Supply chain control tower optimization system and method based on quantum computing

Abstract

The invention discloses a supply chain control tower optimization system and method based on quantum computing, and relates to the technical field of quantum computing, the system comprises a data acquisition module, a quantum computing interface module, a decision optimization module and a visual display module, the data acquisition module is used for acquiring real-time data from each link of a supply chain and displaying the real-time data in the visual display module; the real-time data comprises order information, inventory level, production progress, transportation state and market demand, and the collected data is preprocessed and stored in a data warehouse. The method is excellent in performance when the complex combination optimization problem is processed, inventory management can be reasonably optimized, and the risk of inventory overstock or stockout is reduced. Compared with a traditional algorithm which is easy to fall into local optimum, the method can find a global optimal solution in a shorter time, can integrate factors such as transportation cost, time, vehicle capacity and traffic conditions in the aspect of logistics path planning, can find an optimal logistics path, and reduces the transportation cost and time.

Description

Technical Field

[0001] The present invention relates to the field of quantum computing technology, and in particular to a supply chain control tower optimization system and method based on quantum computing. Background Art

[0002] The supply chain control tower is a digital supply chain management solution that helps companies effectively manage complex supply chains by integrating data, providing visibility, early warning and forecasting, and collaborative decision-making.

[0003] As supply chains become more globalized and complex, the amount of data generated is exploding. Existing supply chain control towers have the following shortcomings:

[0004] Decision-making problems in the supply chain often involve multiple variables and constraints. For example, production scheduling must consider production capacity, raw material supply, order demand, and delivery dates; logistics routing must integrate transportation costs, time, vehicle capacity, and traffic conditions. Traditional optimization algorithms are prone to local optimal solutions when dealing with these complex combinatorial optimization problems, and computational time is long, making it difficult to find the global optimal solution within a limited timeframe.

[0005] Based on this, a supply chain control tower optimization system and method based on quantum computing is now provided, which can eliminate the drawbacks of the existing system. Summary of the Invention

[0006] The purpose of the present invention is to provide a supply chain control tower optimization system and method based on quantum computing to solve the shortcomings of modern systems in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A supply chain control tower optimization system based on quantum computing, including a data acquisition module, a quantum computing interface module, a decision optimization module, and a visualization display module;

[0009] The data collection module is used to collect real-time data from various links in the supply chain. The real-time data includes order information, inventory levels, production progress, transportation status, and market demand. The collected data is pre-processed and stored in the data warehouse;

[0010] The quantum computing interface module is used to convert data in the classical computing system into a format suitable for quantum computing and then send it to the quantum computer, and receive the calculation results returned by the quantum computer and convert them into a format that can be understood by the classical computing system;

[0011] The decision optimization module is used to optimize supply chain decisions, including production planning optimization, inventory management optimization, and logistics path planning optimization;

[0012] The visualization display module is used to display the optimized supply chain decision results to users in the form of charts and graphs, providing real-time supply chain status information.

[0013] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0014] In an optional solution: the quantum computing model in the quantum computing interface module is:

[0015] Raw material constraints:

[0016] Equipment production capacity constraints:

[0017] Each task can only be produced on one device:

[0018] Meet order requirements:

[0019] Task assignment variables:

[0020] Among them, xi,j is the decision variable for assigning task ti to equipment mj. Suppose the set of production tasks is T = {t1, t2, …, tn}, each production task ti has a corresponding production time pi, the required raw material quantity ri, the raw material type k, and the order demand quantity di; the set of production equipment is M = {m1, m2, …, mm}, and the production capacity of each equipment mj is cj; the goal is to minimize the total production time Ttotal while meeting the order demand and equipment production capacity.

[0021] In an optional solution, the inventory management optimization model in the decision optimization module is:

[0022]

[0023] The types of inventory items are S = {s1, s2, …, ss}, the unit inventory cost of each item sl is hl, the unit shortage cost is bl, and the demand forecast probability distribution is P(Dl, t), where t represents the demand for item sl at time t.

[0024] In an optional solution, the logistics path planning optimization model in the decision optimization module is:

[0025]

[0026]

[0027] Among them, E represents the set of all possible edges, the set of logistics nodes is N = {n1,n2,…,nn}, the transportation cost from node ni to node nj is ci,j, the transportation time is ti,j, the vehicle capacity is Q, and the cargo demand of each node is qi.

[0028] In one optional solution: Step S1: using a data acquisition module, connect to the information system interfaces of each link in the supply chain to collect data, clean and remove duplicate and erroneous data, normalize the data into a format suitable for quantum computing, and then store it;

[0029] Step S2: Encode the data in the data warehouse into quantum bits through the quantum computing interface module, compress and encrypt it, and then send it to the quantum computer. After receiving the calculation results, decode and denormalize them.

[0030] Step S3: Through the decision optimization module, production tasks are assigned based on the quantum computing results, production progress is monitored in real time, and plans are adjusted according to actual conditions;

[0031] Step S4: Manage inventory through the decision optimization module and based on the optimal inventory level and replenishment strategy obtained by quantum computing, monitor inventory level changes in real time, trigger the replenishment process and dynamically adjust the strategy;

[0032] Step S5: Arrange transportation based on the optimal logistics path obtained by quantum computing through the decision optimization module, track transportation status in real time, and adjust the path;

[0033] Step S6: Through the visualization display module, the optimized supply chain decision results are displayed to the user in intuitive charts and graphs, providing real-time supply chain status information, data analysis and report generation functions.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This invention excels in handling complex combinatorial optimization problems and can efficiently optimize complex decision-making issues. Compared to traditional algorithms that are prone to falling into local optimality, it can find the global optimal solution in a much shorter time. In production planning, it can simultaneously consider multiple constraints such as production capacity, raw material supply, order demand, and delivery time to quickly determine the optimal production plan, thereby improving production efficiency and resource utilization. In logistics route planning, it integrates factors such as transportation cost, time, vehicle capacity, and traffic conditions to find the optimal logistics route, reducing transportation costs and time.

[0036] 2. This invention can more accurately simulate and predict the impact of uncertainties on the supply chain, demonstrating its robust ability to address uncertainty. Leveraging quantum machine learning algorithms, it provides in-depth analysis of uncertainties such as market demand fluctuations and supplier delivery delays. By applying quantum computing to analyze market demand data, it can capture more subtle trends, proactively adjust inventory strategies and production plans, and mitigate the risk of inventory overhangs or stockouts. In the face of emergencies such as natural disasters, it can quickly assess the impact on the supply chain and formulate response strategies, enhancing supply chain resilience and risk mitigation.

[0037] 3. This invention builds a more secure and efficient information sharing and collaboration platform for all supply chain participants, effectively promoting supply chain collaboration. Its powerful computing capabilities enable real-time processing of data uploaded by all parties, rapidly generating collaborative decision-making solutions. In supply chain collaborative planning, it integrates supplier production capacity, manufacturer order requirements, and logistics provider transportation capabilities to rapidly develop optimal collaborative plans, thereby improving supply chain collaboration efficiency and overall competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0040] In one embodiment, Figure 1 As shown, a supply chain control tower optimization system based on quantum computing includes a data acquisition module, a quantum computing interface module, a decision optimization module, and a visualization display module;

[0041] The data collection module is used to collect real-time data from various links in the supply chain. The real-time data includes order information, inventory levels, production progress, transportation status, and market demand. The collected data is pre-processed and stored in the data warehouse;

[0042] The quantum computing interface module is used to convert data in the classical computing system into a format suitable for quantum computing and then send it to the quantum computer, and receive the calculation results returned by the quantum computer and convert them into a format that can be understood by the classical computing system;

[0043] The decision optimization module is used to optimize supply chain decisions, including production planning optimization, inventory management optimization, and logistics path planning optimization;

[0044] The visualization display module is used to display the optimized supply chain decision results to users in the form of charts and graphs, providing real-time supply chain status information.

[0045] In one embodiment, the quantum computing model in the quantum computing interface module is:

[0046] Raw material constraints:

[0047] Equipment production capacity constraints:

[0048] Each task can only be produced on one device:

[0049] Meet order requirements:

[0050] Task assignment variables:

[0051] Among them, xi,j is the decision variable for assigning task ti to equipment mj. Suppose the set of production tasks is T = {t1, t2, …, tn}, each production task ti has a corresponding production time pi, the required raw material quantity ri, the raw material type k, and the order demand quantity di; the set of production equipment is M = {m1, m2, …, mm}, and the production capacity of each equipment mj is cj; the goal is to minimize the total production time Ttotal while meeting the order demand and equipment production capacity.

[0052] In one embodiment, the inventory management optimization model in the decision optimization module is:

[0053]

[0054] The types of inventory items are S = {s1, s2, …, ss}, the unit inventory cost of each item sl is hl, the unit shortage cost is bl, and the demand forecast probability distribution is P(Dl, t), where t represents the demand for item sl at time t.

[0055] In one embodiment, the logistics path planning optimization model in the decision optimization module is:

[0056]

[0057]

[0058] Among them, E represents the set of all possible edges, the set of logistics nodes is N = {n1,n2,…,nn}, the transportation cost from node ni to node nj is ci,j, the transportation time is ti,j, the vehicle capacity is Q, and the cargo demand of each node is qi.

[0059] The above embodiment discloses a supply chain control tower optimization system and method based on quantum computing. The specific principles and processes are as follows:

[0060] 1. System Architecture:

[0061] (1) Data collection module: This module is responsible for collecting real-time data from various links in the supply chain (such as suppliers, manufacturers, logistics providers, retailers, etc.), including but not limited to order information, inventory levels, production progress, transportation status, market demand, etc. The collected data is pre-processed and stored in the data warehouse, providing a basis for subsequent analysis and decision-making.

[0062] (2) Quantum computing interface module: This module is used to implement communication and data exchange between the supply chain control tower system and the quantum computer. It converts data in the classical computing system into a format suitable for quantum computing and sends it to the quantum computer for calculation. At the same time, it receives the calculation results returned by the quantum computer and converts them into a format that the classical computing system can understand.

[0063] (3) Decision Optimization Module: Based on the results of quantum computing, this module optimizes supply chain decisions. Specifically, it includes production plan optimization, inventory management optimization, and logistics route planning optimization. In production plan optimization, factors such as production capacity, raw material supply, and order demand are considered, and quantum computing is used to solve the optimal production plan arrangement. In inventory management optimization, the optimal inventory level and replenishment strategy are determined by combining market demand forecasts and inventory costs. In logistics route planning optimization, factors such as transportation cost, transportation time, and vehicle capacity are considered, and the optimal logistics route is found through quantum computing.

[0064] (4) Visualization display module: The optimized supply chain decision results are displayed to users in the form of intuitive charts and graphs, which facilitates user monitoring and decision-making. At the same time, it provides real-time supply chain status information, such as inventory changes, order execution status, logistics and transportation progress, etc., so that users can understand the operation status of the supply chain in a timely manner.

[0065] 2. Quantum computing optimization methods:

[0066] (1) Production planning optimization: Let the set of production tasks be T = {t1, t2, …, tn}. Each production task ti has a corresponding production time pi, a required raw material quantity ri,k (k represents the type of raw material), and an order quantity di. The set of production equipment is M = {m1, m2, …, mm}, and the production capacity of each equipment mj is cj. The goal is to minimize the total production time Ttotal while satisfying the order requirements and equipment production capacity.

[0067] The quantum computing model can be expressed as:

[0068] (Raw material constraints)

[0069] (Equipment production capacity constraints)

[0070] (Each task can only be produced on one device)

[0071] (Meet order requirements)

[0072] (Task allocation variable, 0 means no allocation, 1 means allocation)

[0073] Among them, xi,j is the decision variable for assigning task ti to device mj.

[0074] Inventory management optimization: Let S = {s1, s2, …, ss}, the unit inventory cost of each item sl be hl, the unit stockout cost be bl, and the demand forecast probability distribution be P(Dl,t) (Dl,t represents the demand for item sl at time t). The goal is to determine the optimal inventory level Il,t to minimize the total inventory cost Ctotal.

[0075] The quantum computing model can be expressed as: (Inventory levels are non-negative).

[0076] Logistics path planning optimization: Let the set of logistics nodes be N = {n1, n2, …, nn}, the transportation cost from node ni to node nj be ci,j, the transportation time be ti,j, the vehicle capacity be Q, and the cargo demand at each node be qi. The goal is to find the optimal logistics path from the starting point to the end point, minimizing the total transportation cost Cpath. The quantum computing model can be expressed as:

[0077]

[0078] (vehicle capacity constraint),

[0079] (Path selection variable, 0 means not selected, 1 means selected)

[0080] Where E represents the set of all possible edges.

[0081] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A supply chain control tower optimization system based on quantum computing, characterized by: Including data acquisition module, quantum computing interface module, decision optimization module, and visualization display module; The data collection module is used to collect real-time data from various links in the supply chain. The real-time data includes order information, inventory levels, production progress, transportation status, and market demand. The collected data is pre-processed and stored in the data warehouse; The quantum computing interface module is used to convert data in the classical computing system into a format suitable for quantum computing and then send it to the quantum computer, and receive the calculation results returned by the quantum computer and convert them into a format that can be understood by the classical computing system; The decision optimization module is used to optimize supply chain decisions, including production planning optimization, inventory management optimization, and logistics path planning optimization; The visualization display module is used to display the optimized supply chain decision results to users in the form of charts and graphs, providing real-time supply chain status information.

2. A supply chain control tower optimization system based on quantum computing according to claim 1, characterized in that: The quantum computing model in the quantum computing interface module is: Raw material constraints: Equipment production capacity constraints: Each task can only be produced on one device: Meet order requirements: Task assignment variables: Among them, xi,j is the decision variable for assigning task ti to equipment mj. Suppose the set of production tasks is T = {t1, t2, …, tn}, each production task ti has a corresponding production time pi, the required raw material quantity ri, the raw material type k, and the order demand quantity di; the set of production equipment is M = {m1, m2, …, mm}, and the production capacity of each equipment mj is cj; the goal is to minimize the total production time Ttotal while meeting the order demand and equipment production capacity.

3. The supply chain control tower optimization system based on quantum computing according to claim 1 is characterized in that: The inventory management optimization model in the decision optimization module is: The types of inventory items are S = {s1, s2, …, ss}, the unit inventory cost of each item sl is hl, the unit shortage cost is bl, and the demand forecast probability distribution is P(Dl, t), where t represents the demand for item sl at time t.

4. The supply chain control tower optimization system based on quantum computing according to claim 1, characterized in that: The logistics path planning optimization model in the decision optimization module is: Among them, E represents the set of all possible edges, the set of logistics nodes is N = {n1,n2,…,nn}, the transportation cost from node ni to node nj is ci,j, the transportation time is ti,j, the vehicle capacity is Q, and the cargo demand of each node is qi.

5. A supply chain control tower optimization method based on quantum computing, characterized in that: The following steps are involved: Step S1: Through the data acquisition module, connect to the information system interfaces of each link in the supply chain to collect data, clean and remove duplicate and erroneous data, normalize it into a format suitable for quantum computing, and then store it; Step S2: Encode the data in the data warehouse into quantum bits through the quantum computing interface module, compress and encrypt it, and then send it to the quantum computer. After receiving the calculation results, decode and denormalize them. Step S3: Through the decision optimization module, production tasks are assigned based on the quantum computing results, production progress is monitored in real time, and plans are adjusted according to actual conditions; Step S4: Manage inventory through the decision optimization module and based on the optimal inventory level and replenishment strategy obtained by quantum computing, monitor inventory level changes in real time, trigger the replenishment process and dynamically adjust the strategy; Step S5: Arrange transportation based on the optimal logistics path obtained by quantum computing through the decision optimization module, track transportation status in real time, and adjust the path; Step S6: Through the visualization display module, the optimized supply chain decision results are displayed to the user in intuitive charts and graphs, providing real-time supply chain status information, data analysis and report generation functions.

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