CN-121998525-A - Method and system for optimizing fleet route allocation

CN121998525ACN 121998525 ACN121998525 ACN 121998525ACN-121998525-A

Abstract

The invention provides a fleet route allocation optimization method and system, which comprises the steps of firstly obtaining ship data of ships in a fleet and route data of each route, taking the minimum total biofuel consumption of the fleet in a planning period and the minimum total economic loss caused by the fact that the maximum deployment quantity of the routes is not met as targets, respectively establishing a first objective function and a second objective function under a plurality of constraint conditions established by taking the total number limit of the ships deployed each route each year, the fuel consumption limit, the route change total number limit of the fleet in any year and the CII grade standard reaching limit of each ship as constraints, constructing a multi-objective optimization model based on the first objective function and the second objective function, calculating the multi-objective optimization mathematical model by adopting a non-dominant sequencing genetic algorithm to obtain a pareto optimal solution, and selecting a final fleet route allocation scheme according to the pareto optimal solution, thereby realizing the dual minimization of the total biofuel consumption and the total economic loss.

Inventors

ZHANG YANFEI
KE JIA
JIN JIANGANG
GAO YULING
HUANG ZHENPING
GE JIAPING
HE PING

Assignees

上海船舶运输科学研究所有限公司
中远海运集装箱运输有限公司
上海交通大学

Dates

Publication Date: 20260508
Application Date: 20251205

Claims (10)

1. The fleet route allocation optimization method is characterized by comprising the following steps of: The method comprises the steps of acquiring ship data of each ship in a fleet and route data of each route, wherein the ship data comprises biofuel values required by the ship in a certain year, and the route data comprises the maximum number of ships deployed on each route, the number of ships deployed on each route in a certain year and economic loss of each ship caused by less deployment of one ship on the route; A multi-objective optimization mathematical model building step of building a multi-objective optimization mathematical model based on a first objective function according to biofuel values required by ships in a certain year under a plurality of constraint conditions established by taking the total number limit of ships deployed each course each year, the fuel consumption limit, the course change total number limit of the fleet in any year and the CII grade standard reaching limit of each ship as constraint, building a second objective function according to the maximum number of ships deployed on the course, the number of ships deployed on the course in a certain year and the economic loss of each ship; The method comprises the steps of constructing an initial parent population consisting of a plurality of individuals, wherein each individual represents an annual fleet route allocation scheme, and the annual fleet route allocation scheme comprises the steps of randomly allocating the ships meeting the minimum ship deployment number requirement for each route until all routes reach the minimum ship deployment number requirement; The method comprises the steps of calculating an objective function value, namely calculating a first objective function value and a second objective function value corresponding to each individual in an initial parent population according to the first objective function and the second objective function, and carrying out rapid non-dominant sorting on all the individuals in the initial parent population based on the calculated first objective function value and second objective function value, so as to divide the initial parent population into a plurality of first non-dominant levels arranged from high to low; The generation step of temporary population, namely processing the current parent population according to a first non-dominant level and adopting selection, crossover and mutation operations in a non-dominant sorting genetic algorithm to generate a child population, combining the current parent population with the child population to form a temporary population; the method comprises the steps of selecting a temporary population, namely, selecting a first objective function value and a second objective function value corresponding to each individual in the temporary population, and selecting a crowding degree distance according to the first objective function value and the second objective function value corresponding to each individual in the temporary population; A new parent population generation step, namely screening a predetermined number of individuals from the temporary population to form a new parent population according to a second non-dominant level and a crowding degree distance by adopting a preset screening rule; And an optimal solution set calculation step, namely repeatedly executing a temporary population generation step, a non-dominant sorting and crowding degree distance calculation step and a new parent population generation step based on the new parent population until the preset iteration times are reached, taking an individual with the smallest objective function value in the new parent population when the preset iteration times are reached as a pareto optimal solution set of a multi-objective optimized mathematical model, and selecting a final fleet route allocation scheme according to the pareto optimal solution set, wherein the scheme realizes double minimization of the total biofuel consumption and the total economic loss on the premise of meeting all constraint conditions.
2. The fleet line allocation optimization method according to claim 1, wherein in the multi-objective optimization mathematical model creation step, the plurality of constraints include a first constraint and a second constraint that are created with a constraint on a total number of vessels deployed per line per year, a third constraint, a fourth constraint and a fifth constraint that are created with a constraint on fuel consumption, a sixth constraint that is created with a constraint on a total number of line changes of the fleet at any one year, and a seventh constraint, an eighth constraint and a ninth constraint that are created with a constraint on a carbon intensity index level achievement of each vessel.
3. The method according to claim 2, wherein the data obtaining step further obtains CII regulation data, the CII regulation data including a target CII value of the ship in a certain year, an actual CII value of the ship in a certain year, and a required CII value of the ship in a certain year, calculates a boundary value of the ship reaching a CII level C in a certain year and a boundary value of the ship reaching a CII level D in a certain year according to the required CII value, the ship data further including a total fuel consumption value required by the ship in a certain year, a conventional fuel value required by the ship in a certain year, a biofuel usage amount of the ship in a certain year, a load ton of the ship, and a total annual navigational distance of the ship, the line data further including a minimum number of ships deployed on each line, and a maximum number of times the ship is allowed to change the line; In the multi-objective optimized mathematical model building step, the first constraint condition is built according to the maximum number of vessels deployed on the airlines and the number of vessels deployed on the airlines in a certain year, and the second constraint condition is built according to the minimum number of vessels deployed on the airlines and the number of vessels deployed on the airlines in a certain year, so that the number of vessels deployed on each airline in each year is ensured to be not smaller than the minimum number of vessels deployed on the airlines and not larger than the maximum number of vessels deployed on the airlines; The third constraint condition is constructed according to a total fuel consumption value required by the ship in a certain year, a biofuel value required by the ship in a certain year and a traditional fuel value required by the ship in a certain year, the fourth constraint condition is constructed according to the biofuel value required by the ship in a certain year, and the fifth constraint condition is constructed according to the traditional fuel value required by the ship in a certain year so as to ensure that the total fuel consumption of the ship is the sum of the biofuel usage amount and the traditional fuel usage amount, and the biofuel usage amount and the traditional fuel usage amount are both non-negative numbers; The sixth constraint condition is constructed according to the biofuel usage amount of the ship in a certain year and the maximum number of times the fleet is allowed to change the route, so that the total number of times the fleet is allowed to change the route in a planning period does not exceed the preset maximum number of times; The seventh constraint condition is constructed according to a target CII value of the ship in a certain year and an actual CII value of the ship in a certain year, the eighth constraint condition is constructed according to the actual CII value of the ship in a certain year, a boundary value of the ship with the CII grade of C, the target CII value of the ship in a certain year and a boundary value of the ship with the CII grade of D in a certain year, and the ninth constraint condition is constructed according to the actual CII value of the ship in a certain year, the target CII value of the ship in a certain year, a biofuel value required by the ship in a certain year, a total fuel consumption value required by the ship in a certain year, the carrying capacity of the ship and the total annual sailing distance of the ship so as to ensure that the CII grade of any ship in any single year cannot be E grade and the CII grade cannot be D in three consecutive years.
4. The method according to claim 1, wherein in the step of generating the new parent population, the screening rule includes selecting all individuals in the non-dominant hierarchy layer by layer in order of the non-dominant hierarchy from high to low, and selecting the individuals in the non-dominant hierarchy in order of the crowdedness distance from high to low until the total number of selected individuals reaches the predetermined number when the total number of selected individuals in a certain non-dominant hierarchy exceeds the predetermined number.
5. The method of optimizing fleet line allocation according to claim 1, wherein in the initial parent population constructing step, each individual is encoded with an integer number of codes, the length of the codes being equal to the total number of vessels in the fleet.
6. A fleet route allocation optimization system is characterized by comprising a data acquisition module, a multi-objective optimization mathematical model building module, an initial parent population building module, an objective function value calculation module, a temporary population generation module, a non-dominant sorting and crowding degree distance calculation module, a new parent population generation module and an optimal solution set calculation module which are connected in sequence, The system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module acquires ship data of each ship in a fleet and route data of each route, and the ship data comprises biofuel values required by the ship in a certain year; The multi-objective optimization mathematical model building module aims at minimizing the total biofuel consumption of a fleet in a planning period and minimizing the total economic loss caused by the fact that the maximum deployment quantity of the airlines is not met, builds a first objective function according to biofuel values required by the vessels in a certain year under a plurality of constraint conditions of constraint building by taking the total number limit of vessels deployed each airline each year, the fuel consumption limit, the total number limit of airlines changed by the fleet in any year and the CII grade standard reaching limit of each vessel as constraint building, builds a second objective function according to the maximum number of vessels deployed on the airlines, the number of vessels deployed on the airlines in a certain year and the economic loss of each vessel, and builds a multi-objective optimization mathematical model based on the first objective function and the second objective function; The initial parent population construction module constructs an initial parent population consisting of a plurality of individuals, wherein each individual represents an annual fleet route allocation scheme which comprises the steps of randomly allocating the ships meeting the minimum ship deployment number requirement for each route until all routes reach the minimum ship deployment number requirement; The objective function value calculation module is used for respectively calculating a first objective function value and a second objective function value corresponding to each individual in the initial parent population according to the first objective function and the second objective function, and carrying out rapid non-dominant sorting on all the individuals in the initial parent population based on the calculated first objective function value and second objective function value, so as to divide the initial parent population into a plurality of first non-dominant levels arranged from high to low; The temporary population generation module processes the current parent population according to a first non-dominant level and by adopting selection, crossover and mutation operations in a non-dominant ordering genetic algorithm to generate a child population, and combines the current parent population and the child population to form a temporary population; The non-dominant ranking and crowding distance calculating module is used for carrying out rapid non-dominant ranking on all individuals in the temporary population based on a first objective function value and a second objective function value corresponding to each individual in the temporary population, and dividing the temporary population into a plurality of second non-dominant levels arranged from high to low; The new parent population generation module screens out a preset number of individuals from the temporary population according to the second non-dominant level and the crowding degree distance by adopting a preset screening rule to form a new parent population; The optimal solution set calculation module is used for repeatedly executing the temporary population generation module, the non-dominant sorting and crowding degree distance calculation module and the new parent population generation module based on the new parent population, taking an individual with the smallest objective function value in the new parent population when the preset iteration number is reached as the pareto optimal solution set of the multi-objective optimal mathematical model until the preset iteration number is reached, and selecting a final fleet route allocation scheme according to the pareto optimal solution set.
7. The fleet line allocation optimization system according to claim 6, wherein the plurality of constraints in the multi-objective optimization mathematical model creation module include a first constraint and a second constraint that are created with a constraint on a total number of vessels deployed per line per year, a third constraint, a fourth constraint, and a fifth constraint that are created with a constraint on fuel consumption, a sixth constraint that is created with a constraint on a total number of line changes of the fleet at any one year, and a seventh constraint, an eighth constraint, and a ninth constraint that are created with a constraint on a carbon strength index level achievement of each vessel.
8. The fleet line allocation optimization system according to claim 7, wherein the data acquisition module further acquires CII regulation data including a target CII value for the ship at a certain year, an actual CII value for the ship at a certain year, and a required CII value for the ship at a certain year, calculates a boundary value for the ship to reach CII class C at a certain year and a boundary value for the ship to reach CII class D at a certain year, respectively, based on the required CII values, and further includes a total fuel consumption value required for the ship at a certain year, a conventional fuel value required for the ship at a certain year, a bio-fuel usage amount for the ship at a certain year, a load of the ship, and a total annual voyage distance for the ship, and the line data further includes a minimum number of ships deployed on each line, and a maximum number of times the fleet is allowed to change the line; In the multi-objective optimized mathematical model building module, the first constraint condition is built according to the maximum number of vessels deployed on the airlines and the number of vessels deployed on the airlines in a certain year, and the second constraint condition is built according to the minimum number of vessels deployed on the airlines and the number of vessels deployed on the airlines in a certain year so as to ensure that the number of vessels deployed on each airline in each year is not smaller than the minimum number of vessels deployed on the airlines and not larger than the maximum number of vessels deployed on the airlines; The third constraint condition is constructed according to a total fuel consumption value required by the ship in a certain year, a biofuel value required by the ship in a certain year and a traditional fuel value required by the ship in a certain year, the fourth constraint condition is constructed according to the biofuel value required by the ship in a certain year, and the fifth constraint condition is constructed according to the traditional fuel value required by the ship in a certain year so as to ensure that the total fuel consumption of the ship is the sum of the biofuel usage amount and the traditional fuel usage amount, and the biofuel usage amount and the traditional fuel usage amount are both non-negative numbers; The sixth constraint condition is constructed according to the biofuel usage amount of the ship in a certain year and the maximum number of times the fleet is allowed to change the route, so that the total number of times the fleet is allowed to change the route in a planning period does not exceed the preset maximum number of times; The seventh constraint condition is constructed according to a target CII value of the ship in a certain year and an actual CII value of the ship in a certain year, the eighth constraint condition is constructed according to the actual CII value of the ship in a certain year, a boundary value of the ship with the CII grade of C, the target CII value of the ship in a certain year and a boundary value of the ship with the CII grade of D in a certain year, and the ninth constraint condition is constructed according to the actual CII value of the ship in a certain year, the target CII value of the ship in a certain year, a biofuel value required by the ship in a certain year, a total fuel consumption value required by the ship in a certain year, the carrying capacity of the ship and the total annual sailing distance of the ship so as to ensure that the CII grade of any ship in any single year cannot be E grade and the CII grade cannot be D in three consecutive years.
9. The fleet route allocation optimization system according to claim 6, wherein the new parent population generation module wherein the screening rules comprise selecting all individuals in the non-dominant hierarchy layer by layer in order of the non-dominant hierarchy from high to low, and selecting individuals in a non-dominant hierarchy in order of the crowdedness distance from high to low until the total number of selected individuals reaches the predetermined number when selecting all individuals in a non-dominant hierarchy results in the total number exceeding the predetermined number.
10. The fleet route allocation optimization system according to claim 9, wherein each individual is encoded in the initial parent population building module in an integer encoding manner with a length equal to the total number of vessels in the fleet.

Description

Method and system for optimizing fleet route allocation Technical Field The invention relates to the technical field of shipping management, in particular to a fleet route allocation optimization method and system. Background With increasing global concern over climate change, the International Maritime Organization (IMO) has implemented carbon strength regulations (CII regulations) since 2023, according to which international operating vessels will be classified into 5 grades, A, B, C, D, E, the vessels cannot be rated D for consecutive 3 years, or rated E for any one year, if the vessels are rated D for three consecutive years, or rated E for any one year, the vessels are judged to be out of compliance, and measures are required to raise the grade of CII to C. In addition, according to CII regulations, the reference line of the ship class per year is further lowered, i.e. if a ship is kept operating unchanged, no operation emission reduction measures are taken, and the ship is also changed to class E. Therefore, it is necessary to use clean energy or implement energy saving technology for operating ships to avoid the occurrence of the non-compliance. In airlines, fleet scheduling is a complex process and a practical engineering problem. As shown in fig. 1, a shipping company has a total of V vessels that can be assigned to R routes, each year. Finally, as shown in FIG. 2, each route will be assigned to V vessels. However, the number of vessels allocated to each course each year and the specific allocation of vessels varies. The same type of vessel can be deployed to multiple airlines. However, port distances and planned speeds differ between airlines, which results in different fuel consumption effects at different airlines deployments. Furthermore, each line has maximum and minimum requirements on the number of deployed vessels. For example, deploying fewer vessels to a line may result in a continuous decrease in overall quality of service. Thus, it is a critical issue to ensure that the airlines meet maximum deployment requirements while minimizing economic impact. Furthermore, even if the same type of ship is deployed to the same route, the fuel consumption may vary due to the difference in the fuel consumption performance of the ship. For example, a serviced vessel may exhibit better fuel consumption performance than other vessels. Thus, deployment of vessels to different airlines can have different effects, particularly when considering the operational constraints imposed by international maritime organization carbon strength index (CII) regulations. In order to meet annual CII requirements, the entire fleet operation must take into account vessels that are both poorly fuel consuming and better to ensure compliance, thereby providing a solid foundation for subsequent fleet operations. To increase CII levels annually, biofuels may be used to reduce carbon emissions. However, the cost of biofuels is higher than conventional fuels. Therefore, minimizing the use of biofuels in a fleet has become an important goal. In summary, most of the existing fleet scheduling and optimization methods focus on the traditional economic objectives (such as cost minimization and profit maximization), or only consider the static energy efficiency constraint, but fail to systematically incorporate multiple factors such as the dynamic compliance requirement of CII regulations, the individual fuel consumption performance degradation and maintenance improvement effect of ships, the cost difference between biofuel and traditional fuel, etc. into the unified optimization framework. This limitation makes it difficult for existing methods to support shipping enterprises in making optimal fleet allocation schemes under CII regulations that compromise compliance, economy and operational feasibility. Therefore, there is an urgent need to develop a new optimization method to solve the above-mentioned complex decision problem of multiple objectives and constraints. Disclosure of Invention In order to solve the problems that the constraint of CII regulations and the influence of the change of the fuel consumption performance of the ship are not considered in the current fleet route layout optimization process, the invention provides a fleet route distribution optimization method, and the fleet route distribution scheme with CII compliance, minimum bio-fuel consumption and minimum route economic loss is achieved by considering the compliance layout of the fleet for the next years under the constraint of the CII regulations, so that the operation cost is effectively reduced. The invention also relates to a fleet route allocation optimization system. The technical scheme of the invention is as follows: The fleet route allocation optimization method is characterized by comprising the following steps of: The method comprises the steps of acquiring ship data of each ship in a fleet and route data of each route, wherein the ship data comprise