Method and device for network reliability evaluation of stochastic resilient supply chain considering disruption and recovery strategy

Abstract

The present invention proposes an evaluation method and device for network reliability, considering disruptions and recovery strategies, representing nodes as vendors, transfer centers, or markets, and paths as carriers, with evaluations conducted on processor-based devices. The method is stored in a readable media device, facilitating the evaluation of network reliability. The present invention establishes the initial resilience performance level (IRPL) by evaluating the initial performance of the supply chain. Subsequently, an analysis of capacity during disruption is conducted to calculate the minimal quantity the current vendors can provide if the IRPL is sustained, and calculate the required quantity supplied of the transfer centers. Supply chain reliability is measured by aligning the quantity supplied allocation.

Description

[Technical Field]

[0001] This invention relates to a method and apparatus for assessing the network reliability of a stochastically resilient supply chain, and more particularly to a method and apparatus for assessing the network reliability of a stochastically resilient supply chain that considers chain failure and recovery strategies. [Previous Technology]

[0002] To assist supply chains in various sectors in assessing the feasibility of meeting customer demand and making decisions related to transportation routes, this invention aims to construct a practically resilient supply chain system as a stochastic resilient supply chain network (SRSCN) topology that considers multi-level capacity and transportation capacity, and store it in a readable media device. Suppliers, transshipment centers, and markets are inputs as nodes, and multiple transportation path inputs between two nodes are transmission edges. SRSCN considers that supplier capacity may vary due to material shortages, labor shortages, or machine malfunctions, resulting in diverse quantities provided. Furthermore, the number of available vehicles on transportation paths may be multi-level due to carriers needing to serve other customers.

[0003] In recent years, due to the increasing frequency of epidemics and environmental disasters, there has been a growing emphasis on whether supply chain members can provide goods normally. However, past patents and literature have rarely discussed the above-mentioned situations, that is, the impact of supply chain disruptions, and there is a lack of methods to assess the performance changes of the supply chain after implementing recovery strategies.

[0004] Therefore, this patent employs network analysis techniques to perform network topology analysis on the SRSCN to calculate its network reliability. This patent can assess supply chain performance during supply chain disruptions and deeply analyze changes in the supply chain after implementing recovery strategies. Furthermore, by using network reliability as a quantitative indicator, the method and apparatus invented in this patent help managers understand changes in supply chain performance during disruptions caused by supply chain shocks. This indicator not only assesses the feasibility of meeting demand but also observes how long the supply chain needs to recover under existing strategies over time. Supply chain managers can further adjust the configuration of recovery strategies in a timely manner based on this quantified performance indicator to meet the needs of all markets and serve as a basis for future related decisions to achieve intelligent and data-driven supply chain management. [Summary of the Invention]

[0005] One objective of this invention is to provide a reliability assessment method for stochastic resilient supply chain networks, applicable to stochastic resilient supply chain networks. The method includes: calculating a first capacity probability table for at least one supplier's supply chain disruption in the stochastic resilient supply chain network; listing all transportation paths from the at least one supplier to the market; identifying at least one feasible flow vector that satisfies initial demand, and listing at least one conditional expression for the at least one feasible flow vector; converting the at least one feasible flow vector into a capacity vector; using comparison rules in vector operations to obtain a first minimum capacity vector, and calculating a first network reliability based on the first capacity probability table and the first minimum capacity vector; if the first network reliability does not reach the initial network reliability, calculating the stochastic resilient supply chain network reliability... In a supply chain network, after replenishment begins from at least one backup supplier, a second capacity probability table is generated during the period of supply chain disruption. The first demand for the first working day after replenishment is calculated. The projected supply for the first working day is calculated, and based on the first demand, the supply allocation for the first working day is calculated, wherein the supply allocation is based on the supply allocation where network reliability reaches the initial network reliability. According to the supply allocation, a second minimum capacity vector is obtained using comparison rules in vector operations, and then, using the second minimum capacity vector, a second network reliability is calculated based on the second capacity probability table. If the second network reliability does not reach the initial network reliability, the demand for the next working day is calculated, and the network reliability for the next working day is calculated, until the network reliability reaches the initial network reliability.

[0006] In one embodiment, in the first capacity probability table, the probability of maintaining non-zero capacity is the probability of the non-zero capacity before the chain break multiplied by the probability of maintaining the same capacity during the chain break, while the probability of zero capacity is 1 minus the probability of maintaining each non-zero capacity.

[0007] In one embodiment, each component of the feasible flow vector corresponds to one of the transport paths.

[0008] In one embodiment, each component of the feasible flow vector satisfies the supplier’s capacity limit and the vehicle quantity limit of the transport route.

[0009] In one embodiment, the reliability of the first network is: where k is the number of the first minimum capacity vector.

[0010] In one embodiment, the first demand on the first working day is the initial demand minus an initial inventory level, and the inventory level at the start of the second working day is the expected supply on the first working day minus the first demand on the first working day.

[0011] In one embodiment, the expected supply is calculated based on the second capacity probability table, wherein the supply of each supplier and the backup supplier is the sum of the products of each possible output of the supplier or the backup supplier and the output probability, and the expected supply is the sum of the supply of each supplier and the backup supplier.

[0012] In one embodiment, the first demand is the sum of the supply from the supplier and the supply from the backup supplier, and the supply from the backup supplier satisfies the following condition: where s is the number of backup suppliers, p is the number of main suppliers, n is the number of transportation routes, xt is the capacity, wt is the maximum capacity of the backup supplier, and QI1 is the supply from the backup supplier.

[0013] In one embodiment, the second network reliability is: where k is the number of the second minimum capacity vector, QV1 is the supplier supply, and QI1 is the backup supplier supply.

[0014] Another object of the present invention is to provide a reliability assessment device for a stochastic resilient supply chain network, including a memory for storing the stochastic resilient supply chain network, and a method as described above, and including a processor for executing the method to calculate the network reliability of the stochastic resilient supply chain network.

Implementation Method

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making progressive contributions are within the scope of protection of the present invention.

[0016] This invention, based on a supply chain system, considers the multi-stage states of the capacity of each existing supplier, the inventory provided by backup suppliers, and transportation vehicles. It calculates a performance indicator: network reliability, to assess the probability of meeting demand under the impact of supply chain disruptions, quantify the effectiveness of recovery strategies, and evaluate the resilience of the supply chain through this performance indicator. Compared to other previous literature or patents, this invention, from the perspective of a transshipment center, calculates a performance indicator that considers the uncertainty of each supplier's capacity and the number of transportation vehicles for each route under the impact of supply chain disruptions. It assesses the possibility that the current supply chain network can meet demand, providing managers with a basis for decision-making. Furthermore, through continuous monitoring over time, it obtains information on changes in current network reliability and further provides feasible supply allocation. Through sensitivity analysis, it can calculate the probability of fulfilling transportation needs under different supply allocation scenarios, providing managers with a reference on how to allocate supply allocation in the face of the impact of supply chain disruptions.

[0017] Please refer to Figure 1. The analysis method used in this invention will be described below using the embodiment shown in Figure 1, but this invention is not limited thereto. Figure 1 is an example of a supply chain network diagram, in which the nodes include three suppliers S1, S2, and S3, two transshipment centers C1 and C2, and a market M. The nodes are interconnected by eight transportation paths, a1 to a8. The capacity that the three suppliers can provide and their corresponding probabilities are shown in Table 1, and the number of available vehicles (e.g., trucks) for each transportation path varies depending on the bookings of other customers, and their available quantities and corresponding probabilities are shown in Table 2. Table 1: Probability of Capacity Provided by Each Supplier Production capacity supplier 0 1 2 3 4 S1 0.01 0.04 0.15 0.3 0.5 S2 0.01 0.14 0.3 0.55 S3 0.02 0.11 0.27 0.6 Table 2: Probability of Vehicles Provided for Each Transportation Route Number of available vehicles Transportation routes 0 1 2 3 4 5 6 7 8 9 10 a1 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.1 0.15 0.2 0.4 a2 0.01 0.01 0.02 0.03 0.04 0.05 0.07 0.1 0.15 0.15 0.37 a3 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.1 0.1 0.15 0.5 a4 0.01 0.01 0.02 0.03 0.05 0.04 0.07 0.1 0.15 0.15 0.37 a5 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.01 0.14 0.25 0.45 a6 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.1 0.15 0.2 0.4 a7 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.1 0.15 0.15 0.45 a8 0.01 0.01 0.01 0.02 0.02 0.03 0.05 0.1 0.1 0.15 0.5

[0018] In recent years, due to the impact of the pandemic and environmental disasters on suppliers, the supply volume has not met expectations, resulting in a reduction in the production capacity of each supplier and thus causing supply chain disruptions. In this embodiment, based on the order data of the previous quarter, nearly 20% of orders were delayed or canceled, and the current supply chain recovery strategy is for the two transshipment centers C1 and C2 to replenish their stock from backup suppliers (not shown in the figure).

[0019] In this embodiment, the initial weekly demand for goods is 6. The probability that suppliers S1, S2, and S3 can maintain the same capacity during the chain break is PN = 80%. The network reliability before the chain break is R0 = 0.947536. The maximum capacity of suppliers S1, S2, and S3 are (4, 3, 3), and the number of available vehicles for each transportation path a1~a8 are (10, 10, 10, 10, 10, 10, 10, 10). The maximum capacity of the backup supplier to transport goods to the two transshipment centers C1 and C2 is (3, 2). For ease of calculation, the above data is combined into a vector W = (4, 3, 3, 10, 10, 10, 10, 10, 10, 10, 10, 3, 2).

[0020] The probability table of production capacity during the supply chain disruption and the calculation of network reliability are as follows. First, based on the impact of the supply chain disruption, the probability table of production capacity during the disruption is calculated. Please refer to Table 3. Taking supplier S1 as an example, since the probability of maintaining the same production capacity during the disruption is PN = 80%, the probability of maintaining a production volume of 4 is 80% of the probability of reaching this production capacity before the disruption, i.e., PB{x1 = 4}PN = 0.5 0.8 = PA{x1 = 4} = 0.4, where PB is the probability before the disruption and PA is the probability during the disruption. Similarly, the probability of a production volume of 3 is 0.3 0.8 = 0.24, the probability of a production volume of 2 is 0.15 0.8 = 0.12, and the probability of a production volume of 1 is 0.04 0.8 = 0.032. The probability of zero production (i.e., no products available) is: 1 - 0.8[0.5 + 0.3 + 0.15 + 0.04] = PA{x1 = 0} = 0.208. It should be noted that the probability of zero production during the supply chain disruption is 1 minus the probabilities of production at levels 1, 2, 3, or 4, not the probability of zero production before the disruption multiplied by 80%. The probability tables of supplier S2 and S3's capacity during the supply chain disruption can be obtained in the same way, as shown in Tables 4 and 5. Table 3: Probability Table of Supplier S1's Capacity Provided During the Supply Chain Disruption. Production capacity Supplier S1 production Before the chain broke During the chain break 4 0.5 0.4 3 0.3 0.24 2 0.15 0.12 1 0.04 0.032 0 0.01 0.208 Table 4: Probability of Supply Capacity Provided by Supplier S2 During Supply Chain Disruption Production capacity Supplier S2 production Before the chain broke During the chain break 3 0.55 0.44 2 0.3 0.24 1 0.14 0.112 0 0.01 0.208 Table 5: Probability of Supply Capacity Provided by Supplier S3 During Supply Chain Disruption Production capacity Supplier S3 production Before the chain broke During the chain break 3 0.6 0.48 2 0.27 0.216 1 0.11 0.088 0 0.02 0.216

[0021] Next, list all transportation routes Pj from the supplier (any one of S1, S2 and S3) to the market M. Based on the network diagram, identify all transportation routes as follows: P1 = {S1, a1, a7}, P2 = {S1, a2, a8}, P3 = {S2, a3, a7}, P4 = {S2, a4, a8}, P5 = {S3, a5, a7}, P6 = {S3, a6, a8}. Then, identify all feasible flow vectors that meet the demand: F = (f1, f2, f3, f4, f5, f6), where f1~f6 correspond to P1~P6 respectively, and f1 + f2 + f3 + f4 + f5 + f6 = 6. Check whether the available capacity from all suppliers and the number of vehicles used on each route exceed the maximum capacity, ensuring that f1~f6 satisfy the following conditions: f1 + f2 ≤ 4, f3 + f4 ≤ 3, f5 + f6 ≤ 3, f1 ≤ 10, f2 ≤ 10, f3 ≤ 10. f4 ≤ 10, f5 ≤ 10, f6 ≤ 10, f1 + f3 + f5 ≤ 10, f2 + f4 + f6 ≤ 10.

[0022] Subsequently, a capacity vector X = (x1, x2, …, x11) is generated, representing the number of vehicles used on each path in the network. Each feasible flow vector is converted into a capacity vector X = (x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11), as follows: x1 = f1 + f2, x2 = f3 + f4, x3 = f5 + f6, x4 = f1, x5 = f2, x6 = f3, x7 = f4, x8 = f5, x9 = f6, x10 = f1 + f3 + f5, x11 = f2 + f4 + f6. Subsequently, using the comparison rules in vector operations, the minimum capacity vectors (MCVs) are obtained, and the network reliability is calculated using the minimum capacity vectors according to the capacity probability table: where 269 is the number of minimum capacity vectors under the aforementioned conditions, and Xt is the capacity. Through the above steps, the network reliability at the moment of the supply chain disruption was calculated to be 0.680243. This value represents the probability that the supply chain can successfully meet market demand while meeting capacity constraints. At this point, the network reliability has not reached the initial resilience performance level (IRPL) of R0 = 0.947536 before the disruption. Therefore, it is necessary to start replenishing from backup suppliers to restore the network reliability to the level before the disruption, in order to maintain production capacity and meet market demand.

[0023] Subsequently, after the transshipment centers begin replenishing from backup suppliers, the network reliability is calculated over time. Table 6 lists the probability of the capacity provided by suppliers S1, S2, and S3 and backup suppliers (represented by transshipment centers C1 and C2) during the chain disruption in this embodiment. The capacity of suppliers S1, S2, and S3 is the same as in Tables 3, 4, and 5, while the maximum capacity of C1 is 3 and the maximum capacity of C2 is 2. Table 6: Probability of Capacity Provided by Suppliers and Backup Suppliers During the Chain Disruption Production capacity supplier 0 1 2 3 4 S1 0.208 0.032 0.12 0.24 0.4 S2 0.208 0.112 0.24 0.44 S3 0.216 0.088 0.216 0.48 C1 0.001 0.027 0.243 0.729 C2 0.01 0.18 0.81

[0024] First, calculate the demand D1 for the first working day after replenishment: D1 = d1 – I1 = 6 – 0 = 6 where d1 is the initial demand and I1 is the initial inventory on the first day. Since there is no inventory at the start of the replenishment process, I1 is 0.

[0025] Subsequently, the supply allocation (Qv1, QI1) and network reliability for the first working day are calculated, where Qv1 is the quantity of goods supplied by the primary supplier and QI1 is the quantity of goods supplied by the backup supplier. First, the expected supply E for the first working day is calculated; then, the supply allocation (Qv1, QI1) for the first working day is calculated. Since the network reliability R0 = 0.947536 when the chain is not interrupted, the minimum capacity vector is obtained by using the comparison rules in vector operations. After calculating the network reliability under different Qv1 using the minimum capacity vector, Table 7 is obtained. Table 7: Probability of the capacity provided by the supplier and backup supplier during the chain interruption. Q v 1 Minimum number of capacity vectors Network reliability 6 269 0.680243 5 194 0.801043 4 116 0.88066 3 56 0.950619 As shown in Table 7, to achieve the same network reliability as when the link is not broken, Qv1 = 3. Therefore: QI1 = D1 – Qv1 = 6 – 3 = 3. Here, s represents the number of backup suppliers, p represents the number of primary suppliers, n represents the number of transport paths, xt represents capacity, and wt represents the maximum capacity of backup suppliers. Therefore, in this embodiment, these three factors satisfy the above inequality relationship.

[0026] Subsequently, based on the supply allocation (Qv1,QI1)=(3,3), all feasible solutions of the flow vector F = (f1, f2,…, f8) are calculated. First, find all feasible flow vectors that satisfy the supply requirements: F = (f1, f2, ..., f8) f1 + f2 + f3 + f4 + f5 + f6 = 3 f7 + f8 = 3. Then check whether the available capacity from all suppliers and the number of vehicles used along the route exceed the maximum capacity, ensuring that f1~f8 satisfy the following conditions: f1 + f2 ≤ 4 f3 + f4 ≤ 3 f5 + f6 ≤ 3 f1 ≤ 10 f2 ≤ 10 f3 ≤ 10 f4 ≤ 10 f5 ≤ 10 f6 ≤ 10 f1 + f3 + f5 + f7 ≤ 10 f2 + f4 + f6 + f8 ≤ 10 f7 ≤ 3 f8 ≤ 2. Next, convert each feasible flow vector into a capacity vector X = (x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, ..., x8). x12, x13) are as follows: x1 = f1 + f2 x2 = f3 + f4 x3 = f5 + f6 x4 = f1 x5 = f2 x6 = f3 x7 = f4 x8 = f5 x9 = f6 x10 = f1 + f3 + f5 x11 = f2 + f4 + f6 x12 = f7 x13 = f8 Then, using the comparison rules in vector operations, the minimum capacity vector is obtained. Based on the capacity probability table after replenishment by the backup supplier, the network reliability for the first working day after replenishment begins is calculated using the minimum capacity vector: where 168 is the number of minimum capacity vectors under the aforementioned conditions, and Xt is the capacity. At this point, the network reliability has not yet recovered to the level before the connection was interrupted, R0 = 0.947536, therefore, calculations for the second working day are required.

[0027] Calculate the inventory level I2 at the start of the second working day, and repeat the above calculation process to calculate the network reliability corresponding to the second working day. The inventory level I2 at the start of the second working day is: IT+1 = E – DT = 7 – 6 = 1 where T=1. As shown in Table 8, in this embodiment, the supply chain shown in Figure 1 can recover to a network reliability R0 = 0.947536 or higher than before the chain break on the second working day after the chain break with the support of the backup supplier. Therefore, this supply chain has sufficient resilience under the current backup strategy. Please refer to Figure 2, which shows the change of the supply chain network reliability over time in this embodiment. As shown in Figure 2, the network reliability decreases at the moment of the chain break (T=0), but with the intervention of the backup supplier, this supply chain can recover to a network reliability R0 = 0.947536 or higher than before the chain break on the second working day (T=2). Therefore, this supply chain has sufficient resilience under the current backup strategy. Table 8 Network Reliability Changes T I T D T (Q v T Q I T ) 1 0 6 (3,3) 0.930507 2 1 5 (2,3) 0.956434

[0028] Figure 3 is a flowchart of the reliability assessment method for stochastic resilient supply chain networks provided by the present invention. Please refer to Figure 3, and also to Figures 1 and 2, for a more detailed understanding of the operation flow of the reliability assessment method for stochastic resilient supply chain networks provided by the present invention.

[0029] First, in step S301, the production capacity probability table for each supplier during the chain break is calculated based on the degree of impact caused by the chain break.

[0030] Next, in step S302, all time factors are analyzed and all transportation routes from the supplier to the market are listed.

[0031] Subsequently, in step S303, all feasible flow vectors that meet the requirements are identified, and it is checked whether the capacity that all suppliers can provide and the number of vehicles used in the route exceed the maximum capacity, and the conditional expressions of the feasible flow vectors are listed.

[0032] Subsequently, in step S304, each feasible flow vector is converted into a capacity vector, representing the number of vehicles used by each path in the network.

[0033] Subsequently, in step S305, the comparison rules in vector operations are used to obtain the minimum capacity vectors (MCVs), and the network reliability is calculated using the minimum capacity vectors based on the first capacity probability table.

[0034] Subsequently, in step S306, the process is divided into two paths based on whether the network reliability has reached the level before the link failure. If yes, the process proceeds to step S307 and ends. If no, the process proceeds to step S308.

[0035] In step S308, calculate the probability table of the capacity provided by each supplier and the backup supplier (or the transshipment center) during the supply chain disruption after the transshipment center starts replenishing from the backup supplier.

[0036] Subsequently, in step S309, the demand for the first working day after replenishment is calculated.

[0037] Subsequently, in step S310, the expected supply for the first working day is calculated, and the supply allocation for the first working day is calculated. Using the comparison rules in vector operations, the minimum capacity vector is obtained, and the network reliability under different supply allocations is calculated using the minimum capacity vector, with the supply allocation based on the network reliability reaching the level before the link is broken.

[0038] Subsequently, in step S311, based on the supply allocation, all feasible solutions for the flow vector are calculated, and each feasible flow vector is then converted into a capacity vector. Using the comparison rules in vector operations, the minimum capacity vector is obtained, and then the network reliability for the first working day after replenishment begins is calculated using the minimum capacity vector.

[0039] Finally, in step S312, the process is divided into two paths based on whether the network reliability has reached the level before the link loss. If yes, the process proceeds to step S313 and ends. If no, the process returns to step S309, and the network reliability for the second working day is calculated in the same way. If the network reliability for the second working day reaches the level before the link loss, the process proceeds to step S313 and ends. If no, the process returns to step S309, and the network reliability for the third working day is calculated in the same way. This calculation is repeated until the network reliability recovers to the level before the link loss.

[0040] The present invention further provides a stochastic resilience supply chain network reliability assessment device for performing the aforementioned stochastic resilience supply chain network reliability assessment method. Referring to Figure 4, this stochastic resilience supply chain network reliability assessment device 400 includes a memory 402 for storing the aforementioned stochastic resilience supply chain network (as shown in Figure 1) and the aforementioned algorithm (as shown in Figure 3), and includes a processor 404 connected to the memory 402 for executing the aforementioned algorithm to calculate the network reliability of the aforementioned stochastic resilience supply chain network.

[0041] The memory 402 described above may include any type of volatile memory and / or non-volatile memory (NVRAM), such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Read-Only Memory (ROM), Flash memory, Cache memory, etc. The processor 404 may be implemented through an integrated circuit chip, a system on chip (SoC), a complex programmable logic device (CPLD), a field programmable gate array (FPGA), etc. The program instructions that execute the operations of this invention can be assembly language instructions, instruction set architecture instructions, machine instructions, machine-dependent instructions, microinstructions, firmware instructions, or source code or object code written in any combination of one or more programming languages. The programming languages ​​include object-oriented programming languages ​​such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, and PHP, as well as conventional procedural programming languages ​​such as C or similar programming languages.

[0042] This invention employs network analysis techniques to perform network topology analysis on a Stochastic Resilience Supply Chain Network (SRSCN) and calculate the network reliability of the SRSCN. This patent can assess supply chain performance during supply chain disruptions and deeply analyze changes in the supply chain after implementing recovery strategies. Furthermore, by using network reliability as a quantitative indicator, the method and apparatus provided by this invention can help managers understand changes in supply chain performance during supply chain disruptions. This indicator not only assesses the likelihood of meeting demand but also observes over time how much time the supply chain needs to recover under existing strategies. Supply chain managers can further adjust the configuration of recovery strategies in a timely manner based on this quantified performance indicator to meet the needs of all markets and serve as a basis for future related decisions to achieve intelligent and data-driven supply chain management.

[0043] The methods and apparatus provided by the present invention can be widely applied to various industries, including but not limited to manufacturing, food, logistics, and various transportation industries, which are complex and related to production and transportation, require consideration of customer needs, potential chain disruption risks faced by each system, and the effectiveness of current backup strategies, and require the provision of various performance indicators and decision-making basis.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. [Simplified Explanation of the Diagram]

[0045] The accompanying drawings are provided to enable those skilled in the art to further understand the present invention and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present invention and are used together with the specification to explain the principles of the invention. The following is a brief description of the figures: Figure 1 is a network diagram of a stochastic resilient supply chain network according to an embodiment of the present invention. Figure 2 shows the change in network reliability over time of a stochastic resilient supply chain network according to an embodiment of the present invention. Figure 3 is a flowchart of the reliability assessment method for a stochastic resilient supply chain network according to an embodiment of the present invention. Figure 4 is a schematic diagram of the architecture of a reliability assessment device for a stochastic resilient supply chain network according to an embodiment of the present invention. [Biomaterial Storage]

[0047] None

Claims

1. A reliability assessment method for a stochastically resilient supply chain network, applicable to a stochastically resilient supply chain network, the reliability assessment method comprising: Calculate a first capacity probability table for at least one supplier disruption in this stochastic resilient supply chain network; List all transportation routes from the at least one supplier to a market; identify at least one feasible flow vector that satisfies an initial demand, and list at least one condition for the at least one feasible flow vector; convert the at least one feasible flow vector into a capacity vector. Using comparison rules in vector operations, a first minimum capacity vector is obtained, and a first network reliability is calculated based on the first capacity probability table. If the first network reliability does not reach an initial network reliability, a second capacity probability table is calculated for the stochastic resilient supply chain network during a supply chain disruption after replenishment from at least one backup supplier begins. A first demand is calculated for the first working day after replenishment. A projected supply for the first working day is calculated, and a supply allocation for the first working day is calculated based on the first demand, wherein the supply allocation is based on the supply allocation where network reliability reaches the initial network reliability. Based on the supply allocation, using comparison rules in vector operations, a second minimum capacity vector is obtained, and a second network reliability is calculated based on the second minimum capacity vector and the second capacity probability table. If the second network reliability does not reach the initial network reliability, a demand for the next working day is calculated, and a network reliability for the next working day is calculated, until the network reliability reaches the initial network reliability.

2. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, In the first capacity probability table, the probability of maintaining a non-zero capacity is the probability of that non-zero capacity before the supply chain disruption multiplied by the probability of maintaining the same capacity during the supply chain disruption, while the probability of zero capacity is 1 minus the probability of maintaining each non-zero capacity.

3. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, Each component in the feasible flow vector corresponds to one of the equivalent transport paths.

4. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, Each component of the feasible flow vector satisfies the capacity constraints of the suppliers and the vehicle quantity constraints of the transport routes.

5. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, The reliability of the first network is: where k is the number of the first minimum capacity vectors, and Xt is the capacity.

6. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, The initial demand on the first working day is the initial demand minus an initial inventory level, and the inventory level at the start of the second working day is the projected supply on the first working day minus the initial demand on the first working day.

7. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, The projected supply is calculated based on the second capacity probability table, wherein the supply of each supplier and the backup supplier is the sum of the products of each possible output of the supplier or the backup supplier and the probability of that output, rounded down, and the projected supply is the sum of the supply of each supplier and the backup supplier.

8. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, The first demand is the sum of the supply from one supplier and the supply from a backup supplier, and the supply from the backup supplier satisfies the following condition: where s is the number of backup suppliers, p is the number of primary suppliers, n is the number of transportation routes, xt is the capacity, wt is the maximum capacity of the backup supplier, and QI1 is the supply from the backup supplier.

9. The reliability assessment method for stochastic resilient supply chain networks as described in claim 1, wherein, The reliability of the second network is: where k is the number of the second minimum capacity vectors, QV1 is the supplier supply, QI1 is the backup supplier supply, and Xt is the capacity.

10. A reliability assessment apparatus for a stochastically resilient supply chain network having the method described in claim 1, comprising: A memory for storing the random resilience supply chain network, and the method as described in claim 1; And a processor for executing the method to calculate a network reliability of the stochastic resilient supply chain network.

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