Product management method based on one-station inspection platform

Abstract

The invention relates to the technical field of logistics and supply chain management, and discloses a one-station inspection platform-based product management method, which comprises the following steps of S1, constructing an optimal transportation problem model, defining a resource circulation relationship between a supplier and an order, and calculating an optimal scheme allocated to the order by the supplier by taking the minimum transportation cost as a target; s2, based on a game theory model, strategy interaction among a plurality of suppliers is considered, a supplier resource allocation strategy is optimized through a game, and balance of resource scheduling is ensured; s3, a Markov decision process is adopted, and order circulation and resource scheduling strategies are adjusted through real-time feedback so as to adapt to dynamic changes of service requirements; and S4, carrying out multi-objective optimization. According to the invention, through a dynamic optimization and real-time data feedback mechanism based on the one-station inspection platform, efficient resource scheduling and order circulation are realized, the maximization of the resource utilization rate is ensured, the delivery timeliness is improved, and the cost and the service quality are optimized at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and supply chain management, and specifically to a product management method based on a one-stop inspection platform. Background Art

[0002] In modern society, with the rapid development of e-commerce and logistics industries, the efficiency of order flow and resource scheduling has become the core of competitiveness of major enterprises. In order to meet the growing market demand, enterprises need to maximize the use of existing resources and reduce operating costs while ensuring service quality and delivery timeliness. In this context, how to improve the efficiency of the entire supply chain through reasonable resource scheduling and optimization strategies has become an urgent problem to be solved.

[0003] In existing technologies, resource scheduling and order management usually rely on static models and pre-set rules. These methods mainly optimize time and cost by planning resource allocation and order flow in advance to ensure efficient operation of the supply chain. Through these technologies, enterprises can reduce resource waste to a certain extent, improve the timeliness of order delivery, and ensure the rational use of resources during operations. Most existing systems can handle standardized resource allocation tasks well, optimize order allocation, and reduce the need for manual intervention, thereby improving work efficiency.

[0004] However, there are still some shortcomings in the existing technology. In actual applications, market demand changes rapidly and supplier resources often fluctuate. The existing scheduling system is often unable to adapt to such changes, resulting in inflexible resource allocation and delayed order flow. In traditional solutions, due to the lack of real-time feedback mechanism, it is difficult for the system to adjust resource allocation in a timely manner, resulting in uneven burden on suppliers, some suppliers are overloaded, while other suppliers are idle. In addition, many traditional systems still focus on the optimization of a single goal, such as cost control or time management, and fail to fully consider the needs of multi-objective balance. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a product management method based on a one-stop inspection platform, which solves the problems of inflexible resource scheduling, delayed order flow and lack of real-time data feedback adjustment in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A product management method based on a one-stop inspection platform comprises the following steps: S1: Construct an optimal transportation problem model, define the resource flow relationship between suppliers and orders, and calculate the optimal solution for suppliers to allocate to orders with the goal of minimizing transportation costs; S2: Based on the game theory model, considering the strategic interactions among multiple suppliers, the supplier resource allocation strategy is optimized through game theory to ensure the balance of resource scheduling; S3: Adopting Markov decision process, adjusting order flow and resource scheduling strategies through real-time feedback to adapt to the dynamic changes of business needs; S4: Perform multi-objective optimization, comprehensively consider cost, time and service quality, calculate the optimal resource allocation plan, and ensure optimal resource scheduling in a changing market environment; S5: Dynamically adjust resource scheduling strategies based on real-time data to ensure optimization of order flow efficiency and resource utilization.

[0007] Preferably, the constructing of the optimal transportation problem model includes: Define the resource flow relationship between suppliers and orders, and establish a transportation cost matrix by considering the order delivery time and the supplier's service capacity; Through the optimal transportation problem model, the linear programming method is used to calculate the optimal supplier and order pairing plan to minimize the total transportation cost.

[0008] Preferably, the game theory-based model includes: Set the strategy for each supplier as the way to allocate resources, and analyze the competition and cooperation relationship between multiple suppliers; Use the Nash equilibrium model to evaluate the optimal strategy of each supplier in the resource allocation game; The Q-learning algorithm is used to dynamically optimize supplier strategies to ensure equilibrium during the game and achieve the optimal resource allocation solution.

[0009] Preferably, the Markov decision process includes: Define the state space of order flow, including order processing progress, supplier load and resource utilization; Defining the space of actions the platform can take, including decisions about selecting suppliers and allocating resources; The optimal resource scheduling strategy is solved through the Bellman equation in the Markov decision process to achieve dynamic resource adjustment and ensure the optimization of order flow when business demand changes.

[0010] Preferably, the multi-objective optimization includes: Taking cost, time and service quality as objective functions, multi-objective optimization methods are used to optimize them simultaneously; Use non-dominated sorting genetic algorithm to solve the Pareto optimal solution between multiple optimization objectives to ensure the optimal balance between different objectives; Dynamically adjust target weights based on real-time orders and supplier capabilities to ensure optimized resource scheduling in a changing market environment.

[0011] Preferably, the dynamically adjusting resource scheduling strategy includes: Analyze current resource scheduling status based on real-time order volume, supplier load, and resource usage data; The resource scheduling strategy is adjusted through adaptive algorithms to ensure that order processing time is minimized while ensuring resource utilization; the order flow path is dynamically optimized, and the real-time feedback mechanism enables the platform to adapt to demand fluctuations and adjust resource allocation in a timely manner.

[0012] Preferably, the steps of the Q-learning algorithm include: Define state space and action space. The state space includes supplier load, order progress and resource status, and the action space includes supplier selection and resource allocation. Set a reward function, evaluate the benefits of each supplier adopting a certain strategy through real-time feedback, and adjust the strategy based on the reward; continuously update the Q value through the Q-learning algorithm, so that suppliers can adjust resource allocation strategies according to current market conditions, thereby achieving the optimal game equilibrium.

[0013] Preferably, the Bellman equation includes: According to the state space and action space of the platform, calculate the transition probability P(s′|s,a) of taking action in each state; According to the immediate reward R(s,a), evaluate the benefit after taking a certain action in each state; Solve the Bellman equation to obtain the optimal state value V(s), and optimize resource scheduling by selecting the best action.

[0014] The present invention provides a product management method based on a one-stop inspection platform. It has the following beneficial effects: 1. The present invention enables the system to quickly respond to market demand fluctuations through real-time data feedback and dynamic optimization algorithms. Compared with the fixed scheduling method in the prior art, the present invention ensures the efficiency and flexibility of order flow and resource allocation. Especially in the case of unstable market demand, the system can automatically adjust the optimization plan to avoid order processing delays caused by changes in demand.

[0015] 2. The present invention innovatively uses a multi-objective optimization algorithm to comprehensively consider multiple objectives such as cost, delivery time and service quality, avoiding the overly single objective optimization in traditional solutions. By dynamically adjusting the weights, the present invention can minimize costs without sacrificing other objectives and ensure that orders are delivered on time and with high quality.

[0016] 3. By dynamically adjusting resource allocation and optimizing goals, the platform can not only reduce overall operating costs, but also ensure efficient order flow. Compared with the static cost control in the prior art, the present invention achieves dual optimization of cost and time, making the platform more accurate in resource use and effectively avoiding resource waste and ineffective operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Please see attached Figure 1 ,The embodiment of the present invention provides a product management method based on a one-stop inspection platform, including the following steps: S1: constructing an optimal transportation problem model, defining the resource flow relationship between suppliers and orders, and with the goal of minimizing the transportation cost, calculating the optimal solution assigned by the supplier to the order; S1 aims to optimize the matching between suppliers and orders by minimizing transportation costs, thereby improving the efficiency of overall resource allocation.

[0020] In the platform, the flow of resources between suppliers and orders is a crucial part. In order to ensure that the platform can manage supplier resources efficiently and accurately, the optimal transportation problem (OTP) model is used to optimize resource scheduling. This step defines the transportation cost matrix, calculates and determines the optimal match between each supplier and order, so as to reduce costs and improve resource utilization efficiency. The platform's resource scheduling not only considers costs, but also includes factors such as service quality and delivery time, further ensuring that the system can flexibly respond to different business needs.

[0021] In this embodiment, the purpose of constructing the optimal transportation problem model is to calculate the optimal pairing solution between suppliers and orders to ensure that the transportation cost is minimized. First, it is necessary to describe the resource flow relationship by establishing a transportation cost matrix between suppliers and orders.

[0022] In the optimal transportation problem model, the platform has m suppliers and n orders. The transportation cost between suppliers and orders can be calculated based on a variety of factors, usually including the order delivery time, supplier service capabilities, transportation costs, etc. Assume C ij represents the service cost of supplier i for order j.

[0023] In this matrix, C ij It does not just represent a fixed cost, but is dynamically adjusted based on real-time business data (such as the urgency of order demand, supplier service quality, etc.). For example, when an order has a strong time requirement, C ij The value of will increase accordingly, reflecting the additional cost required to meet the order time requirements. Similarly, the supplier's historical service quality can also affect C ij If the service quality provided by a supplier is poor, the corresponding element in the cost matrix will also increase, reducing the order allocation to that supplier.

[0024] The goal of the optimal transportation problem is to minimize the total transportation cost between all suppliers and orders. The objective function is as follows: Where: C ij represents the transportation cost of supplier i for order j, including time, cost and service quality; X ij is a decision variable. If order j is assigned to supplier i, then X ij =1, otherwise X ij =0; m is the number of suppliers; n is the number of orders.

[0025] To ensure that each order is processed by a supplier and each supplier can only process one order, add the following constraints: Each supplier can only process one order: This means that for each supplier i, only one order j is assigned to that supplier.

[0026] Each order can only be assigned to one supplier: This means that each order j can only be processed by one supplier i.

[0027] By solving this optimization problem, the system can derive the optimal resource allocation plan and determine the orders allocated to each supplier.

[0028] To solve this optimal transportation problem, linear programming (LP) methods such as the simplex method or other optimization algorithms can be used. These algorithms can effectively calculate the optimal solution required to minimize the objective function while satisfying the constraints. Through optimization calculations, the platform can accurately allocate resources, reduce costs and improve efficiency.

[0029] In the actual business process, order requirements and supplier status often change. Therefore, in order to cope with these changes, the platform will dynamically adjust the transportation cost matrix. For example, when an order has a temporary change (such as an increase in demand or urgent delivery), the platform will update the C ij The optimal transportation plan is recalculated based on the value of .

[0030] These adjustments are based on real-time data feedback from the platform, including the supplier's current load, resource usage, and the urgency of the order. By dynamically adjusting the transportation cost matrix, the platform can better respond to market demand, ensure that resources are reasonably allocated, and avoid resource waste or overload.

[0031] Assume there are 3 suppliers and 5 orders. The system will construct a 5x3 cost matrix based on each supplier's processing capacity, service quality and other information, where each element C ij Represents the processing cost of supplier i for order j. For example: Where: C 11 =10 means the cost for supplier 1 to process order 1 is 10; C 12 =15 means the cost for supplier 1 to process order 2 is 15; C 13 =20 means the cost for supplier 1 to process order 3 is 20; C 21 =12 means the cost for supplier 2 to process order 1 is 12; C 22 =18 means the cost for supplier 2 to process order 2 is 18; C 23 =15 means the cost for supplier 2 to process order 3 is 15; C 31 =11 means the cost for supplier 3 to process order 1 is 11; C 32 =14 means the cost for supplier 3 to process order 2 is 14; C 33 =19 means the cost for supplier 3 to process order 3 is 19.

[0032] The platform solves this matrix using a linear programming algorithm and ultimately determines which supplier is best suited to assign each order to ensure that the total transportation cost is minimized.

[0033] The optimal transportation problem model is not limited to basic cost minimization, but can be extended to more constraints and optimization objectives. For example, more factors such as transportation time and service quality can be introduced and optimized as additional objectives. At the same time, when supplier resources are limited, the supplier's capacity can also be used as one of the constraints to ensure that the system can make full use of the supplier's resources while avoiding overloading.

[0034] In addition, in some embodiments, the platform can also make predictions based on historical order data, market demand trends and other factors, and calculate and adjust the transportation cost matrix in advance to improve the response speed of the system.

[0035] By defining the transportation cost matrix and applying the linear programming optimization algorithm, the platform can effectively calculate the optimal supplier and order matching solution. The dynamic adjustment and real-time update mechanism ensures that the system can adapt to the ever-changing market demand and improve resource utilization efficiency and order processing capabilities.

[0036] S2: Based on the game theory model, considering the strategic interactions among multiple suppliers, the supplier resource allocation strategy is optimized through game theory to ensure the balance of resource scheduling; In a multi-supplier competition environment, models that only rely on minimizing costs are not sufficient to handle the strategic interaction between suppliers. Therefore, game theory models, especially Nash equilibrium models, are used in S2 to optimize supplier resource allocation and ensure balanced resource scheduling among multiple suppliers. This process can not only optimize the resource competition relationship between suppliers, but also achieve long-term stability and balance in supplier strategies.

[0037] In this embodiment, the core idea of ​​the game theory model is to optimize the decision-making of each supplier in resource allocation by simulating the competition and cooperation among multiple suppliers, and finally reach the equilibrium state of resource allocation through the game process. Specifically, we use the concept of Nash equilibrium in game theory to calculate the optimal strategy of suppliers, so that given the strategies of other suppliers, each supplier cannot obtain better benefits by unilaterally adjusting its own strategy.

[0038] In general, game theory models involve multiple participants (in this case, suppliers), each of which affects the system's resource scheduling and cost by choosing a resource allocation strategy. In this case, the goal of the supplier is to choose a resource allocation strategy that maximizes their gains in the game with other suppliers. Game theory can help the platform find an equilibrium resource allocation solution between suppliers by analyzing the interactions between them.

[0039] In the specific game theory model, the revenue function R of each supplier is i All depend on the strategy x chosen by supplier i i and other vendor selected strategies x j The ultimate goal of the game is to find a strategy combination that allows all suppliers to adopt this combination strategy without any supplier being able to gain greater benefits by unilaterally changing its strategy. This is the definition of Nash equilibrium.

[0040] Specifically, in this embodiment, m suppliers are assumed to participate in the game, and each supplier i chooses a resource allocation strategy x i (For example, selecting the number of services, resource allocation, etc.) The revenue function R of each supplier i Depends on the strategy x it chooses i and strategies of other suppliers x j The Nash equilibrium model in game theory requires finding a strategy combination (x 1 ,x 2 ,...,x m ), so that each supplier meets the following conditions: in: Is supplier i taking the strategy The income at that time; is the optimal resource allocation strategy of supplier i under Nash equilibrium; R i (x 1 ,x 2 ,...,x i ,...,x m ) is supplier i adopting other strategies x i The income at time x 1 ,x 2 ,...,x m is the strategy combination of all suppliers; x 1 represents the resource allocation strategy selected by supplier i.

[0041] This formula means that when the strategies of other suppliers are known, supplier i cannot obtain higher returns by unilaterally changing its own strategy, that is, the strategy combination is stable.

[0042] In order to effectively realize the dynamic optimization of the game theory model, the Q-learning algorithm is introduced in this embodiment, so that suppliers can autonomously learn and adjust their resource allocation strategies according to market feedback. The Q-learning algorithm learns through interaction with the environment and optimizes the supplier's decision-making strategy by continuously updating the Q value.

[0043] In Q-learning, the state space S represents all possible situations that the supplier may face, and the action space A represents all resource allocation strategies that the supplier can choose. By setting the reward function r i , the platform can evaluate the effect of a supplier's strategy and use the result to adjust future decisions. The update formula of Q-learning is: Where: Q(xi ,a i ) is supplier i in state x i Take action a i Q value; α is the learning rate, which controls the speed of Q value update; r i It is supplier i who is taking action a i The immediate reward obtained later; γ is the discount factor, indicating the importance of future rewards; is in state x i ′, the Q value of the optimal strategy selected by supplier i.

[0044] This formula indicates that through interaction with the environment, the supplier updates its Q value based on the feedback of the current strategy, so that future decisions can continue to move closer to the optimal strategy.

[0045] In actual operations, suppliers’ strategies are not fixed. As market demand, supplier resource load, order urgency, etc. change, the platform needs to collect and analyze new data in real time to adjust strategies. In one possible implementation, the platform continuously adjusts the input parameters of the game theory model, such as supplier processing capacity, changes in market demand, etc., by collecting real-time data on orders and feedback from suppliers. These real-time data are used to dynamically update the cost matrix and reward function in order to optimize the supplier’s strategy.

[0046] Through the Nash equilibrium model in game theory, the platform can optimize resource allocation strategies among multiple suppliers to ensure balanced resource scheduling and a balance between cooperation and competition among suppliers. Combined with the Q-learning algorithm, the platform can continuously adjust the supplier's resource allocation strategy based on real-time feedback, thereby optimizing the overall scheduling effect.

[0047] S3: Adopting Markov decision process, adjusting order flow and resource scheduling strategies through real-time feedback to adapt to the dynamic changes of business needs; S3 introduces the Markov Decision Process (MDP) to continuously adjust resource scheduling and order flow strategies through a real-time feedback mechanism to ensure that the platform can adapt to changing market demands.

[0048] In this embodiment, the core of step S3 is to use the Markov decision process (MDP) model to dynamically adjust the order flow and resource scheduling based on real-time feedback to achieve the optimal resource allocation and processing effect. This step is closely related to the results of the optimal resource allocation and game optimization made in the above steps S1 and S2, and further enhances the flexibility and optimization capabilities of the platform.

[0049] In general, the Markov decision process (MDP) is an optimization decision model that is widely used in dynamic environments to select the best action based on the current state to maximize long-term goals. In the process of resource scheduling and order flow on the platform, the state of the system will continue to change with the passage of time and external factors (such as changes in orders, fluctuations in supplier load, etc.). Therefore, the platform needs to rely on the MDP model to make the best scheduling decision at each moment to ensure efficient use of resources and smooth completion of orders.

[0050] Specifically, the MDP model consists of the following basic components: The state space S represents all possible states of the platform system. In this embodiment, the state may include but is not limited to the processing state of the order, the resource load of the supplier, the change of market demand, etc.

[0051] The action space A represents all possible actions that the platform can take in each state. For example, the platform can choose different resource allocation schemes, scheduling strategies, etc. according to the current state.

[0052] The transition probability P(s′|s,a) represents the probability of transitioning from state s to a new state s′ after executing action a. This transition describes the evolution of the system state after each decision.

[0053] The reward function R(s,a) represents the immediate reward for taking action a in a given state s. The reward function is usually designed based on factors such as order processing efficiency, service quality, and cost-effectiveness.

[0054] The discount factor γ indicates the relative importance of future rewards. The larger the γ value, the more the platform values ​​long-term returns; the smaller the γ value, the more the platform values ​​current immediate rewards.

[0055] In the platform, the goal of the MDP model is to maximize the total reward of the system in the long term through dynamic decision-making on order flow and resource scheduling. The optimization goal of the platform can be expressed by the value function V(s): V(s)=max a∈A [R(s,a)+γ·∑ s′ P(s′|s,a)·V(s′)]; Where: V(s) is the value function of state s, which represents the maximum expected reward obtained by executing the optimal strategy starting from state s; R(s,a) is the immediate reward obtained after taking action a in state s; γ is the discount factor, which measures the relative importance of future rewards; P(s′|s,a) is the probability of transitioning from state s to state s′ after taking action a; ∑ s′P(s′|s,a)·V(s′) is the expected reward of entering all possible states s′ after taking action a in state s; s is the current state of the platform. The state can include supplier load, order urgency, service quality requirements, etc.; a is the action that the platform can choose, usually representing a resource allocation decision, such as choosing which supplier to assign an order to, or adjusting the scheduling method of resources.

[0056] The function of this formula is to calculate the expected maximum reward of taking different strategies (resource scheduling, order allocation, etc.) under the current state and select the optimal strategy. This enables the platform to dynamically adjust resource allocation and order flow decisions, thereby maintaining efficient operations in a constantly changing environment.

[0057] As the platform operation progresses, the external market environment and internal operating conditions may change. Therefore, the MDP model in step S3 can be dynamically adjusted in combination with real-time feedback. For example, when a supplier's resource load is too high, the platform incorporates this change into the MDP model through real-time feedback, adjusts the resource scheduling strategy, ensures a more balanced resource allocation among suppliers, and avoids overload.

[0058] In actual operation, the platform will dynamically update the reward function R(s,a), transition probability P(s′|s,a) and other parameters by continuously monitoring the progress of orders, the resource status of suppliers and market demand, etc. This enables the system to make quick adjustments when the market changes to maximize overall benefits.

[0059] In order to further improve the optimization effect of the MDP model, the platform can combine the Q-learning algorithm for reinforcement learning. Q-learning can optimize the decision-making process of the platform through interaction with the environment, thereby achieving more refined resource scheduling in a dynamic and complex environment.

[0060] Through Q-learning, the platform can continuously update the Q value and optimize decisions based on real-time feedback, so that each supplier's strategy gradually tends to be optimal, thereby maintaining efficient resource allocation and order flow in a complex and dynamic market environment.

[0061] Through the optimization of the MDP model, the platform can make the best resource allocation decision at any time to ensure that the system always maintains efficient operation in a dynamic market environment. Combined with the reinforcement learning of the Q-learning algorithm, the platform can continuously optimize the supplier resource scheduling and order flow strategy, so that the system can still achieve the best resource allocation and service quality when facing complex market changes.

[0062] S4: Perform multi-objective optimization, comprehensively consider cost, time and service quality, calculate the optimal resource allocation plan, and ensure optimal resource scheduling in a changing market environment; S4 introduces a multi-objective optimization model, which optimizes multiple objectives through a weighted combination method to ensure that the platform can obtain an optimal resource allocation solution while considering cost, time, and service quality. The following will describe the multi-objective optimization method in detail, and give a complete formula and parameter definition to ensure that technical personnel can clearly understand the meaning of each parameter.

[0063] In multi-objective optimization, the platform needs to optimize multiple objective functions at the same time, and each objective function corresponds to an optimization goal of the platform. In this embodiment, the platform's goals include: Minimizing the cost C(x) is to reduce the costs associated with resource scheduling, transportation, and supplier services.

[0064] Minimize the time T(x) to reduce the processing and delivery time of the order and try to improve the timeliness of the order.

[0065] Maximizing service quality Q(x) is to ensure order accuracy and customer satisfaction.

[0066] Usually, there may be conflicts between multiple objectives, so it is necessary to combine multiple objective functions into a comprehensive objective function by weighted summation. The objective function of the platform can be expressed as: min(α·C(x)+β·T(x)-γ 2 Q(x)); Where: C(x) represents the cost function, which measures the overall cost of resource scheduling and order flow; T(x) represents the time function, which measures the total time required from order placement to delivery; Q(x) is the quality function, which measures service quality or customer satisfaction; α, β, γ 2 is the weight coefficient, α determines the weight of cost, β determines the weight of time, and γ 2 determines the weight of quality; x is the decision variable, which represents resource scheduling plan, order allocation strategy, etc.

[0067] Multi-objective optimization is not just about solving the objective function, but also about considering the constraints in actual operation. These constraints may come from resource limitations, order requirements, supplier capabilities, etc. The following are some of the constraints that the platform may encounter: The supplier's resource constraints represent the maximum number of orders each supplier can handle; The latest delivery time of the order to ensure that the order is delivered on time; Service quality standards to ensure delivery meets customer requirements; The system has the maximum processing capacity to avoid overload operation.

[0068] These constraints can be expressed using the following inequalities: Where: x ij is a decision variable indicating whether order j is served by supplier i (usually 0 or 1); S i is the maximum processing capacity of supplier i, indicating the maximum number of orders that each supplier can process; D j is the demand for order j, indicating the quantity of resources or services required for the order; T j is the delivery time of order j, indicating the delivery time point of the order; T max It is the maximum acceptable delivery time for an order, meaning that the order must be delivered before a certain point in time.

[0069] These constraints ensure that the platform can meet the needs of actual operations when performing multi-objective optimization and avoid unrealistic optimization solutions.

[0070] Multi-objective optimization problems are usually nonlinear, so the solution method needs to consider the balance between multiple objectives. The platform can choose the following common solution methods: Weighted method: Multiple objective functions are combined into a single objective function by weighted summation. The platform achieves a balance between objectives by adjusting the weights of different objective functions.

[0071] ε-constraint method: optimize one objective function as the main objective and transform other objective functions into constraints for processing. This method is suitable for situations where the objectives have different importance.

[0072] Pareto optimal solution: Find a set of solutions such that no solution is better than other solutions in all objectives. This method is suitable for situations where there is no clear priority between multiple objectives, and the optimal solution can be determined by comparing the advantages and disadvantages of multiple solutions.

[0073] In some embodiments, the platform uses a weighted summation method and dynamically adjusts the weight coefficients α, β, γ 2 , which enables the system to make appropriate adjustments to optimization goals based on market demand, customer feedback, etc. This enables the platform to obtain flexible optimization results in different business environments.

[0074] The platform obtains a set of optimal resource scheduling solutions through a multi-objective optimization algorithm. In order to evaluate the effectiveness of the optimization results, the platform will track and evaluate them based on the actual execution. For example, the platform will monitor indicators such as the actual delivery time, actual cost, and customer feedback of the order. If the optimization plan fails to achieve the expected results, the platform will readjust the objective function or weight coefficient through the feedback mechanism.

[0075] For example, in one embodiment, the platform processes multiple orders simultaneously and needs to allocate resources among multiple suppliers. In this process, the platform considers three key goals: Reduce transportation costs; Ensure timely delivery of orders; Improve customer satisfaction.

[0076] The platform uses a multi-objective optimization model to comprehensively consider the weight of each goal and come up with a balanced resource scheduling solution. In the actual implementation process, the platform dynamically adjusts the weight coefficient according to the changes in orders to ensure that the optimization goals are reasonably balanced under different market demands.

[0077] By introducing multi-objective optimization models and constraints, the platform can maintain the operability and practical feasibility of the system while considering the trade-offs between multiple objectives. In addition, the platform can dynamically adjust the optimization scheme based on real-time feedback, thereby improving the flexibility and adaptability of the system in complex environments.

[0078] S5: Dynamically adjust resource scheduling strategies based on real-time data to ensure the optimization of order flow efficiency and resource utilization; S5 introduces a dynamic adjustment mechanism based on real-time data to ensure that the platform can dynamically optimize order flow and resource scheduling strategies based on the latest real-time information. Through step S5, the platform can dynamically adjust the objective function, decision variables and weight coefficients based on real-time feedback data in a changing market environment to optimize multiple goals such as cost, delivery time and service quality. This adjustment mechanism can respond to environmental changes in real time and ensure the optimization of resource scheduling.

[0079] Generally, the platform collects data related to orders, suppliers and market demand from multiple data sources in real time. This data usually includes: Supplier status data: including real-time data such as inventory levels, order processing capacity, and transportation capacity; Order flow data: order receipt time, processing time, transportation status and delivery status, etc.; Market demand data: real-time demand fluctuation data obtained by the platform based on factors such as customer orders, geographic location, and demand changes.

[0080] These data are collected in real time through IoT sensors, big data analysis tools, API interfaces, etc., and transmitted to the platform's real-time data processing system.

[0081] In this embodiment, the platform can monitor the real-time dynamics of suppliers and the market by integrating big data analysis technology and real-time sensor monitoring systems. For example, the platform can dynamically adjust resource allocation strategies to ensure optimal resource scheduling by real-time monitoring of suppliers' inventory information, transportation capacity, and order processing capacity.

[0082] In the above steps S1 to S4, the platform calculates the optimal resource scheduling solutions through the optimization model, but these solutions may no longer be optimal when facing the dynamically changing market and resource environment. Therefore, in step S5, the platform dynamically adjusts the above optimization models (such as the optimal transportation problem model, game theory model, Markov decision process model and multi-objective optimization algorithm) according to real-time data to optimize the current resource scheduling and order flow strategy.

[0083] In this embodiment, the core of dynamic adjustment is to update the objective function and constraint conditions based on real-time data and dynamically adjust the weight coefficient α t , β t , γ t and the decision variable X ij Through these adjustments, the platform can continue to optimize resource allocation in a real-time changing environment.

[0084] Specifically, the platform dynamically updates the cost function C(x), time function T(x) and quality function Q(x) through real-time data analysis, and adjusts the weight coefficient in the objective function to ensure efficient resource scheduling in a changing market environment.

[0085] In step S5, the platform dynamically adjusts the objective function in step S4 through real-time data feedback to reflect current market changes and actual needs. min(α t C(x)+β t T(x)-γ t Q(x)); Among them: C(x) is the cost function, which measures the total cost of resource scheduling, usually including transportation costs, supplier costs, inventory management, etc. This function is dynamically updated based on real-time supplier load and market demand; T(x) is the time function, which measures the total time required from order placement to delivery, including transportation time, processing time, etc. In real-time adjustment, the system will update the time function according to the actual transportation progress and order status; Q(x) is the quality function, which measures service quality or customer satisfaction, including order accuracy, delivery timeliness, customer satisfaction, etc. The quality function will be dynamically adjusted based on customer feedback, order accuracy, etc. in real-time data; α t , β t , γ tis the weight coefficient, which is used to indicate the relative importance of each objective in real-time optimization. During the real-time adjustment process, the platform adjusts these coefficients based on factors such as market demand, supplier status, and order urgency. For example, when market demand increases sharply, the platform may increase the time weight and reduce the cost weight; ij is a decision variable, indicating whether order j is served by supplier i. Based on real-time data and resource load, the platform will adjust the matching relationship between orders and suppliers in real time.

[0086] To ensure that the optimization plan meets the constraints of real-world operations, the platform dynamically updates the constraints based on real-time data. For example, when the supplier's resource load reaches the upper limit, the system will adjust the resource allocation strategy to ensure that it does not exceed the supplier's processing capacity, using the constraints expressed by the aforementioned inequality.

[0087] These constraints will be adjusted as real-time data is updated. For example, when the platform monitors the supplier's load in real time, if it finds that a supplier's capacity is close to saturation, the system will automatically update the supplier's maximum processing capacity S i , and adjust the order allocation strategy accordingly.

[0088] When implementing real-time adjustments, the platform needs an effective feedback mechanism to ensure the rationality and timeliness of dynamic adjustments. By continuously monitoring order status, customer feedback, market demand, etc., the platform can evaluate the execution effect of the current strategy in real time and adjust the optimization strategy according to actual conditions.

[0089] For example, if a supplier's transportation capacity decreases, resulting in a delay in the delivery of an order, the system can increase the weight of the delivery time in real time, adjust the resource scheduling strategy, and prioritize the allocation of orders to suppliers with surplus resources to ensure that delays are minimized as much as possible.

[0090] For example, when implementing an order processing task, the platform finds through real-time data that the processing capacity of a supplier has reached saturation and the demand for orders has increased dramatically. The platform uses the dynamic adjustment mechanism of step S5 to adjust the objective function in real time, increase the time weight, and give priority to assigning orders to suppliers with strong inventory and transportation capabilities to ensure that orders can be delivered on time and minimize costs.

[0091] Through real-time data feedback, the platform can continuously adjust optimization goals such as cost, time, and quality, so that it can maintain efficient operation in a changing environment.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A product management method based on a one-stop inspection platform, characterized in that: The following steps are involved: S1: Construct an optimal transportation problem model, define the resource flow relationship between suppliers and orders, and calculate the optimal solution for suppliers to allocate to orders with the goal of minimizing transportation costs; S2: Based on the game theory model, considering the strategic interactions among multiple suppliers, the supplier resource allocation strategy is optimized through game theory to ensure the balance of resource scheduling; S3: Adopting Markov decision process, adjusting order flow and resource scheduling strategies through real-time feedback to adapt to the dynamic changes of business needs; S4: Perform multi-objective optimization, comprehensively consider cost, time and service quality, calculate the optimal resource allocation plan, and ensure optimal resource scheduling in a changing market environment; S5: Dynamically adjust resource scheduling strategies based on real-time data to ensure optimization of order flow efficiency and resource utilization.

2. The product management method based on the one-stop inspection platform according to claim 1 is characterized in that: The construction of the optimal transportation problem model includes: Define the resource flow relationship between suppliers and orders, and establish a transportation cost matrix by considering the order delivery time and the supplier's service capacity; Through the optimal transportation problem model, the linear programming method is used to calculate the optimal supplier and order pairing plan to minimize the total transportation cost.

3. The product management method based on the one-stop inspection platform according to claim 1 is characterized in that: The game theory-based model includes: Set the strategy for each supplier as the way to allocate resources, and analyze the competition and cooperation relationship between multiple suppliers; Use the Nash equilibrium model to evaluate the optimal strategy of each supplier in the resource allocation game; The Q-learning algorithm is used to dynamically optimize supplier strategies to ensure equilibrium during the game and achieve the optimal resource allocation solution.

4. The product management method based on the one-stop inspection platform according to claim 1 is characterized in that: The Markov decision process includes: Define the state space of order flow, including order processing progress, supplier load and resource utilization; Defining the space of actions the platform can take, including decisions about selecting suppliers and allocating resources; The optimal resource scheduling strategy is solved through the Bellman equation in the Markov decision process to achieve dynamic resource adjustment and ensure the optimization of order flow when business demand changes.

5. The product management method based on the one-stop inspection platform according to claim 1 is characterized in that: The multi-objective optimization includes: Taking cost, time and service quality as objective functions, multi-objective optimization methods are used to optimize them simultaneously; Use non-dominated sorting genetic algorithm to solve the Pareto optimal solution between multiple optimization objectives to ensure the optimal balance between different objectives; Dynamically adjust target weights based on real-time orders and supplier capabilities to ensure optimized resource scheduling in a changing market environment.

6. The product management method based on the one-stop inspection platform according to claim 1 is characterized in that: The dynamic adjustment of resource scheduling strategy includes: Analyze current resource scheduling status based on real-time order volume, supplier load, and resource usage data; The resource scheduling strategy is adjusted through adaptive algorithms to ensure that order processing time is minimized while ensuring resource utilization; the order flow path is dynamically optimized, and the real-time feedback mechanism enables the platform to adapt to demand fluctuations and adjust resource allocation in a timely manner.

7. The product management method based on the one-stop inspection platform according to claim 3 is characterized in that: The steps of the Q-learning algorithm include: Define state space and action space. The state space includes supplier load, order progress and resource status, and the action space includes supplier selection and resource allocation. Set a reward function, evaluate the benefits of each supplier adopting a certain strategy through real-time feedback, and adjust the strategy based on the reward; continuously update the Q value through the Q-learning algorithm, so that suppliers can adjust resource allocation strategies according to current market conditions, thereby achieving the optimal game equilibrium.

8. The product management method based on a one-stop inspection platform according to claim 4 is characterized in that: The Bellman equation includes: According to the state space and action space of the platform, the transition probability P(s) of taking action in each state is calculated. ′ |s,a); According to the immediate reward R(s,a), evaluate the benefit of taking an action in each state; Solve the Bellman equation to obtain the optimal state value V(s), and optimize resource scheduling by selecting the best action.

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