Oil port resource optimization scheduling method

Abstract

The present invention is an oil port resource optimization scheduling method, which includes the following steps: step S11 of establishing a lower-level scheduling model; step S12 of establishing an upper-level scheduling model; step S13 of obtaining the lower-level scheduling model and the upper-level scheduling model using a multi-objective evolutionary algorithm; and step S14 of determining the target berth and berthing sequence of the tanker based on the obtained results. According to the present invention, on the one hand, under the factor conditions of multiple storage tanks and multiple pipelines, the operation of the entire oil port can be relatively balanced and relatively stable, and on the other hand, the control objective of maximizing the profit of the oil port terminal and minimizing the tanker's stay time in port can be achieved. The present invention is an optimal scheduling solution, which is obviously improved in both intelligence and streamlining compared with the traditional manual creation method.

Description

Technical Field

[0001] The present invention belongs to the technical field of port equipment, and particularly relates to an oil port resource optimization scheduling method. Background Technology

[0002] An oil port is a dedicated port for loading and unloading crude oil and refined petroleum products. Oil port resource scheduling is of great significance to the construction of smart ports. Rational scheduling can equalize berth occupancy time, improve the profits of oil ports, and optimize the work efficiency of oil ports.

[0003] At present, oil port resource scheduling generally adopts the form of manual unloading plan creation: before the tanker enters the port, the land side provides the ship side with information such as the number of connecting berths and pipelines between the ship and the land side, the ship side provides the land side with information such as the oil type and tonnage to be loaded during the voyage, and both the ship side and the land side confirm the information such as the type and amount of crude oil, the unloading sequence and the unloading rate, and then formulate and execute the unloading plan. However, due to the possibility that multiple pipelines are transported at the same time and multiple tanks are stored at the same time during the unloading operation, the traditional manual unloading plan creation technology has difficulty in optimizing the oil port resource allocation, and the oil port resources cannot be fully utilized, which has the problem of intelligence and rationalization that needs to be improved.

Summary of the invention

Problems to be solved by the invention

[0004] The present invention designs and provides a method for optimally scheduling oil port resources, in contrast to the existing manual unloading work planning technology of oil ports, which is difficult to optimize resource allocation in oil ports, cannot fully utilize the resources of oil ports, and has problems in terms of improving intelligence and rationalization.

Means for solving the problems

[0005] To achieve the above object of the invention, the present invention is realized by using the following technical solutions.

[0006] In the oil port resource optimization scheduling method, including the following steps, namely, Step S11 of establishing a lower-level scheduling model, where the lower-level scheduling model is as follows, namely,

Number

Equation

Advantages of the Invention

[0007] Compared with the prior art, the advantages and positive effects of the present invention are as follows. That is, on the one hand, under the factor conditions of a plurality of storage tanks and a plurality of pipelines, the overall operation of the oil port can be relatively balanced and made relatively stable. On the other hand, the profit of the oil port terminal can be maximized, and the control goal of minimizing the in-port time of the tanker can be achieved. The present invention is an optimal scheduling solution, and compared with the conventional manual preparation method, both intelligence and rationalization are significantly improved.

Brief Description of the Drawings

[0008] To more clearly explain the technical solution in the embodiment of the present invention, the drawings required for the embodiment are briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention, and those skilled in the art can obtain other drawings from these drawings without creative effort.

[0009]

Figure 1

Figure 2

Figure 3

Embodiments for Carrying Out the Invention

[0010] In order to make the object, technical solution and advantages of the present invention clearer and easier to understand, in the following, the present invention will be further described in detail in conjunction with the accompanying drawings and embodiments.

[0011] In the description of the present invention, terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the accompanying drawings, and are for the convenience of description only, and do not indicate or imply that the device or element must be configured and operated in a specific direction. Therefore, it should not be construed as a limitation of the present invention. Furthermore, the terms "first" and "second" are used only for the purpose of description and are not to be understood as indicating or implying relative importance.

[0012] FIG. 1 is a schematic diagram showing the structure of an oil port to which the oil port resource optimization scheduling method proposed by the present invention is applied. As shown in FIG. 1, when unloading operations are performed, the oil port resources can be divided into a tanker layer, a berth layer, and a tank layer. In FIG. 1, the tanker layer includes, for example, tanker 1, tanker 2, …, tanker n, the berths include, for example, berth 1, berth 2, …, berth m, and the tanks include, for example, tank 1, tank 2, …, tank r. The berth layer and the tank layer are communicated through a plurality of pipelines, and when unloading the same type of oil, a plurality of pipelines can be transported in parallel and simultaneously. The unloaded oil can be stored in the target tank installed according to the type of oil, and each tank is used to store one type of oil, and a plurality of tanks can be installed arbitrarily according to demand.

[0013] The oil port resource optimization scheduling method proposed by the present invention is intended to allocate berths. The incoming tankers select berths according to the principle of efficiency, allocate berths to the tankers in the optimal order, and the tankers dock and perform unloading operations, aiming to control the total stay time of the tankers at the oil port and improve the benefits of the oil port. The allocation of berths is the basis of oil port resource scheduling and the core of the entire optimization scheduling method.

[0014] The berth allocation has the following characteristics, that is, (1) After the tanker arrives at the port, it is judged whether there is an empty berth. If there is an empty berth, the next berth allocation is performed. If there is no empty berth, the tanker waits at the anchorage. (2) Only one tanker can always berth at one berth, and one tanker can only occupy one berth. (3) After the tanker completes the unloading operation, it is permitted to leave the port, and one unloading operation ends. Once a tanker that has left the port enters the port again and enters the berthing operation, it is recognized that the next unloading operation is executed. (4) When a single tanker stores multiple types of oils, it can only unload them sequentially and cannot unload multiple types of oils simultaneously. (5) It is assumed that factors such as delays in the arrival of tankers due to force majeure such as weather and equipment failures are not considered.

[0015] From the schematic diagram of the oil port structure in FIG. 1 and the characteristics of berth allocation, when unloading crude oil, multiple pipelines can transport oil simultaneously, multiple tanks can store oil simultaneously, and different tanks can store different types of oils. Therefore, the oil transportation volume by pipelines affects the oil transportation time. The greater the oil transportation volume by pipelines, the shorter the oil transportation time. Conversely, the smaller the oil transportation volume by pipelines, the longer the oil transportation time. On the other hand, the berth where the tanker docks and the docking sequence affect the berth occupancy period and the oil transportation time. If the berth where the tanker docks and the docking sequence are reasonable, the berth occupancy period and the oil transportation time will be shortened. Conversely, if the berth where the tanker docks and the docking sequence are not reasonable, the berth occupancy period and the oil transportation time will be lengthened. Generally, the total stay time of the tanker is the sum of the tanker's docking waiting time, the crude oil transportation preparation time, and the crude oil transportation time. A reasonable berth allocation can minimize the tanker waiting time, the crude oil transportation preparation time, and the crude oil transportation time, shorten the total stay time of the tanker, improve the effective unloading operation time of the berth, and improve the oil port profit.

[0016] To achieve the above object, the oil port resource optimization scheduling method provided by the present invention includes a plurality of steps as described in detail below. The oil port resource optimization scheduling method provided by the present invention may be executed in an oil port resource management system. The oil port resource management system may be realized by a controller having a processor. The controller further includes a storage device connected to the controller and necessary peripheral circuits.

[0017] Step S11: Establishing a lower-level scheduling model is Step S11. The control objectives of the lower-level scheduling model include the maximum berth utilization equalization rate (minimum standard deviation) and the maximum pipeline transportation time equalization rate. The lower-level scheduling model can be expressed by the following formula, that is,

Equation

[0018] An executable solution, i.e., an ideal berth for a tanker, can be obtained by the lower-level scheduling model, and both the occupancy time of the berth and the transportation time through the pipeline are kept in an equilibrium state. That is, in the multi-element conditions of multiple tanks and multiple pipelines, the tanker, the berth, the pipeline, and the tanks can perform the unloading operation in an interrelated state, realizing a relative equilibrium state and a relative stable state.

[0019] Considering the characteristics of the unloading operation at the oil port, the lower-level scheduling model further includes the following constraint conditions, that is, (1) The tanker berthing constraint condition is that the tanker can only perform the unloading operation when it enters the oil port, and the tanker can only berth at one berth. The tanker berthing constraint condition can be expressed by the following formula. That is, JPEG0007679050000023.jpg10136 Here, i is the tanker, i = 1, 2, ···, n, and n represents the number of tankers. (2) The tanker loading capacity constraint condition is that the tanker is permitted to enter the target berth only when the loading capacity of the tanker is less than the berthing capacity of the target berth. The tanker loading weight constraint condition is expressed by the following formula, that is, JPEG0007679050000024.jpg5134 Here, W i represents the loading capacity of tanker i, and W k represents the berthing capacity of the berth. In the tanker berthing constraint condition and the tanker loading weight constraint condition, y i,k satisfies the following formula. JPEG0007679050000025.jpg14137 (3) The pipeline constraint condition is that at least one pipeline is installed between the target berth and the tank to realize the oil unloading operation. The pipeline constraint condition can be shown as follows. JPEG0007679050000026.jpg11135Here, q is the number of pipelines, j is the type of oil, where j = 1, 2, ···, u, u is the number of oil types, s is the tank, where s = 1, 2, ···, r, and r is the number of tanks. JPEG0007679050000027.jpg12138(4) The unloading capacity constraint condition is that during the set planning period, the types of oil to be unloaded must match the types of oil that can be accommodated in the target tank, and the target unloading amount of the oil in the tanker is less than the remaining capacity of the target tank. The constraint of the unloading capacity can be shown as follows, that is, JPEG0007679050000028.jpg12132Here, F s represents the remaining storage capacity of tank s. JPEG0007679050000029.jpg13137Here, E i,j represents the number of tons of the j-th type of oil unloaded by tanker i. The number of tons of the j-th type of oil unloaded by tanker i is a known value and can be obtained by scheduling from the tanker side. The remaining storage capacity of tank s can be detected in real time, for example, by using a radar level sensor to detect the real-time liquid level of tank s. (5) The unloading target constraint condition is that the oil in the tanker must be completely unloaded. The unloading target constraint condition can be shown as follows, that is, JPEG0007679050000030.jpg10136Here, E i,j,p represents the number of tons of the j-th type of oil transported to tanker i through pipeline p.

[0020] The executable solution obtained by the lower-level scheduling model, namely the berth where the tanker berths, the determined oil storage tank, the pipeline, and the pipeline transportation volume, can ensure the effective implementation of the unloading operation. At the same time, it can ensure that both the berth occupancy time and the pipeline transportation time are maintained in a balanced state, guaranteeing that the overall operation of the oil port is effective and stable, and in a relative balance and relative stability. Based on the lower-level scheduling model, a higher-level scheduling model is further constructed.

[0021] Step S12: The oil port resource optimization scheduling method provided by the present invention includes the following steps, that is, step S12 of establishing a higher-level scheduling model. The control objective of the higher-level scheduling model is to maximize the profitability of the oil port terminal and minimize the total stay time of the tankers. The higher-level scheduling model is represented by the following formula. That is,

Equation

[0022] The executable solution obtained from the upper-level scheduling model, that is, the berthing sequence of tankers, can maximize the profit of the oil port terminal and minimize the total residence time of tankers.

[0023] Considering the characteristics of the unloading operation at the oil port, the upper-level scheduling model further includes the following constraints, that is, (1) The pipeline constraint is that at least one pipeline is installed between the target berth and the tank to realize the oil unloading operation. The pipeline constraint can be shown as follows. JPEG0007679050000033.jpg12136 Here, k is the berth, k = 1, 2, ···, m, where m is the number of berths at the oil port. p is the pipeline, p = 1, 2, ···, q, where q is the number of pipelines. j is the oil type, j = 1, 2, ···, u, where u is the number of oil types. JPEG0007679050000034.jpg14144(2) The berth access permission constraint is that only one tanker can berth at one berth. The berth access permission condition can be shown as follows. That is, JPEG0007679050000035.jpg6136Here, TB i+1.kindicates the time when the (i + 1)-th tanker berths at berth k, TL i,k indicates the time when tanker i completes its operation at berth k and departs. That is, the time when the (i + 1)-th tanker berths at berth k is later than the time when tanker i completes its operation at berth k and departs. (3) The unloading completion and departure constraint condition is that the tanker is permitted to depart after completing the unloading operation, and one unloading operation is completed. The unloading completion and departure constraint condition can be shown as follows. That is, JPEG0007679050000036.jpg12134 Here, TL i,k indicates the time when tanker i completes its operation at berth k and departs, TB i,k indicates the time when tanker i berths at berth k, JPEG0007679050000037.jpg7136 indicates the maximum value of the sum of the preparation time and the transportation time when transporting the j-th oil of tanker i through the pipeline. The preparation time TR for transporting the j-th oil of tanker i through pipeline p i,j,p is calculated by the following formula, that is, JPEG0007679050000038.jpg6136 Here, TR p indicates the preparation time for transporting oil through pipeline p, and the preparation time includes the startup preparation time of fluid control components such as pumps and valves installed in pipeline p, but is not limited thereto. TR p is generated based on the detected history data and may be pre-stored in a storage device so that it can be retrieved on demand. The transportation time for transporting the j-th oil of tanker i through pipeline p is calculated by the following formula. That is, JPEG0007679050000039.jpg11143 Here, E i,j,p indicates the number of tons of oil j transported through pipeline p for tanker i, and v j,p indicates the flow rate of the j-th oil transported through pipeline p. In the above two equations, W i,j,p satisfies the following equation. JPEG0007679050000040.jpg15154That is, when transporting the j-th oil of tanker i to the tank via pipeline p, W i,j,p is 1. Otherwise, W i,j,p is 0.

[0024] Step S13: Step S13 is a step of obtaining a lower-level scheduling model and a higher-level scheduling model using a multi-objective evolutionary algorithm. The lower-level scheduling model and the higher-level scheduling model are obtained by software, such as Matlab. Step S14 determines the target berth and berthing sequence of the tanker based on the obtained results.

[0025] In an optional embodiment, a multi-objective evolutionary algorithm is used to solve the lower-level scheduling model. If necessary, using a multi-objective evolutionary algorithm to solve the lower-level scheduling model includes the following steps. That is, Step S21. Step S21 is an individual encoding that represents the berth number corresponding to the feasible solution of the lower-level scheduling model in binary. In this embodiment, the berth where the tanker berths corresponding to the feasible solution (also called an individual) of the lower-level scheduling model has a unique berth number and is represented in binary. The berth number represented in binary constitutes the genotype of the individual (i.e., the genotype of the feasible solution). The genotype and the phenotype (berth number) can be mutually converted by an encoding and decoding program. Step S22. Step S22 is to generate an initial population of individuals. Set the scale of the initial population of individuals, randomly select individuals of the initial population of individuals in the feasible solution of the lower-level scheduling model, and define the initial population of individuals as pop1. Step S23. Fitness calculation step S23 for calculating the fitness of the sample individual. Since each objective in the lower-level scheduling model does not satisfy linear independence, it is preferable to construct a fitness function using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The fitness function is shown as follows: JPEG0007679050000041.jpg11132 Here, g l represents the fitness value of the l-th individual in the initial population of the lower-level scheduling model, and the smaller g l is, the better the individual is. JPEG0007679050000042.jpg6137 represents the distance from the objective function value of the first individual in the initial population of the lower-level scheduling model to the negative ideal solution of the upper-level scheduling model. JPEG0007679050000043.jpg6137 represents the distance from the objective function value of the first individual in the initial population of the lower-level scheduling model to the positive ideal solution of the lower-level scheduling model. The objective function is selectable and set in the model function of the lower-level scheduling model. Step S24. Selection operation step S24 for performing a selection operation based on the fitness of the sample individual and selecting an individual sample. Calculate the sum of the fitnesses of all individuals in the initial population. Calculate the relative fitness of each individual in the initial population, where the relative fitness is the ratio of the individual fitness to the sum of the fitnesses. Each relative fitness can be represented as a probability, and since the sum of all probability expressions is 1, the region corresponding to all relative fitnesses with corresponding probability values can be considered to form a complete region (e.g., a disk). The selection marker is randomly placed within this complete region, and the selected independent region is the selected individual. That is, individuals are selected from the population as if rotating a disk. The number of individuals equal to the target number is selected from the initial population by rotating the target sub-disk, that is, by the standard Roulette wheel method. When the fitness of an individual is normalized in the step, it can also be directly used as the Roulette wheel probability. Step S25. The crossover operation step S25 of performing a crossover operation on the individual samples after the selection operation. Randomly select two individuals from the selected individual samples and perform a crossover with a predetermined crossover probability. Linearly interpolate the factors corresponding to the random positions with a coefficient, and leave the remaining positions unchanged to obtain two new individuals. The crossover probability is adjusted according to the fitnesses of the two individuals involved in the crossover operation. First, set the crossover probability interval [pc min , pc max , calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population, and the crossover probability is determined by the following formula. JPEG0007679050000044.jpg23136 Here, f’ is the larger fitness of the two individuals involved in the crossover operation. Step S26. The mutation operation step S26 of performing a mutation operation on the individual samples after the crossover operation. According to the mutation probability, invert the values of one or more mutation points in the individual samples after the crossover. First, the mutation probability interval [pm min , pm maxSet [[ID=]], and adjust the mutation probability according to the individual fitness. The mutation probability is determined by the following formula. JPEG0007679050000045.jpg23135 Here, pm represents the mutation probability, and pm min represents the minimum value of the set mutation probability interval, and pm max represents the maximum value of the set mutation probability interval, and f min represents the minimum fitness value in the population of the sample individuals after crossover, and f avg represents the average fitness value in the population of the sample individuals after crossover, and f represents the fitness value of the mutant individual. Step S27. Step S27 determines whether the iteration condition is satisfied. If the iteration condition is satisfied, the calculation is terminated, and the optimized population pop1-opt of the lower-level scheduling model is obtained. If the iteration condition is not satisfied, the individual sample after the mutation operation is used as the next-generation population, and steps S23 to S26 are repeatedly executed until the iteration condition is satisfied. Specifically, through the selection operation, crossover operation, and mutation operation, the next-generation population is obtained. Furthermore, the processes of the selection calculation, crossover operation, and mutation operation are repeatedly executed until the number of iterations reaches the upper limit (the iteration condition is satisfied), the calculation is terminated, and the optimized population pop1-opt corresponding to the maximum fitness of the lower-level scheduling model is obtained. Decode the genotype of the individuals in the optimized population pop1-opt, that is, obtain the berth where the oil port berths, and determine the oil storage tank, oil transportation pipeline, and pipeline transportation volume. Step S28. Step S28 calculates the E i,j,p corresponding to each individual in the optimized population pop1-opt. E i,j,p represents the number of tons of oil j of tanker i transported through pipeline p. Step S29. Step S29 substitutes the calculated E i,j,p into the upper-level scheduling model. The multi-objective evolutionary algorithm is used to solve the upper-level scheduling model into which E i,j,p is substituted.

[0026] If necessary, using a multi-objective evolutionary algorithm to solve the upper-level scheduling model with E i,j,p substituted includes the following steps That is Step S30. The individual encoding step S30 that represents the birth number corresponding to the executable solution of the upper-level scheduling model in binary. In this embodiment, the birth of the tanker corresponding to the executable solution (also called an individual) of the upper-level scheduling model has a unique birth number and is represented in binary. The birth number represented in binary constitutes the genotype of the individual (i.e., the genotype of the executable solution). The genotype and the phenotype (birth number) can be mutually converted by encoding and decoding programs. Step S31. The step S31 of generating an initial population of individuals. Set the size of the initial population of individuals, randomly select individuals of the initial population of individuals in the executable solution of the upper-level scheduling model, and define the initial population of individuals as pop2. Step S32. The fitness calculation step S32 of calculating the fitness of the sample individual. Since each objective in the upper-level scheduling model does not satisfy linear independence, it is preferable to construct a fitness function using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The fitness function is shown as follows. JPEG0007679050000046.jpg11135 Here, g l represents the fitness value of the I-th individual in the initial population of the upper-level scheduling model, and g l indicates that the smaller the value, the better the individual. JPEG0007679050000047.jpg6137 indicates the distance from the objective function value of the first individual in the initial population of the upper-level scheduling model to the negative ideal solution of the upper-level scheduling model. JPEG0007679050000048.jpg6136 indicates the distance from the objective function value of the first individual in the initial population of the upper-level scheduling model to the positive ideal solution of the upper-level scheduling model. The objective function is selectable and set in the model function of the upper-level scheduling model. For a function that takes the maximum value, it can be converted to take the minimum value by inverting the numerical form, and the overflow of the function can be prevented by adding 1 to the denominator. Step S33. A selection operation is performed based on the fitness of the sample individuals, and the selection operation step S33 for selecting individual samples. Calculate the sum of the fitnesses of all individuals in the initial population. Calculate the relative fitness of each individual in the initial population, and the relative fitness is the ratio of the individual fitness to the sum of fitnesses. Each relative fitness can be represented by a probability, and since the sum of all probability expressions is 1, the region corresponding to all relative fitnesses with corresponding probability values can be regarded as forming a complete region (such as a disk). The selection marker is randomly placed within this complete region, and the selected independent region is the selected individual. That is, individuals are selected from the population in a way similar to rotating a disk. The target number of individuals is selected from the initial population by rotating the target sub-disk, that is, by the standard Roulette wheel method. When the fitness of an individual is normalized in the step, it can also be directly used as the Roulette wheel probability. Step S34. The crossover operation step S34 for performing a crossover operation on the individual samples after the selection operation. Randomly select two individuals from the selected individual samples and perform a crossover with a predetermined crossover probability. Linearly interpolate the factors corresponding to the random positions with coefficients, and do not change the remaining positions to obtain two new individuals. The crossover probability is adjusted according to the fitness of two individuals involved in the crossover operation. First, set the crossover probability interval [pc min , pc max , calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population, and the crossover probability is determined by the following formula. JPEG0007679050000049.jpg23136 Here, f’ is the larger fitness of the two individuals involved in the crossover operation. Step S35. Mutation operation step S35 of mutating the sample individuals after the crossover operation. According to the mutation probability, reverse the values of one or more mutation points in the individual sample after crossover. First, set the mutation probability interval [pm min , pm max , adjust the mutation probability according to the individual fitness, and the mutation probability is determined by the following formula. JPEG0007679050000050.jpg23136 Here, pm represents the mutation probability, pm min represents the minimum value of the set mutation probability interval, pm max represents the maximum value of the set mutation probability interval, f min represents the minimum fitness value in the population of the sample individuals after crossover, f avg represents the average fitness value in the population of the sample individuals after crossover, and f represents the fitness value of the mutated individual. Step S36. Step S36 determines whether the iteration condition is satisfied. If the iteration condition is satisfied, the calculation ends, and the optimized population pop1-opt of the upper-level scheduling model is obtained. If the iteration condition is not satisfied, the individual sample after the mutation operation is used as the next-generation population, and steps S32 to S35 are repeatedly executed until the iteration condition is satisfied.

[0027] Decode the genotype of the individuals in the optimized population pop2-opt to obtain the docking sequence of the oil ports. The genetic algorithm is solved twice, and based on the optimal solution of the lower-level scheduling model, the optimal solution of the upper-level scheduling model is obtained, and finally the berth where the oil port docks, the oil storage tank, the oil pipeline, the pipeline transportation volume, and the docking sequence are obtained. Under the conditions of multiple storage tanks and multiple pipelines, it is possible to ensure that the operation of the entire oil port is relatively balanced and stable. On the other hand, the control objectives of maximizing the profit of the oil port terminal and minimizing the in-port time of the tanker can be achieved. This is an optimized scheduling plan, and compared with the conventional manual preparation method, the intelligence and rationality are significantly improved.

[0028] The above embodiments are only used to explain the technical solutions of the present invention and are not used for limitation. Although the present invention has been described in detail with reference to the above embodiments, for those skilled in the art, it is still possible to make modifications to the technical solutions described in the above embodiments or perform equivalent substitutions for some of the technical features therein. Such modifications or substitutions do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions protected by the present invention.

Claims

1. An oil port resource optimization scheduling method in which a program of an oil port resource management system causes a controller to execute the method, A step S11 in which the controller establishes a lower-level scheduling model, The low-level scheduling model is as follows: [0010] Where: k is a berth, k=1, 2, ..., m, m is the number of berths of the oil port, T ｋ is the usage time of berth k within the set planning period, [0025] Where: 【number】 is the average usage time of all m berths within the set planning period, [0030] TR ｉ，ｊ，ｐ denotes the preparation time for transporting the jth oil of tanker i through pipeline p, p is a pipeline, p=1, 2, ..., q, q is the number of pipelines, j is an oil type, j=1, 2, ..., u, u is the number of oil types, i is a tanker, i=1, 2, ..., n, n is the number of tankers; TR ｉ，ｊ，ｐ =w ｉ，ｊ，ｐ TR ｐ , T.R. Ｐ is the preparation time for transporting oil through pipeline p, and w ｉ，ｊ，ｐ satisfies the following formula, that is, 【number】 TZ ｉ，ｊ，ｐ denotes the transportation time of oil j of tanker i transported through pipeline p, [0045] where E ｉ，ｊ，ｐ denotes the tonnage of oil j of tanker i transported through pipeline p, and v ｊ、ｐ denotes the flow rate of the jth oil transported through the pipeline p, 【number】 When the condition for transporting the jth oil of tanker i through pipeline p is satisfied, the sum of the preparation time and the transportation time is TR ｉ，ｊ，ｐ +TZ ｉ，ｊ，ｐ Show that 【number】 denotes the maximum sum of preparation time and transportation time when transporting the jth oil of the tanker i through the pipeline p, 【number】 Step S11, in which t denotes the minimum value of the sum of the preparation time and the transportation time when transporting the j-th oil of the tanker i through the pipeline p; A step S12 in which the controller establishes a high-level scheduling model, The high-level scheduling model is as follows: [0050] Here, u ｉ，ｊ denotes the price per ton paid by tanker i to unload the jth oil, E ｉ，ｊ denotes the tonnage of the jth oil unloaded by tanker i, 【number】 and L Ｐ denotes the length of the pipeline p, PC ｊ，ｐ denotes the unit cost of transporting the jth oil through pipeline p, E ｉ，ｊ，ｐ denotes the tonnage of the jth oil of tanker i transported through pipeline p, DC ｉ indicates the penalty cost for the tanker i's dwell time, TL ｉ，ｋ indicates the departure time of tanker i after the work at berth k is completed, T.D. ｉ denotes the latest allowable departure time for tanker i, T.A. ｉ indicates the arrival time of the tanker i, step S12; A step S13 in which the controller uses a multi-objective evolutionary algorithm to find an optimal solution of the lower-level scheduling model and the upper-level scheduling model; Step S14 in which the controller determines a target berth and a berthing sequence of the tanker based on the obtained optimal solution; An oil port resource optimization scheduling method comprising:

2. The lower-level scheduling model further includes the following constraints: The constraints for a tanker to berth are as follows: [006] and The tanker load constraints are: [0070] And W ｉ denotes the loaded weight of tanker i, and W ｋ indicates the berthing capacity of berth k, The pipeline constraints are: [0080] where s is a tank, s=1, 2, ..., r, r is the number of tanks, 【number】 and The constraints on the unloading capacity are: [0090] where F ｓ is the remaining capacity of tank s, 【number】 and E ｉ，ｊ is the tonnage of oil j unloaded from tanker i, The constraints for the unloading goal are: [0010] 2. The oil port resource optimization scheduling method according to claim 1, wherein the program of the oil port resource management system causes a controller to execute the method.

3. The lower-level scheduling model further includes the following constraints: The pipeline constraints are: ##EQU00011## and Where: 【number】 and The permission constraints for berth approval are: ##EQU00012## where: T.B. ｉ＋１，ｋ indicates the time when the i+1th tanker arrives at berth k, TL ｉ，ｋ indicates the departure time of tanker i after the work at berth k is completed, The departure constraints for unloading completion are: ##EQU00013## and Here, T.L. ｉ，ｋ indicates the departure time of tanker i after the work is completed at berth k, and TB ｉ，ｋ indicates the time when the i-th tanker arrives at berth k, 【number】 denotes the maximum value of the sum of the preparation time and the transportation time for transporting the jth oil of the tanker i through the pipeline p.

3. An oil port resource optimization scheduling method that is executed by a controller by a program of the oil port resource management system according to claim 1 or 2.

4. The step of the controller using the multi-objective evolutionary algorithm to find the optimal solution of the lower-level scheduling model and the upper-level scheduling model includes the following steps, namely: Step S21: indicating, in binary, a berth number corresponding to a feasible solution of the lower-level scheduling model; A step S22 of setting a size of an initial population, randomly selecting individuals of the initial population in a feasible solution of the lower-level scheduling model, and defining the initial population as pop1; A step S23 of calculating the fitness of the sample individuals; A step S24 of performing a selection operation based on the fitness of the sample individuals to select an individual sample; A step S25 of performing an intersection operation on the individual samples after the selection operation; A step S26 of performing a mutation operation on the individual sample after the crossover operation; Step S27: determining whether an iteration condition is satisfied, and if the iteration condition is satisfied, terminating the calculation and obtaining an optimized population pop1-opt of the lower-level scheduling model; if the iteration condition is not satisfied, using the individual sample after the mutation operation as the next generation population, and repeatedly executing steps S23 to S26 until the iteration condition is satisfied; E corresponding to each individual of the optimized population pop1-opt ｉ，ｊ，ｐ Calculating E ｉ，ｊ，ｐ step S28, where p denotes the tonnage of oil j of tanker i transported through pipeline p; The calculated E ｉ，ｊ，ｐ into the upper level scheduling model; a step S30 of representing in binary form tanker berthing sequences corresponding to feasible solutions of the upper level scheduling model; Step S31: setting an initial population size, randomly selecting individuals of the initial population in a feasible solution of the upper-level scheduling model, and defining the initial population as pop2; A step S32 of calculating the fitness of the sample individuals; A step S33 of performing a selection operation based on the fitness of the sample individuals to select individual samples; A step S34 of performing a crossover operation on the sample individuals after the selection operation; A step S35 of performing a mutation operation on the sample individual after the crossover operation; If the iteration condition is satisfied, the calculation is terminated, and the optimized population pop2-opt of the upper level scheduling model is obtained. If the iteration condition is not satisfied, the individual sample after the mutation operation is used as the next generation population, and steps S32 to S36 are repeatedly executed until the iteration condition is satisfied. and S37. decoding the optimized population of the upper level scheduling model to obtain tanker target berths and berthing sequences.

2. An oil port resource optimization scheduling method according to claim 1, wherein the program of the oil port resource management system causes a controller to execute the method.

5. When the crossover calculation is performed in step S25 or step S34, the crossover probability is determined by the following formula: ##EQU00015## where p is the crossover probability, and p ｍｉｎ is the minimum value of the set crossing probability interval, and p ｍａｘ is the maximum value of the set crossover probability interval, f′ is the individual with the greater fitness among the two individuals involved in the crossover operation, and f ａｖｇ 5. The oil port resource optimization scheduling method according to claim 4, characterized in that the program of the oil port resource management system causes the controller to execute the method.

6. When the mutation operation is performed in step S26 or step S35, the mutation probability is determined by the following formula: ##EQU00016## where pm is the mutation probability and pm ｍｉｎ indicates the minimum value of the set mutation probability interval, and pm ｍａｘ indicates the maximum value of the set mutation probability interval, and f ｍｉｎ indicates the fitness minimum in the population of sample individuals after the crossover operation, and f ａｖｇ An oil port resource optimization scheduling method as described in claim 4, characterized in that the program of the oil port resource management system causes a controller to execute, where f indicates the average fitness value in the population of sample individuals after the crossover operation, and f indicates the fitness value of a mutant individual.

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