A port ship scheduling optimization method and device and a storage medium

Abstract

The application discloses a port ship scheduling optimization method and device and a storage medium. The method comprises the following steps: a ship scheduling optimization mixed integer linear programming model is established according to a pre-constructed ship scheduling objective function and a constraint condition; a genetic algorithm is used to solve the ship scheduling optimization mixed integer linear programming model to obtain a ship scheduling optimization scheme; wherein the ship scheduling objective function is obtained according to a pre-determined ship scheduling optimization demand; and the constraint condition comprises a ship arrival time conflict constraint, a berth and loading sequence conflict constraint, a ship and berth matching constraint and a ship time constraint. The ship scheduling optimization scheme obtained by using the method provided in the embodiment of the application can guide the in-out port scheduling and berth allocation of the ship, help to improve the utilization efficiency of the wharf berth, and ensure the efficiency of the coal and iron port integrated operation.

Description

Technical Field

[0001] This invention relates to a method, apparatus, and storage medium for optimizing port vessel scheduling, belonging to the field of vessel scheduling technology. Background Technology

[0002] In the integrated coal transportation process of large energy enterprises, port operations are relatively efficient, with some large coal ports often having a turnaround time of less than three days. Therefore, any port congestion caused by special factors will lead to port inventory buildup, directly affecting the efficiency of integrated coal transportation. Thus, when coal port inventory is too high, it is necessary to quickly reduce inventory to meet the port's unloading needs for coal from upstream railways.

[0003] Currently, vessel scheduling at coal ports is often based on matching vessels to berths according to their arrival order. Scheduling personnel lack consideration for the integrated supply chain and the urgency of vessel arrivals when formulating berth plans, and scheduling often relies on manual experience. Especially with high inventory levels at coal ports, how to rationally schedule vessels and allocate berths to improve berth utilization efficiency and rapidly reduce inventory is of great significance for integrated coal rail-port intermodal transport. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a port vessel scheduling optimization method, device and storage medium that can improve the utilization efficiency of terminal berths.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a port vessel scheduling optimization method, the method comprising the following steps:

[0007] Based on the pre-constructed objective function and constraints for ship scheduling, a mixed-integer linear programming model for ship scheduling optimization is established.

[0008] The ship scheduling optimization mixed integer linear programming model is solved using a genetic algorithm to obtain the ship scheduling optimization scheme;

[0009] The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints.

[0010] In conjunction with the first aspect, the ship scheduling objective function is further constructed and obtained based on the predetermined ship scheduling optimization requirements, including:

[0011] The objective functions are constructed respectively with the following objectives: maximizing the loading capacity within the shortest time, maximizing the loading capacity within the optimization period, minimizing the changes in loading sequence and berth, and minimizing the delay in port entry of ships to power plants in urgent need.

[0012] Determine the corresponding weights based on the importance of each objective function;

[0013] The final ship scheduling objective function is obtained by weighted summation of each objective function and its corresponding weight.

[0014] Building upon the first aspect, the objective function f1, which aims to maximize the ship's cargo load within the shortest possible time, is expressed as follows:

[0015]

[0016] In the formula, f1 represents the maximum load capacity loaded onto the ship in the most recent time, Y represents the most recent time; x ibk Let x represent the probability that the i-th ship is loaded at berth b in order k. ibk When x is 0, it indicates that the i-th ship is not loaded at berth b in sequence k; ibk When y is 1, it means that the i-th ship is not loaded in order k at berth b; I represents the number of ships participating in the loading process, y i V represents the berthing time of the i-th ship. i This represents the load capacity of the i-th ship.

[0017] The objective function f2, which aims to maximize the loading capacity within the optimization cycle, is expressed as follows:

[0018]

[0019] In the formula, f2 represents the maximum load capacity of the ship within the optimization period;

[0020] The objective function f3, which aims to minimize the changes in loading sequence and berth, is expressed as follows:

[0021]

[0022] In the formula, f3 represents the minimum change relative to the original calculated loading sequence and loading berth. Let represent the probability that the i-th ship is originally scheduled to load at berth b in the k-th order. When the value is 1, it means that the i-th ship was originally scheduled to be loaded at berth b in sequence k. When the value is 0, it means that the i-th ship was not originally scheduled to be loaded at berth b in sequence k.

[0023] The objective function f4, which aims to minimize the delay in the arrival of ships at the emergency power plant, is expressed as follows:

[0024]

[0025] In the formula, This represents the expected arrival time of the i-th emergency power plant vessel.

[0026] The expression for the ship scheduling objective function f is as follows:

[0027] f=ω1f1+ω2f2+ω3f3+ω4f4 (6)

[0028] Where ω1, ω2, ω3, and ω4 represent weight coefficients, and according to the importance of the objective function, ω4>ω1>ω2>ω3.

[0029] In conjunction with the first aspect, furthermore, the berth and loading sequence conflict constraints include: a constraint that each vessel can be served at most once; a constraint that each berth can serve at most one vessel per sequence; and a constraint that a vessel can only be scheduled for the next sequence if a vessel has been loaded in the previous sequence at each berth. The corresponding constraint expressions are as follows:

[0030]

[0031]

[0032]

[0033] In the formula, B represents the total number of berths in the port, and K represents the maximum sequential number for each berth;

[0034] The vessel arrival time conflict constraint means that the vessel's arrival time must not be later than its arrival confirmation time, and its constraint expression is as follows:

[0035] y i ≥A i (10)

[0036] In the formula, A i This indicates the arrival confirmation time of the i-th ship;

[0037] The vessel time constraints include: the constraint that vessels departing from berth within the optimization period must also enter berth within the optimization period; the constraint that at most one vessel can enter berth at each berth within the optimization period but not depart within the optimization period; and the constraint that the total loading time of vessels departing from each berth within the optimization period must not exceed the optimization period. The corresponding constraint expressions are as follows:

[0038] x ibk ≥y ibk (11)

[0039]

[0040]

[0041] In the formula, y ibk Let y represent the probability that the i-th vessel, loaded at berth b in sequence k, departs before the end of the optimization period. ibk When y is 0, it means that the i-th vessel is not loaded at berth b in sequence k or does not depart within the optimization period; ibk When the value is 1, it means that the i-th ship is loaded at the b-th berth in sequence k and departs within the optimization period; T represents the time when the b-th berth becomes available. end Δt represents the time when the optimization cycle ends. ib This indicates the loading time of the i-th vessel at the b-th berth;

[0042] The vessel-berth matching constraint means that the vessel's draft must match the berth's allowable draft. The corresponding constraint expression is:

[0043]

[0044] In the formula, D i D represents the draft of ship i when fully loaded. b This represents the permissible draft of berth b, where M is a constant.

[0045] In conjunction with the first aspect, the expression for the mixed-integer linear programming model for ship scheduling optimization is as follows:

[0046]

[0047] In the formula, x and y are the corresponding variables in each constraint condition, and h(x,y)≤0 and g(x,y)=0 represent the inequality constraints and equality constraints in the constraint conditions, respectively.

[0048] In conjunction with the first aspect, furthermore, the method of using a genetic algorithm to solve the mixed-integer linear programming model for ship scheduling optimization includes:

[0049] Step A: Use the loading berth, berthing order, and berthing time of each ship as optimization variables, and encode the optimization variables of all ships to form a one-dimensional genetic code;

[0050] Step B: Initialize the genetic code under random rules to generate an initial population;

[0051] Step C: Calculate the fitness of the initial population and use the roulette wheel method to select parent pairs;

[0052] Step D: Perform crossover and mutation operations on the paired parents to generate a new population;

[0053] Step E: Perform gene repair on the new population according to the constraints;

[0054] Step F: Return to step B and iterate continuously until the error requirement is met, thus forming the optimal solution.

[0055] In conjunction with the first aspect, further, the gene repair of the new population according to the constraints includes:

[0056] The genetic code corresponding to the new population is decoded. If the berth corresponding to any ship is not in the ship's list of available berths, a new berth is selected from the list of available berths.

[0057] In conjunction with the first aspect, further, the gene repair of the new population according to the constraints includes:

[0058] The genetic code corresponding to the new population is decoded, all ships are classified according to berth, and a set of berth loading order is constructed.

[0059] For each ship, iterate through it and determine if the ship's loading order is in the set. If not, add the loading order to the set. If it is in the set, randomly select a loading order from those not in the set and add it to the set. If the set is full or all ships have been traversed, end the gene repair process.

[0060] Secondly, the present invention provides a port vessel scheduling optimization device, the device comprising:

[0061] Model building module: used to build a mixed-integer linear programming model for ship scheduling optimization based on a pre-constructed ship scheduling objective function and constraints;

[0062] Solution module: Used to solve the mixed integer linear programming model for ship scheduling optimization using a genetic algorithm to obtain the ship scheduling optimization scheme;

[0063] The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints.

[0064] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0065] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0066] The method provided in this embodiment of the invention establishes a mixed-integer linear programming model for ship scheduling optimization based on a pre-constructed ship scheduling objective function and constraints; it then uses a genetic algorithm to solve the mixed-integer linear programming model to obtain an optimized ship scheduling scheme. The ship scheduling objective function is constructed based on pre-determined ship scheduling optimization requirements. The constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship-berth matching constraints, and ship time constraints. The optimized ship scheduling scheme obtained using the method provided in this embodiment of the invention can guide ship arrival and departure scheduling and berth allocation, helping to improve the utilization efficiency of terminal berths and ensure the efficiency of integrated coal and railway port operations. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the single-ship berthing time provided in an embodiment of the present invention;

[0068] Figure 2 This is a flowchart of a port vessel scheduling optimization method provided in an embodiment of the present invention;

[0069] Figure 3 This is a flowchart of a method for solving a mixed-integer linear programming model for ship scheduling optimization using a genetic algorithm, provided by an embodiment of the present invention.

[0070] Figure 4 This is a flowchart of a gene repair method provided in an embodiment of the present invention when the loading order does not meet the constraints.

[0071] Figure 5 This is a schematic diagram illustrating the iterative convergence of the hyperparameter settings of the genetic algorithm provided in this embodiment of the invention;

[0072] Figure 6 This is a schematic diagram of the optimized results of ship scheduling and berth allocation provided by an embodiment of the present invention. Detailed Implementation

[0073] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0074] Example 1:

[0075] Figure 2 This is a flowchart of a port vessel scheduling optimization method according to Embodiment 1 of the present invention. This flowchart only illustrates the logical sequence of the method described in this embodiment. Provided there are no conflicts, different methods may be used in other possible embodiments of the present invention. Figure 2 Complete the steps shown or described in the order indicated.

[0076] The method provided in this embodiment can be applied to a terminal and can be executed by a port vessel scheduling optimization device. This device can be implemented in software and / or hardware and can be integrated into the terminal, such as any smartphone, tablet, or computer device with communication capabilities. See also Figure 2 The method implemented in this way specifically includes the following steps:

[0077] Step 1: Based on the pre-constructed objective function and constraints for ship scheduling, establish a mixed-integer linear programming model for ship scheduling optimization;

[0078] Step 2: Use a genetic algorithm to solve the mixed-integer linear programming model for ship scheduling optimization to obtain the ship scheduling optimization scheme;

[0079] The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints.

[0080] It should be understood that the ship scheduling optimization requirements and the constraints can be adjusted according to actual needs.

[0081] Taking a coal port as an example, the objective function under high inventory is generally constructed by comprehensively considering the loading sequence of ships and the situation of power plants in urgent need, with the goal of rapidly reducing port coal inventory and maximizing ship loading capacity within the optimization period. Therefore, as an embodiment of the present invention, the method for constructing and obtaining the ship scheduling objective function based on predetermined ship scheduling optimization requirements includes:

[0082] The objective functions are constructed respectively with the following objectives: maximizing the loading capacity within the shortest time, maximizing the loading capacity within the optimization period, minimizing the changes in loading sequence and berth, and minimizing the delay in port entry of ships to power plants in urgent need.

[0083] Determine the corresponding weights based on the importance of each objective function;

[0084] The final ship scheduling objective function is obtained by weighted summation of each objective function and its corresponding weight.

[0085] The following details the construction methods of the objective functions, with the objectives of maximizing the loading capacity within the shortest time, maximizing the loading capacity within the optimization period, minimizing changes in loading sequence and berth, and minimizing the delay in port entry for ships at emergency power plants:

[0086] The objective function f1, which aims to maximize the ship's cargo load in the shortest possible time, is expressed as follows:

[0087]

[0088] In the formula, f1 represents the maximum loading capacity within the most recent time, Y represents the most recent time, and ship scheduling generally requires inventory to be reduced as quickly as possible within 24 hours; therefore, Y can be taken as the most recent 24 hours; x ibk Let x represent the probability that the i-th ship is loaded at berth b in order k. ibk When x is 0, it indicates that the i-th ship is not loaded at berth b in sequence k; ibk When 1 is selected, it indicates that the i-th ship is not loaded in sequence k at berth b; I represents the number of ships participating in the loading process, and V i y represents the load capacity of the i-th ship; i Let y represent the tidal time corresponding to the i-th ship entering berth. i ∈[1,2,3,4,…,N], where N represents the total number of tides within the optimization time, when y i When y = N, it can be understood as the i-th ship entering berth at the Nth tidal time. Since ships can only enter and leave the port at tidal times, y i Take an integer variable.

[0089] The objective function f2, which aims to maximize the loading capacity within the optimization cycle, is expressed as follows:

[0090]

[0091] In the formula, f2 represents the maximum load capacity of the ship within the optimization period;

[0092] The objective function is constructed with the goal of minimizing changes in loading sequence and berth. This considers the loading sequence from the perspective of the loading order. For ships whose arrival times and loading berths have already been scheduled, their arrival sequence and loading berths should be kept as unchanged as possible. The corresponding objective function f3 is expressed as follows:

[0093]

[0094] In the formula, f3 represents the minimum change relative to the original calculated loading sequence and loading berth. This represents the probability that the i-th ship is originally scheduled to load at berth b in the k-th order; When the value is 1, it means that the i-th ship was originally scheduled to be loaded at berth b in sequence k. When the value is 0, it means that the i-th ship was not originally scheduled to be loaded at berth b in sequence k.

[0095] Based on the severity of their urgency, vessels can be categorized as either in urgent or non-urgent situations. Therefore, setting the expected arrival time for vessels in urgent situations should ensure that they arrive before their expected arrival time. To this end, the objective function f4 constructed in this embodiment of the invention, with the goal of minimizing the delayed arrival time of vessels in urgent situations, is expressed as follows:

[0096]

[0097] In the formula, This represents the penalty coefficient for the i-th ship failing to enter port on time. This represents the expected arrival time of the i-th emergency power plant vessel.

[0098] The final ship scheduling objective function is obtained by weighted summation of each objective function and its corresponding weight. Therefore, the expression for the ship scheduling objective function f is as follows:

[0099] f=ω1f1+ω2f2+ω3f3+ω4f4 (6)

[0100] Where ω1, ω2, ω3, and ω4 represent weight coefficients. According to the importance of the objective function, ω4>ω1>ω2>ω3, the priority should be to ensure that power plants in urgent need of supplies are loaded onto ships, followed by ensuring rapid destocking and maximizing loading efficiency.

[0101] The distribution of operating time for a single vessel in port is as follows: Figure 1 As shown, since the loading waiting time and departure time of a single vessel, as well as the berth vacancy time, are relatively fixed and less affected by a single vessel, the larger the vessel, the greater its loading rate. Therefore, priority should be given to ensuring that large vessels enter the port first to achieve rapid depletion of port capacity. In this embodiment of the invention, the constraints include berth and loading sequence conflict constraints, vessel arrival time conflict constraints, vessel and berth matching constraints, and vessel time constraints.

[0102] The berth and loading sequence conflict constraints include a vessel service frequency constraint, i.e., a constraint that each vessel can serve at most once. The corresponding constraint expression is as follows:

[0103]

[0104] In the formula, B represents the total number of berths in the port, and K represents the maximum sequential number for each berth;

[0105] The berth and loading sequence conflict constraints also include the constraint that each berth and each sequence can serve at most one vessel, and the corresponding constraint expressions are as follows:

[0106]

[0107] To improve berth utilization, the loading sequence of ships at each berth should ideally be consecutive, meaning that a ship can only be loaded into the next berth if a ship has been loaded into the previous berth. Therefore, the berth and loading sequence conflict constraint also includes the constraint that a ship can only be loaded into the next berth if a ship has been loaded into the previous berth. The corresponding constraint expressions are as follows:

[0108]

[0109] The vessel arrival time conflict constraint means that the vessel's arrival time must not be later than its arrival confirmation time, and its constraint expression is as follows:

[0110] y i ≥A i (10)

[0111] In the formula, A i This indicates the arrival confirmation time of the i-th ship.

[0112] Since the optimization parameter is the vessel arrival time within the optimization period, some vessels only enter the port for loading within the optimization period and are not fully loaded. Therefore, the vessel time constraints include the constraint that vessels departing from the berth within the optimization period must have entered the berth within the optimization period, the constraint that at most one vessel can enter the port within the optimization period but not depart from the berth within the optimization period, and the constraint that the total loading time of vessels departing from the berth within the optimization period for each berth must not exceed the optimization period's time limit.

[0113] For the constraint that vessels departing from port within the optimization period must also enter port within the optimization period, the corresponding constraint expression is:

[0114] x ibk ≥y ibk (11)

[0115] In the formula, y ibk Let y represent the probability that the i-th vessel, loaded at berth b in sequence k, departs before the end of the optimization period. ibk When y is 0, it means that the i-th vessel is not loaded at berth b in sequence k or does not depart within the optimization period; ibk When the value is 1, it means that the i-th ship is loaded at the b-th berth in sequence k and departs within the optimization period;

[0116] For the constraint that at most one ship can enter the port at each berth during the optimization period but not leave the port during the optimization period, the corresponding constraint expression is:

[0117]

[0118] For the constraint that the total loading time of ships departing from each berth within the optimization period must not exceed the optimization period, the corresponding constraint expression is:

[0119]

[0120] In the formula, T represents the time when the b-th berth becomes available. end Δt represents the time when the optimization cycle ends. ib Δt represents the loading time of the i-th ship at berth b. ib It is related to the ship's load capacity and the loading rate of the berth loader.

[0121] The vessel-berth matching constraint means that the vessel's draft must match the berth's allowable draft. The corresponding constraint expression is:

[0122]

[0123] In the formula, D i D represents the draft of ship i when fully loaded. b M represents the allowable draft of berth b, and M is a constant. In this embodiment of the invention, M can be a number greater than 0.

[0124] Based on the objective function of formula (6) and the constraints of formulas (7)-(14), the expression of the mixed integer linear programming model for ship scheduling optimization is as follows:

[0125]

[0126] In the formula, f is the objective function. x and y are the variables, and h(x,y)≤0 and g(x,y)=0 represent the inequality constraints and equality constraints in the model constraints, respectively.

[0127] like Figure 3 As shown, based on the genetic rules for processing the objective function and constraints, a genetic algorithm is designed to solve the mixed-integer linear programming model for ship scheduling optimization, including:

[0128] 1) Encode the optimization variables and use a random generation strategy to generate the initial population;

[0129] 2) Calculate the fitness of the initial population and use the roulette wheel method to select parent pairs;

[0130] 3) Perform crossover and mutation operations on the paired parents to generate a new population;

[0131] 4) Perform gene repair on the new population according to the constraints, and then return to step 2) to iterate continuously until the error requirement is met and the optimal solution is formed.

[0132] In one embodiment of the present invention, the optimization variables include the loading berth, berthing order, and berthing time for each ship. The optimization variables for all ships are encoded and then linked together to form a one-dimensional genetic code.

[0133] Initializing the population requires initializing the optimization variables under random rules. When initializing the berth variable, the matching between the ship and the berth must be considered. When initializing the berth order and berth time, ship order conflicts must be considered.

[0134] To calculate the fitness of the initial population and make selections, the genetic code of the initial population first needs to be segmented according to the ships. The objective function is then calculated based on the berth, order, and time of loading for each ship in each individual, and the fitness of each individual is calculated.

[0135] The selection of the parent generation using the roulette wheel method includes:

[0136] 1) Sort individuals according to the ratio of their fitness to the total fitness of the entire population;

[0137] 2) Stack the ratios according to the sorting order to form a new accumulated array;

[0138] 3) Generate a random number between 0 and 1, and use the position of the random number in the array as the basis for selecting the parent; 4) After an individual is selected, repeat step 3) until a new population size is reached.

[0139] Genetic repair includes fixing mismatches between ships and berths and conflicts in ship loading sequence. During the generation of new individuals, the following may occur: a) the ship's displacement is too large, exceeding the capacity of its loading berth; b) multiple ships may be loading in the same order at the same berth.

[0140] For question a), the specific steps are as follows: First, the genetic code corresponding to the new population is decoded. For any ship, if the corresponding berth is not in the ship's list of available berths, then a new berth is selected from the list of available berths.

[0141] For question b), the flowchart of gene repair for ship loading sequence conflict constraints is as follows: Figure 4 As shown:

[0142] 1) First, the genetic code corresponding to the new population is decoded, the ships are classified according to berths, and a set of berth loading order is constructed;

[0143] 2) Then, loop through each ship and determine whether the ship's loading order k is in the set. If it is not, add the order to the set. If it is, randomly select a loading order from the loading orders that are not in the set to update the ship's position and put it into the set.

[0144] 3) If the loading sequence in the set is full or all ships have been cycled, then the gene repair process ends.

[0145] The method provided in this embodiment of the invention is verified below using specific data. This embodiment is coded using Python and analyzes a scenario with 3 berths and 9 ships. All 3 berths are over 70,000 tons, with allowable drafts of 15m, 12m, and 15m respectively. The ship information is shown in the table below:

[0146] Table 1

[0147]

[0148]

[0149] The population size was set to 800, the number of iterations to 1000, the mating probability to 0.6, and the mutation probability to 0.03. A schematic diagram illustrating the convergence of the genetic algorithm iteration is shown below. Figure 5 As shown in the figure. A Gantt chart analysis of the optimization results is performed as follows. Figure 6 As shown, the loading capacity of ships increased from 90,000 tons to 160,000 tons within 24 hours. Furthermore, the loading sequence and the order of ships urgently needed for power plants were optimized, guiding ship scheduling and berth allocation under high coal port inventory conditions. It is evident that the method provided in this embodiment of the invention has achieved good technical results in both reducing port inventory and increasing port efficiency.

[0150] Example 3:

[0151] This invention provides a port vessel scheduling optimization device, the device comprising:

[0152] Model building module: used to build a mixed-integer linear programming model for ship scheduling optimization based on a pre-constructed ship scheduling objective function and constraints;

[0153] Solution module: Used to solve the mixed integer linear programming model for ship scheduling optimization using a genetic algorithm to obtain the ship scheduling optimization scheme;

[0154] The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints.

[0155] The apparatus provided in the embodiments of the present invention can execute the method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0156] Example 3:

[0157] This invention also provides a port vessel scheduling optimization device, including a processor and a storage medium;

[0158] The storage medium is used to store instructions;

[0159] The processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0160] Example 4:

[0161] This invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0162] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0166] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing port vessel scheduling, characterized in that, The method includes the following steps: Based on the pre-constructed objective function and constraints for ship scheduling, a mixed-integer linear programming model for ship scheduling optimization is established. The ship scheduling optimization mixed integer linear programming model is solved using a genetic algorithm to obtain the ship scheduling optimization scheme; The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints. The method of solving the mixed-integer linear programming model for ship scheduling optimization using a genetic algorithm includes: Step A: Use the loading berth, berthing order, and berthing time of each ship as optimization variables, and encode the optimization variables of all ships to form a one-dimensional genetic code; Step B: Initialize the genetic code under random rules to generate an initial population; Step C: Calculate the fitness of the initial population and use the roulette wheel method to select parent pairs; Step D: Perform crossover and mutation operations on the paired parents to generate a new population; Step E: Perform gene repair on the new population according to the constraints; Step F: Return to step B and iterate continuously until the error requirement is met, thus forming the optimal solution; The gene repair includes the repair of ship-berth mismatch and ship sequence conflict. In response to the mismatch between ships and berths, the gene repair of the new population based on the constraints includes: The genetic code corresponding to the new population is decoded. If the berth corresponding to any ship is not in the ship's list of available berths, a new berth is selected from the ship's list of available berths. In the event of a ship sequence conflict, the gene repair of the new population based on the constraints includes: The genetic code corresponding to the new population is decoded, all ships are classified according to berth, and a set of berth loading order is constructed. Iterate through each ship and determine whether the ship's loading order is in the set. If not, add the loading order to the set. If it is in the set, randomly select a loading order from the loading orders that are not in the set and add it to the set. If the set is full or all ships have been iterated, end the gene repair process. The ship scheduling objective function is constructed and obtained based on predetermined ship scheduling optimization requirements, including: The objective functions are constructed respectively with the following objectives: maximizing the loading capacity within the shortest time, maximizing the loading capacity within the optimization period, minimizing the changes in loading sequence and berth, and minimizing the delay in port entry of ships to power plants in urgent need. Determine the corresponding weights based on the importance of each objective function; The final ship scheduling objective function is obtained by weighted summation of each objective function and its corresponding weight. The objective function is constructed with the goal of maximizing the loading capacity of the ship in the shortest possible time. The expression is as follows: (1) In the formula, This indicates the maximum load capacity that can be loaded onto a ship within the most recent time period. Indicates the most recent time; Let represent the probability that the i-th ship is loaded at berth b in order k. When the value is 0, it means that the i-th ship is not loaded in order k at the b-th berth; When 1 is selected, it indicates that the i-th ship is not loaded in sequence k at berth b; I represents the number of ships participating in the loading process. This indicates the berthing time of the i-th ship. This represents the load capacity of the i-th ship. The objective function is constructed with the goal of maximizing the loading capacity within the optimization period. The expression is as follows: (2) In the formula, This indicates the maximum load capacity on board during the optimization period; The objective function is constructed with the goal of minimizing the loading sequence and berth changes. The expression is as follows: (3) In the formula, This indicates the minimum change relative to the original calculated loading sequence and loading berth. Let represent the probability that the i-th ship is originally scheduled to load at berth b in the k-th order. When the value is 1, it means that the i-th ship was originally scheduled to be loaded at berth b in sequence k. When the value is 0, it means that the i-th ship was not originally scheduled to be loaded at berth b in sequence k. The objective function is constructed with the goal of minimizing the delay in the arrival of ships at the emergency power plant. The expression is as follows: （5） In the formula, , This represents the expected arrival time of the i-th emergency power plant vessel. The ship scheduling objective function The expression is as follows: (6) in, , , , These represent weighting coefficients, based on the importance of the objective function. > > > .

2. The port vessel scheduling optimization method according to claim 1, characterized in that, The berth and loading sequence conflict constraints include: a constraint that each vessel can be served at most once; a constraint that each berth and each sequence can serve at most one vessel; and a constraint that a vessel can only be scheduled for the next sequence if a vessel has been loaded in the previous sequence at each berth. The corresponding constraint expressions are as follows: (7) (8) (9) In the formula, B represents the total number of berths in the port, and K represents the maximum sequential number for each berth; The vessel arrival time conflict constraint means that the vessel's arrival time must not be later than its arrival confirmation time, and its constraint expression is as follows: (10) In the formula, This indicates the arrival confirmation time of the i-th ship; The vessel time constraints include: the constraint that vessels departing from berth within the optimization period must also enter berth within the optimization period; the constraint that at most one vessel can enter berth at each berth within the optimization period but not depart within the optimization period; and the constraint that the total loading time of vessels departing from each berth within the optimization period must not exceed the optimization period. The corresponding constraint expressions are as follows: (11) (12) (13) In the formula, Let represent the probability that the i-th vessel, loaded at berth b in sequence k, departs before the end of the optimization period. When the value is 0, it means that the i-th ship is not loaded at the b-th berth in order k or does not depart within the optimization period; When the value is 1, it means that the i-th ship is loaded at the b-th berth in sequence k and departs within the optimization period; This indicates the time when the b-th berth becomes available. Indicates the end time of the optimization cycle. This indicates the loading time of the i-th vessel at the b-th berth; The vessel-berth matching constraint means that the vessel's draft must match the berth's allowable draft. The corresponding constraint expression is: (14) In the formula, This indicates the draft of ship i when it is fully loaded. This represents the permissible draft of berth b, where M is a constant.

3. The port vessel scheduling optimization method according to claim 2, characterized in that, The expression for the mixed-integer linear programming model for ship scheduling optimization is as follows: （15） In the formula, x and y are the corresponding variables in each constraint condition. , These represent inequality constraints and equality constraints in the constraint conditions, respectively.

4. A port vessel scheduling optimization device, characterized in that, The device includes: Model building module: used to build a mixed-integer linear programming model for ship scheduling optimization based on a pre-constructed ship scheduling objective function and constraints; Solution module: Used to solve the mixed integer linear programming model for ship scheduling optimization using a genetic algorithm to obtain the ship scheduling optimization scheme; The objective function for ship scheduling is constructed based on predetermined ship scheduling optimization requirements; the constraints include ship arrival time conflict constraints, berth and loading sequence conflict constraints, ship and berth matching constraints, and ship time constraints. The method of solving the mixed-integer linear programming model for ship scheduling optimization using a genetic algorithm includes: Step A: Use the loading berth, berthing order, and berthing time of each ship as optimization variables, and encode the optimization variables of all ships to form a one-dimensional genetic code; Step B: Initialize the genetic code under random rules to generate an initial population; Step C: Calculate the fitness of the initial population and use the roulette wheel method to select parent pairs; Step D: Perform crossover and mutation operations on the paired parents to generate a new population; Step E: Perform gene repair on the new population according to the constraints; Step F: Return to step B and iterate continuously until the error requirement is met, thus forming the optimal solution; The gene repair includes the repair of ship-berth mismatch and ship sequence conflict. In response to the mismatch between ships and berths, the gene repair of the new population based on the constraints includes: The genetic code corresponding to the new population is decoded. If the berth corresponding to any ship is not in the ship's list of available berths, a new berth is selected from the ship's list of available berths. In the event of a ship sequence conflict, the gene repair of the new population based on the constraints includes: The genetic code corresponding to the new population is decoded, all ships are classified according to berth, and a set of berth loading order is constructed. Iterate through each ship and determine whether the ship's loading order is in the set. If not, add the loading order to the set. If it is in the set, randomly select a loading order from the loading orders that are not in the set and add it to the set. If the set is full or all ships have been iterated, end the gene repair process. The ship scheduling objective function is constructed and obtained based on predetermined ship scheduling optimization requirements, including: The objective functions are constructed respectively with the following objectives: maximizing the loading capacity within the shortest time, maximizing the loading capacity within the optimization period, minimizing the changes in loading sequence and berth, and minimizing the delay in port entry of ships to power plants in urgent need. Determine the corresponding weights based on the importance of each objective function; The final ship scheduling objective function is obtained by weighted summation of each objective function and its corresponding weight. The objective function is constructed with the goal of maximizing the loading capacity of the ship in the shortest possible time. The expression is as follows: (1) In the formula, This indicates the maximum load capacity that can be loaded onto a ship within the most recent time period. Indicates the most recent time; Let represent the probability that the i-th ship is loaded at berth b in order k. When the value is 0, it means that the i-th ship is not loaded in order k at the b-th berth; When 1 is selected, it indicates that the i-th ship is not loaded in sequence k at berth b; I represents the number of ships participating in the loading process. This indicates the berthing time of the i-th ship. This represents the load capacity of the i-th ship. The objective function is constructed with the goal of maximizing the loading capacity within the optimization period. The expression is as follows: (2) In the formula, This indicates the maximum load capacity on board during the optimization period; The objective function is constructed with the goal of minimizing the loading sequence and berth changes. The expression is as follows: (3) In the formula, This indicates the minimum change relative to the original calculated loading sequence and loading berth. Let represent the probability that the i-th ship is originally scheduled to load at berth b in the k-th order. When the value is 1, it means that the i-th ship was originally scheduled to be loaded at berth b in sequence k. When the value is 0, it means that the i-th ship was not originally scheduled to be loaded at berth b in sequence k. The objective function is constructed with the goal of minimizing the delay in the arrival of ships at the emergency power plant. The expression is as follows: （5） In the formula, , This represents the expected arrival time of the i-th emergency power plant vessel. The ship scheduling objective function The expression is as follows: (6) in, , , , These represent weighting coefficients, based on the importance of the objective function. > > > .

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 3.

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