A voyage optimization method and system for new fuel dry bulk carrier routes

Abstract

The present invention provides a voyage optimization method and system for routes of dry bulk carriers with new fuels. The method first obtains the ship parameters and navigation parameters of the dry bulk carriers with new fuels, and establishes a first objective function according to the maximum loading capacity of the ship and the decision variables under multiple constraint conditions established with the value range of the decision variables, the limit range of the fleet size, the minimum number of voyages, and the limit range of the cumulative loading capacity of the ship at the loading port as constraints. The method also establishes a second objective function according to the voyage cost determined by the starting sailing time and the sailing duration of the voyage and the decision variables. The method then combines the first objective function and the second objective function based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function. The method then designs a customized non-dominated sorting genetic algorithm to solve the comprehensive objective function and obtains the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume while minimizing the total voyage cost, which can effectively improve operational efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of ship operation, and in particular to a voyage optimization method and system for a new fuel dry bulk carrier route. Background Art

[0002] Conventional fuel vessels generate significant amounts of exhaust and pollutants during operation, severely impacting the environment. The fuels used by conventional fuel vessels, such as heavy oil or diesel, release significant amounts of carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur oxides (SOx) when burned. These emissions are major greenhouse gases and air pollutants, directly contributing to climate change and air quality. Furthermore, combustion on conventional fuel vessels produces other chemical pollutants, such as carbon monoxide (CO), volatile organic compounds (VOCs), and particulate matter (PM). These pollutants harm air quality and human health, particularly fine particulate matter (PM2.5), which can cause respiratory and cardiovascular problems. Furthermore, conventional fuel vessels generate significant amounts of wastewater during navigation, including ship drainage, wash water, and toilet wastewater. This wastewater contains petroleum residues, heavy metals, and other hazardous substances. If not properly treated and discharged directly into the ocean, it can have serious impacts on aquatic life and marine ecosystems. The operation of conventional fuel vessel engines and propellers generates noise pollution, which disrupts and harms marine life, particularly aquatic animals. Noise pollution can affect the migration, communication, and reproduction of aquatic organisms, negatively impacting the ecological balance. Therefore, to reduce the environmental impact of traditional fuel vessels, it is crucial to promote and apply new fuel technologies, employ advanced exhaust gas treatment equipment, implement effective oil-water separation and wastewater treatment, and control noise. The introduction of new fuel vessels, such as liquefied natural gas (LNG) vessels and electric ships, can significantly reduce pollutant emissions and noise pollution, contributing positively to environmental protection.

[0003] However, the operation of new fuel vessels differs significantly from that of conventional vessels. For example, changes in refueling points will require adjustments to routes, and changes in Emission Restriction Areas (ERAs) must also be considered. Therefore, optimizing route planning is a crucial research topic to improve operational efficiency and reduce costs. First, changes in refueling points will directly impact route planning. New fuel vessels may require more frequent refueling at specific ports, so route planning must consider the location and timing of refueling points. This requires route planners to fully understand the energy needs of new fuel vessels and comprehensively consider the feasibility of port facilities and supply chains. Second, with increasing environmental protection requirements, the scope and restrictions of Emission Restriction Areas (ERAs) are constantly evolving. Route planning must comply with relevant emission restrictions and rationally arrange routes to avoid or minimize entry into Emission Restriction Areas. This requires timely access to the latest Emission Restriction Area information and incorporating it into route planning considerations. To improve operational efficiency and reduce costs, optimal route planning is crucial. Route planning requires comprehensive consideration of multiple factors, including voyage distance, sea conditions, traffic volume, port facilities, and navigation safety. Proper route planning can reduce voyage distance and time, lowering fuel consumption and operating costs. At the same time, taking into account the requirements of environmental protection, route planning should also try to avoid entering sensitive ecological areas and reduce the impact on marine life.

[0004] After the route plan is finalized, developing a reasonable annual sailing plan for new fuel vessels is a key step in optimizing fleet operations, reducing costs, and increasing cargo transshipment. To achieve this goal, operations optimization methods can be applied to rationally deploy the fleet through network optimization scheduling. Network optimization scheduling is a complex issue, requiring multiple factors to be considered to arrive at the optimal decision. Firstly, the vessel's load capacity, voyage time, and port call time all need to be considered. A vessel's load capacity directly affects the amount of cargo it can carry on each voyage, while voyage time and port call time affect the vessel's transportation efficiency and time costs. Furthermore, fluctuating loading information is a crucial consideration. By keeping abreast of the port's supply conditions, fleet operational strategies can be better adjusted. For example, if loading times are prolonged during the rainy season, vessel sailing times can be increased to meet the port's loading and unloading capacity, thereby reducing fuel consumption and costs. Conversely, if loading rates are high during a particular season, voyages can be accelerated to increase terminal throughput. Intelligent scheduling algorithms are crucial tools for achieving efficient fleet operations. Through network optimization and intelligent scheduling algorithms, fleets can achieve efficient operations and improve overall transportation efficiency. This will bring many benefits, including reduced operating costs, increased cargo transshipment volume, reduced empty runs and waiting time, and reduced energy consumption and environmental pollution. The application of intelligent scheduling algorithms can help achieve efficient fleet operations, maximize profits, and improve transportation efficiency. In short, from the perspective of operational optimization, after the route plan is determined, it is crucial for the fleet to develop a reasonable annual sailing plan. Summary of the Invention

[0005] To address the current issues of low operational efficiency and high costs in voyage optimization, the present invention provides a voyage optimization method for dry bulk carrier routes using new fuels. This method aims to maximize the total dry bulk cargo transshipment volume and minimize the total voyage cost. Constrained by the range of decision variables, the limited range of fleet size, the minimum number of voyages, and the limited range of the cumulative cargo volume of the ship at the loading port, the method establishes first and second objective functions, then constructs a comprehensive objective function. A customized non-dominated sorting genetic algorithm is designed to obtain the optimal solution to the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume while minimizing the total voyage cost, effectively improving operational efficiency and reducing costs. The present invention also relates to a voyage optimization system for dry bulk carrier routes using new fuels.

[0006] The technical solutions of the present invention are as follows:

[0007] A method for optimizing the voyage of a dry bulk carrier route using a new fuel, characterized by comprising the following steps:

[0008] Parameter acquisition step: obtaining ship parameters and navigation parameters of the new fuel dry bulk carrier, wherein the ship parameters include the maximum loading capacity of the ship, fleet size, minimum number of voyages, and cumulative loading capacity of the ship at the loading port; the navigation parameters include the starting time of the voyage, the duration of the voyage, and the voyage cost;

[0009] Objective function establishment steps: constructing decision variables based on the judgment result of whether the ship starts sailing at the starting sailing time of the voyage and the sailing duration, and under multiple constraints established by the value range of the decision variables, the limit range of the fleet size, the minimum number of voyages, and the limit range of the cumulative loading volume of the ship at the loading port, establishing a first objective function based on the maximum loading capacity of the ship and the decision variables, and establishing a second objective function based on the voyage cost determined by the starting sailing time and the sailing duration of the voyage and the decision variables, and then combining the first objective function and the second objective function based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function;

[0010] Initial population generation step: Divide the total sailing time of the ship into multiple time periods in chronological order to generate the initial population, and treat each time period in the initial population as an individual;

[0011] Fitness value calculation and offspring individual generation step: the fitness value of each individual in the initial population is calculated according to the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and the genetic operator in the non-dominated sorting genetic algorithm is used to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and the fitness value of each new offspring individual is calculated;

[0012] New population generation step: merge the new offspring individuals with the parent individuals to generate a temporary population, and compare the number of individuals in the temporary population with the preset number threshold. If the number of individuals in the temporary population is greater than the preset number threshold, sort the fitness value of each individual in the temporary population by size, and delete the individuals that exceed the number threshold in the sorted temporary population from small to large to obtain a new population;

[0013] Optimal solution calculation steps: Based on the new population, the fitness value calculation and offspring individual generation steps and the new population generation step are repeated until the preset number of iterations is reached. The individual with the smallest fitness value in the new population when the preset number of iterations is reached is taken as the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost.

[0014] Preferably, in the fitness value calculation and offspring individual generation steps, the genetic operators include a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator; and using the genetic operators in the non-dominated sorting genetic algorithm to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals specifically includes:

[0015] S1: The selection operator selects multiple individuals as parent individuals from the initial population based on the fitness value and adopts the roulette wheel selection method;

[0016] S2: The new blood operator randomly generates multiple new individuals and adds them to the parent individuals;

[0017] S3: Determine whether to perform a crossover operation based on the randomly generated probability value and the preset crossover rate. If yes, the crossover operator randomly selects two individuals from the parent individuals, then randomly selects two time units with relative positions from the two individuals, and swaps the codes of these two time units to generate two offspring individuals. If no, the number of parent individuals remains unchanged, and all individuals in the parent individuals are used as offspring individuals.

[0018] S4: Repeat step S3 until the preset number of iterations is reached to obtain multiple offspring individuals;

[0019] S5: The insertion operator performs an insertion operation on each offspring individual to obtain multiple inserted offspring individuals;

[0020] S6: The mutation operator performs mutation operations on each inserted offspring individual to obtain multiple new offspring individuals.

[0021] Preferably, in the parameter acquisition step, port parameters are also acquired, and the port parameters include the initial inventory of the loading port and the loading capacity of the loading port at a certain moment.

[0022] Preferably, in the objective function establishment step, the multiple constraints include a first constraint, a second constraint, a third constraint and a fourth constraint, the first constraint is constructed based on the decision variable and a preset numerical threshold, the second constraint is constructed based on the decision variable and the fleet size, the third constraint is constructed based on the decision variable and the minimum number of voyages, and the fourth constraint is constructed based on the cumulative loading volume of the ship at the loading port and the maximum loading volume of the ship.

[0023] Preferably, in the objective function establishment step, the multiple constraints also include a fifth constraint, a sixth constraint, a seventh constraint and an eighth constraint. The fifth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the initial inventory of the loading port, the loading capacity of the loading port at a certain moment, the maximum loading volume of the ship and the decision variables. The sixth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the loading capacity of the loading port at a certain moment, the maximum loading volume of the ship and the decision variables. The seventh constraint is constructed based on the initial inventory of the loading port, the maximum loading volume of the ship and the decision variables. The eighth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the maximum loading volume of the ship and the decision variables.

[0024] Preferably, in the initial population generation step, each time period is further divided into a plurality of time units in chronological order, and each time unit is encoded to obtain the encoded time units.

[0025] A voyage optimization system for a new fuel dry bulk carrier route is characterized by comprising a parameter acquisition module, an objective function establishment module, an initial population generation module, a fitness value calculation and offspring individual generation module, a new population generation module and an optimal solution calculation module connected in sequence.

[0026] The parameter acquisition module acquires the ship parameters and navigation parameters of the new fuel dry bulk carrier, wherein the ship parameters include the maximum loading capacity of the ship, the fleet size, the minimum number of voyages, and the cumulative loading capacity of the ship at the loading port; the navigation parameters include the starting time of the voyage, the duration of the voyage, and the cost of the voyage;

[0027] The objective function establishment module constructs decision variables based on a determination result of whether a ship has started sailing at the start sailing time of the voyage and the sailing duration, and establishes a first objective function based on the maximum loading capacity of the ship and the decision variables under multiple constraints established based on the value range of the decision variables, the limited range of the fleet size, the minimum number of voyages, and the limited range of the cumulative loading capacity of the ship at the loading port. A second objective function is established based on the voyage cost determined by the start sailing time and the sailing duration of the voyage and the decision variables, and then the first objective function and the second objective function are combined based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function;

[0028] The initial population generation module divides the total sailing time of the ship into multiple time periods in chronological order to generate an initial population, and regards each time period in the initial population as an individual;

[0029] The fitness value calculation and offspring individual generation module calculates the fitness value of each individual in the initial population according to the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and uses the genetic operator in the non-dominated sorting genetic algorithm to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and calculates the fitness value of each new offspring individual;

[0030] The new population generation module merges the new offspring individuals with the parent individuals to generate a temporary population, and compares the number of individuals in the temporary population with a preset number threshold. If the number of individuals in the temporary population is greater than the preset number threshold, the fitness value of each individual in the temporary population is sorted according to size, and the individuals exceeding the number threshold in the sorted temporary population are deleted in order from small to large to obtain a new population;

[0031] The optimal solution calculation module is based on the new population and repeats the fitness value calculation and the work of the offspring individual generation module and the new population generation module until a preset number of iterations is reached. The individual with the smallest fitness value in the new population when the preset number of iterations is reached is used as the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost.

[0032] Preferably, in the fitness value calculation and offspring individual generation module, the genetic operators include a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator; and using the genetic operators in the non-dominated sorting genetic algorithm to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals specifically includes:

[0033] S1: The selection operator selects multiple individuals as parent individuals from the initial population based on the fitness value and adopts the roulette wheel selection method;

[0034] S2: The new blood operator randomly generates multiple new individuals and adds them to the parent individuals;

[0035] S3: Determine whether to perform a crossover operation based on the randomly generated probability value and the preset crossover rate. If yes, the crossover operator randomly selects two individuals from the parent individuals, then randomly selects two time units with relative positions from the two individuals, and swaps the codes of these two time units to generate two offspring individuals. If no, the number of parent individuals remains unchanged, and all individuals in the parent individuals are used as offspring individuals.

[0036] S4: Repeat step S3 until the preset number of iterations is reached to obtain multiple offspring individuals;

[0037] S5: The insertion operator performs an insertion operation on each offspring individual to obtain multiple inserted offspring individuals;

[0038] S6: The mutation operator performs mutation operations on each inserted offspring individual to obtain multiple new offspring individuals.

[0039] Preferably, the parameter acquisition module further acquires port parameters, which include the initial inventory of the loading port and the loading capacity of the loading port at a certain moment.

[0040] Preferably, in the objective function establishment module, the multiple constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, a sixth constraint, a seventh constraint and an eighth constraint; the first constraint is constructed based on the decision variable and a preset numerical threshold, the second constraint is constructed based on the decision variable and the fleet size, the third constraint is constructed based on the decision variable and the minimum number of voyages, the fourth constraint is constructed based on the cumulative loading volume of the ship at the loading port and the maximum loading volume of the ship, the fifth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the initial inventory of the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variable, the sixth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variable, the seventh constraint is constructed based on the initial inventory of the loading port, the maximum loading volume of the ship and the decision variable, and the eighth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the maximum loading volume of the ship and the decision variable.

[0041] The beneficial effects of the present invention are:

[0042] The present invention provides a voyage optimization method for a new fuel dry bulk carrier route. The method first obtains the ship parameters and navigation parameters of the new fuel dry bulk carrier, constructs decision variables based on the judgment result of whether the ship starts sailing at the starting sailing time of the voyage and the sailing duration, and establishes a first objective function according to the maximum loading capacity of the ship and the decision variables under multiple constraint conditions established by the value range of the decision variables, the limit range of the fleet size, the minimum number of voyages, and the limit range of the cumulative loading capacity of the ship at the loading port as constraints, and establishes a second objective function according to the voyage cost determined by the starting sailing time and the sailing duration of the voyage and the decision variables, and then combines the first objective function and the second objective function based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function; then divides the total sailing time of the ship into multiple time periods in chronological order to generate an initial population, and each time period in the initial population is divided into a plurality of time periods. Each time period is regarded as an individual, and then the fitness value of each individual in the initial population is calculated. The genetic operator in the non-dominated sorting genetic algorithm is used to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals. The new offspring individuals are merged with the parent individuals to generate a temporary population, and the number of individuals in the temporary population is compared with a preset number threshold. The individuals that exceed the number threshold in the sorted temporary population are deleted to obtain a new population. Finally, based on the new population, the fitness value calculation and offspring individual generation steps and the new population generation step are repeated until the preset number of iterations is reached. The individual with the largest fitness value in the new population when the preset number of iterations is reached is taken as the optimal solution of the comprehensive objective function, thereby maximizing the total transshipment volume of dry bulk cargo (such as coal, ore, timber and other bulk dry bulk cargoes) and minimizing the total voyage cost, effectively improving operational efficiency and reducing costs.

[0043] The present invention optimizes the scheduling of the new fuel dry bulk carrier network under different speeds and different fuel filling strategies by considering factors such as multiple ship parameters and navigation parameters, which can better adjust the fleet's operating strategy, thereby reducing fuel consumption and reducing costs. It also establishes a comprehensive objective function, which can also be called a dual-objective mixed integer programming function, by adopting a specific method, and designs a customized non-dominated sorting genetic algorithm to obtain the optimal solution of the comprehensive objective function, thereby realizing intelligent scheduling of new fuel dry bulk carriers, enabling the fleet to achieve efficient operation and improve overall transportation efficiency. This will bring many benefits, including reducing operating costs, increasing dry bulk cargo transshipment volume, reducing empty driving and waiting time, and reducing energy consumption and environmental pollution. At the same time, it can also help the fleet achieve efficient operation, maximize profits and achieve improved transportation efficiency.

[0044] The present invention also relates to a voyage optimization system for new fuel dry bulk carrier routes, which corresponds to the above-mentioned voyage optimization method for new fuel dry bulk carrier routes and can be understood as a system for realizing the above-mentioned voyage optimization method for new fuel dry bulk carrier routes, including a parameter acquisition module, an objective function establishment module, an initial population generation module, a fitness value calculation and offspring individual generation module, a new population generation module and an optimal solution calculation module connected in sequence. Each module works in coordination with each other, which can be understood as studying the intelligent scheduling algorithm for the operation deduction of new fuel ships, developing an intelligent scheduling decision module, and making decisions on different speeds and different fuel filling strategies. The new fuel dry bulk carrier network under the new fuel strategy is optimized and scheduled, with the goal of maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost. The first and second objective functions are established with the value range of decision variables, the limit range of fleet size, the minimum number of voyages, and the limit range of the cumulative loading volume of ships at the loading port as constraints. Then, a comprehensive objective function is constructed, and a customized non-dominated sorting genetic algorithm is designed to obtain the optimal solution of the comprehensive objective function, so as to maximize the total dry bulk cargo transshipment volume while minimizing the total voyage cost, realize the intelligent scheduling of new fuel dry bulk carriers, and effectively improve operational efficiency and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of a voyage optimization method for a dry bulk carrier route using a new fuel.

[0046] Figure 2 It is a flow chart of the non-dominated sorting genetic algorithm of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be described below with reference to the accompanying drawings.

[0048] Fleet operators need to plan the fleet's annual sailing schedule. Assuming the optimal refueling strategy for each voyage of a new fuel vessel has been determined, the fleet's annual sailing schedule needs to be scheduled due to limitations such as fleet size, seasonal fluctuations in loading rates at the loading port, and the capacity of the loading platform at the loading port. From the fleet operator's perspective, the need to transport and complete more dry bulk cargo transshipments at the lowest possible cost necessitates the dual decision-making objectives of minimizing fleet operating costs and maximizing dry bulk cargo transshipment. New fuel dry bulk vessels transport bulk dry cargoes such as coal, ore, and timber. This is illustrated below using ore as an example, where the dual decision-making objectives are minimizing fleet operating costs and maximizing ore transshipment.

[0049] The present invention relates to a voyage optimization method for a new fuel dry bulk carrier route. The flow chart of the method is as follows: Figure 1 As shown, the following steps are included in sequence:

[0050] Parameter Acquisition Step: Obtain vessel and voyage parameters for the new fuel dry bulk carrier. Vessel parameters include the vessel's maximum load capacity, fleet size, minimum number of voyages, and cumulative cargo loads at the loading port. Voyage parameters include the voyage start time, voyage duration, and voyage cost. Furthermore, port parameters are obtained, including the loading port's initial inventory and the loading capacity at a specific time.

[0051] 2. Objective Function Establishment Steps: Decision variables are constructed based on the judgment result of whether a ship begins sailing at the voyage's starting time and the voyage duration. A first objective function is established based on the ship's maximum loading capacity and the decision variables, subject to multiple constraints established based on the value range of the decision variables, the limited range of fleet size, the minimum number of voyages, and the limited range of the ship's cumulative cargo volume at the loading port. A second objective function is established based on the voyage cost determined by the voyage's starting time and duration and the decision variables. The first and second objective functions are then combined using a weighted sum method based on preset weight coefficients to obtain a comprehensive objective function. Furthermore, the value range of the decision variables includes the decision variables and a preset numerical threshold, the limited range of fleet size includes the decision variables and fleet size, and the limited range of the ship's cumulative cargo volume at the loading port includes the ship's cumulative cargo volume at the loading port and the ship's maximum loading capacity.

[0052] Specifically, the goal is to maximize the total ore transfer volume and minimize the total voyage cost. First, the decision variables are constructed based on the judgment result of whether the ship starts sailing at the starting time of the voyage and the duration of the voyage. Under the constraints of the decision variable value range, fleet size limit, minimum number of voyages, and cumulative cargo volume limit of the ship at the loading port, the maximum loading capacity Q of the ship and the decision variable are used as the constraints. Establish the first objective function f1 and express it as follows:

[0053]

[0054] In the above formula, Whether the ship starts sailing at the starting sailing time t and the sailing duration is p, if so, then Is 1, if not, then is 0, Q is the maximum loading capacity of the ship (also known as the maximum loading capacity of the ship), T is the time range of the voyage, and p is the duration of the voyage, which is determined according to the longest and shortest voyage times.

[0055] Then the voyage cost is determined based on the voyage start time and voyage duration. Joint decision variables The second objective function f2 is established and expressed as follows:

[0056]

[0057] In the above formula, It represents the cost of a voyage with a starting sailing time of t and a sailing duration of p.

[0058] Among them, the multiple constraints include the first constraint, according to the decision variable The first constraint condition ensures that at most one voyage departs per time period and is constructed with a preset numerical threshold. It is expressed as follows:

[0059]

[0060] The second constraint is based on the decision variables The second constraint condition limits the number of ships in the same time period to within the range of the fleet size, which can be expressed as follows:

[0061]

[0062] In the above formula, Indicates whether there is a The voyage that starts and lasts for p is N, the fleet size is d p b) the possible duration of the voyage; ti It represents the processing time before the ship starts sailing in time period ti.

[0063] The third constraint condition is based on the decision variables The third constraint is the minimum number of voyages required and the minimum profit value of each voyage, which can be expressed as follows:

[0064]

[0065] In the above formula, K is the minimum number of flights.

[0066] The fourth constraint is constructed based on the cumulative cargo volume of the ship at the loading port and the maximum loading capacity of the ship. This fourth constraint represents the capacity limit of the loading port, that is, the loading port can only serve one ship at a time, and is expressed as follows:

[0067]

[0068] In the above formula, y t represents the cumulative loading volume of the ship at the loading port, and y t ≥0, becomes 0 when the ship leaves the port, Q represents the maximum loading capacity (loading capacity) of the ship.

[0069] Furthermore, the fifth constraint condition is based on the cumulative loading volume y of the ship at the loading port t , the initial inventory h0 of the loading port, the loading capacity l of the loading port at a certain time tn tn , the maximum loading capacity Q of the ship and the decision variables Construction; The sixth constraint condition is based on the cumulative loading volume y of the ship at the loading port t , the loading capacity of the loading port at a certain moment l tn , the maximum loading capacity Q of the ship and the decision variables The fifth and sixth constraints follow the real loading logic that the cumulative loading volume gradually increases with the real-time loading capacity. The fifth constraint is as shown in the following formula (7), and the sixth constraint is as shown in the following formula (8):

[0070]

[0071] Furthermore, the seventh constraint condition is based on the initial inventory h0 of the loading port, the maximum loading capacity Q of the ship, and the decision variable Construction; The eighth constraint condition is based on the cumulative loading volume y of the ship at the loading port t , the maximum loading capacity Q of the ship and the decision variables The seventh and eighth constraints ensure that the voyage can only begin when the loading is completed. The seventh constraint is as shown in the following formula (9), and the eighth constraint is as shown in the following formula (10):

[0072]

[0073] Furthermore, the domain of the decision variable can be shown as (11) and (12):

[0074]

[0075] Since the problem has multiple objectives, the general solution method cannot be directly solved. Therefore, based on the preset weight coefficient, the first objective function f1 and the second objective function f2 are combined using the weighted sum method that combines the two objective functions to obtain a comprehensive objective function, which is expressed as follows:

[0076] minλ·f2-(1-λ)·f1 (13)

[0077] In the above formula, λ is the preset weight coefficient.

[0078] Considering that a ship must complete the loading work of the previous voyage before it can start the next voyage, two constraints are set for the comprehensive objective function to ensure that the loading and unloading processes between consecutive voyages do not overlap or conflict, thereby ensuring the smooth progress of the transportation process. As shown in the following effective inequalities (14) and (15), they serve as two constraints respectively. This is achieved by ensuring the minimum loading and unloading time between two consecutive voyages. For example, constraint (14) stipulates that the start time of the new voyage should be greater than or equal to the start time of the previous voyage plus the time required for loading. The definition of constraint (15) is similar.

[0079]

[0080] In the above formula, f t0 represents the loading time required to load the goods starting from time t0, b ti It represents the processing time before the ship starts sailing in time period ti.

[0081] 3. Initial population generation step: The total voyage time of the ship is divided into multiple time periods in chronological order to generate the initial population, and each time period in the initial population is regarded as an individual. Furthermore, each time period is divided into multiple time units in chronological order, and each time unit is encoded to obtain the encoded time units.

[0082] Compared to single-objective programming problems, which usually have only one exact optimal solution, multi-objective programming problems have a set of Pareto optimal solutions. In order to obtain as many Pareto optimal solutions as possible within a reasonable computing time, the comprehensive objective function is solved based on the non-dominated sorting genetic algorithm (NSGA-II) to obtain the optimal solution when the preset number of iterations is reached. The overall flow chart of the NSGA-II algorithm is shown below. Figure 2 shown.

[0083] Specifically, the total sailing time of the ship is first divided into multiple time periods in chronological order to form an initial population, and each time period in the initial population is regarded as an individual. Then, each individual in the initial population is divided into multiple time units in chronological order, and each time unit is encoded, and the number of the initial population is set. Among them, the encoding includes a description of the start time and sailing duration of the voyage in each time unit. By encoding each time unit, a time series can be formed to clearly represent the voyage arrangement. The initial population can be understood as the beginning of the NSGA-II algorithm, which affects the iterative evolution and convergence effect of the NSGA-II algorithm. The random generation sequence can be used, and the number of initial populations can be set. Set each time period in the initial population as S = (s1, s2,…, s i ,…,s T), S represents a time series with T elements (i.e., time units), where s i ≠-1 indicates the duration of the voyage starting at the i-th time unit, s i = -1 indicates that no voyage begins in that time unit. As shown in Table 1, assuming an individual has 10 time units (the length of a time unit is set to one day in this study; it can also be set to 2 days or 0.5 days based on actual requirements for the time precision of voyage planning, and is not a unique constraint here), the initial population plans two voyages. The first voyage is a non-negative unit in s3 (i.e., the third time unit), indicating that if the voyage duration set is [15, 30, 45] (indicating that the allowed voyage durations are 15, 30, and 45 days), then a voyage begins in time unit 3 and returns after 30 time units (i.e., 30 days, the voyage duration is selected here). The next voyage is in s8 (i.e., the eighth time unit), indicating that the voyage begins in the eighth time unit and returns after 15 time units (i.e., 15 days, the voyage duration is selected here).

[0084] Table 1

[0085] -1 -1 2 -1 -1 -1 -1 1 -1 -1

[0086] It's important to note that each individual (also called a region) improves independently, acting as a self-individual that continuously updates itself during the subsequent improvement search process. Furthermore, since the NSGA-II algorithm introduces random factors to achieve continuous iterative updates, random factors can lead to local optima. Therefore, the use of isolated regions can also avoid this problem, ensuring stable search across each region. Furthermore, after encoding each time unit, the encoding is evaluated. The fitness value is used to assess the quality of the individual, playing a crucial role in the algorithm.

[0087] 4. Fitness value calculation and offspring individual generation step: The fitness value of each individual in the initial population is calculated based on the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and a genetic operator in a non-dominated sorting genetic algorithm is used to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and the fitness value of each new offspring individual is calculated. Preferably, the genetic operator includes a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator.

[0088] Specifically, the fitness value of each individual in the initial population is first calculated based on the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient. Then, a variety of genetic operators are designed to improve and upgrade the individuals in the initial population. Genetic operators may include selection operators, new blood operators, crossover operators, insertion operators, and mutation operators.

[0089] 1. Selection operator: This is the beginning of the evolutionary operator. It plays a key role in maintaining the continuous inheritance of good individuals and temporarily suspending the spread of bad individuals during the evolutionary process. The selection operator selects multiple individuals from the initial population as parent individuals based on fitness values ​​and uses the roulette wheel selection method. The specific steps are as follows:

[0090] 1) Calculate the cumulative probability value of each individual based on the proportion of the individual fitness value in the overall total fitness value (the total fitness value is the sum of the fitness values ​​of all individuals);

[0091] 2) Randomly generate a number in the interval [0,1] and use it as the discrimination value to determine the selected individual. If the random number falls within the cumulative probability value interval of an individual, the individual is selected;

[0092] 3) Add the selected individuals to the candidate improvement set, and repeat the above steps continuously until the number of candidate improvement sets reaches a certain number, and use the candidate improvement set as the parent individual.

[0093] 2. Crossover operator: This is to randomly cross the two annual sailing plans of the fleet. The specific steps are as follows:

[0094] 1) First, a probability value is randomly generated (usually a random floating point number between 0 and 1), and then the probability value is compared with the pre-set crossover rate; when the generated probability value is less than the crossover rate, the crossover operation is selected; when the generated probability value is greater than the crossover rate, the crossover step is skipped directly, the number of parent individuals remains unchanged, and all individuals in the parent individual are used as offspring individuals;

[0095] 2) After determining to perform the crossover operation, randomly select two individuals from the parent individuals;

[0096] 3) Randomly select two time units that are opposite to each other from the two individuals, and swap the codes of these two time units to generate two offspring individuals; for example, as shown in Table 2, the code of the eighth time unit is selected and swapped, and the eighth time unit of code 1 is changed from "1" to "-1", and the eighth time unit of code 2 is changed from "-1" to "1";

[0097] 4) Repeat steps 2 and 3 until the preset number of iterations is reached to obtain multiple offspring individuals.

[0098] Table 2

[0099]

[0100] It should be noted that the crossover rate is a set value between 0 and 1, indicating the probability of performing a crossover operation. For example, if the crossover rate is set to 0.8, then during each crossover operation, there is an 80% probability of performing a crossover and a 20% probability of not performing a crossover. In the case of no crossover, the parent individual is directly copied to the child individual.

[0101] 3. Insertion Operator: Perform an insertion operation on each offspring individual, obtaining multiple inserted offspring individuals. Specifically, a time unit at a random position in a offspring individual is randomly selected, and the code of that time unit is randomly inserted before a time unit at another position. As shown in Table 3, the code -1 of the fifth time unit is inserted before the code 2 of the third time unit, forming the inserted offspring individual.

[0102] Table 3

[0103]

[0104] 4. Mutation Operator: Mutating each inserted offspring individual produces multiple mutated new offspring individuals. This prevents the algorithm from falling into local optima and increases regional diversity, a key factor in enabling global exploration. Specifically, two time units are randomly selected from a new offspring individual and their codes are swapped. As shown in Table 4, the code of the fifth time unit is swapped with the code of the third time unit, resulting in a mutated new offspring individual. The fitness value of each new offspring individual is then calculated.

[0105] Table 4

[0106]

[0107] 5. New blood operator: In order to ensure the diversity of regional individual evolution, the new blood operator is selected to randomly generate multiple new individuals and add them to the parent individuals.

[0108] 5. New population generation step: Merge the new offspring individuals with the parent individuals to generate a temporary population. To avoid slow calculations due to the increasing number of individuals in the temporary population, the number of individuals in the temporary population is compared with a preset threshold. If the number of individuals in the temporary population is greater than the preset threshold, the fitness value of each individual in the temporary population is sorted by size, and the individuals exceeding the threshold are deleted from the sorted temporary population in descending order to obtain a new population. This step can be understood as setting a size-maintaining operator for the regions. When the number of regions reaches a certain number, they are sorted from small to large according to their fitness value, and the redundant individuals at the end are deleted.

[0109] 6. Optimal solution generation step: Based on the new population, the fitness value calculation and offspring individual generation steps and the new population generation step are repeated until the preset number of iterations is reached. In other words, the number of evolutions (iterations) is used as the condition for the algorithm to terminate. When the number of iterations exceeds a certain number, the algorithm is terminated and the individual with the smallest fitness value in the new population when the preset number of iterations is reached is used as the optimal solution of the comprehensive objective function, thereby maximizing the total ore transportation volume and minimizing the total voyage cost.

[0110] Based on the actual scale of operations, data is generated and numerical experiments are conducted to verify the applicability of the voyage optimization method (involving a comprehensive objective function and a non-dominated sorting genetic algorithm) for dry bulk carrier routes using the new fuel of the present invention.

[0111] This case study is based on two key parameters: the voyage timeframe, T, and the voyage duration (or voyage time span), P. The detailed configuration is shown in Table 5. Other basic parameters are generated or selected based on the actual range: the fleet size is set to 16 ships; the loading capacity of ore ports collected from historical data is averaged over each time period, resulting in a ship loading capacity, Q, of 210,000 tons; the speed set, V, is the set of real numbers {9, 10, 11, 12, 13}; the cost is the unit fuel consumption collected from historical data; the unloading time, t0, is set to 5 days; and the minimum and maximum voyage times, as well as the mean and variance of the voyage times, are all derived from historical data.

[0112] Table 5

[0113]

[0114] The computation time in the experimental results does not include the data processing time. After weighing the computation time and solution quality, the parameters of NSGA-II are set as follows: population size 50; iteration time 10; crossover rate 0.9; mutation rate 0.9; and variation rate 0.1.

[0115] The performance of effective inequalities (14) and (15) is then evaluated through computational experiments. As shown in Table 6, the computational results for different configurations of T and P are reported. "Time (1)", "Gap (1)", "Time (2)", and "Gap (2)" are the computational time (s) and optimization degree of the solution with and without effective inequalities, respectively. By comparison, it can be seen that the good effect of effective inequalities in accelerating the solution process is demonstrated. Due to the addition of effective inequalities, the computational time is reduced from 3600 seconds to only 278.58 seconds.

[0116] Table 6

[0117]

[0118]

[0119] The effectiveness of the NSGA-II algorithm was also verified. Table 7 lists multiple solutions using the CPLEX algorithm and the NSGA-II algorithm used in this paper. It can be seen that the NSGA-II algorithm can provide more Pareto solutions within a reasonable computation time, allowing managers to make trade-offs based on their actual needs.

[0120] Table 7

[0121]

[0122] The present invention also relates to a voyage optimization system for new fuel dry bulk carrier routes. The system corresponds to the above-mentioned voyage optimization method for new fuel dry bulk carrier routes and can be understood as a system for implementing the above-mentioned method. The system includes a parameter acquisition module, an objective function establishment module, an initial population generation module, a fitness value calculation and offspring individual generation module, a new population generation module and an optimal solution calculation module connected in sequence. Specifically,

[0123] The parameter acquisition module acquires the ship parameters and navigation parameters of the new fuel dry bulk carrier, wherein the ship parameters include the maximum loading capacity of the ship, the fleet size, the minimum number of voyages, and the cumulative loading capacity of the ship at the loading port; the navigation parameters include the starting time of the voyage, the duration of the voyage, and the cost of the voyage;

[0124] The objective function establishment module constructs decision variables based on a determination result of whether a ship has started sailing at the start sailing time of the voyage and the sailing duration, and establishes a first objective function based on the maximum loading capacity of the ship and the decision variables under multiple constraints established based on the value range of the decision variables, the limited range of the fleet size, the minimum number of voyages, and the limited range of the cumulative loading capacity of the ship at the loading port. A second objective function is established based on the voyage cost determined by the start sailing time and the sailing duration of the voyage and the decision variables, and then the first objective function and the second objective function are combined based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function;

[0125] The initial population generation module divides the total sailing time of the ship into multiple time periods in chronological order to generate an initial population, and regards each time period in the initial population as an individual;

[0126] The fitness value calculation and offspring individual generation module calculates the fitness value of each individual in the initial population according to the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and uses the genetic operator in the non-dominated sorting genetic algorithm to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and calculates the fitness value of each new offspring individual;

[0127] The new population generation module merges the new offspring individuals with the parent individuals to generate a temporary population, and compares the number of individuals in the temporary population with a preset number threshold. If the number of individuals in the temporary population is greater than the preset number threshold, the fitness value of each individual in the temporary population is sorted according to size, and the individuals exceeding the number threshold in the sorted temporary population are deleted in order from small to large to obtain a new population;

[0128] The optimal solution calculation module is based on the new population and repeats the fitness value calculation and the work of the offspring individual generation module and the new population generation module until a preset number of iterations is reached. The individual with the smallest fitness value in the new population when the preset number of iterations is reached is used as the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost.

[0129] Preferably, in the fitness value calculation and offspring individual generation module, the genetic operators include a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator; and using the genetic operators in the non-dominated sorting genetic algorithm to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals specifically includes:

[0130] S1: The selection operator selects multiple individuals as parent individuals from the initial population based on the fitness value and adopts the roulette wheel selection method;

[0131] S2: The new blood operator randomly generates multiple new individuals and adds them to the parent individuals;

[0132] S3: Determine whether to perform a crossover operation based on the randomly generated probability value and the preset crossover rate. If yes, the crossover operator randomly selects two individuals from the parent individuals, then randomly selects two time units with relative positions from the two individuals, and swaps the codes of these two time units to generate two offspring individuals. If no, the number of parent individuals remains unchanged, and all individuals in the parent individuals are used as offspring individuals.

[0133] S4: Repeat step S3 until the preset number of iterations is reached to obtain multiple offspring individuals;

[0134] S5: The insertion operator performs an insertion operation on each offspring individual to obtain multiple inserted offspring individuals;

[0135] S6: The mutation operator performs mutation operations on each inserted offspring individual to obtain multiple new offspring individuals.

[0136] Preferably, the parameter acquisition module further acquires port parameters, which include the initial inventory of the loading port and the loading capacity of the loading port at a certain moment.

[0137] Preferably, in the objective function establishment module, the multiple constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, a sixth constraint, a seventh constraint and an eighth constraint; the first constraint is constructed based on the decision variable and a preset numerical threshold, the second constraint is constructed based on the decision variable and the fleet size, the third constraint is constructed based on the decision variable and the minimum number of voyages, the fourth constraint is constructed based on the cumulative loading volume of the ship at the loading port and the maximum loading volume of the ship, the fifth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the initial inventory of the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variable, the sixth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variable, the seventh constraint is constructed based on the initial inventory of the loading port, the maximum loading volume of the ship and the decision variable, and the eighth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the maximum loading volume of the ship and the decision variable.

[0138] Preferably, in the initial population generation module, each time period is further divided into a plurality of time units according to the time sequence, and each time unit is encoded to obtain the encoded time units.

[0139] The present invention provides an objective and scientific voyage optimization method and system for new fuel dry bulk carrier routes, with the goals of maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost, and taking the value range of decision variables, the limit range of fleet size, the minimum number of voyages, the limit range of the cumulative loading volume of the ship at the loading port, etc. as constraints, establishes the first and second objective functions, and then constructs a comprehensive objective function, and designs a customized non-dominated sorting genetic algorithm to obtain the optimal solution of the comprehensive objective function, so as to maximize the total dry bulk cargo transshipment volume while minimizing the total voyage cost, which can effectively improve operational efficiency and reduce costs.

[0140] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be included in the scope of protection of the patent for the present invention.

Claims

1. A voyage optimization method for a new fuel dry bulk carrier route, characterized in that: The following steps are involved: Parameter acquisition step: obtaining ship parameters and navigation parameters of the new fuel dry bulk carrier, wherein the ship parameters include the maximum loading capacity of the ship, fleet size, minimum number of voyages, and cumulative loading capacity of the ship at the loading port; the navigation parameters include the starting time of the voyage, the duration of the voyage, and the voyage cost; Objective function establishment steps: constructing decision variables based on the judgment result of whether the ship starts sailing at the starting sailing time of the voyage and the sailing duration, and under multiple constraints established by the value range of the decision variables, the limit range of the fleet size, the minimum number of voyages, and the limit range of the cumulative loading volume of the ship at the loading port, establishing a first objective function based on the maximum loading capacity of the ship and the decision variables, and establishing a second objective function based on the voyage cost determined by the starting sailing time and the sailing duration of the voyage and the decision variables, and then combining the first objective function and the second objective function based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function; Initial population generation step: Divide the total sailing time of the ship into multiple time periods in chronological order to generate the initial population, and treat each time period in the initial population as an individual; Fitness value calculation and offspring individual generation step: the fitness value of each individual in the initial population is calculated according to the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and the genetic operator in the non-dominated sorting genetic algorithm is used to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and the fitness value of each new offspring individual is calculated; New population generation step: merge the new offspring individuals with the parent individuals to generate a temporary population, and compare the number of individuals in the temporary population with the preset number threshold. If the number of individuals in the temporary population is greater than the preset number threshold, sort the fitness value of each individual in the temporary population by size, and delete the individuals that exceed the number threshold in the sorted temporary population from small to large to obtain a new population; Optimal solution calculation steps: Based on the new population, the fitness value calculation and offspring individual generation steps and the new population generation step are repeated until the preset number of iterations is reached. The individual with the smallest fitness value in the new population when the preset number of iterations is reached is taken as the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost.

2. The voyage optimization method for new fuel dry bulk carrier routes according to claim 1, characterized in that: In the fitness value calculation and offspring individual generation steps, the genetic operators include a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator; using the genetic operators in the non-dominated sorting genetic algorithm to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals specifically includes: S1: The selection operator selects multiple individuals as parent individuals from the initial population based on the fitness value and adopts the roulette wheel selection method; S2: The new blood operator randomly generates multiple new individuals and adds them to the parent individuals; S3: Determine whether to perform a crossover operation based on the randomly generated probability value and the preset crossover rate. If yes, the crossover operator randomly selects two individuals from the parent individuals, then randomly selects two time units with relative positions from the two individuals, and swaps the codes of these two time units to generate two offspring individuals. If no, the number of parent individuals remains unchanged, and all individuals in the parent individuals are used as offspring individuals. S4: Repeat step S3 until the preset number of iterations is reached to obtain multiple offspring individuals; S5: The insertion operator performs an insertion operation on each offspring individual to obtain multiple inserted offspring individuals; S6: The mutation operator performs mutation operations on each inserted offspring individual to obtain multiple new offspring individuals.

3. The voyage optimization method for a new fuel dry bulk carrier route according to claim 1 or 2, characterized in that: In the parameter acquisition step, port parameters are also acquired, and the port parameters include the initial inventory of the loading port and the loading capacity of the loading port at a certain moment.

4. The voyage optimization method for new fuel dry bulk carrier routes according to claim 3, characterized in that: In the objective function establishment step, the multiple constraints include a first constraint, a second constraint, a third constraint and a fourth constraint. The first constraint is constructed based on the decision variable and a preset numerical threshold, the second constraint is constructed based on the decision variable and the fleet size, the third constraint is constructed based on the decision variable and the minimum number of voyages, and the fourth constraint is constructed based on the cumulative loading volume of the ship at the loading port and the maximum loading volume of the ship.

5. The voyage optimization method for new fuel dry bulk carrier routes according to claim 4, characterized in that: In the objective function establishment step, the multiple constraints also include a fifth constraint, a sixth constraint, a seventh constraint and an eighth constraint. The fifth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the initial inventory of the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variables. The sixth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship and the decision variables. The seventh constraint is constructed based on the initial inventory of the loading port, the maximum loading volume of the ship and the decision variables. The eighth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the maximum loading volume of the ship and the decision variables.

6. The voyage optimization method for a new fuel dry bulk carrier route according to claim 1, characterized in that: In the initial population generation step, each time period is further divided into a plurality of time units according to the time sequence, and each time unit is encoded to obtain the encoded time units.

7. A voyage optimization system for dry bulk carrier routes using new fuels, characterized in that: It includes a parameter acquisition module, an objective function establishment module, an initial population generation module, a fitness value calculation and offspring individual generation module, a new population generation module and an optimal solution calculation module, which are connected in sequence. The parameter acquisition module acquires the ship parameters and navigation parameters of the new fuel dry bulk carrier, wherein the ship parameters include the maximum loading capacity of the ship, the fleet size, the minimum number of voyages, and the cumulative loading capacity of the ship at the loading port; the navigation parameters include the starting time of the voyage, the duration of the voyage, and the cost of the voyage; The objective function establishment module constructs decision variables based on a determination result of whether a ship has started sailing at the start sailing time of the voyage and the sailing duration, and establishes a first objective function based on the maximum loading capacity of the ship and the decision variables under multiple constraints established based on the value range of the decision variables, the limited range of the fleet size, the minimum number of voyages, and the limited range of the cumulative loading capacity of the ship at the loading port. A second objective function is established based on the voyage cost determined by the start sailing time and the sailing duration of the voyage and the decision variables, and then the first objective function and the second objective function are combined based on a preset weight coefficient and a weighted sum method to obtain a comprehensive objective function; The initial population generation module divides the total sailing time of the ship into multiple time periods in chronological order to generate an initial population, and regards each time period in the initial population as an individual; The fitness value calculation and offspring individual generation module calculates the fitness value of each individual in the initial population according to the first objective function value and its weight coefficient, and the second objective function value and its weight coefficient, and uses the genetic operator in the non-dominated sorting genetic algorithm to perform a neighborhood search evolution operation on multiple individuals in the initial population to generate multiple new offspring individuals, and calculates the fitness value of each new offspring individual; The new population generation module merges the new offspring individuals with the parent individuals to generate a temporary population, and compares the number of individuals in the temporary population with a preset number threshold. If the number of individuals in the temporary population is greater than the preset number threshold, the fitness value of each individual in the temporary population is sorted according to size, and the individuals exceeding the number threshold in the sorted temporary population are deleted in order from small to large to obtain a new population; The optimal solution calculation module is based on the new population and repeats the fitness value calculation and the work of the offspring individual generation module and the new population generation module until a preset number of iterations is reached. The individual with the smallest fitness value in the new population when the preset number of iterations is reached is used as the optimal solution of the comprehensive objective function, thereby maximizing the total dry bulk cargo transshipment volume and minimizing the total voyage cost.

8. The voyage optimization system for new fuel dry bulk carrier routes according to claim 7, characterized in that: In the fitness value calculation and offspring individual generation module, the genetic operators include a selection operator, a new blood operator, a crossover operator, an insertion operator, and a mutation operator; using the genetic operators in the non-dominated sorting genetic algorithm to perform neighborhood search evolution operations on multiple individuals in the initial population to generate multiple new offspring individuals specifically includes: S1: The selection operator selects multiple individuals as parent individuals from the initial population based on the fitness value and adopts the roulette wheel selection method; S2: The new blood operator randomly generates multiple new individuals and adds them to the parent individuals; S3: Determine whether to perform a crossover operation based on the randomly generated probability value and the preset crossover rate. If yes, the crossover operator randomly selects two individuals from the parent individuals, then randomly selects two time units with relative positions from the two individuals, and swaps the codes of these two time units to generate two offspring individuals. If no, the number of parent individuals remains unchanged, and all individuals in the parent individuals are used as offspring individuals. S4: Repeat step S3 until the preset number of iterations is reached to obtain multiple offspring individuals; S5: The insertion operator performs an insertion operation on each offspring individual to obtain multiple inserted offspring individuals; S6: The mutation operator performs mutation operations on each inserted offspring individual to obtain multiple new offspring individuals.

9. The voyage optimization system for dry bulk carrier routes using new fuel according to claim 7 or 8, characterized in that: The parameter acquisition module also acquires port parameters, which include the initial inventory of the loading port and the loading capacity of the loading port at a certain moment.

10. The voyage optimization system for new fuel dry bulk carrier routes according to claim 9, characterized in that: In the objective function establishment module, the multiple constraints include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, a sixth constraint, a seventh constraint, and an eighth constraint; the first constraint is constructed based on a decision variable and a preset numerical threshold, the second constraint is constructed based on the decision variable and the fleet size, the third constraint is constructed based on the decision variable and the minimum number of voyages, the fourth constraint is constructed based on the cumulative loading volume of the ship at the loading port and the maximum loading volume of the ship, the fifth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the initial inventory of the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship, and the decision variable, the sixth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the loading capacity of the loading port at a certain time, the maximum loading volume of the ship, and the decision variable, the seventh constraint is constructed based on the initial inventory of the loading port, the maximum loading volume of the ship, and the decision variable, and the eighth constraint is constructed based on the cumulative loading volume of the ship at the loading port, the maximum loading volume of the ship, and the decision variable.

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