A distributed localization fulfillment network construction method for clothing POD

Abstract

The application discloses a distributed localized performance network construction method for clothing POD, which realizes optimal matching of orders and global flexible manufacturing resources by constructing a factory comprehensive capability Token evaluation system, an order intelligent splitting mechanism and a multi-target optimization model, so as to construct a localized performance network of 'where the order is, where the factory is'. The application can realize order production and delivery nearby, and significantly shorten the delivery cycle.

Description

Technical Field

[0001] This invention belongs to the field of clothing POD (Print On Demand) technology, specifically relating to a method for constructing a distributed localized fulfillment network for clothing POD. Background Technology

[0002] In recent years, with the rapid growth of cross-border e-commerce and personalized consumption demand, the POD (Personalized Delivery) model has gradually become an important development direction for the apparel industry. Unlike the traditional mass production model, the POD model is characterized by "small orders, multiple styles, fast delivery, and zero inventory," which places higher demands on the supply chain's responsiveness and fulfillment capabilities.

[0003] Currently, most POD platforms still adopt a centralized production model, which involves centrally distributing global orders to at least a few large factories for production and then completing delivery through international logistics. While this model offers advantages in large-scale production, its drawbacks gradually become apparent as order volume increases and market areas expand. These drawbacks mainly include the following:

[0004] 1. First, orders require cross-border transportation, resulting in a longer overall delivery cycle. When the target market is far from the production center, logistics time usually accounts for a large part of the entire fulfillment cycle, making it difficult to meet consumers' demand for fast delivery.

[0005] 2. Secondly, international logistics is significantly affected by policy changes, customs inspections, shipping resource shortages, and unforeseen public events, resulting in higher fulfillment risks. Logistics disruptions can easily lead to order delays and even customer complaints.

[0006] 3. Furthermore, centralized production models are prone to inventory and capacity pressures. When order peaks arrive, production resources become congested, while resources in other regional factories remain idle, resulting in low overall resource utilization.

[0007] 4. In addition, most existing order allocation methods rely on simple regional divisions or human experience-based decision-making, selecting production factories solely based on geographical location. This fails to comprehensively consider multiple factors such as factory capacity, quality level, production costs, and delivery capabilities, resulting in low order matching efficiency.

[0008] Existing technologies lack a method for constructing a distributed fulfillment network that can intelligently match global orders with local production resources, dynamically optimize fulfillment paths, and continuously improve network operating efficiency. Therefore, a new technical solution is urgently needed to address these issues. Summary of the Invention

[0009] The purpose of this invention is to provide a method for constructing a distributed localized fulfillment network for apparel PODs, which enables orders to be produced and delivered locally, significantly shortening the delivery cycle.

[0010] The objective of this invention is achieved through the following technical solution:

[0011] A method for constructing a distributed localized fulfillment network for apparel PODs is characterized by: firstly, establishing a distributed manufacturing node database containing data from all cooperating factories, and then normalizing the corresponding indicators of the factories to obtain the comprehensive capability Token value S of each factory.

[0012] Once a user order is received, the following steps are performed:

[0013] S1. Perform feature analysis on the orders and establish an order demand vector D. Based on the order demand vector D, select qualified factories from the distributed manufacturing node database as candidate factories to form a candidate factory set.

[0014] S2 combines the candidate factory's comprehensive capability Token value S, the transportation distance from the factory to the target market, logistics costs, logistics timeliness, and the factory's current load rate to obtain the matching degree between the order and the candidate factory;

[0015] S3. Sort the candidate factories from high to low according to the matching degree, and select N candidate factories at the top.

[0016] S4 allocates orders to the selected N candidate factories.

[0017] A further technical solution of the present invention is as follows: when a single factory cannot meet the order delivery requirements, the order is split into m sub-orders, and each sub-order is processed by steps S1-S4 respectively.

[0018] A further technical solution of the present invention is as follows: when the fulfillment cost of a single factory among the N candidate factories selected in step S3 is high, the order is split into m sub-orders, and the m sub-orders are allocated to different candidate factories according to the fulfillment cost.

[0019] A further technical solution of the present invention is: the fulfillment cost of a single factory. The calculation formula is:

[0020] ;

[0021] In the formula: For production costs, For logistics costs, For tariff costs, For packaging costs, For risk costs;

[0022] Further calculate the average overall fulfillment cost of the N candidate factories selected in step S3. :

[0023] ;

[0024] In the formula That is, the fulfillment cost corresponding to each factory. ;

[0025] The fulfillment cost of a single factory is considered high when the following formula is met:

[0026]

[0027] In the formula: θ is the cost fluctuation threshold.

[0028] A further technical solution of the present invention is as follows: the order demand vector D = {Q, P, M, T, C, F, S, L}, where: Q represents the order quantity, P represents the product category, M represents the target market, T represents the delivery time limit, C represents the process complexity, F represents the fabric type, S represents the printing process, and L represents the customer level.

[0029] A further technical solution of the present invention is as follows: the candidate factories that meet the requirements in step S1 must meet the following conditions: the area where the factory is located can cover the target market of the order; the current remaining capacity of the factory is greater than the capacity required by the order; the factory's equipment capacity meets the process requirements of the order; the sum of the expected production cycle and logistics cycle does not exceed the order delivery time limit; and the historical quality pass rate and on-time delivery rate reach the threshold set by the system.

[0030] A further technical solution of the present invention is: the matching degree is determined by the matching score. This indicates the matching score. The calculation formula is as follows:

[0031]

[0032] In the formula: Token represents the comprehensive capability Token value; Distance represents the transportation distance score from the factory to the target market of the order; Logistics represents the comprehensive score of logistics cost and logistics timeliness; Load represents the current load rate score of the factory; Risk represents the comprehensive score of logistics risk, equipment operation risk and regional operation risk; w1 to w5 are the weight coefficients corresponding to each evaluation indicator.

[0033] A further technical solution of the present invention is as follows: The formula for calculating the comprehensive capability Token value S is as follows:

[0034]

[0035] In the formula: α, β, γ, and δ are weighting coefficients. As a production capacity token, For quality tokens, For cost tokens, This is the delivery date token.

[0036] A further technical solution of the present invention is as follows: In step S4, a multi-objective optimization model is established with N candidate factories as targets, and the objective function is:

[0037] Min Z = A×Production Costs + B×Logistics Costs + C×Delivery Time + D×Risk Costs - E×Customer Satisfaction;

[0038] In the objective function: A, B, C, D, and E are the objective weights.

[0039] A further technical solution of the present invention is as follows: A reward function is constructed, and a reward value R is calculated for each order, using the following formula:

[0040] R = a × cost savings rate + b × delivery timeliness improvement rate + c × on-time delivery rate + d × customer satisfaction - e × delay penalty - f × complaint penalty;

[0041] In the formula: a, b, c, d, e, and f are the target weights.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. This invention can build a globally distributed localized fulfillment network, enabling orders to be produced and delivered locally, significantly shortening the delivery cycle.

[0044] 2. This invention achieves a unified quantitative evaluation of the fulfillment capabilities of different factories through a comprehensive capability token value.

[0045] 3. This invention further proposes an order splitting mechanism to improve the utilization rate of production resources and order processing capacity.

[0046] 4. This invention further establishes a multi-objective optimization model to achieve a comprehensive optimal balance between cost, delivery time and quality.

[0047] 5. This invention further establishes a reward function and uses reinforcement learning to continuously optimize the performance strategy, thereby improving the system's intelligent decision-making level. Attached Figure Description

[0048] Figure 1 A flowchart of a system incorporating the methods of embodiments of the present invention;

[0049] Figure 2 This is a flowchart illustrating the calculation of the comprehensive capability Token value for a specific clothing POD order, as described in this embodiment of the invention.

[0050] Figure 3 This is a flowchart for when orders are split. Detailed Implementation

[0051] The present invention will be further described below with reference to embodiments.

[0052] Example:

[0053] The distributed localized fulfillment network construction method for apparel PODs in this embodiment achieves optimal matching between orders and global flexible manufacturing resources by constructing a comprehensive factory capability token evaluation system, an intelligent order splitting mechanism, and a multi-objective optimization model, thereby building a localized fulfillment network where "the factory is wherever the order is".

[0054] This method first establishes a distributed manufacturing node database, which contains data from all cooperating factories. The collected factory data includes capacity, equipment capabilities, production efficiency, quality level, unit cost, historical delivery cycle, on-time delivery rate, and order processing capacity. This data forms a standardized factory profile, and the system performs unified digital modeling on each manufacturing node. These factories include cooperating factories located in different countries or regions, self-operated factories, and third-party flexible manufacturing centers.

[0055] To achieve a unified evaluation of factory capabilities, this method constructs a comprehensive factory capability token evaluation system. This system quantifies a factory's comprehensive capability by using a comprehensive capability token value S to represent its overall capability. It normalizes the relevant indicators of each factory to obtain a comprehensive capability token value for each factory. These indicators include production capacity, quality, cost, and delivery time. The formula for calculating the comprehensive capability token value S is as follows:

[0056]

[0057] In the formula: α, β, γ, and δ are weighting coefficients. As a production capacity token, For quality tokens, For cost tokens, This is the delivery date token.

[0058] The system normalizes indicators such as capacity, quality, cost, and delivery time, resulting in capacity tokens, quality tokens, cost tokens, and delivery time tokens. The capacity token reflects the factory's available production capacity; the quality token reflects historical product quality levels and customer satisfaction; the cost token reflects unit production costs and overall operating costs; and the delivery time token reflects order response speed and on-time delivery capability. α, β, γ, and δ can be dynamically adjusted based on order type: increasing the weight of the delivery time token for orders with high timeliness requirements; increasing the weight of the quality token for high-end customized clothing orders; and increasing the weight of the cost token for price-sensitive orders. By obtaining the factory's comprehensive capacity token value, standardized comparisons between different factories can be achieved.

[0059] Once a user order is received, this method performs the following processing steps:

[0060] S1. Perform feature analysis on the order. Order features include order quantity, product category, target market, delivery time limit, process requirements, production complexity, and customer level. Then, establish an order demand vector D = {Q, P, M, T, C, F, S, L} based on the order features, where: Q represents order quantity, P represents product category, M represents target market, T represents delivery time limit, C represents process complexity, F represents fabric type, S represents printing process, and L represents customer level.

[0061] Based on the order characteristics protected by the order demand vector D, factories that meet the conditions are selected from the distributed manufacturing node database as candidate factories, forming a candidate factory set.

[0062] Eligible candidate factories must meet the following conditions: 1. The factory's location can cover the target market of the order; 2. The factory's current remaining capacity is greater than the capacity required by the order; 3. The factory's equipment capacity meets the process requirements of the order; 4. The sum of the expected production cycle and logistics cycle does not exceed the order delivery deadline; 5. The historical quality pass rate and on-time delivery rate reach the thresholds set by the system. Factories that meet all of the above conditions will be included in the candidate factory set.

[0063] S2 combines the candidate factory's comprehensive capability token value S, the transportation distance from the factory to the target market, logistics costs, logistics timeliness, and the factory's current load rate to obtain the matching degree between the order and the candidate factory. The matching degree is determined by the matching score. This indicates the matching score. The calculation formula is as follows:

[0064]

[0065] In the formula: Token represents the comprehensive capability Token value; Distance represents the transportation distance score from the factory to the target market of the order; Logistics represents the comprehensive score of logistics cost and logistics timeliness; Load represents the current load rate score of the factory; Risk represents the comprehensive score of logistics risk, equipment operation risk and regional operation risk; w1 to w5 are the weight coefficients corresponding to each evaluation indicator, which can be dynamically adjusted according to the order type.

[0066] Among them, the comprehensive capability Token value is used to characterize the factory's comprehensive fulfillment capability. It is calculated by weighting the capacity Token, quality Token, cost Token and delivery date Token, and reflects the factory's static production capacity. Transportation distance, logistics cost and factory load rate reflect the real-time fulfillment status when the order is executed, which are dynamic decision factors.

[0067] The matching score is used to comprehensively evaluate the factory's suitability for the current order, reflecting the factory's overall capabilities, logistics conditions, and real-time operating status.

[0068] S3. Sort the candidate factories from high to low according to the matching score, and select N candidate factories at the top.

[0069] S4. Using the selected N candidate factories as targets, a multi-objective optimization model is established. The optimization objectives are to minimize overall fulfillment cost, shortest delivery time, and minimum fulfillment risk, to solve for the order allocation scheme. The objective function is:

[0070] Min Z = A×Production Costs + B×Logistics Costs + C×Delivery Time + D×Risk Costs - E×Customer Satisfaction;

[0071] In the objective function: A, B, C, D, and E are the objective weights.

[0072] This method, through its objective function, selects the order allocation scheme that minimizes overall cost, delivery cycle, fulfillment risk, and customer satisfaction, while satisfying constraints related to capacity, quality, timeliness, and process. Compared to traditional methods that allocate orders solely based on geographical distance, this method provides a more comprehensive assessment of fulfillment capabilities, thereby improving overall fulfillment efficiency.

[0073] The method in this embodiment also constructs an intelligent order splitting mechanism, which mainly includes splitting in the following two cases:

[0074] 1. In the selection process of step S1, when a single factory cannot meet the order delivery requirements, the order is split into m sub-orders, and each sub-order is processed separately using steps S1-S4. The aforementioned delivery requirements include whether the factory's estimated completion time is within the order delivery deadline and whether the factory's current load rate is within a set threshold.

[0075] 2. When the fulfillment cost of a single factory among the N candidate factories selected in step S3 is high, the order is split into m sub-orders, and the m sub-orders are allocated to different candidate factories according to their fulfillment costs, with the goal of minimizing the overall fulfillment cost.

[0076] Orders will be split according to product category, size, color, target region, process type, and order quantity. For example, orders from different countries within the same batch can be split by target region; different clothing categories can be split by product type; and large-volume orders for the same product can be split by quantity proportion. After splitting into m sub-orders, each sub-order includes information such as quantity, process requirements, delivery address, and time limit.

[0077] The fulfillment cost of a single factory The calculation formula is:

[0078] ;

[0079] In the formula: For production costs, For logistics costs, For tariff costs, For packaging costs, For risk costs;

[0080] Further calculate the average overall fulfillment cost of the N candidate factories selected in step S3. :

[0081] ;

[0082] In the formula That is, the fulfillment cost corresponding to each factory. ;

[0083] The fulfillment cost of a single factory is considered high when the following formula is met:

[0084]

[0085] In the formula: θ is the cost fluctuation threshold, which is generally taken as 10% to 20% and can be dynamically adjusted according to the operation strategy.

[0086] This method also introduces a reinforcement learning optimization mechanism. The system continuously records data such as order allocation results, fulfillment costs, delivery timeliness, and customer evaluations, and constructs a reward function to calculate the reward value R for each order. The calculation formula is: R = a × cost saving rate + b × delivery timeliness improvement rate + c × on-time delivery rate + d × customer satisfaction − e × delay penalty − f × complaint penalty, where a, b, c, d, e, and f are the target weights.

[0087] The system rewards orders on time, reduces costs, and generates high customer ratings; it also provides negative feedback for delayed orders, cost overruns, quality issues, or customer complaints. Based on these rewards, the system continuously optimizes factory selection rules, order splitting ratios, and fulfillment path strategies, enabling the distributed, localized fulfillment network to learn and adapt. Positive rewards are given when orders are delivered at lower costs and in shorter cycles; negative feedback is given for delayed deliveries or customer complaints. The reinforcement learning model continuously learns from historical order data to optimize factory matching rules and network scheduling strategies, giving the fulfillment network adaptive and self-learning capabilities.

[0088] Ultimately, this method achieves efficient matching of global orders with local production resources through the synergistic effect of a factory comprehensive capability token evaluation system, an intelligent order splitting mechanism, and a multi-objective optimization model, forming a distributed localized fulfillment network covering multiple countries and regions.

[0089] To adapt to changes in the market environment, this method can further construct a dynamic adjustment and restructuring mechanism for the localized fulfillment network. The system monitors global logistics status, factory capacity utilization, changes in order demand, and operational costs in real time. When a logistics disruption, capacity shortage, or cost anomalies occur in a certain region, the system automatically recalculates the optimal fulfillment path and switches orders to new manufacturing nodes, achieving dynamic restructuring of the fulfillment network. This mechanism effectively enhances the supply chain's resilience and network stability.

[0090] like Figure 1 The flowchart shown is for a system incorporating this method: Users place orders at the user level, with order information including order quantity, product type, process, target market, delivery time, and customer level. After order placement, the system enters the order processing layer, where feature analysis is performed and an order demand vector D is established. Next is the intelligent decision-making layer, where the calculation of the comprehensive capability token value, selection of candidate factories, calculation of matching scores, solving multi-objective optimization (Min Z), order splitting and allocation, dynamic adjustment and reconstruction, and reinforcement learning optimization are all performed. Following this is the execution layer, which handles subsequent factory production execution, logistics transportation, and order delivery after allocation using this method. Finally, there is the order completion feedback optimization layer, where the system collects data on order completion costs, timeliness, quality, and customer feedback, feeding this data back to the distributed manufacturing node database. Simultaneously, the system updates the model based on this data, updating the weights of each calculation formula, ultimately optimizing the strategy. Figure 3 The flowchart for adding order splitting.

[0091] The following section uses a specific apparel POD order to illustrate in detail the distributed local fulfillment network construction method for apparel PODs in this embodiment:

[0092] Suppose a consumer in the New York area purchases 100 DTG digital direct-to-garment short-sleeved T-shirts through a clothing POD platform, requesting delivery within 5 days. Order information includes: order quantity 100 pieces, product category 100 cotton short-sleeved T-shirts, printing technology DTG digital direct-to-garment, target market New York, USA, customer level VIP, and delivery time limit 5 days.

[0093] Once a customer places an order, the order first enters the order management system. The system automatically parses information from the order, such as quantity, product category, fabric type, printing process, target market, delivery time, and customer level, and constructs an order demand vector:

[0094] D={100, T-shirt, Cotton, DTG, USA-New York, 5 Day, VIP}

[0095] Subsequently, the system invokes the distributed manufacturing node database to search for all cooperating factories. Assume the current database contains four manufacturing nodes: Los Angeles (Factory A), Chicago (Factory B), Mexico (Factory C), and Guangzhou (Factory D).

[0096] The system first conducts an initial screening based on process capabilities, equipment capabilities, and remaining capacity. Factory A was eliminated because its current available capacity is only 80 units, which cannot meet the order requirement of 100 units. Factory B, Factory C, and Factory D all have DTG digital direct injection manufacturing capabilities, and their remaining capacity meets the order requirements, so they are included in the candidate factory set.

[0097] Subsequently, the system reads the historical operating data of each candidate factory, including indicators such as capacity utilization rate, product quality pass rate, unit production cost, on-time delivery rate, historical delivery cycle and logistics transportation time, and establishes a factory capability token evaluation model.

[0098] For example, the main data for each factory are as follows:

[0099] Factory B: Capacity utilization rate 85%, quality pass rate 99.5%, unit production cost $7 / piece, logistics and transportation time 2 days, historical on-time delivery rate 99%.

[0100] Factory C: Capacity utilization rate 60%, quality pass rate 98.8%, unit production cost $6 / piece, logistics and transportation time 3 days, historical on-time delivery rate 94%.

[0101] Factory D: Capacity utilization rate 40%, quality pass rate 99.2%, unit production cost $4 / piece, logistics and transportation time 8 days, historical on-time delivery rate 97%.

[0102] The system first normalizes the above indicators and calculates the capacity token, quality token, cost token, and delivery time token respectively. Then, it calculates the factory's overall capacity token value according to the formula.

[0103]

[0104] Wherein, α, β, γ, and δ represent the weighting coefficients corresponding to production capacity, quality, cost, and delivery time, respectively. In this specific case, they are taken as 0.30, 0.30, 0.20, and 0.20, respectively.

[0105] After calculation, the overall capability token value of Factory B is 92.5, the overall capability token value of Factory C is 90.3, and the overall capability token value of Factory D is 85.1. Figure 2 As shown.

[0106] The system further combines factors such as the factory's comprehensive capability token value, transportation distance, logistics costs, current factory load rate, and regional logistics risks to calculate the matching score between orders and each candidate factory:

[0107]

[0108] Among them, Token represents the comprehensive capability Token value; Distance represents the transportation distance score from the factory to the target market of the order; Logistics represents the comprehensive score of logistics cost and logistics timeliness; Load represents the current load rate score of the factory; and Risk represents the comprehensive score of logistics risk, equipment operation risk and regional operation risk.

[0109] The calculations show that Factory B has a matching score of 91.8, Factory C has a matching score of 89.6, and Factory D has a matching score of 76.4.

[0110] The system sorts the matching scores from high to low and selects Factory B and Factory C, which rank at the top, as candidate fulfillment nodes to enter the multi-objective optimization stage. Factory D, due to its long logistics distance and long transportation time, is not included in this round of optimization calculation.

[0111] Subsequently, the system establishes a multi-objective optimization model, with the optimization objectives being the lowest overall fulfillment cost, the shortest delivery time, and the lowest fulfillment risk. Its objective function is:

[0112] MinZ = A × Production Cost + B × Logistics Cost + C × Delivery Time + D × Risk Cost − E × Customer Satisfaction

[0113] The system calculates different order splitting schemes and calculates the objective function value MinZ for each scheme. The calculation results are as follows:

[0114] When all 100 orders are produced by Factory B, the total fulfillment time is about 4 days. However, since Factory B is expected to add about 300 new orders to be produced in the next 24 hours, its production load rate will increase to more than 95%, and the fulfillment risk will increase significantly, resulting in a high objective function value MinZ.

[0115] When the allocation scheme of Factory B producing 60 units and Factory C producing 40 units is adopted, the total fulfillment cost is approximately $960, the total fulfillment time is approximately 4 days, the overall risk is the lowest, and the corresponding objective function value MinZ is minimized. Therefore, this scheme is determined to be the optimal fulfillment scheme.

[0116] The final system determination is as follows:

[0117] Factory B is responsible for producing 60 units;

[0118] Factory C is responsible for producing 40 units.

[0119] Factory B is primarily responsible for orders from the Midwestern and Eastern United States, while Factory C is responsible for orders from the Southern United States. The two factories produce and ship simultaneously, which can further shorten the overall fulfillment cycle.

[0120] The system further calculates the overall fulfillment cost. Factory B's production and logistics costs are approximately $600, Factory C's production and logistics costs are approximately $360, and the total fulfillment cost is approximately $960.

[0121] If all orders were fulfilled by the factory in Guangzhou, China, while production costs would be lower, the total fulfillment cost would be approximately US$1,450 due to increased international transportation, tariffs, and cross-border logistics expenses, with an estimated delivery cycle exceeding 8 days. Therefore, the system automatically abandoned the centralized production plan and adopted a collaborative production plan between the United States and Mexico, reducing fulfillment costs by approximately 33% while still meeting the 5-day delivery requirement.

[0122] During order production, the system continuously monitors the logistics status and the operational status of each manufacturing node. On the second day of production, extreme blizzard weather occurred in the northeastern United States, causing a decrease in the transportation capacity of the logistics route corresponding to Factory B. After receiving the logistics monitoring data in real time, the system immediately re-executed the fulfillment path optimization algorithm to recalculate the optimal manufacturing node for the 20 orders that had not yet started production.

[0123] System analysis revealed that Factory C still had sufficient spare capacity and that logistics and transportation were not affected. Therefore, the remaining 20 orders were automatically switched to Factory C for production, realizing dynamic reconstruction of the fulfillment network without manual intervention and effectively ensuring on-time delivery of orders.

[0124] After an order is completed, the system automatically calculates data such as actual production cost, logistics cost, delivery time, customer rating, complaint status, and fulfillment success rate, and constructs a reinforcement learning reward function to calculate the reward value R.

[0125] R = a × Cost Savings Rate + b × Delivery Timeliness Improvement Rate + c × On-Time Delivery Rate + d × Customer Satisfaction - e × Delay Penalty - f × Complaint Penalty

[0126] In this embodiment, the order was delivered one day ahead of schedule, logistics costs were reduced by approximately 31%, customer satisfaction score reached 4.9, and the system received a high reward value. Based on this, the reinforcement learning model automatically updated the factory matching strategy, increasing the priority of allocating orders from the northeastern United States to the Chicago and Mexico factories for collaborative production.

[0127] The above embodiments of the present invention are not intended to limit the scope of protection of the present invention. The implementation of the present invention is not limited thereto. All other modifications, substitutions or alterations made to the above structure of the present invention based on the above content of the present invention, in accordance with ordinary technical knowledge and common practice in the field, without departing from the basic technical idea of ​​the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for constructing a distributed, localized fulfillment network for apparel PODs, characterized in that: First, establish a distributed manufacturing node database containing data from all cooperating factories, and normalize the corresponding indicators of the factories to obtain the comprehensive capability Token value S for each factory. Once a user order is received, the following steps are performed: S1. Perform feature analysis on the orders and establish an order demand vector D. Based on the order demand vector D, select qualified factories from the distributed manufacturing node database as candidate factories to form a candidate factory set. S2 combines the candidate factory's comprehensive capability Token value S, the transportation distance from the factory to the target market, logistics costs, logistics timeliness, and the factory's current load rate to obtain the matching degree between the order and the candidate factory; S3. Sort the candidate factories from high to low according to the matching degree, and select N candidate factories at the top. S4 allocates orders to the selected N candidate factories.

2. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: When a single factory cannot meet the order delivery requirements, the order is split into m sub-orders, and each sub-order is processed using steps S1-S4.

3. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: When the fulfillment cost of a single factory among the N candidate factories selected in step S3 is high, the order is split into m sub-orders, and the m sub-orders are allocated to different candidate factories according to their fulfillment costs.

4. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 3, characterized in that: Fulfillment cost of a single factory The calculation formula is: ； In the formula: For production costs, For logistics costs, For tariff costs, For packaging costs, For risk costs; Further calculate the average overall fulfillment cost of the N candidate factories selected in step S3. : ； In the formula That is, the fulfillment cost corresponding to each factory. ; The fulfillment cost of a single factory is considered high when the following formula is met: ； In the formula: θ is the cost fluctuation threshold.

5. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: The order demand vector D = {Q, P, M, T, C, F, S, L}, where: Q represents the order quantity, P represents the product category, M represents the target market, T represents the delivery time limit, C represents the process complexity, F represents the fabric type, S represents the printing process, and L represents the customer level.

6. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: In step S1, eligible candidate factories must meet the following conditions: the factory's location can cover the target market of the order; the factory's current remaining capacity is greater than the capacity required by the order; the factory's equipment capacity meets the process requirements of the order; the sum of the expected production cycle and logistics cycle does not exceed the order delivery time limit; and the historical quality pass rate and on-time delivery rate reach the thresholds set by the system.

7. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: The matching degree is determined by the matching score. This indicates the matching score. The calculation formula is as follows: ； In the formula: Token represents the comprehensive capability Token value; Distance represents the transportation distance score from the factory to the target market of the order; Logistics represents the comprehensive score of logistics cost and logistics timeliness; Load represents the current load rate score of the factory; Risk represents the comprehensive score of logistics risk, equipment operation risk and regional operation risk; w1 to w5 are the weight coefficients corresponding to each evaluation indicator.

8. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: The formula for calculating the overall capability token value S is as follows: ； In the formula: α, β, γ, and δ are weighting coefficients. As a production capacity token, For quality tokens, For cost tokens, This is the delivery date token.

9. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: In step S4, a multi-objective optimization model is established with N candidate factories as the targets, and its objective function is: Min Z = A×Production Costs + B×Logistics Costs + C×Delivery Time + D×Risk Costs - E×Customer Satisfaction; In the objective function: A, B, C, D, and E are the objective weights.

10. The method for constructing a distributed localized fulfillment network for apparel PODs according to claim 1, characterized in that: Construct a reward function to calculate the reward value R for each order. The calculation formula is as follows: R = a × cost savings rate + b × delivery timeliness improvement rate + c × on-time delivery rate + d × customer satisfaction - e × delay penalty - f × complaint penalty; In the formula: a, b, c, d, e, and f are the target weights.

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