maximum number of iterations;

[0034] S6, reaches the maximum number of iterations and obtains a series of Pareto optimal solutions.

[0035] Optionally, for each of the populations, evaluating all individuals in the population includes:

[0036] Decode all individuals to obtain the scheduling sequence;

[0037] For each of the scheduling sequences, determine whether it satisfies the constraints and discard individuals that do not meet the constraints;

[0038] For each of the scheduling sequences satisfying the constraint conditions, calculating the objective function thereof;

[0039] Use the fast non-dominated solution algorithm to perform non-dominated sorting on all individuals;

[0040] Calculate the crowding degree between all individuals;

[0041] For each individual, the fitness is calculated based on the crowding degree and non-dominated ordering.

[0042] Optionally, the step of performing selection, crossover and mutation on each population to select and generate the next generation population includes:

[0043] Use the binary tournament algorithm to generate the parent population P;

[0044] For the parent population P, the offspring population Q is generated according to the adaptive crossover probability and mutation probability;

[0045] Merge the parent population P and the offspring population Q into a new population R;

[0046] Evaluate the new population R according to S2 and select the new population according to the elite retention strategy.

[0047] Optionally, the method further comprises: a rescheduling mechanism, wherein: when a disturbance event occurs causing the scheduling task to be interrupted, the above steps are repeated to obtain the job scheduling plan again, and the unfinished tasks within the scheduling period are rescheduled starting from the current time.

[0048] A second aspect of the present invention provides an oil terminal oil transportation operation scheduling system, comprising:

[0049] An information collection module, the information collected by the module includes at least the transshipment and / or unloading task information, and the equipment information of the oil terminal;

[0050] A model building module, which builds a scheduling model based on the information collected by the information collection module, wherein the scheduling model adopts a multi-objective genetic algorithm; wherein the building of the scheduling model at least includes: determining an optimization target and setting constraints;

[0051] An iteration module, which iterates the established scheduling model to obtain an optimal solution;

[0052] A scheduling plan determination module selects an oil transportation operation scheduling plan according to the optimal solution.

[0053] A third aspect of the present invention provides an electronic terminal, comprising:

[0054] a memory for storing instructions executable by a processor;

[0055] The processor is used to execute the instructions to implement the above-mentioned method for scheduling oil transportation operations at an oil terminal.

[0056] A fourth aspect of the present invention provides a computer-readable medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the above-mentioned method for scheduling oil transportation operations at an oil terminal is executed.

[0057] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:

[0058] In view of the complexity of the oil transportation scheduling problem at the oil terminal, the embodiment of the present invention proposes an oil transportation operation scheduling technology for the oil terminal based on NSGA-II, which can obtain a scheduling plan closer to the theoretical optimal solution, realize the oil transportation operation scheduling of the oil terminal, and improve the efficiency of the oil transportation operation of the oil terminal.

[0059] In view of the adverse effects that various uncertain factors may have on the operation process in actual oil transportation operations, the embodiment of the present invention proposes a reactive rescheduling mechanism, which brings better robustness to oil transportation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0061] Figure 1 This is a flow chart of a method for scheduling oil transportation operations at an oil terminal according to an embodiment of the present invention;

[0062] Figure 2 It is a flowchart of algorithm iteration according to an embodiment of the present invention;

[0063] Figure 3 A flowchart of an algorithm evaluating all individuals in a population according to an embodiment of the present invention;

[0064] Figure 4 This is a flow chart of the algorithm population elite strategy of one embodiment of the present invention;

[0065] Figure 5 This is a schematic diagram of individual coding of a preferred embodiment of the present invention;

[0066] Figure 6 This is a flowchart of individual decoding of a preferred embodiment of the present invention;

[0067] Figure 7 A spatial trajectory diagram of an iterative process of a preferred embodiment of the present invention;

[0068] Figure 8 It is a Hv trajectory diagram of the iterative process of a preferred embodiment of the present invention;

[0069] Fig. 9 A Pareto optimal solution set distribution diagram of a preferred embodiment of the present invention;

[0070] Fig.10 This is a Gantt chart of the optimal plan of a preferred embodiment of the present invention;

[0071] Fig.11 This is a Gantt chart of the optimal plan for rescheduling oil transportation tasks in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0073] In the current actual oil transportation operation of the oil terminal, a scheduling plan closer to the theoretical optimal solution cannot be obtained, and disturbance events in the actual operation process are not considered, and there is a lack of a mechanism to solve the problem in a timely manner. In response to these problems, an embodiment of the present invention proposes a scheduling technology for oil transportation operation of the oil terminal.

[0074] Specifically, refer to Figure 1 As shown, in one embodiment of the present invention, a method for scheduling oil transportation operations at an oil terminal is provided, comprising:

[0075] S100, collecting information, the information at least including transshipment and / or unloading task information, and equipment information of the oil terminal;

[0076] In this step, the equipment information of the oil terminal may include: basic attributes of all equipment waiting for scheduling; real-time availability information of equipment availability; connection or assembly relationship of terminal equipment, etc. Specifically:

[0077] The basic properties of all equipment waiting to be dispatched, such as the maximum capacity of the oil tank, the maximum flow rate of the valve, etc.

[0078] Real-time availability information of equipment availability, for example, a tank will be performing other tasks from now to a foreseeable time in the future, and a new task can only be scheduled after the current task is completed;

[0079] The connection or assembly relationship of terminal equipment mainly includes the connection relationship of oil pipelines, the path logic of each endpoint, etc.

[0080] After the above information is collected, it is used to establish a subsequent scheduling model to constrain various information or conditions. Of course, the above is only part of the equipment information listed in this embodiment. In practice, more selections or designs can be made according to the specific conditions of the oil terminal.

[0081] S200, establishing a scheduling model based on the collected information, wherein the scheduling model adopts a multi-objective genetic algorithm; wherein establishing the scheduling model at least includes: determining an optimization target and setting constraints;

[0082] In this step, the multi-objective genetic algorithm can be an improved multi-objective genetic algorithm (NSGA-II). As a possible preferred implementation, the multi-objective genetic algorithm uses a fast non-dominated sorting method, and adopts an elite strategy and introduces crowding. Among them, the fast non-dominated sorting method can reduce the computational complexity, the elite strategy merges the parent individuals and the child individuals and then performs non-dominated sorting, and adopts the crowding method as a criterion for selecting excellent individuals among individuals of the same level.

[0083] In this step, the optimization objectives may include one or more of the following: minimizing the time cost of each tanker using the port; minimizing the cost of using the oil tank; minimizing the cost of switching the oil tank; minimizing the cost of mixed oil loss in the oil tank; minimizing the cost of mixed oil loss in the pipeline; minimizing the cost of pipeline transportation, etc. Of course, the above are only part of the optimization objectives listed in this embodiment. In practice, more optimization objectives can be selected or designed according to the specific conditions and scheduling requirements of the oil terminal.

[0084] In this step, the constraints may include one or more of the following: oil tank time constraint, that is, the start time of an oil transfer operation is greater than the end time of the previous oil transfer operation and must meet the residence time constraint, that is, the oil can only be discharged after a period of time after the filling is completed; oil tank capacity limitation, that is, the oil volume in the oil tank must be less than its maximum volume; oil type limitation of the oil tank, that is, the oil tank can only store one type of oil; tanker working time constraint, the next tanker needs to start unloading a period of time after the previous tanker finishes unloading; at the end, all oil unloading tasks and oil transfer tasks should be completed.

[0085] Of course, the above are only some of the constraints listed in this embodiment. In practice, more constraints may be selected or designed according to the specific conditions and scheduling requirements of the oil terminal.

[0086] Through the above operations, the corresponding optimization objectives and constraints are selected, and in this step a scheduling model that meets the scheduling operation requirements of each oil terminal is established.

[0087] S300, iterating the established scheduling model to obtain an optimal solution;

[0088] This step is based on the scheduling model established in the previous step to obtain the optimal solution for the scheduling operation of the actual oil terminal, which is used to determine the final scheduling plan and is also a key part of the optimization in this embodiment.

[0089] Reference Figure 2 As shown, in a possible implementation, the following steps may be specifically used to obtain the optimal solution:

[0090] S1, based on the scheduling model, generates multiple populations of different sizes, initializes these populations with different parameters, and sets the maximum number of iterations;

[0091] S2, for each population, evaluate all individuals in the population;

[0092] S3, select, crossover and mutate each population to generate the next generation population;

[0093] S4, repeat S2 and S3 several times to carry out inter-population migration;

[0094] S5, repeat S2 to S4 before the number of iterations reaches the maximum number of iterations;

[0095] S6, reaches the maximum number of iterations and obtains a series of Pareto optimal solutions.

[0096] The optimal solution is obtained through iteration of the above steps, which can be used to determine the final scheduling plan according to the actual scheduling needs of the oil terminal.

[0097] Reference Figure 3 As shown, in a possible implementation, in order to better achieve the above purpose and reduce the complexity of calculation, the above S2 can adopt the following steps when evaluating all individuals in each population:

[0098] S21, decode all individuals to obtain the scheduling sequence;

[0099] S22, for each scheduling sequence, determine whether it meets the constraints and discard the individuals that do not meet the constraints;

[0100] S23, for each scheduling sequence that satisfies the constraint conditions, calculate its objective function;

[0101] S24, use the fast non-dominated solution algorithm to perform non-dominated sorting on all individuals;

[0102] S25, calculate the crowding degree between all individuals;

[0103] S26, for each individual, the fitness is calculated based on the crowding degree and non-dominated sorting.

[0104] In this preferred embodiment, the NSGA-II algorithm uses a fast non-dominated sorting method, which reduces the algorithm's computational complexity from O(mN3) to O(mN2), greatly reducing the algorithm's computational time. At the same time, the NSGA-II algorithm introduces crowding, and uses the crowding method to replace the fitness sharing strategy that requires a specified sharing radius, and uses it as a criterion for selecting excellent individuals among individuals of the same level, which ensures the diversity of the population.

[0105] Reference Figure 4 As shown, in a possible implementation, in order to obtain a better population and ensure that the optimal solution that better meets the actual needs can be obtained in the end, the above S3 implements selection, crossover and mutation for each population, and when selecting to generate the next generation population, the following steps can be adopted:

[0106] S31, using the binary tournament algorithm to generate the parent population P;

[0107] S32, for the parent population P, generate the offspring population Q according to the adaptive crossover probability and mutation probability;

[0108] S33, merge the parent population P and the child population Q into a new population R;

[0109] S34, evaluate the new population R according to the above S2, and select the new population according to the elite retention strategy.

[0110] In this preferred embodiment, NSGA-II adopts an elite strategy, merging the parent individuals with the offspring individuals and performing non-dominated sorting to enlarge the search space, selecting individuals with higher priority in order when generating the next generation of parent populations, and selecting individuals of the same level using crowding, ensuring that excellent individuals have a greater probability of being retained. At the same time, this embodiment adopts a parallel iteration method of multiple populations, and adds an immigration mechanism to achieve communication between populations.

[0111] S400: Select an oil transportation operation scheduling plan according to the optimal solution.

[0112] In this step, after a series of Pareto optimal solutions are obtained in S300, an optimal solution is selected as a scheduling plan using the analytic hierarchy process according to the importance of each optimization target on site. Then, the scheduling is performed according to the determined scheduling plan until the scheduling task is completed.

[0113] In the actual scheduling process, various disturbance events that affect the operation process often occur, which may cause the interruption of the execution of the scheduling task, etc., and the existing scheduling method lacks a mechanism to solve the problem in a timely manner. In order to solve this problem, as a possible implementation method, when a task is interrupted due to a disturbance event, the above steps can be repeated to obtain the job scheduling plan again, that is, trigger the rescheduling mechanism, and reschedule the unfinished tasks within the scheduling period from the current time. This embodiment realizes dynamic scheduling based on disturbance by setting a rescheduling mechanism, which overcomes the adverse effects that various uncertain factors may have on the operation process, and brings better robustness to oil transportation scheduling.

[0114] This embodiment uses an end-to-end optimization method, directly uses field data as input to the scheduling model to obtain the optimal solution and implement an optimal job scheduling plan.

[0115] The above embodiment of the present invention uses the above NSGA-II algorithm to obtain a scheduling plan that is closer to the theoretical optimal solution, thereby improving the efficiency of the oil transportation operation of the oil terminal.

[0116] Based on the same technical concept, in another embodiment of the present invention, a system for dispatching oil transportation operations at an oil terminal is provided, comprising:

[0117] An information collection module, the information collected by the module includes at least the transshipment and / or unloading task information, and the equipment information of the oil terminal;

[0118] A model building module, which builds a scheduling model based on the information collected by the information collection module, and the scheduling model adopts a multi-objective genetic algorithm; wherein building the scheduling model at least includes: determining the optimization target and setting the constraint conditions;

[0119] Iteration module, which iterates the established scheduling model to obtain the optimal solution;

[0120] The scheduling plan determination module selects the oil transportation operation scheduling plan according to the optimal solution.

[0121] The specific implementation of the above modules can refer to Figure 1-Figure 4 The implementation technology corresponding to the steps of the oil terminal oil transportation operation scheduling method shown will not be repeated here.

[0122] In order to better understand the above technical solution of the present invention, refer to Figure 5-Figure 11 , further described in detail in combination with specific applications and preferred embodiments, but it should be understood that the following embodiments are not intended to limit the scope of protection of the present invention.

[0123] Reference Figure 5-Figure 11 In this embodiment, a method for scheduling oil transportation operations at an oil terminal is provided, which can be specifically implemented according to the following steps:

[0124] S100, collecting information;

[0125] In this embodiment, the collected information includes transshipment task information, unloading task information and oil terminal information.

[0126] Specifically, the receiving transfer task information is shown in Table 1, and the unloading task information is shown in Table 2:

[0127] Table 1: Transshipment and departure tasks within the scheduling period of this embodiment

[0128]

[0129]

[0130] Table 2: Tanker unloading tasks within the scheduling period of this embodiment

[0131]

[0132] The oil terminal information obtained in this embodiment is as follows: Figure 3 As shown, only the information of currently available oil tanks in the terminal tank area is shown in Table 3:

[0133] Table 3 Oil storage tank information at the beginning of the scheduling cycle

[0134]

[0135]

[0136] S200, based on the above collected information, a scheduling model is established; first, the symbols in the model are described as shown in Table 4:

[0137] Table 4 Symbols

[0138]

[0139]

[0140]

[0141] Specifically, in this step, it is necessary to determine the optimization objectives and constraints, where:

[0142] The optimization target in this embodiment is shown in the following formula:

[0143] minf1=K1∑ i∈ST (Te i -Ts i ) (1)

[0144] minf2=K2U st (2)

[0145] min f3=K3S (3)

[0146]

[0147] Among them, formula (1) represents minimizing the time cost of each tanker using the port; formula (2) minimizes the cost of using the oil tank; formula (3) minimizes the cost of switching the oil tank; formula (4) minimizes the cost of mixed oil loss in the oil tank; formula (5) minimizes the cost of mixed oil loss in the pipeline; formula (6) minimizes the cost of pipeline transportation;

[0148] The constraint conditions in this embodiment are shown in the following formula:

[0149]

[0150] Among them, formulas (7)-(8) represent the time constraint of the oil storage tank, that is, the start time of an oil transfer operation is greater than the end time of the previous oil transfer operation, and the residence time constraint must be met, that is, the oil can only be discharged after a period of time after the filling is completed; formula (9) represents the capacity constraint of the oil storage tank, that is, the oil volume of the oil storage tank must be less than its maximum volume; formula (10) represents the oil type constraint of the oil storage tank, that is, the oil storage tank can only store one type of oil; formula (11) is the working time constraint of the oil tanker, and the next oil tanker needs to start unloading a period of time after the previous oil tanker finishes unloading; formulas (12)-(13) mean that at the end, all oil unloading tasks and oil transfer tasks should be completed.

[0151] S300, iteratively optimize the scheduling model to obtain the optimal solution;

[0152] Reference Figure 2 As shown, follow these steps:

[0153] 1.1. Initialize the algorithm according to the parameters in Table 5:

[0154] Table 5 Algorithm parameter settings

[0155] parameter value Population 3 Population size 50,60,70 Immigration frequency (how often does immigration occur) 5 Immigration rate 0.2 Maximum crossover probability 1，0.9，0.8 Minimum crossover probability 0.6，0.55，0.5 Maximum mutation probability 0.05，0.04，0.03 Minimum mutation probability 0.002，0.0015，0.001 Distribution Index 20 Maximum number of iterations 360

[0156] 1.2 Set the binary chromosome encoding method. The implementation details are as follows:

[0157] For multi-objective genetic algorithms, when implementing them, we can generally first clarify the encoding and decoding methods of the target problem.

[0158] In this embodiment, the chromosome gene is divided into two parts, representing the oil unloading task order gene (US) and the oil tank selection gene (SC). Figure 5 Schematic diagram of chromosome coding.

[0159] The US gene part is encoded with integers, and each code value represents the oil unloading task number. Figure 5 As shown in the figure, the values ​​at positions 1 to 2 represent the unloading order of the oil unloading task of tanker 1, and the values ​​at positions 3 to 5 represent the unloading order of the oil unloading task of tanker 2. The order of oil unloading tasks at positions 3-5 can be changed at will, and the order of oil unloading at positions 5-7 can also be changed at will, but the unloading order between tankers cannot be changed. The length of this part of the code is the number of unloading tasks of each tanker in the scheduling period, that is, formula 13.

[0160]

[0161] Where lengthUS is the US code length, N i represents the number of oil unloading tasks of tanker i, and n is the total number of tankers.

[0162] The SC gene part uses real number coding. Each code represents one of the available oil storage tanks during the unloading process. It may also represent that the tanker stops unloading oil for a period of time. Generally speaking, one code value of the US part corresponds to multiple code values ​​of the SC part. Since the maximum amount of oil is taken each time, when the tanker stops unloading, the code can also represent the stop of unloading. The code length is shown in Formula 14.

[0163]

[0164] where lengthSC is the SC coding length, V i,j represents the volume of the jth oil unloading task of the ith tanker, CST min is the minimum volume of the storage tanks in the storage area.

[0165] Of course, the above coding method is only one method adopted in this embodiment. In other embodiments, other coding designs can also be adopted.

[0166] 1.3 Set the decoding method as shown in the figure, specifically as follows:

[0167] Step1: Input the tanker information Ships, storage tank information StorageTank, transfer task information TransferSchedul, the current time t, the oil delivery speed v, the information TEndExpected of the latest end time of oil unloading for each tanker, and the coding set variables within the scheduling period. Let the tanker number shipCount = 0, and the total number of tankers be Q.

[0168] Step2: Expand the coding variables into the codings of multiple tankers according to the oil delivery tasks of the tankers. The coding of each tanker is variable, and the coding of each tanker also includes the coding of the tanker unloading sequence and the storage tank selection. The coding length of the tanker unloading sequence is length1, and the coding length of the storage tank selection is length2. Define unloadCount = 0, mCount = length1.

[0169] Step3: Judge whether shipCount < Q. If so, execute step4; otherwise, it means that the oil products in all tankers have been unloaded, and the decoding ends.

[0170] Step4: Convert the US part in the coding variable into the unloading oil product type sequence OilTypes according to the corresponding oil product type in Tasks.

[0171] Step5: Judge three conditions: the remaining oil volume AllOilshipCount of the tanker ShipsshipCount > 0, unloadCount < length1, and mCount < length2 + length2. If not, shipCount++, and execute Step3. If all hold simultaneously, obtain the arrival time arrivalTimeshipCount and the oil unloading task information Tasks of the tanker, update the current time t = min{arrivalTime, t}, and proceed to Step6.

[0172] Step 6: Determine the oil type at the corresponding position of US according to the value of OilTypesunloadCount at the unloadCount position in OilTypes, scan the oil storage tank information StorageTank, determine whether the oil storage tank is idle according to the unloadTime and the historical scheduling information of the oil storage tank, and add the oil storage tank that meets the conditions to the optional oil storage tank set usedStorageTank.

[0173] Step 7: Determine whether usedStorageTank is not empty. If so, wait and find the empty oil tank that is about to complete the transfer task according to the oil tank scheduling information table, select the oil tank for unloading, calculate the waiting time twait, update the current time t=twait+t, and execute Step 10. If not, execute Step 8.

[0174] Step8: Select the storage tank or shut it down from chosenStorageTank according to the mCountth code variablemCount in variabl.

[0175] Step 9: Determine whether to stop operation. If so, select a stop period of time tstop, update the current time t=tstop+t, mCount++, and execute Step 5.

[0176] Step 10: Calculate the unloaded oil volume Vunload, calculate the unloading time tunload, and determine whether Vunload<=0 is true. If so, the code is considered illegal and the decoding ends. If not, execute Step 11.

[0177] Step 11: Update the scheduling information in this scheduling to the corresponding oil storage tank information StorageTank, update the tanker information Ships, transfer task information TransferSchedule, add the scheduling information to chosenStorageTank for storage, and update the time t=t+tunload, mCount++.

[0178] Step 12: Determine whether the oil currently unloaded in the tanker has been unloaded. If so, unloadCount++. Execute Step 5.

[0179] Of course, the above decoding method is only a preferred implementation method. In other embodiments, other decoding designs can also be used.

[0180] 1.4 Based on the above settings, follow Figure 2 The process shown is iterated.

[0181] 1.5 Spacing changes during iteration as follows Figure 7 As shown, the Hv changes in the iterative process as Figure 8 As shown, after the iteration, the Pareto solution set is obtained, and the distribution is as follows Fig. 9 shown.

[0182] 1.6 Using the analytic hierarchy process, a comparison matrix is ​​specified based on the priority of different optimization objectives at the site. Through the comparison matrix, the degree of fit of the obtained solution set to the current site requirements can be compared, so as to select the optimal solution. The specified comparison matrix Jahp is as follows:

[0183]

[0184] 1.7 Use a Gantt chart to represent the optimal solution.

[0185] S400, selecting an oil transportation operation scheduling plan according to the optimal solution.

[0186] In this embodiment, the optimal scheduling plan is determined according to the obtained optimal solution. Fig.10 shown.

[0187] In a possible implementation, based on the above embodiment, a dynamic scheduling mechanism based on disturbance, i.e., a rescheduling mechanism, is further included to achieve robust scheduling. Specifically, if the optimal scheduling plan obtained in the above embodiment is executed, for example, at the 37th hour, a tanker is temporarily added to be unloaded. According to the disturbance event handling strategy, after the unloading task of the current tanker is completed, the temporary task is performed. If the subsequent unloading task is postponed, the transshipment demand cannot be met. Therefore, rescheduling is performed after the unloading of the first tanker is completed. The situation of the added third tanker is shown in Table 6:

[0188] Table 6 Temporary uninstallation tasks

[0189] Tanker Uninstall task number Oil type Oil volume (t) Arrival time(h) Estimated unloading speed (t / h) Ship3 1 Oil4 14000 48 2000 Ship3 2 Oil0 18000 48 2000

[0190] It is determined that the existing scheduling plan cannot meet the transfer demand of transfer task 2, that is, it is impossible to unload enough oil products in the oil storage tank at the beginning of transfer task 2. At this time, rescheduling will be triggered. Starting from the current time, the unfinished oil transportation tasks within the scheduling cycle will be rescheduled. The scheduling Gantt chart is as follows Fig.11 shown.

[0191] This embodiment realizes dynamic scheduling based on disturbance by setting up a rescheduling mechanism, which can eliminate the adverse effects that various uncertain factors may have on the operation process and bring better robustness to oil transportation scheduling.

[0192] Based on the same technical concept, in another embodiment of the present invention, an electronic terminal is also provided, including: a memory and a processor, the memory is used to store instructions executable by the processor; the processor is used to execute instructions to implement the oil terminal oil transportation operation scheduling method as in any of the above embodiments.

[0193] Based on the same technical concept, in another embodiment of the present invention, a computer-readable medium is provided, on which computer instructions are stored, wherein when the computer instructions are executed by a processor, the oil terminal oil transportation operation scheduling method in any of the above embodiments is executed.

[0194] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as separate components.

[0195] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0196] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0197] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0199] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A method for dispatching oil transportation operations at an oil terminal, characterized in that: include: Collecting information, the information at least including transshipment and / or unloading task information, and equipment information of the oil terminal; A scheduling model is established based on the collected information, and the scheduling model adopts a multi-objective genetic algorithm; wherein the establishment of the scheduling model at least includes: determining an optimization target and setting constraints; Iterating the established scheduling model to obtain an optimal solution; selecting an oil transportation operation scheduling plan according to the optimal solution; The optimization objectives include one or more of the following: - Minimize the time cost of each tanker using the port; - Minimize the cost of using oil storage tanks; - Minimize tank switching costs; - Minimize the cost of mixed oil loss in oil storage tanks; - Minimize pipeline mixing loss costs; - Minimize pipeline transportation costs; The constraints include one or more of the following: - Oil tank time constraint, that is, the start time of a fuel transfer operation is greater than the end time of the previous fuel transfer operation and must meet the residence time constraint, that is, the oil can only be discharged after a period of time after the filling is completed; - Oil storage tank capacity limit, that is, the oil volume of the oil storage tank must be less than its maximum volume; - Oil type restrictions in oil storage tanks, i.e. oil storage tanks can only store one type of oil; - Tanker working time constraints, the next tanker needs to start unloading some time after the previous tanker finishes unloading; - At the end, all oil unloading and oil transfer tasks should be completed; The optimization objective is determined and the constraint conditions are set, wherein: The optimization objective is shown in the following formula: min f1=K1∑ i∈ST (Tea i -Ts i ) (1) <h2 style=";text-align:left;direction:ltr">min f2=K2U<h2 style=";text-align:left;direction:ltr"> st <h2 style=";text-align:left;direction:ltr"> (2) min f3=K3S (3) Among them, formula (1) represents minimizing the time cost of each tanker using the port; formula (2) minimizes the cost of using the oil tank; formula (3) minimizes the cost of switching the oil tank; formula (4) minimizes the cost of mixed oil loss in the oil tank; formula (5) minimizes the cost of mixed oil loss in the pipeline; formula (6) minimizes the cost of pipeline transportation; The constraint condition is shown in the following formula: Among them, formulas (7)-(8) represent the time constraint of the oil tank, that is, the start time of an oil transfer operation is greater than the end time of the previous oil transfer operation, and the residence time constraint must be met, that is, the oil can only be discharged after a period of time after the filling is completed; formula (9) represents the capacity constraint of the oil tank, that is, the oil volume of the oil tank must be less than its maximum volume; formula (10) represents the oil type constraint of the oil tank, that is, the oil tank can only store one type of oil; formula (11) is the working time constraint of the oil tanker, and the next oil tanker needs to start unloading a period of time after the previous oil tanker finishes unloading; formulas (12)-(13) mean that at the end, all oil unloading tasks and oil transfer tasks should be completed; In the above formula:

2. The method for dispatching oil transportation operations at an oil terminal according to claim 1, characterized in that: The equipment information of the oil terminal shall at least include: -Basic properties of all devices waiting to be scheduled; - Real-time availability information of equipment availability; -The connection or assembly relationship of dock equipment.

3. The method for dispatching oil transportation operations at an oil terminal according to claim 1, characterized in that: The iterating the established scheduling model to obtain an optimal solution includes: S1, based on the scheduling model, generate multiple populations of different sizes, initialize these populations with different parameters, and set the maximum number of iterations; S2, for each of the populations, evaluating all individuals in the population; S3, performing selection, crossover and mutation on each of the populations to generate the next generation population; S4, repeat S2 and S3 several times to carry out inter-population migration; S5, repeat S2 to S4 before the number of iterations reaches the maximum number of iterations; S6, reaches the maximum number of iterations and obtains a series of Pareto optimal solutions.

4. The method for dispatching oil transportation operations at an oil terminal according to claim 3, characterized in that: For each of the populations, all individuals in the population are evaluated, including: Decode all individuals to obtain the scheduling sequence; For each of the scheduling sequences, determine whether it satisfies the constraints and discard individuals that do not meet the constraints; For each of the scheduling sequences satisfying the constraint conditions, calculating the objective function thereof; Use the fast non-dominated solution algorithm to perform non-dominated sorting on all individuals; Calculate the crowding degree between all individuals; For each individual, the fitness is calculated based on the crowding degree and non-dominated ordering.

5. The method for dispatching oil transportation operations at an oil terminal according to claim 3, characterized in that: The selection, crossover and mutation are performed on each population to select and generate the next generation population, including: Use the binary tournament algorithm to generate the parent population P; For the parent population P, the offspring population Q is generated according to the adaptive crossover probability and mutation probability; Merge the parent population P and the offspring population Q into a new population R; Evaluate the new population R according to S2 and select the new population according to the elite retention strategy.

6. The method for dispatching oil transportation operations at an oil terminal according to any one of claims 1 to 5, characterized in that: Also includes: Rescheduling mechanism, where: When a disturbance event occurs and causes the scheduling task to be interrupted, repeat the above steps to obtain the job scheduling plan again, and reschedule the unfinished tasks within the scheduling period starting from the current time.

7. An oil terminal oil transportation operation scheduling system, used to implement the oil terminal oil transportation operation scheduling method described in claim 1, characterized in that: include: An information collection module, the information collected by the module includes at least the transshipment and / or unloading task information, and the equipment information of the oil terminal; A model building module, which builds a scheduling model based on the information collected by the information collection module, wherein the scheduling model adopts a multi-objective genetic algorithm; wherein the building of the scheduling model at least includes: determining an optimization target and setting constraints; An iteration module, which iterates the established scheduling model to obtain an optimal solution; A scheduling plan determination module selects an oil transportation operation scheduling plan according to the optimal solution.

8. An electronic terminal, comprising: a memory for storing instructions executable by a processor; A processor, configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the method according to any one of claims 1 to 6 is executed.

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