Bulk shipping ship cargo matching method, device and equipment based on heuristic algorithm and medium

Abstract

The invention relates to the technical field of waterway transportation, in particular to a bulk cargo ship cargo matching method, device, equipment and medium based on a heuristic algorithm, and the method comprises the steps: obtaining the current bulk cargo ship route loading data, and recognizing the bulk cargo return route cargo quantity according to the current bulk cargo ship route loading data; generating a market quotation index according to the bulk transportation return goods quantity, and analyzing according to the current market quotation index to obtain goods package demand data; when a goods package completion message triggered according to the goods package demand data is obtained, a preset ship constraint condition is obtained, and feasible ship information is obtained according to the ship constraint condition; calculating a ship scheduling initial scheme according to the feasible ship information, and calculating the ship scheduling initial scheme to obtain a to-be-matched ship scheme; and simulating each to-be-matched ship scheme, generating a ship scheme sequence according to a simulation result, and generating a ship scheduling scheme according to the ship scheme sequence. The method has the effect of solving the problems of low efficiency and slow decision of bulk cargo ships in the aspect of ship and cargo matching.

Description

Technical Field

[0001] The present application relates to the technical field of water transport, and in particular to a bulk cargo matching method, device, equipment and medium based on a heuristic algorithm. Background Art

[0002] At present, route planning refers to the reasonable arrangement of the voyage tasks that ships need to perform in each time period under the premise of meeting shipping needs, so as to achieve the best economic benefits and service quality. Unlike the stable liner route transportation of containers, bulk carrier transportation often has the characteristics of diverse cargo types but concentrated cargo owners and flexible route adjustment. In particular, when bulk carriers perform coastal coal transportation business, there are a large number of immediate ship-cargo matching needs. At present, they mostly rely on senior business personnel to complete the ship-cargo matching operation with the help of past business experience.

[0003] The above-mentioned prior art solutions have the following defects: Faced with market demand fluctuations and uncertainties in cargo sources, there are multiple internal and external factors intertwined, and ship scheduling work mainly relies on manual operation, which is time-consuming and labor-intensive. It is difficult to process massive amounts of data in a short period of time and compare to obtain the most efficient plan. Summary of the invention

[0004] In order to solve the problems of low efficiency and slow decision-making in bulk carriers in ship-cargo matching, the present application provides a bulk carrier-cargo matching method, device, equipment and medium based on a heuristic algorithm.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A bulk cargo matching method based on a heuristic algorithm, the bulk cargo matching method based on a heuristic algorithm comprising: obtaining current bulk cargo ship route loading data, identifying the quantity of bulk return cargo according to the current bulk cargo ship route loading data, generating a market index according to the quantity of bulk return cargo, and obtaining cargo collection demand data according to the current market index analysis; After obtaining a cargo collection completion message triggered by the cargo collection demand data, obtaining a preset ship constraint condition, and obtaining feasible ship information according to the ship constraint condition; Calculate an initial ship scheduling plan according to the feasible ship information, calculate the initial ship scheduling plan, and obtain a ship plan to be matched; Each of the to-be-matched ship plans is simulated, a ship plan ranking is generated according to the simulation results, and a ship scheduling plan is generated according to the ship plan ranking.

[0006] By adopting the above technical solution, by collecting and analyzing the loading data of the current bulk carrier routes, a market index is generated, and then the cargo collection demand data is analyzed, which can not only accurately reflect the market dynamics, but also provide a scientific basis for subsequent cargo collection operations, avoiding operational losses caused by misjudgment of the market situation; when the cargo collection completion message triggered by the cargo collection demand data is obtained, the preset ship constraints are obtained, and the feasible ship information that meets the conditions is screened out according to these constraints. By accurately obtaining the ship parameters and cargo parameters and screening them according to the ship constraints, the appropriate ship can be quickly and effectively selected for cargo loading, thereby ensuring that the ship is matched under the constraints of deadweight, cargo capacity, location, loading and unloading time, etc., and reducing the probability of matching failure due to violation of constraints; after determining the feasible ship information, the initial ship scheduling plan is calculated, and the initial ship scheduling plan is further calculated, and the ship plan to be matched is generated according to the dynamic adjustment mechanism, and the ship scheduling plan can be generated according to the ship plan sorting, thereby not only considering multiple factors such as market conditions, ship constraints, port environmental data, etc., but also fully evaluating the comprehensive benefits of different plans in the simulation calculation. Therefore, the generated ship scheduling plan can not only maximize the operating profit of the ship, but also reduce idle time, reduce fuel and operating costs, thereby significantly improving the overall operational efficiency and economic benefits. At the same time, it can also make the best decisions in different business scenarios and enhance the scientificity and rationality of decision support.

[0007] In a preferred example, the present application may be further configured as follows: upon obtaining a cargo collection completion message triggered by the cargo collection demand data, obtaining a preset ship constraint condition, and obtaining feasible ship information according to the ship constraint condition, specifically including: Obtaining ship parameter information and cargo parameter information, and calculating the ship parameter information and the cargo parameter information according to the ship constraint conditions to obtain corresponding constraint calculation results; The constraint calculation result is compared with the ship constraint condition, and the feasible ship information is obtained by screening according to the comparison result.

[0008] By adopting the above technical solution, after obtaining the cargo collection completion message, the preset ship constraint conditions are obtained, and the ship parameter information and cargo parameter information are calculated according to the ship constraint conditions to obtain the corresponding constraint calculation results, thereby reducing blind screening in the absence of constraints, ensuring the feasibility of matching between ships and cargoes, and significantly improving the matching accuracy.

[0009] In a preferred example, the present application may be further configured as follows: the calculating of the initial ship scheduling plan according to the feasible ship information specifically includes: The corresponding ship daily profit information is obtained according to the feasible ship information, and the information is sorted according to the ship daily profit information; and the initial ship scheduling plan is generated according to the sorting result from high to low of the ship daily profit information.

[0010] By adopting the above technical solution, the corresponding daily profit information of the ship is obtained according to the feasible ship information, and the ship daily profit information is sorted, which can intuitively reflect the profitability of different ships when performing different voyage tasks, so that the feasible ships can be sorted from high to low according to the calculated daily profit information of the ship, so as to give priority to ships with higher daily profit information in the subsequent ship-cargo matching process, thereby maximizing the overall economic benefits. By sorting the daily profit information of the ship and generating the initial ship scheduling plan accordingly, the present invention can improve the economy and rationality of the matching plan while meeting the ship constraints, give priority to ships with higher daily profit information to perform tasks, and ensure the maximization of the benefits of each voyage; on the other hand, the scheduling plan generated by this method is more in line with changes in market conditions and can dynamically adapt to operational needs under different market environments.

[0011] In a preferred example, the present application may be further configured as follows: the step of obtaining corresponding ship daily profit information according to the feasible ship information specifically includes: The voyage income, voyage variable cost, voyage operating day and voyage daily fixed cost are obtained from the feasible ship information; the ship daily profit information is calculated according to the following formula: Ship daily profit information = (voyage income - voyage variable costs) / voyage operating days - voyage daily fixed costs.

[0012] By adopting the above technical solution, key parameters such as specific voyage income, voyage variable costs, voyage operating days and voyage daily fixed costs are extracted from feasible ship information. Based on these parameters, the daily profit information of the ship is calculated using the formula. This calculation process can accurately evaluate the relationship between the cost and revenue of each ship voyage, thereby providing a reliable profit reference for subsequent scheduling plans.

[0013] In a preferred example, the present application can be further configured as follows: the calculation of the initial ship scheduling plan to obtain the ship plan to be matched specifically includes: Acquire port environment data, calculate the initial ship scheduling plan according to each of the port environment data, and record the success rate of each calculated result; The acceptance probability of inferior solutions is set, and the probability of selecting each calculation is dynamically adjusted according to the acceptance probability of inferior solutions and the success rate to obtain the ship solution to be matched.

[0014] By adopting the above technical solution, port environmental data is obtained, and the initial ship scheduling plan is calculated by analyzing each type of port environmental data, and the success rate of each calculation result is recorded. The success rate is the degree of conformity between the ship scheduling plan calculated in each environment and the actual effect. By recording these success rates, the scheduling effects in different environments can be dynamically reflected; an adaptive mechanism is introduced to dynamically adjust the influence weights of various port environmental data in the calculation process, and the probability of selecting each calculation is adaptively adjusted according to the previously calculated success rate to ensure that the ship scheduling plans generated in different environments can best adapt to the current environment; in addition, the acceptance probability of inferior solutions is set, allowing inferior solutions to be accepted with a certain probability during the calculation process. The probability is dynamically adjusted with the success rate during the calculation process. This dynamic adjustment mechanism avoids the search process from falling into the local optimal solution and enhances the robustness and global optimization ability of the search. This randomness and temperature control mechanism enables the system to find better ship scheduling plans in a large-scale search space.

[0015] The second object of the invention is achieved by the following technical solutions: A bulk cargo matching device based on a heuristic algorithm, the bulk cargo matching device based on a heuristic algorithm comprising: a cargo collection demand acquisition module, used to acquire current bulk cargo ship route loading data, identify the quantity of bulk return cargo according to the current bulk cargo ship route loading data, generate a market index according to the quantity of bulk return cargo, and obtain cargo collection demand data according to the current market index analysis; A feasible ship acquisition module is used to obtain a cargo collection completion message triggered by the cargo collection demand data, obtain preset ship constraints, and obtain feasible ship information according to the ship constraints; A scheduling calculation module, used to calculate an initial ship scheduling plan according to the feasible ship information, and calculate the initial ship scheduling plan to obtain a ship plan to be matched; The scheduling recommendation module is used to simulate each of the ship plans to be matched, generate a ship plan ranking according to the simulation results, and generate a ship scheduling plan according to the ship plan ranking.

[0016] By adopting the above technical solution, by collecting and analyzing the loading data of the current bulk carrier routes, a market index is generated, and then the cargo collection demand data is analyzed, which can not only accurately reflect the market dynamics, but also provide a scientific basis for subsequent cargo collection operations, avoiding operational losses caused by misjudgment of the market situation; when the cargo collection completion message triggered by the cargo collection demand data is obtained, the preset ship constraints are obtained, and the feasible ship information that meets the conditions is screened out according to these constraints. By accurately obtaining the ship parameters and cargo parameters and screening them according to the ship constraints, the appropriate ship can be quickly and effectively selected for cargo loading, thereby ensuring that the ship is matched under the constraints of deadweight, cargo capacity, location, loading and unloading time, etc., and reducing the probability of matching failure due to violation of constraints; after determining the feasible ship information, the initial ship scheduling plan is calculated, and the initial ship scheduling plan is further calculated, and the ship plan to be matched is generated according to the dynamic adjustment mechanism, and the ship scheduling plan can be generated according to the ship plan sorting, thereby not only considering multiple factors such as market conditions, ship constraints, port environmental data, etc., but also fully evaluating the comprehensive benefits of different plans in the simulation calculation. Therefore, the generated ship scheduling plan can not only maximize the operating profit of the ship, but also reduce idle time, reduce fuel and operating costs, thereby significantly improving the overall operational efficiency and economic benefits. At the same time, it can also make the best decisions in different business scenarios and enhance the scientificity and rationality of decision support.

[0017] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned bulk cargo matching method based on a heuristic algorithm when executing the computer program.

[0018] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned bulk cargo matching method based on a heuristic algorithm.

[0019] In summary, the present application includes at least one of the following beneficial technical effects: 1. By obtaining the current bulk cargo ship route loading data and generating a market index, combined with the market index analysis to obtain cargo demand data, the ship-cargo matching is completed automatically, the efficiency of ship-cargo matching is improved, and the time and labor cost of manual operation are reduced; 2. After obtaining the ship parameter information and cargo parameter information, compare and screen according to the ship constraint conditions to obtain feasible ship information, sort them according to the ship daily profit information, generate the initial ship scheduling plan, and ensure the efficiency and rationality of the matching plan; 3. By simulating the initial ship scheduling plans under different port environments, dynamically adjusting the probability of selecting each calculation, generating ship plans to be matched, and generating ship plan rankings based on simulation results, optimizing routes and ship-cargo matching plans, and improving overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of a bulk cargo matching method based on a heuristic algorithm in an embodiment of the present application; Figure 2 is a flowchart for implementing step S20 of the method for bulk cargo matching based on a heuristic algorithm in one embodiment of the present application; Figure 3 is a flowchart for implementing step S30 of the method for bulk cargo matching based on a heuristic algorithm in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S311 of the method for bulk cargo matching based on a heuristic algorithm in one embodiment of the present application; Figure 5 is another implementation flow chart of step S30 of the method for bulk cargo matching based on a heuristic algorithm in one embodiment of the present application; Figure 6 It is a principle block diagram of a bulk cargo matching system based on a heuristic algorithm in one embodiment of the present application; Figure 7 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The present application is further described in detail below in conjunction with the accompanying drawings.

[0022] In one embodiment, if Figure 1 As shown, the present application discloses a bulk cargo matching method based on a heuristic algorithm, which specifically includes the following steps: S10: obtaining the current bulk cargo ship route loading data, identifying the bulk return cargo quantity according to the current bulk cargo ship route loading data, generating a market index according to the bulk return cargo quantity, and obtaining cargo collection demand data according to the current market index analysis.

[0023] In this embodiment, the current bulk cargo ship route loading data refers to the ship AIS track data and cargo status data that have occurred within a period of time. Generally speaking, bulk cargo ships are mostly one-way transportation. Through the above AIS track data and cargo status, a typical transportation route a in which a ship carries a certain cargo from area A to area B can be identified. The loaded cargo of other voyages between the typical transportation routes is bulk return cargo b. The proportion of bulk return cargo within a period of time can reflect the bulk transportation market situation during that period. The market situation index h is calculated according to the formula h=1-b / (a+b).

[0024] Specifically, when cargo needs to be transported by ship, the freight rate index published by the shipping transaction and the current market sales of the cargo are combined as the current bulk cargo ship route loading data, and the corresponding market index is calculated through the model. Further, the cargo demand data is calculated and analyzed based on the current market index. For example, for coal transportation volume: A large h means good market conditions. In extreme cases, only coal routes can be used, p=α*h, q=β*h; the long-term coal transportation volume x does not change with the market and can be regarded as a constant X; the market coal transportation volume y, y=r*h; the market non-coal transportation volume z=n*Np*(x+y); n*N represents the transportation volume of non-coal goods through atypical coal routes under extremely bad market conditions (h=0). If the market conditions are taken into account, it will be affected by the transportation volume of typical routes. The calculated x, y, and z are used as the cargo collection demand data.

[0025] S20: After obtaining a cargo collection completion message triggered by cargo collection demand data, obtain preset ship constraints, and obtain feasible ship information according to the ship constraints.

[0026] Specifically, after the cargo collection demand data is calculated and the cargo collection completion message is triggered, the cargo is sorted according to priority, and for each shipment of cargo, the preset ship constraint conditions are obtained, and the ship that meets the conditions is obtained based on the ship constraint conditions, and the obtained ship information is used as the feasible ship information.

[0027] S30: Calculate an initial ship scheduling plan based on feasible ship information, calculate the initial ship scheduling plan, and obtain a ship plan to be matched.

[0028] Specifically, the transport profit of each ship is calculated according to the feasible ship information, and the cargo is allocated to the ship in descending order according to the calculated transport profit, as the initial ship scheduling plan. When constructing the initial ship scheduling plan, the method of calculating the initial ship scheduling plan in steps S20-S30 is cyclically executed until all cargoes are allocated or cannot be allocated anymore.

[0029] After obtaining the initial ship scheduling plan corresponding to all cargoes, the degree of conformity between the plan and the effect of the ship scheduling in each environment is calculated, and the matching ship plan is obtained based on the calculation results.

[0030] S40: Simulate each ship plan to be matched, generate a ship plan ranking according to the simulation results, and generate a ship scheduling plan according to the ship plan ranking.

[0031] Specifically, the ship plans to be matched are simulated in different priority modes, such as high freight rate priority, long-term contract fulfillment priority, long voyage priority, short voyage priority, coal cargo priority, and least empty load priority, to obtain the corresponding plan benefits, and then sort the benefits of the ship plans to be matched from high to low, and generate a ship scheduling plan based on the sorting results.

[0032] In this embodiment, by collecting and analyzing the loading data of the current bulk carrier route, a market index is generated, and then the cargo collection demand data is analyzed, which can not only accurately reflect the market dynamics, but also provide a scientific basis for subsequent cargo collection operations, avoiding operational losses caused by market market misjudgment; when the cargo collection completion message triggered by the cargo collection demand data is obtained, the preset ship constraints are obtained, and the feasible ship information that meets the conditions is screened out according to these constraints. By accurately obtaining the ship parameters and cargo parameters and screening them according to the ship constraints, a suitable ship can be quickly and effectively selected for cargo loading, thereby ensuring that the ship is matched under the constraints of deadweight, cargo capacity, location, loading and unloading time, etc., and reducing the probability of matching failure due to violation of constraints; after determining the feasible ship information, the initial ship scheduling plan is calculated, and the initial ship scheduling plan is further calculated, and the ship plan to be matched is generated according to the dynamic adjustment mechanism, and the ship scheduling plan can be generated according to the ship plan sorting, so that not only the market conditions, ship constraints, port environmental data and other factors are considered, but also the comprehensive benefits of different plans are fully evaluated in the simulation calculation. Therefore, the generated ship scheduling plan can not only maximize the operating profit of the ship, but also reduce idle time, reduce fuel and operating costs, thereby significantly improving the overall operational efficiency and economic benefits. At the same time, it can also make the best decisions in different business scenarios and enhance the scientificity and rationality of decision support.

[0033] In one embodiment, if Figure 2 As shown, in step S20, that is, when the cargo collection completion message triggered by the cargo collection demand data is obtained, the preset ship constraint conditions are obtained, and the feasible ship information is obtained according to the ship constraint conditions, specifically including: S21: obtaining ship parameter information and cargo parameter information, calculating the ship parameter information and cargo parameter information according to the ship constraint conditions, and obtaining corresponding constraint calculation results.

[0034] Specifically, the type of the ship constraint is obtained, for example, deadweight, cargo capacity, location, loading period, cargo type, draft restriction, etc., and the information of each ship and cargo is obtained according to the type of the ship constraint as ship parameter information and cargo parameter information.

[0035] Furthermore, corresponding calculations are performed based on the acquired ship parameter information and cargo parameter information to obtain data for determining whether the constraint conditions of each type of ship are met, as the constraint calculation result.

[0036] S22: Compare the constraint calculation results with the ship constraint conditions, and select feasible ship information according to the comparison results.

[0037] Specifically, after the constraint calculation result is calculated, the constraint calculation result is compared with the corresponding ship constraint condition, for example, for the ship load constraint: 90%*W i ≤w j ≤,W i represents the maximum load of ship i, w j represents the weight of cargo j; for loading period constraints: t j,m,start ≤z i,j,m ≤t j.m,end , z i,j,m represents the time when ship i carrying cargo j arrives at port m, t j,m,start and t j.m,end represents the loading period of cargo j at port m.

[0038] Furthermore, after comparison, the ship information whose constraint calculation result satisfies the constraint condition is taken as the feasible ship information.

[0039] In one embodiment, if Figure 3 As shown, in step S30, the initial ship scheduling plan is calculated according to the feasible ship information, which specifically includes: S311: Obtain corresponding ship daily profit information according to feasible ship information, and sort according to the ship daily profit information.

[0040] Specifically, the corresponding revenue data is obtained from the feasible ship information obtained through screening, so as to calculate the ship daily profit information of each ship. Furthermore, after the ship daily profit is calculated, the profit values ​​are sorted.

[0041] S312: Generate an initial ship scheduling plan based on the sorting results of the ship daily profit information from high to low.

[0042] Specifically, the ship that carries the cargo is identified in descending order of the profit information of the ship as an initial scheduling plan for the ship.

[0043] In one embodiment, if Figure 4 As shown, in step S311, the corresponding ship daily profit information is obtained according to the feasible ship information, which specifically includes: S3111: Obtain voyage revenue, voyage variable costs, voyage operating days and voyage daily fixed costs from feasible ship information.

[0044] Specifically, according to the feasible ship information, the voyage income, voyage variable cost, voyage operating days and voyage daily fixed cost of each ship are obtained from historical data.

[0045] S3112: Calculate the daily profit information of the ship according to the following formula: Ship daily profit information = (voyage income - voyage variable costs) / voyage operating days - voyage daily fixed costs.

[0046] Specifically, the calculation formula for the ship's daily profit information is calculated by the following function: Among them, p j Freight rate for cargo j, k j The priority of cargo j, w j The weight of cargo j, W i Maximum deadweight of ship i, D m The draft of port m corresponds to the load limit, [t i,m,start ,t j,m,end ] The loading period of cargo j at port m, d m,n The distance from port m to port n, t m,n Estimated sailing time from port m to port n, l i,m The loading time of ship i at port m, u i,n The time for vessel i to unload at port n, C i The daily fixed cost of the ship b1 is the profit coefficient, c1, c2, c3, c4 are the corresponding cost coefficients, the cargo set C = {1, 2, ..., m}, the ship set S = {1, 2, ..., n}, and the port set P = {1, 2, ..., k}.

[0047] In one embodiment, if Figure 5 As shown, in step S30, the initial ship scheduling plan is calculated to obtain a ship plan to be matched, which specifically includes: S321: Acquire port environment data, calculate the initial ship scheduling plan according to each port environment data, and record the success rate of each calculated result.

[0048] Specifically, the environmental data of the port in the past period of time is obtained, such as weather conditions, channel blockage conditions, port congestion level and other information.

[0049] Furthermore, an adaptive mechanism is adopted to retain an operation pool, in which the initial ship scheduling plan is calculated according to each different port environment data, and the success rate of each calculation result is recorded. The success rate is the degree of conformity between the ship scheduling plan calculated in each environment and the actual effect.

[0050] S322: Setting the acceptance probability of inferior solutions, dynamically adjusting the probability of selecting each calculation according to the acceptance probability of inferior solutions and the success rate, and obtaining the ship solution to be matched.

[0051] Specifically, a simulated annealing mechanism is adopted to set an allowable acceptance probability of inferior solutions. The probability of selecting each calculation is dynamically adjusted according to the acceptance probability and success rate of inferior solutions to obtain the ship solution to be matched, so as to jump out of the local optimal solution.

[0052] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0053] In one embodiment, a bulk cargo matching device based on a heuristic algorithm is provided, and the bulk cargo matching device based on a heuristic algorithm corresponds one-to-one to the bulk cargo matching method based on a heuristic algorithm in the above embodiment. Figure 6 As shown, the bulk cargo matching device based on heuristic algorithm includes a cargo demand acquisition module, a feasible ship acquisition module, a scheduling calculation module and a scheduling recommendation module. The detailed description of each functional module is as follows: The cargo collection demand acquisition module is used to obtain the current bulk cargo ship route loading data, generate a market index based on the current bulk cargo ship route loading data, and obtain the cargo collection demand data based on the current market index analysis; A feasible ship acquisition module is used to obtain the cargo collection completion message triggered by the cargo collection demand data, obtain the preset ship constraint conditions, and obtain the feasible ship information according to the ship constraint conditions; The scheduling calculation module is used to calculate the initial ship scheduling plan according to the feasible ship information, calculate the initial ship scheduling plan, and obtain the ship plan to be matched; The scheduling recommendation module is used to simulate each ship plan to be matched, generate a ship plan ranking based on the simulation results, and generate a ship scheduling plan based on the ship plan ranking.

[0054] Optional, feasible ship acquisition modules include: The constraint condition calculation submodule is used to obtain the ship parameter information and the cargo parameter information, calculate the ship parameter information and the cargo parameter information according to the ship constraint conditions, and obtain the corresponding constraint calculation results; The feasible ship acquisition submodule is used to compare the constraint calculation results with the ship constraint conditions, and obtain feasible ship information based on the comparison results.

[0055] Optionally, the scheduling calculation module includes: The profit calculation submodule is used to obtain the corresponding ship daily profit information according to the feasible ship information and sort according to the ship daily profit information; The scheme sorting submodule is used to generate an initial ship scheduling scheme based on the sorting results of the ship daily profit information from high to low.

[0056] Optionally, the profit calculation submodule includes: The ship revenue acquisition unit is used to obtain voyage revenue, voyage variable cost, voyage operating day and voyage daily fixed cost from feasible ship information; The ship daily profit calculation unit is used to calculate the ship daily profit information according to the following formula: Ship daily profit information = (voyage income - voyage variable costs) / voyage operating days - voyage daily fixed costs.

[0057] Optionally, the scheduling calculation module includes: The adaptive calculation submodule is used to obtain port environment data, calculate the initial ship scheduling plan according to each port environment data, and record the success rate of each calculated result; The simulated annealing mechanism calculation submodule is used to set the acceptance probability of inferior solutions, dynamically adjust the probability of selecting each calculation according to the acceptance probability and success rate of inferior solutions, and obtain the ship solution to be matched.

[0058] For the specific definition of the bulk cargo matching device based on heuristic algorithm, please refer to the definition of the bulk cargo matching method based on heuristic algorithm in the above text, which will not be repeated here. Each module in the above-mentioned bulk cargo matching device based on heuristic algorithm can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0059] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a bulk cargo matching method based on a heuristic algorithm is implemented.

[0060] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain the current bulk cargo ship route loading data, generate a market index based on the current bulk cargo ship route loading data, and obtain cargo collection demand data based on the current market index analysis; After obtaining the cargo collection completion message triggered by the cargo collection demand data, obtain the preset ship constraint conditions, and obtain feasible ship information according to the ship constraint conditions; Calculate the initial ship scheduling plan according to the feasible ship information, calculate the initial ship scheduling plan, and obtain the ship plan to be matched; Simulate each ship plan to be matched, generate a ship plan ranking based on the simulation results, and generate a ship scheduling plan based on the ship plan ranking.

[0061] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain the current bulk cargo ship route loading data, generate a market index based on the current bulk cargo ship route loading data, and obtain cargo collection demand data based on the current market index analysis; After obtaining the cargo collection completion message triggered by the cargo collection demand data, obtain the preset ship constraint conditions, and obtain feasible ship information according to the ship constraint conditions; Calculate the initial ship scheduling plan according to the feasible ship information, calculate the initial ship scheduling plan, and obtain the ship plan to be matched; Simulate each ship plan to be matched, generate a ship plan ranking based on the simulation results, and generate a ship scheduling plan based on the ship plan ranking.

[0062] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0063] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0064] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A bulk cargo matching method based on heuristic algorithm, characterized in that: The bulk cargo matching method based on heuristic algorithm includes: Acquire current bulk cargo ship route loading data, identify the quantity of bulk return cargo according to the current bulk cargo ship route loading data, generate a market index according to the quantity of bulk return cargo, and obtain cargo collection demand data according to the current market index analysis; After obtaining a cargo collection completion message triggered by the cargo collection demand data, obtaining a preset ship constraint condition, and obtaining feasible ship information according to the ship constraint condition; Calculate an initial ship scheduling plan according to the feasible ship information, calculate the initial ship scheduling plan, and obtain a ship plan to be matched; Simulate each of the to-be-matched ship plans, generate a ship plan ranking according to the simulation results, and generate a ship scheduling plan according to the ship plan ranking.

2. The bulk cargo matching method based on heuristic algorithm according to claim 1 is characterized in that: The method of obtaining a cargo collection completion message triggered by the cargo collection demand data, obtaining a preset ship constraint condition, and obtaining feasible ship information according to the ship constraint condition specifically includes: Obtaining ship parameter information and cargo parameter information, and calculating the ship parameter information and the cargo parameter information according to the ship constraint conditions to obtain corresponding constraint calculation results; The constraint calculation result is compared with the ship constraint condition, and the feasible ship information is obtained by screening according to the comparison result.

3. The bulk cargo matching method based on heuristic algorithm according to claim 2 is characterized in that: The calculating of the initial ship scheduling plan according to the feasible ship information specifically includes: Acquire corresponding ship daily profit information according to the feasible ship information, and sort according to the ship daily profit information; The initial ship scheduling plan is generated according to the sorting results of the ship daily profit information from high to low.

4. The bulk cargo matching method based on heuristic algorithm according to claim 3 is characterized in that: The obtaining corresponding ship daily profit information according to the feasible ship information specifically includes: Obtaining voyage revenue, voyage variable cost, voyage operating days and voyage daily fixed cost from the feasible vessel information; The daily profit information of the ship is calculated according to the following formula: Ship daily profit information = (voyage income - voyage variable costs) / voyage operating days - voyage daily fixed costs.

5. The bulk cargo matching method based on heuristic algorithm according to claim 1, characterized in that: The calculating of the initial ship scheduling plan to obtain the ship plan to be matched specifically includes: Acquire port environment data, calculate the initial ship scheduling plan according to each of the port environment data, and record the success rate of each calculated result; The acceptance probability of inferior solutions is set, and the probability of selecting each calculation is dynamically adjusted according to the acceptance probability of inferior solutions and the success rate to obtain the ship solution to be matched.

6. A bulk cargo matching device based on heuristic algorithm, characterized in that: The bulk cargo matching device based on heuristic algorithm comprises: A cargo collection demand acquisition module is used to obtain current bulk cargo ship route loading data, identify the quantity of bulk return cargo according to the current bulk cargo ship route loading data, generate a market index according to the quantity of bulk return cargo, and obtain cargo collection demand data according to the current market index analysis; A feasible ship acquisition module is used to obtain a cargo collection completion message triggered by the cargo collection demand data, obtain preset ship constraints, and obtain feasible ship information according to the ship constraints; A scheduling calculation module, used to calculate an initial ship scheduling plan according to the feasible ship information, and calculate the initial ship scheduling plan to obtain a ship plan to be matched; The scheduling recommendation module is used to simulate each of the ship plans to be matched, generate a ship plan ranking according to the simulation results, and generate a ship scheduling plan according to the ship plan ranking.

7. The bulk cargo matching device based on heuristic algorithm according to claim 6, characterized in that: The feasible ship acquisition module includes: A constraint condition calculation submodule is used to obtain ship parameter information and cargo parameter information, calculate the ship parameter information and cargo parameter information according to the ship constraint conditions, and obtain corresponding constraint calculation results; The feasible ship acquisition submodule is used to compare the constraint calculation result with the ship constraint condition, and obtain the feasible ship information according to the comparison result.

8. The bulk cargo matching device based on heuristic algorithm according to claim 7, characterized in that: The scheduling calculation module includes: A profit calculation submodule, used for obtaining corresponding ship daily profit information according to the feasible ship information, and sorting according to the ship daily profit information; The scheme sorting submodule is used to generate the initial ship scheduling scheme according to the sorting results of the ship daily profit information from high to low.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the bulk cargo matching method based on a heuristic algorithm as claimed in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the bulk cargo matching method based on a heuristic algorithm as claimed in any one of claims 1 to 5 are implemented.

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