Search

CN-122022377-A - Travel scene package ticket project resource allocation method, system, equipment and storage medium

CN122022377ACN 122022377 ACN122022377 ACN 122022377ACN-122022377-A

Abstract

The invention provides a travel scene package ticket item resource allocation method, a system, equipment and a storage medium. And (3) taking the investment amount of each project as a decision variable, and constructing a multi-objective optimization model, wherein the objectives comprise maximizing total income, minimizing total risk and total capital occupation cost. Solving a multi-objective optimization model based on an NSGA-II algorithm to generate a pareto optimal solution set, wherein each solution in the pareto optimal solution set corresponds to a fund distribution scheme consisting of ticket-wrapping item resource distribution limits of all travel scenes; and providing a fund distribution scheme under different benefits, risks and fund occupation cost preferences based on the generated pareto optimal solution set. The invention can reasonably distribute limited project funds, balance the benefits, risks and cost, avoid making decisions by only artificial subjective judgment, provide data-driven decision support and realize the fine operation of the funds.

Inventors

  • ZHU WENBANG
  • LIN JIANHUA

Assignees

  • 携程旅游信息技术（上海）有限公司

Dates

Publication Date
20260512
Application Date
20260311

Claims (10)

  1. 1. The method for distributing the resources of the travel scene package ticket project is characterized by comprising the following steps: S110, acquiring a plurality of ticket-wrapping items to be allocated with funds, and constructing a profit function, a risk function and a funds occupation cost function of each ticket-wrapping item; S120, constructing a multi-objective optimization model by taking the amount of money to be invested in each ticket item as a decision variable, wherein the objectives of the multi-objective optimization model comprise maximizing the total income of all the ticket items, minimizing the total risk of all the ticket items and minimizing the total capital occupation cost of all the ticket items; s130, solving the multi-objective optimization model based on an NSGA-II algorithm to generate a pareto optimal solution set, wherein each solution in the pareto optimal solution set corresponds to a fund distribution scheme consisting of ticket-wrapping item resource distribution units of all travel scenes; And S140, providing a fund distribution scheme under different benefits, risks and fund occupation cost preference based on the generated pareto optimal solution set.
  2. 2. The travel scenario pack item resource allocation method according to claim 1, wherein in the step S110, the profit function is determined based on historical sales data, gross interest rate and market heat of the pack item, the risk function is determined based on risk factors comprehensively assessed by performance risk, market fluctuation risk and profit stability of the pack item, and the capital occupation cost function is determined based on the amount of investment, capital occupation period and capital cost rate of a company.
  3. 3. The travel scenario pack item resource allocation method according to claim 1, wherein the multi-objective optimization model in step S120 further comprises constraints including at least that the sum of the funds to be invested for all pack items does not exceed a preset total funds budget upper limit, and that the funds to be invested for a single pack item is not lower than its lowest investment limit and not higher than its highest investment limit.
  4. 4. The method for allocating resources to travel scene pack items according to claim 1, wherein said step S130 comprises: s131, initializing a population comprising a plurality of individuals, wherein each individual is a decision variable vector and represents a fund distribution scheme; s132, non-dominant ranking is carried out on the current population, individuals in the population are divided into different non-dominant layers, and the crowding degree of each individual in a target space is calculated; s133, selecting parent individuals from the current population based on the non-dominant layers and the crowding degree; S134, performing crossing and mutation operation on the selected parent individuals to generate a child population; S135, merging the parent population and the offspring population, performing non-dominant sorting and congestion degree calculation on the merged population, and selecting a new population with the same scale as the initial population as the next generation population according to the sorting result and the congestion degree; s136, repeating the steps S132 to S135 until the preset iteration times are reached or convergence conditions are met, and taking the pareto optimal solution set in the final generation population as a candidate set of the fund distribution scheme.
  5. 5. The method for allocating resources to travel scene pack items according to claim 4, wherein the non-dominant ranking in step S132 specifically comprises: s1321, for any two individuals p and q in the population, if all objective function values of the individual p are not inferior to the individual q and at least one objective function value is superior to the individual q, judging that the individual p dominates the individual q; S1322, dividing all individuals not subject to any other individuals into a first non-subject layer, then finding out the individuals not subject to any other individuals again as a second non-subject layer among the remaining individuals, and so on until all the individuals are layered; s1323, sequencing individuals in the same non-dominant layer according to each objective function value, and setting the crowding degree of the boundary individuals to infinity; s1324, for the middle individual, the crowding degree is equal to the sum of normalized difference values of two adjacent individuals on the target on each target function.
  6. 6. The method for allocating resources for travel scene pack ticket items according to claim 4, wherein in step S133, parent individuals are selected based on non-dominant layers and crowdedness, and a tournament selection method is adopted, wherein two individuals with non-dominant layers lower than a preset threshold are randomly selected, and if the two individuals are in the same non-dominant layer, the individual with higher crowdedness is selected.
  7. 7. The method for allocating resources for travel scene pack items according to claim 4, wherein the cross operation in step S134 uses a simulated binary cross SBX, and the mutation operation uses a polynomial mutation; when a new population is selected in step S135, individuals are selected from the non-dominant layers of the combined population from low to high, and when the individuals in a certain layer cannot be fully placed into the new population, the individuals with larger crowding degree in the layer are selected until the new population is filled.
  8. 8. A travel scenario pack item resource allocation system for implementing the method of any one of claims 1 to 7, comprising: The project parameter module is used for acquiring a plurality of ticket-wrapping projects of funds to be distributed, and constructing a profit function, a risk function and a funds occupation cost function of each ticket-wrapping project; the optimizing model module is used for constructing a multi-objective optimizing model by taking the amount of money to be invested in each ticket item as a decision variable, wherein the objectives of the multi-objective optimizing model comprise maximizing the total income of all the ticket items, minimizing the total risk of all the ticket items and minimizing the total capital occupation cost of all the ticket items; The algorithm solving module is used for solving the multi-objective optimization model based on an NSGA-II algorithm to generate a pareto optimal solution set, wherein each solution in the pareto optimal solution set corresponds to a fund distribution scheme consisting of the resource distribution units of the package ticket items of all travel scenes, and the method comprises the following steps of And the scheme output module is used for providing a fund distribution scheme under different benefits, risks and fund occupation cost preference based on the generated pareto optimal solution set.
  9. 9. A travel scene pack item resource allocation apparatus, comprising: A processor; a memory having stored therein executable instructions of the processor; Wherein the processor is configured to perform the steps of the travel scenario pack ticket project resource allocation method of any one of claims 1 to 7 via execution of the executable instructions.
  10. 10. A computer-readable storage medium storing a program, wherein the program when executed by a processor implements the steps of the travel scenario pack ticket project resource allocation method of any one of claims 1 to 7.

Description

Travel scene package ticket project resource allocation method, system, equipment and storage medium Technical Field The invention relates to the field of resource optimal allocation, in particular to a method, a system, equipment and a storage medium for allocating resources of travel scene package ticket items. Background In the service expansion of an online travel platform (OTA), particularly in the competitive environment of overseas markets, the "package item" becomes an important operation strategy. The package ticket project is characterized in that the platform acquires the dominant price or inventory from suppliers (such as scenic spots and scenic spots) in a certain period, a certain area and a certain range by adopting cooperation forms such as deposit, sponsorship fee, marketing fee, prestored money buying and buying, and the like, so that the project income is improved or the market share is increased. Such collaboration patterns present a competitive barrier for the platform, as well as placing higher demands on the funding management capabilities of the platform. However, ticketed items of different geographic areas tend to have distinct attributes. For example, tickets in some scenic spots have high gross profit, relatively small scale, considerable profits and scattered volumes, some scenic spots belong to new business states, lack historical data for reference, are greatly influenced by seasons or public opinion, have high risk levels, and some scenic spots tend to cooperate for a long time, and though the overall risk is controllable, the financial cost of the platform is high due to continuous cooperation. When the revenue management team faces the various types of package ticket projects in the same period, and under the condition of limited funds, how to reasonably distribute project funds, how to consider the maximization of the revenue, the minimization of the risk and the minimization of the cost of occupied funds become the core decision problem that each revenue management team must face. From a mathematical and operational perspective, this is essentially a Multi-objective optimization problem (MOP-objective Optimization Problem), requiring as many conflicting objectives as possible of revenue, risk and cost under limited resource (capital) constraints. In conventional business models, project funds are often distributed depending on subjective experience judgment of an expert. In the early stages of business development, the operation means with experience bias can really meet the rapidly-growing demands. However, as traffic volume increases, project funds management requires more stringent, refined operations. There may be numerous bias and misjudgments with human analysis alone, such as excessive focusing on high-yield projects and neglecting potential risks, or missing high-quality investment opportunities due to avoiding risks, and difficulty in systematically and quantitatively weighting multiple conflicting targets. Therefore, there is an urgent need for a method that can provide more data basis and interpretability for subjective, empirically-based decisions through quantitative analysis and scientific solution, providing insight into efficient use and allocation strategies of funds. In view of the above, the invention provides a method, a system, a device and a storage medium for allocating resources of travel scene package tickets. Disclosure of Invention Aiming at the problems in the prior art, the invention aims to provide a travel scene package ticket project resource allocation method, a system, equipment and a storage medium, which overcome the difficulty that the prior art relies on subjective experience to allocate funds and is difficult to balance among multiple conflict targets, realize quantitative allocation of funds among income, risk and cost, and remarkably improve the use efficiency of the funds. The embodiment of the invention provides a method for distributing resources of travel scene package tickets, which comprises the following steps: S110, acquiring a plurality of ticket-wrapping items to be allocated with funds, and constructing a profit function, a risk function and a funds occupation cost function of each ticket-wrapping item; S120, constructing a multi-objective optimization model by taking the amount of money to be invested in each ticket item as a decision variable, wherein the objectives of the multi-objective optimization model comprise maximizing the total income of all the ticket items, minimizing the total risk of all the ticket items and minimizing the total capital occupation cost of all the ticket items; s130, solving the multi-objective optimization model based on NSGA-II algorithm to generate a pareto optimal solution set, wherein each solution in the pareto optimal solution set corresponds to a fund distribution scheme consisting of ticket-wrapping item resource distribution units of various travel scenes, and And S140, providing