Emergency supply chain cooperative intelligent reconstruction method and device

Abstract

The invention provides an emergency supply chain cooperative intelligent reconstruction method and device. The method comprises the following steps: acquiring demand point information; according to the demand point information, the emergency warehouse reserve, the supplier reserve, the emergency warehouse dispatching amount, the emergency material amount purchased from the supplier according to different prices and the material amount transported from the supplier to the demand point are obtained by using the scene robust model constructed in advance; wherein the scene robust model is constructed on the basis of the variance between the expected performance and the actual performance, wherein the expected performance is obtained according to the pre-disaster reserve cost expectation and the post-disaster emergency supply cost expectation in any scene; and reconstructing an emergency supply chain according to the emergency warehouse reserve, the supplier reserve, the emergency warehouse allocation volume, the emergency material volume purchased from the supplier according to different prices and the material quantity transported from the supplier to the demand point. From the perspective of pre-disaster reserve and post-disaster response, rapid repair after emergency supply chain interruption is realized, and emergency material supply is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for collaborative intelligent reconstruction of an emergency supply chain. Background Art

[0002] Every year, many disasters occur around the world, causing serious ecological damage, facility damage, casualties and property losses. In the early stages of a disaster, emergency supplies and rescue personnel need to be quickly transported to the disaster area. In order to improve the ability to respond to disasters, rescue organizations select some strategic locations to establish rescue centers and reserve a predetermined amount of emergency supplies. Since the demand for disaster relief is generated by random events that are unpredictable in terms of time, location, type and scale, it is not easy to develop an effective and robust emergency supply reserve and distribution network.

[0003] Traditional emergency resource pre-positioning is that the rescue organization selects some potential rescue centers and purchases a certain amount of emergency supplies for storage. After the disaster occurs, these disaster relief supplies are transported to the demand points. The rescue organization needs to be responsible for the purchase, storage, rotation and daily management of emergency supplies.

[0004] However, due to the uncertainty of post-disaster demand and supply, there may be a risk of stockouts if the pre-stored emergency supplies are small, and redundancy and waste if the emergency supplies are too large. In addition, the location where the pre-stored supplies are stored may be destroyed in a disaster event, resulting in the loss (partial or complete) of anything stored there, or part of the transportation network used to transport the supplies may be unavailable after the event. Summary of the invention

[0005] The present invention provides a method and device for collaborative intelligent reconstruction of an emergency supply chain, which is used to solve the defects of the prior art due to the uncertainty of post-disaster demand and supply, realize a robust emergency material pre-positioning and distribution network design, and ensure that the supply chain can be quickly reconstructed when the post-disaster emergency supply chain is interrupted, thereby ensuring the supply of emergency materials.

[0006] The present invention provides a method for collaborative intelligent reconstruction of an emergency supply chain, comprising: obtaining demand point information; according to the demand point information, using a previously constructed scenario robust model to obtain emergency warehouse reserve quantity, supplier reserve quantity, emergency warehouse transportation quantity, the quantity of emergency materials purchased from suppliers at different prices, and the quantity of materials transported from suppliers to demand points; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the pre-disaster reserve cost expectation and the post-disaster emergency supply cost expectation in any scenario; and reconstructing the emergency supply chain according to the emergency warehouse reserve quantity, supplier reserve quantity, emergency warehouse transportation quantity, the quantity of emergency materials purchased from suppliers at different prices, and the quantity of materials transported from suppliers to demand points.

[0007] According to a collaborative intelligent reconstruction method for an emergency supply chain provided by the present invention, before obtaining the emergency warehouse reserve, supplier reserve, emergency warehouse transfer volume, emergency material quantity purchased from suppliers at different prices, and material quantity shipped from suppliers to demand points based on demand point information using a previously constructed scenario robust model, the method includes: obtaining expected performance based on the scenario occurrence probability of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost; obtaining actual performance based on the scenario occurrence probability of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost corresponding to all scenario occurrence probabilities variance; for any scenario, a material shortage function is constructed based on the demand for emergency materials at each demand point, the emergency warehouse and the first emergency material quantity transported from the emergency warehouse to the corresponding demand point, the supplier and the second emergency material quantity transported from the supplier to the corresponding demand point; a scenario robust model is constructed based on the expected performance, the actual performance variance and the material shortage function, combined with the auxiliary scalar and the first preset constraint; wherein the first preset constraint is used to limit the sum of the total cost of any scenario based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost and the auxiliary scalar to be no less than the product of the probability of occurrence of all scenarios and the corresponding total cost, and the auxiliary scalar is greater than or equal to 0.

[0008] According to a collaborative intelligent reconstruction method for an emergency supply chain provided by the present invention, before obtaining the expected performance and the actual performance variance based on the probability of occurrence of any scenario and the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, the method includes: constructing an emergency warehouse activation cost function according to the emergency warehouse and the fixed activation cost of the emergency warehouse, wherein the emergency warehouse is selected from the set of emergency warehouses; constructing an emergency material procurement cost function according to the pre-disaster emergency material procurement cost and the emergency material storage quantity of the emergency warehouse; wherein the emergency material storage quantity of the emergency warehouse is less than or equal to the storage capacity of the emergency warehouse, and the emergency material storage quantity of the emergency warehouse is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials in the scenario; and constructing an emergency material procurement cost function according to the emergency warehouse and the fixed activation cost of the emergency warehouse. The emergency material reserve cost function is constructed based on the storage quantity and the reserve cost per unit of the emergency warehouse; the supplier activation cost function is constructed based on the supplier and the supplier's fixed activation cost, and the supplier is selected from the supplier set; the supplier subsidy cost function is constructed based on the supplier, the supplier's emergency material storage quantity and the supplier's subsidy cost per unit; among which, the supplier's emergency material storage quantity is less than or equal to the supplier's maximum reserve quantity, and the supplier's emergency material storage quantity is less than the product of a preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario; according to the emergency warehouse activation cost function, the emergency material procurement cost function, the emergency material reserve cost function, the supplier activation cost function and the supplier subsidy cost function, the expected pre-disaster reserve cost is obtained.

[0009] According to a collaborative intelligent reconstruction method for an emergency supply chain provided by the present invention, before obtaining the expected performance and the actual performance variance based on the probability of occurrence of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, the method also includes: for any scenario, according to the emergency warehouse, the first emergency material quantity transported from any emergency warehouse to any demand point, the first distance between the emergency warehouse and the corresponding demand point, and the transportation cost between the emergency warehouse and the corresponding demand point, constructing an emergency warehouse material transportation cost function; wherein the total amount of the first emergency material transported from the emergency warehouse to all demand points is less than or equal to the emergency material storage quantity of the emergency warehouse, the first emergency material quantity is greater than or equal to 0, and the first emergency material quantity transported from the emergency warehouse to any demand point is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials in the scenario; according to the supplier, the second emergency material quantity transported from any supplier to any demand point, the second distance between the supplier and the corresponding demand point, and the transportation cost between the supplier and the corresponding demand point, constructing a supplier material transportation cost function; wherein the second emergency material quantity is greater than or equal to 0, and the second emergency material quantity transported from the supplier to any demand point is less than The product of a preset positive number and a scenario parameter of whether the supplier can supply emergency materials in the scenario; construct a supplier material procurement cost function according to the first procurement cost, the first purchase quantity purchased from the supplier at the first procurement cost, the second procurement cost and the second purchase quantity purchased from the supplier at the second procurement cost; wherein the first procurement cost is the per-unit emergency material procurement cost within the emergency material reserve quantity range agreed upon when the supplier is determined, the second procurement cost is the per-unit emergency material procurement cost outside the emergency material reserve quantity range agreed upon when the supplier is determined, the second emergency material quantity is the sum of the first purchase quantity and the second purchase quantity, the first purchase quantity is less than or equal to the supplier's emergency material storage quantity, the sum of the first purchase quantity and the second purchase quantity is less than the supplier's maximum reserve quantity, the first purchase quantity is greater than or equal to 0 and less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario, the second purchase quantity is greater than or equal to 0 and less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario; the expected post-disaster emergency supply cost is obtained according to the emergency warehouse material transportation cost function, the supplier material transportation cost function and the supplier material procurement cost function.

[0010] According to a collaborative intelligent reconstruction method of an emergency supply chain provided by the present invention, based on demand point information and using a previously constructed scenario robust model, emergency warehouse reserves, supplier reserves, emergency warehouse transfer volumes, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials shipped from suppliers to demand points are obtained, including: inputting demand point information into the previously constructed scenario robust model to generate an initial emergency warehouse reserve volume and an initial supplier reserve volume based on the expected pre-disaster reserve cost; based on the initial emergency warehouse reserve volume and the initial supplier reserve volume, based on the expected post-disaster emergency supply cost, obtaining the initial emergency warehouse transfer volume, the initial emergency materials purchased from suppliers at different prices, and the initial amount of materials shipped from suppliers to demand points; using a genetic algorithm, optimizing the initial emergency warehouse reserve volume and the initial supplier reserve volume to obtain the optimized emergency warehouse reserve volume and supplier reserve volume, as well as the emergency warehouse transfer volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials shipped from suppliers to demand points corresponding to the optimized emergency warehouse reserve volume and supplier reserve volume; wherein the chromosomes of the genetic algorithm include the emergency warehouse reserve volume and the supplier reserve volume.

[0011] According to an emergency supply chain collaborative intelligent reconstruction method provided by the present invention, a genetic algorithm is used to optimize the initial emergency warehouse reserves and the initial supplier reserves to obtain the optimized emergency warehouse reserves and the supplier reserves, as well as the emergency warehouse transportation volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points corresponding to the optimized emergency warehouse reserves and the supplier reserves, including: evaluating the fitness of the initial emergency warehouse reserves and the initial supplier reserves as chromosomes of the genetic algorithm, and allocating selection probabilities or weights according to the obtained fitness; eliminating corresponding chromosomes according to the allocated selection probabilities or weights; selecting any chromosome from the retained chromosomes; and The method performs crossover and mutation operations on two chromosomes, and uses the mutation operation results to update the chromosomes; using the updated chromosomes, based on the expected post-disaster emergency supply cost, the initial emergency warehouse transfer volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material quantity shipped from suppliers to demand points are obtained again; the updated chromosomes are re-evaluated for fitness, selected with probability or weight distribution, chromosomes are eliminated, crossover and mutation operations are performed until the fitness tends to stabilize or the number of iterations meets the preset number, and the optimized emergency warehouse reserve volume, supplier reserve volume, emergency warehouse transfer volume, emergency material volume purchased from suppliers at different prices, and the material quantity shipped from suppliers to demand points are obtained.

[0012] The present invention also provides an emergency supply chain collaborative intelligent reconstruction device, including: a demand acquisition module, which acquires demand point information; an intelligent reconstruction module, which obtains the emergency warehouse reserve, supplier reserve, emergency warehouse transfer volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points based on the demand point information and a previously constructed scenario robust model; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the pre-disaster reserve cost expectation and the post-disaster emergency supply cost expectation in any scenario; and a supply chain construction module, which reconstructs the emergency supply chain based on the emergency warehouse reserve, supplier reserve, emergency warehouse transfer volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the emergency supply chain collaborative intelligent reconstruction methods described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the emergency supply chain collaborative intelligent reconstruction methods described above.

[0015] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the emergency supply chain collaborative intelligent reconstruction methods described above.

[0016] The method and device for collaborative intelligent reconstruction of an emergency supply chain provided by the present invention reconstruct the emergency supply chain according to demand point information by utilizing a previously constructed scenario robust model, and by considering demand and supply uncertainties, from the perspectives of pre-disaster reserves and post-disaster responses, studies the pre-positioning, storage and transportation of emergency materials, and based on a quantity-flexible option contract signed with suppliers, enables suppliers to replace part of the emergency warehouses for the storage and supply of materials, so that they can be widely used in various emergency rescue and humanitarian rescue work; under the option contract, it can effectively reduce the cost of material reserves and daily rotation for official agencies and rescue organizations, and can also cope with the situation where supplier facilities are damaged due to severe natural disasters, achieve rapid repair after the emergency supply chain is interrupted, and ensure the supply of emergency materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is one of the flow charts of the method for collaborative intelligent reconstruction of an emergency supply chain provided by the present invention; Figure 2 This is the second flow chart of the emergency supply chain collaborative intelligent reconstruction method provided by the present invention; Figure 3 It is a schematic diagram of the convergence effect of the algorithm provided by the present invention; Figure 4 It is a schematic diagram of the change of the expected total cost of the supply chain according to the post-disaster purchase price P2 provided by the present invention; Figure 5 It is a schematic diagram of the change of the total cost of the emergency supply chain provided by the present invention with the amount of the unit emergency material reserve subsidy; Figure 6 It is a structural schematic diagram of the emergency supply chain collaborative intelligent reconstruction device provided by the present invention; Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Figure 1 A schematic diagram of a process of an emergency supply chain collaborative intelligent reconstruction method provided by the present invention is shown, and the method includes the following steps: S11, obtaining demand point information; S12, based on the demand point information, using the previously constructed scenario robust model, obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transportation, the amount of emergency supplies purchased from suppliers at different prices, and the amount of supplies transported from suppliers to the demand point; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost in any scenario; S13, reconstruct the emergency supply chain based on the emergency warehouse reserves, supplier reserves, emergency warehouse transfer volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

[0021] It should be noted that the step numbers "S1N" in this manual do not represent the order of the emergency supply chain collaborative intelligent reconstruction method. Figure 2-Figure 5 The emergency supply chain collaborative intelligent reconstruction method of the present invention is described.

[0022] Step S11, obtaining demand point information.

[0023] In this embodiment, the demand point information includes the demand node k, k∈K, the location of the demand node k (Π k ,Θ k ).

[0024] In an optional embodiment, before obtaining the emergency warehouse reserve, supplier reserve, emergency warehouse transportation volume, emergency material quantity purchased from suppliers at different prices, and material quantity transported from suppliers to the demand point based on the demand point information using the previously constructed scenario robust model, it includes: based on the scenario occurrence probability P of any scenario s , according to the expected pre-disaster reserve cost O1(δ) and the expected post-disaster emergency supply cost O2(δ,γ s ), and get the expected performance∑ (s∈S) P s ξ s ; The probability of occurrence of a scenario based on any scenario P s , according to the expected pre-disaster reserve cost O1(δ) and post-disaster emergency supply cost O2(δ,γ s ), and the actual performance variance ∑ s∈S P s (ξ s -∑ s`∈S P s` ξ s` ) 2 ; For any scenario, the material shortage function is constructed based on the emergency material demand Dk s of each demand point k, the emergency warehouse j and the first emergency material quantity εs jk transported from the emergency warehouse to the corresponding demand point k, the supplier i and the second emergency material quantity Δs ik transported from the supplier to the corresponding demand point k; according to the expected performance ∑ (s∈S) P s ξ s , Actual performance variance ∑ s∈S P s (ξ s -∑ s`∈S P s` ξ s` )2 and the shortage function O3(δ,γ s ), combined with the auxiliary scalar s and the first preset constraint to construct a scenario robust model; wherein the first preset constraint is used to limit any scenario s based on the expected pre-disaster reserve cost O1(δ) and the expected post-disaster emergency supply cost O2(δ,γ s ) to obtain the total cost ξ s With auxiliary scalar s The sum is not less than the probability of all scenarios occurring P s and the corresponding total cost ξ s The product of , that is, the first preset constraint is expressed as: ξ s -∑ s`∈S P s` ξ s` +ο s ≥0,s∈S, and the auxiliary scalar ο s Greater than or equal to 0.

[0025] It should be noted that s =O1(δ)+O2(δ,γ s ), where O1(δ) represents the expected pre-disaster reserve cost of decision δ, O2(δ,γ s ) represents the expected cost of post-disaster emergency supply for decision δ. In addition, the robust objective of the solution is the expected performance ∑ (s∈S) P s ξ s and actual performance variance ∑ s∈S P s (ξ s -∑ s`∈S P s` ξ s` ) 2 The weighted combination of to reflect the cost deviation of decision results in different scenarios, that is, the robust objective is expressed as:

[0026] Where λ represents the solution robustness weight. It should be noted that the larger λ is, the higher the requirement for solution robustness is.

[0027] However, since the solution to the robust objective is nonlinear, the equivalent linear expression is obtained as follows:

[0028] In addition, the infeasibility of decision-making is reflected by the mismatch between supply and demand of materials, and some of the objectives in the scenario robust model are as follows:

[0029] Further,

[0030]

[0031] in, represents the demand for emergency supplies at demand point k under scenario s; Indicates whether the emergency warehouse j in scenario s is the target emergency warehouse parameter for supplying emergency supplies. = 1, j is the target emergency warehouse, = 0, j is not the target emergency warehouse; It represents the first amount of emergency supplies delivered by emergency warehouse j to demand point k in scenario s; Indicates whether supplier i in scenario s is the target supplier parameter for supplying emergency supplies. =1, i is the target supplier, =0, i is not the target supplier; It represents the amount of second emergency supplies delivered by supplier i to demand point k in scenario s. It should be noted that the constraints of each parameter are described below and will not be further elaborated here.

[0032] Therefore, according to the above linear expression, the first preset constraint and some objectives in the scene robust model, the scene robust model is obtained, which is expressed as:

[0033]

[0034] Among them, γ represents the pre-disaster emergency material procurement cost, that is, the unit emergency material procurement cost of purchasing emergency materials from the market and storing them in the emergency warehouse before the disaster; ω represents the model robustness weight. It should be noted that the first two items of the above model are the specific expressions of the solution robustness target in the scenario robust model, and the third item is the model robustness term. The constraint condition is the specific expression of the linearization constraint in the robust model. In addition, the scenario robust model is a mixed integer programming model that can be directly solved. Decision makers can weigh the solution robustness and model robustness, that is, according to the selection of different λ and ω, they can flexibly weigh the cost indicators and supply and demand adaptation.

[0035] Specifically, before obtaining the expected performance and actual performance variance based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost based on the probability of occurrence of any scenario, it includes: SA1, based on emergency warehouse j and emergency warehouse fixed activation cost F j , construct the emergency warehouse activation cost function , emergency warehouse j is selected from the emergency warehouse set J.

[0036] It should be noted that if the corresponding emergency warehouse j is selected from the emergency warehouse set J as the target emergency warehouse for supplying emergency supplies, the corresponding xj =1, other unselected emergency warehouses x j =0,j J, j represents the jth emergency warehouse, and J represents the set of emergency warehouses, that is, x j {0,1}.

[0037] SA2, based on the pre-disaster emergency supplies procurement cost γ and the emergency supplies storage quantity in the emergency warehouse , construct emergency material procurement cost function ; Among them, the number of emergency supplies stored in the emergency warehouse Less than or equal to the emergency warehouse storage capacity h j , the number of emergency supplies stored in the emergency warehouse Less than the preset positive number W and the scenario parameter of whether the emergency warehouse can supply emergency supplies in the scenario The product of , j∈J, to ensure that only the selected emergency warehouse can store emergency supplies. Among them, the emergency supplies procurement cost γ is a pre-set parameter. In addition, ≥0.

[0038] SA3, based on the emergency storage warehouse j, the emergency storage warehouse j’s emergency supplies storage quantity and the emergency warehouse reserve cost per unit f j , construct emergency material reserve cost function .

[0039] SA4, based on supplier i and supplier i fixed activation cost U i , construct supplier activation cost function∑ i∈I y i U i , supplier i is selected from supplier set I. It should be noted that if the corresponding supplier i is selected as the target supplier from supplier set I, the corresponding y i =1, other suppliers y that are not selected i =1, i∈I, I represents the i-th supplier, I represents the supplier set, that is, y i ∈{0,1}.

[0040] SA5, based on supplier i, the emergency material storage quantity G of supplier i i and supplier subsidy costs per unit , construct supplier subsidy cost function ; Among them, the emergency material storage quantity G of supplier i i Less than or equal to the supplier's maximum reserve q i , the emergency supplies storage quantity G of supplier i iLess than the product of the preset positive number W and the scenario parameter of whether the supplier can supply emergency supplies in the scenario y i , that is, G i <Wy i , i∈I, to ensure that only selected suppliers are entrusted with the storage of emergency supplies. In addition, G i ≥0.

[0041] SA6, based on the emergency warehouse activation cost function, emergency material procurement cost function, emergency material reserve cost function, supplier activation cost function and supplier subsidy cost function, obtain the expected pre-disaster reserve cost.

[0042] It should be added that in the pre-disaster stage, the optimization goal of the emergency supply chain is to minimize the total reserve cost, including the fixed cost of emergency warehouse activation, emergency material procurement cost, emergency material reserve cost, fixed cost of agreement supplier activation, and agreement supplier subsidy cost. Therefore, the pre-disaster reserve cost expectation is expressed as:

[0043] It should be noted that by setting the target emergency warehouse parameters and target supplier parameters, it is possible to effectively select emergency warehouses, set up pre-reserve inventories, select suppliers, and entrust the reserve of emergency supplies, thereby facilitating the response to possible supply shortages caused by post-disaster supply disruptions and avoiding greater losses. At the same time, the emergency supply chain based on option contracts can effectively alleviate the financial and human costs of rescue organizations.

[0044] In addition, suppliers and relevant departments need to sign option contracts in advance to effectively reduce the cost of material storage and daily rotation for official agencies and rescue organizations, and by constructing a supplier subsidy cost function, effectively incentivize suppliers to sign option contracts with official agencies or rescue organizations. At the same time, it is possible to flexibly determine whether to sign an option contract and the amount of material storage entrusted to the supplier based on the two different prices provided by the supplier.

[0045] In an optional embodiment, before obtaining the expected performance and the actual performance variance based on the scenario occurrence probability of any scenario according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, the method further includes: SB1, for any scenario s, according to the emergency warehouse j, the first emergency material quantity transported from any emergency warehouse j to any demand point k , the first distance D between the emergency warehouse j and the corresponding demand point k jk and the transportation cost C between emergency warehouse j and the corresponding demand point k jk , construct the emergency warehouse material transportation cost function ; Among them, the total amount of first emergency supplies transported from emergency warehouse j to all demand points Less than or equal to the emergency storage quantity ϑ j ,Right now ≤ϑ j , ∀j J,s S, to ensure that in any scenario, the total amount of emergency supplies provided by each emergency warehouse j to each demand point k is less than or equal to the amount of supplies pre-placed in the emergency warehouse, the first emergency supply quantity is greater than or equal to 0, and the first emergency supply quantity transported from the emergency warehouse to any demand point is greater than or equal to 0. Less than the preset positive number W and the scenario parameter of whether the emergency warehouse can supply emergency supplies in the scenario The product of <W , ∀j J,s S to ensure that only emergency warehouses without interruptions can transport supplies.

[0046] It should be added that in the following application process, the first distance can be calculated using the Euclidean distance based on the location of the emergency warehouse and the selected demand point location. For example, the distance between demand point k and emergency warehouse j is ,in,( , ) represents the location coordinates of the jth emergency warehouse, ( , ) represents the location coordinates of the kth demand point. In addition, the scenario parameters of whether the emergency warehouse can supply emergency supplies in any scenario depend on the scenario.

[0047] SB2, the second emergency material quantity transported from any supplier i to any demand point k according to supplier i , the second distance between supplier i and the corresponding demand point k and the transportation cost between supplier i and the corresponding demand point k , construct supplier material transportation cost function ; Among them, the amount of the second emergency supplies is greater than or equal to 0, and the amount of the second emergency supplies transported from supplier i to any demand point is less than the product of a preset positive number and a scenario parameter indicating whether the supplier can supply emergency supplies in the scenario, that is, <W , ∀i I,s S to ensure that only suppliers with no disruptions are able to ship out supplies.

[0048] It should be added that in the following application process, the second distance can be calculated using the Euclidean distance based on the supplier location and the selected demand point location. For example, the distance between demand point k and supplier i is ,in,( , ) represents the location coordinates of the i-th supplier, ( , ) represents the location coordinates of the kth demand point. In addition, the scenario parameters of whether the supplier can supply emergency supplies in any scenario depend on the scenario.

[0049] SB3, based on the first acquisition cost α i , from supplier i at the first purchase cost α i First purchase quantity purchased , Second purchase cost β i and the second purchase cost β from the supplier i Second purchase amount purchased , construct supplier material procurement cost function∑ i∈I α i + β i ; Among them, the first purchase cost α i The second purchase cost β is the purchase cost per unit of emergency supplies within the emergency supply reserve quantity agreed upon when determining the supplier. i The total amount of second emergency supplies transported from suppliers to all demand points is the purchase cost of each unit of emergency supplies outside the emergency supply reserve quantity agreed upon when determining the supplier. First purchase quantity and the second purchase amount The sum of the first purchase Less than or equal to the emergency supplies storage quantity G of supplier i i , first purchase The sum of the second purchase quantity Ns i is less than the supplier's maximum reserve quantity q i , the first purchase amount is greater than or equal to 0 and less than the preset positive number W and the scenario parameters of whether the supplier can supply emergency supplies in the scenario The product of the second purchase amount is greater than or equal to 0 and less than the preset positive number W and the scenario parameter of whether the supplier can supply emergency supplies in the scenario It should be added that the emergency material storage quantity G of supplier i i It refers to the quantity of emergency supplies entrusted to supplier i for storage, which can be determined based on the storage quantity specified in the option contract signed with the official agency.

[0050] SB4, based on the emergency warehouse material transportation cost function, supplier material transportation cost function and supplier material procurement cost function, obtain the expected post-disaster emergency supply cost.

[0051] It should be added that the expected cost of emergency supply after a disaster is expressed as:

[0052] In addition, a scenario-robust model is constructed based on the expected pre-disaster reserve costs and post-disaster emergency supply costs, so that emergency material procurement and transportation plans can be flexibly formulated according to the actual post-disaster needs. In this way, when faced with interrupted emergency warehouses and suppliers and excess material demand, emergency material procurement and transportation plans can be quickly arranged to achieve emergency supply chain reconstruction and ensure material supply.

[0053] Step S12, based on the demand point information, using the previously constructed scenario robust model, obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transfer quantity, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to the demand points; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the pre-disaster reserve cost expectation and post-disaster emergency supply cost expectation in any scenario.

[0054] In this embodiment, according to the demand point information, the scenario robust model constructed in advance is used to obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transportation volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to the demand point, including: inputting the demand point information into the scenario robust model constructed in advance to generate the initial emergency warehouse reserve and the initial supplier reserve based on the expected pre-disaster reserve cost; according to the initial emergency warehouse reserve and the initial supplier reserve, based on the expected post-disaster emergency supply cost, the initial emergency warehouse transportation volume, the initial emergency materials purchased from suppliers at different prices are obtained. The genetic algorithm is used to optimize the initial emergency warehouse reserves and the initial supplier reserves to obtain the optimized emergency warehouse reserves and supplier reserves, as well as the emergency warehouse transportation volume corresponding to the optimized emergency warehouse reserves and supplier reserves, the emergency material quantity purchased from suppliers at different prices, and the material quantity shipped from suppliers to the demand points; wherein the chromosomes of the genetic algorithm include the emergency warehouse reserves and the supplier reserves, so as to greatly shorten the length of the solution on the basis of ensuring that any solution in the solution space is a feasible solution, thereby completing the optimization process faster.

[0055] Further, refer to Figure 2, using a genetic algorithm, the initial emergency warehouse reserve and the initial supplier reserve are optimized to obtain the optimized emergency warehouse reserve and the supplier reserve, as well as the emergency warehouse transportation volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points corresponding to the optimized emergency warehouse reserve and the supplier reserve, including: evaluating the fitness of the initial emergency warehouse reserve and the initial supplier reserve as chromosomes of the genetic algorithm, and allocating selection probabilities or weights according to the obtained fitness; eliminating corresponding chromosomes according to the allocated selection probabilities or weights; selecting any two chromosomes from the retained chromosomes for crossover operations and mutation operations, and use the mutation operation results to update the chromosomes; use the updated chromosomes to re-obtain the initial emergency warehouse transfer volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material quantity shipped from suppliers to demand points based on the expected post-disaster emergency supply cost; re-perform fitness evaluation, selection probability or weight allocation, chromosome elimination, crossover operation and mutation operation on the updated chromosomes until the fitness tends to be stable or the number of iterations meets the preset number of times, and stop, and obtain the optimized emergency warehouse reserve volume, supplier reserve volume, emergency warehouse transfer volume, emergency material volume purchased from suppliers at different prices, and the material quantity shipped from suppliers to demand points.

[0056] It should be added that the initial emergency warehouse reserve includes the emergency material storage quantity of non-target emergency warehouses, target emergency warehouses and each emergency warehouse; the initial supplier reserve includes the emergency material storage quantity of non-target suppliers, target suppliers, each supplier and non-target suppliers; the initial emergency warehouse transportation quantity includes the first emergency material quantity transported from the target emergency warehouse to any demand point; the initial emergency material quantity purchased from suppliers at different prices includes the first purchase quantity purchased from each target supplier at the first purchase cost and the second purchase quantity purchased from each target supplier at the second purchase cost; the initial material quantity transported from suppliers to demand points includes the first purchase quantity and the second purchase quantity purchased from the corresponding target supplier. It should be noted that the above parameters are all 0 for non-target emergency warehouses and non-target suppliers.

[0057] Step S13, reconstructing the emergency supply chain according to the emergency warehouse reserve, supplier reserve, emergency warehouse transfer quantity, the quantity of emergency materials purchased from suppliers at different prices, and the quantity of materials transported from suppliers to demand points.

[0058] In an optional embodiment, the feasibility of the model and the effectiveness of the algorithm are verified through a large number of calculation examples.

[0059] The numerical case is designed based on the Iranian earthquake data. Referring to the numerical cases used by relevant researchers, the 48 scenarios used in the case were merged according to the regions where they occurred, leaving only 12 scenarios. Table 1 shows the data scale of the case. Mazandaran Province (MZ), Gilan Province (GL), East Azerbaijan Province (EA), Qazvin (QZ), Hamadan (HM), North Khorasan Province (NK), South Khorasan Province (SK), Bushehr (BU), Fars Province (FA), Kerman (KR), Hormozgan Province (HO) and Sistan-Baluchestan Province (SB) are selected as potential disaster occurrence points. Coordinates are randomly extracted from the map as the receiving points of materials. The basic parameters of the demand points are shown in Table 2. At the same time, since each state / province has a central warehouse for pre-deployment of disaster relief supplies; in the absence of sufficient information, the coordinates of a city were randomly selected from each province as the central warehouse. The capacity, activation fee, and unit material storage cost of each central warehouse are shown in Table 3, where the tent capacity is calculated based on the tent area of ​​1 cubic meter per unit.

[0060] Table 1 Problem size of case studies

[0061]

[0062] Since only a single emergency material situation is considered in this study, tent materials are selected. There are two suppliers of tent materials. Table 4 lists the storage capacity of the two suppliers, the two option prices P1 and P2, the fixed cost of signing an option agreement with each supplier, and the subsidy cost per unit of emergency material reserves. The remaining parameters are proposed based on the assumptions of the model, and each parameter is assigned a value within a certain range of values ​​through random generation. In the absence of sufficient information, some parameters are randomly generated taking into account the actual situation.

[0063]

[0064] In addition, since previous case studies did not consider the situation where the disaster relief warehouse was damaged and unable to transfer materials due to earthquake disasters, we added a design for warehouse interruption; in simple terms, the emergency warehouse in the earthquake area was damaged and unable to transfer materials. It is worth noting that supplier S1 is in the QZ area, so in scenario S3, when the disaster occurred in the QZ area, the local emergency warehouse and supplier S1 were interrupted together. The Euclidean distance is used in this study to calculate the distance between the supplier and the demand point. For the sake of simplicity in calculation, the unit emergency material transportation cost between the demand point and the supply point is generated randomly, and the value fluctuates between [8,12], as shown in Table 5.

[0065]

[0066] The model is verified by an example through an improved genetic algorithm. In the study, the pre-disaster purchase price of emergency materials per unit is set to 200, and it is assumed that the penalty cost of the emergency material unit failing to meet the demand is 10 times the pre-disaster purchase cost. This is also equivalent to setting the γ value in the model to 200, the λ value to 1, and the ω value to 2000. Table 6 shows the pre-disaster material reserves of various emergency warehouses and suppliers, as well as the quantity of material transportation under various post-disaster scenarios. It can be seen from Table 3.12 that the number of emergency materials stored in emergency warehouses D1, D5, D6, D7, D11, and D12 reaches the maximum storage capacity, indicating that these warehouses are conducive to reducing the total cost of the emergency supply chain and improving the efficiency of material support; the reserve of supplier S2 reaches the maximum storage capacity, while the reserve of supplier S1 does not reach the maximum storage capacity, indicating that supplier S2 is better than S1 and is more suitable for undertaking the task of emergency material reserves. Figure 3 The convergence effect of the designed algorithm is demonstrated, and convergence can be achieved in the 20th generation.

[0067]

[0068] In an optional embodiment, sensitivity analysis is also performed on different parameters to reveal the impact of their changes on the results. Finally, some management insights are provided to managers based on the calculation examples and sensitivity analysis results.

[0069] The total cost function of the rescue organization varies with different parameters. A sensitivity analysis is performed to illustrate the impact of various parameters on the expected total cost of the proposed emergency supply chain. i and β i is an important price parameter of the option contract, and ϖ is an important parameter that affects the supplier's willingness to reserve. Consider the sensitivity of the objective function value to changes in these pricing parameters. In order to describe this impact, sensitivity analysis is performed for different parameters. Only one parameter is changed in each analysis, and the rest of the parameters remain the same as above.

[0070] First, for option price β iTable 4 shows the sensitivity analysis of the two option prices P1 and P2 provided by the supplier. When conducting the sensitivity analysis, on the basis of the unchanged option price P1, the option price P2 is set to 90%, 95%, 105% and 110% of itself to solve the results. Table 7 shows the change of the supplier's material reserves with the option price P2. It can be seen that with the continuous increase of P2, the material reserves of supplier S1 continue to increase, indicating that in the face of the uncertainty of material demand after the disaster, the higher option price P2 will prompt government departments to choose to entrust suppliers to make more emergency material reserves before the disaster; when P2 increases to a certain value, the reserve quantity of supplier S1 no longer increases, indicating that at this price, the material reserves of supplier S1 have reached saturation, and increasing the reserve quantity cannot provide economic benefits.

[0071]

[0072] As P2 changes, the reserve of supplier S1 does not change, indicating that the government department does not react significantly to the change of supplier S2's option price P2. Combining the geographical location of supplier S2 and the analysis of the various parameters and unit transportation costs of suppliers in Tables 4 and 5, supplier S2 has a superior geographical location and low costs. It is a very economical behavior for the government to entrust supplier S2 to reserve materials, which has a significant effect on reducing the overall cost of the emergency supply chain and ensuring the supply of emergency materials after the disaster. Therefore, no matter how the option price P2 of supplier S2 changes, the government department will entrust supplier S2 to reserve emergency materials to the maximum extent. Figure 4 The figure shows the change of the expected total cost of the supply chain with the post-disaster purchase price P2. It can be seen that as P2 continues to increase, the reserve of supplier S1 continues to increase, and the total cost ratio of the supply chain continues to increase; as the increase in the reserve of supplier S1 gradually decreases, the increase in the total cost of the supply chain also gradually slows down.

[0073] Then, the unit emergency material subsidy amount ϖ is analyzed, and ϖ is set to 5, 10, 15, 20, 25, and 30 to solve the results. Table 8 shows the results of the sensitivity analysis of the supplier's material reserve quantity to the unit emergency material subsidy amount. It can be seen that as the unit emergency material subsidy amount continues to increase, the number of emergency materials entrusted by the government to the supplier to reserve continues to decrease until it reaches 0. It is worth noting that when the unit emergency material subsidy amount of supplier S1 is 15, the government-entrusted reserve quantity has been reduced to 0; when the unit emergency material subsidy amount of supplier S2 is 30, the government-entrusted reserve quantity is reduced to 0. Combined with the above analysis, the various costs of supplier S2 are relatively low, and the government has a strong willingness to entrust it to reserve emergency materials. However, when the unit emergency material subsidy amount provided is too large, the cost of entrusting it to reserve is greater than the benefits it brings, so the government will choose not to entrust it. In addition, for supplier S2, although the subsidy for each unit of emergency materials is 30 when the option price P1 is 200 and the option price P2 is 250, it seems that entrusting the storage is a more economical behavior from a numerical point of view; but it is necessary to consider that there are many scenarios after the disaster, and the materials of supplier S2 will not be called in every scenario. For scenarios that are not called, entrusting it to store will only incur additional storage costs; the same applies to supplier S1. Figure 5 The figure shows that the total cost of the emergency supply chain changes with the amount of subsidies for unit emergency material reserves. As the subsidy amount increases, the total cost of the emergency supply chain gradually increases.

[0074]

[0075] To summarize, the embodiments of the present invention reconstruct the emergency supply chain according to the demand point information by utilizing the previously constructed scenario robust model, and by considering the uncertainty of demand and supply, from the perspective of pre-disaster reserve and post-disaster response, studies the pre-positioning storage and transportation problems of emergency materials, and based on the quantity flexible option contract signed with the supplier, enables the supplier to replace part of the emergency warehouse to store and supply materials, so as to be widely used in various emergency rescue and humanitarian rescue work; under the option contract, it can effectively reduce the cost of official agencies and rescue organizations for material storage and daily rotation, and can also cope with the situation where supplier facilities are damaged due to severe natural disasters, realize rapid repair after the interruption of the emergency supply chain, and ensure the supply of emergency materials.

[0076] The emergency supply chain collaborative intelligent reconstruction device provided by the present invention is described below. The emergency supply chain collaborative intelligent reconstruction device described below and the emergency supply chain collaborative intelligent reconstruction method described above can be referenced to each other.

[0077] Figure 6A schematic diagram of the structure of an emergency supply chain collaborative intelligent reconstruction device is shown, the device comprising: Demand acquisition module 61, acquiring demand point information; The intelligent reconstruction module 62 obtains the emergency warehouse reserve, supplier reserve, emergency warehouse transportation, emergency material quantity purchased from suppliers at different prices, and material quantity transported from suppliers to the demand point based on the demand point information and the scenario robust model constructed in advance; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost in any scenario; The supply chain construction module 63 reconstructs the emergency supply chain according to the emergency warehouse reserve, supplier reserve, emergency warehouse transfer volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

[0078] In an optional embodiment, the device further includes: an expected performance acquisition module, which obtains the expected performance based on the scenario occurrence probability of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, before obtaining the emergency warehouse reserve quantity, supplier reserve quantity, emergency warehouse transportation quantity, the quantity of emergency materials purchased from suppliers at different prices, and the quantity of materials transported from suppliers to the demand point based on the demand point information and the previously constructed scenario robust model; an actual performance variance acquisition module, which obtains the actual performance variance based on the scenario occurrence probability of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost corresponding to all scenario occurrence probabilities; The resource shortage function construction module constructs a resource shortage function for any scenario based on the emergency material demand of each demand point, the emergency warehouse and the first emergency material quantity transported from the emergency warehouse to the corresponding demand point, the supplier and the second emergency material quantity transported from the supplier to the corresponding demand point; the model construction module constructs a scenario robust model based on the expected performance, the actual performance variance and the resource shortage function, combined with the auxiliary scalar and the first preset constraint; wherein the first preset constraint is used to limit the sum of the total cost of any scenario s based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost and the auxiliary scalar to be no less than the product of the probability of occurrence of all scenarios and the corresponding total cost, and the auxiliary scalar is greater than or equal to 0.

[0079] Specifically, the device also includes: a first cost function construction module, which constructs an emergency warehouse activation cost function according to the emergency warehouse and the fixed activation cost of the emergency warehouse, before obtaining the expected performance and the actual performance variance based on the probability of occurrence of any scenario and the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, and the emergency warehouse is selected from the set of emergency warehouses; a second cost function construction module, which constructs an emergency material procurement cost function according to the pre-disaster emergency material procurement costs and the emergency material storage quantity of the emergency warehouse; wherein the emergency material storage quantity of the emergency warehouse is less than or equal to the storage capacity of the emergency warehouse, and the emergency material storage quantity of the emergency warehouse is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials under the scenario; a third cost function construction module, which constructs an emergency material procurement cost function according to the emergency warehouse, the emergency material storage quantity of the emergency warehouse and the emergency storage capacity. The emergency warehouse per unit reserve cost is used to construct an emergency material reserve cost function; the fourth cost function construction module constructs a supplier activation cost function based on the supplier and the supplier's fixed activation cost, and the supplier is selected from the supplier set; the fifth cost function construction module constructs a supplier subsidy cost function based on the supplier, the supplier's emergency material storage quantity and the supplier's per unit subsidy fee; among which, the supplier's emergency material storage quantity is less than or equal to the supplier's maximum reserve quantity, and the supplier's emergency material storage quantity is less than the product of a preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario; the pre-disaster cost acquisition module obtains the expected pre-disaster reserve cost based on the emergency warehouse activation cost function, the emergency material procurement cost function, the emergency material reserve cost function, the supplier activation cost function and the supplier subsidy cost function.

[0080] In addition, the device further includes: a first transportation cost function construction module, before obtaining the expected performance and the actual performance variance based on the scenario occurrence probability of any scenario, according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, For any scenario, an emergency warehouse material transportation cost function is constructed according to the emergency warehouse, the first emergency material quantity transported from any emergency warehouse to any demand point, the first distance between the emergency warehouse and the corresponding demand point, and the transportation cost between the emergency warehouse and the corresponding demand point; wherein the total amount of the first emergency material transported from the emergency warehouse to all demand points is less than or equal to the emergency material storage quantity of the emergency warehouse, and the first emergency material quantity is greater than or equal to, and the first emergency material quantity transported from the emergency warehouse to any demand point is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials in the scenario; a second transportation cost function construction module is constructed according to the supplier, the second emergency material quantity transported from any supplier to any demand point, the second distance between the supplier and the corresponding demand point, and the transportation cost between the supplier and the corresponding demand point; wherein the second emergency material quantity is greater than or equal to, and the second emergency material quantity transported from the supplier to any demand point is less than the product of a preset positive number and a scenario parameter of whether the supplier can supply emergency materials in the scenario; a procurement cost function construction module is constructed according to the first procurement cost, The supplier material procurement cost function is constructed by using the first purchase quantity purchased at the first purchase cost, the second purchase cost and the second purchase quantity purchased from the supplier at the second purchase cost; wherein the first purchase cost is the purchase cost per unit of emergency materials within the emergency material reserve quantity range agreed upon when the supplier is determined, the second purchase cost is the purchase cost per unit of emergency materials outside the emergency material reserve quantity range agreed upon when the supplier is determined, the total amount of the second emergency materials transported from the supplier to all demand points is the sum of the first purchase quantity and the second purchase quantity, the first purchase quantity is less than or equal to the supplier's emergency material storage quantity, the sum of the first purchase quantity and the second purchase quantity is less than the supplier's maximum reserve quantity, the first purchase quantity is greater than or equal to 0, and is less than the product of a preset positive number and a scenario parameter of whether the supplier can supply emergency materials in the scenario; the second purchase quantity is greater than or equal to 0, and is less than the product of a preset positive number and a scenario parameter of whether the supplier can supply emergency materials in the scenario; the post-disaster cost acquisition module obtains the expected post-disaster emergency supply cost according to the emergency warehouse material transportation cost function, the supplier material transportation cost function and the supplier material procurement cost function.

[0081] In this embodiment, the intelligent reconstruction module 62 includes: a solution unit, which inputs the demand point information into a previously constructed scenario robust model to generate an initial emergency warehouse reserve and an initial supplier reserve based on the expected pre-disaster reserve cost; based on the initial emergency warehouse reserve and the initial supplier reserve, the initial emergency warehouse transfer volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material quantity shipped from suppliers to the demand point are obtained based on the expected post-disaster emergency supply cost; an optimization unit, which uses a genetic algorithm to optimize the initial emergency warehouse reserve and the initial supplier reserve to obtain the optimized emergency warehouse reserve and the supplier reserve, as well as the emergency warehouse transfer volume, the emergency material volume purchased from suppliers at different prices, and the material quantity shipped from suppliers to the demand point corresponding to the optimized emergency warehouse reserve and the supplier reserve; wherein the chromosomes of the genetic algorithm include the emergency warehouse reserve and the supplier reserve, so as to greatly shorten the length of the solution on the basis of ensuring that any solution in the solution space is a feasible solution, thereby completing the optimization process faster.

[0082] Furthermore, the optimization unit includes: a fitness evaluation subunit, which evaluates the fitness of the initial emergency warehouse reserve and the initial supplier reserve of the chromosome as the genetic algorithm; an allocation subunit, which allocates selection probability or weight according to the obtained fitness; a elimination subunit, which eliminates the corresponding chromosome according to the allocated selection probability or weight; a chromosome updating subunit, which selects any two chromosomes from the retained chromosomes for crossover operation and mutation operation, and updates the chromosome using the mutation operation result; an iteration subunit, which uses the updated chromosomes to re-obtain the initial emergency warehouse transfer volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material quantity shipped from suppliers to demand points based on the expected post-disaster emergency supply cost; the updated chromosomes are re-evaluated for fitness, selected probabilities or weight allocations, chromosome elimination, crossover operation, and mutation operation until the fitness tends to be stable or the number of iterations meets the preset number of times, and the optimized emergency warehouse reserve, supplier reserve, emergency warehouse transfer volume, emergency material volume purchased from suppliers at different prices, and material quantity shipped from suppliers to demand points are obtained.

[0083] To summarize, the embodiments of the present invention reconstruct the emergency supply chain according to the demand point information by utilizing the previously constructed scenario robust model, and by considering the uncertainty of demand and supply, from the perspective of pre-disaster reserve and post-disaster response, studies the pre-positioning storage and transportation problems of emergency materials, and based on the quantity flexible option contract signed with the supplier, enables the supplier to replace part of the emergency warehouse to store and supply materials, so as to be widely used in various emergency rescue and humanitarian rescue work; under the option contract, it can effectively reduce the cost of official agencies and rescue organizations for material storage and daily rotation, and can also cope with the situation where supplier facilities are damaged due to severe natural disasters, realize rapid repair after the interruption of the emergency supply chain, and ensure the supply of emergency materials.

[0084] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the emergency supply chain collaborative intelligent reconstruction method, which includes: obtaining demand point information; according to the demand point information, using the previously constructed scenario robust model, obtaining the emergency warehouse reserve, the supplier reserve, the emergency warehouse transfer volume, the emergency material quantity purchased from the supplier at different prices and the material quantity shipped from the supplier to the demand point; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained according to the pre-disaster reserve cost expectation and the post-disaster emergency supply cost expectation in any scenario; according to the emergency warehouse reserve, the supplier reserve, the emergency warehouse transfer volume, the emergency material quantity purchased from the supplier at different prices and the material quantity shipped from the supplier to the demand point, reconstruct the emergency supply chain.

[0085] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0086] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the emergency supply chain collaborative intelligent reconstruction method provided by the above methods, which method includes: obtaining demand point information; based on the demand point information, using a previously constructed scenario robust model to obtain emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the pre-disaster reserve cost expectations and post-disaster emergency supply cost expectations in any scenario; reconstructing the emergency supply chain based on the emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

[0087] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the emergency supply chain collaborative intelligent reconstruction method provided by the above-mentioned methods, the method comprising: obtaining demand point information; based on the demand point information, using a previously constructed scenario robust model to obtain emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the pre-disaster reserve cost expectations and post-disaster emergency supply cost expectations in any scenario; and reconstructing the emergency supply chain based on the emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

[0088] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative intelligent reconstruction method for emergency supply chain, characterized in that: include: Obtain demand point information; According to the demand point information, the scenario robust model constructed in advance is used to obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transportation volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to the demand point; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost in any scenario; Reconstruct the emergency supply chain based on the emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points.

2. The method for collaborative intelligent reconstruction of emergency supply chain according to claim 1 is characterized in that: Before obtaining the emergency warehouse reserve, supplier reserve, emergency warehouse transportation volume, emergency material quantity purchased from suppliers at different prices, and material quantity transported from suppliers to the demand point based on the demand point information by using the previously constructed scenario robust model, it includes: Based on the probability of occurrence of any scenario, the expected performance is obtained according to the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost; Based on the probability of occurrence of any scenario, the actual performance variance is obtained according to the expected pre-disaster reserve cost and post-disaster emergency supply cost corresponding to the probability of occurrence of all scenarios; For any scenario, a material shortage function is constructed according to the emergency material demand of each demand point, the emergency warehouse and the first emergency material quantity transported from the emergency warehouse to the corresponding demand point, the supplier and the second emergency material quantity transported from the supplier to the corresponding demand point; According to the expected performance, the actual performance variance and the material shortage function, combined with the auxiliary scalar and the first preset constraint, a scenario robust model is constructed; wherein the first preset constraint is used to limit the sum of the total cost of any scenario based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost and the auxiliary scalar to be no less than the product of the occurrence probability of all scenarios and the corresponding total cost, and the auxiliary scalar is greater than or equal to 0.

3. The method for collaborative intelligent reconstruction of emergency supply chain according to claim 2 is characterized in that: Before obtaining the expected performance and actual performance variance based on the probability of occurrence of any scenario, the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, including: Constructing an emergency warehouse activation cost function according to an emergency warehouse and a fixed activation cost of the emergency warehouse, wherein the emergency warehouse is selected from a set of emergency warehouses; According to the pre-disaster emergency material procurement costs and the emergency material storage quantity of the emergency warehouse, an emergency material procurement cost function is constructed; wherein the emergency material storage quantity of the emergency warehouse is less than or equal to the emergency warehouse storage capacity, and the emergency material storage quantity of the emergency warehouse is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials under the scenario; Constructing an emergency material reserve cost function according to the emergency warehouse, the emergency material storage quantity of the emergency warehouse, and the reserve cost per unit of the emergency warehouse; constructing a supplier activation cost function according to suppliers and supplier fixed activation costs, wherein the suppliers are selected from a supplier set; A supplier subsidy cost function is constructed based on the supplier, the supplier's emergency material storage quantity and the supplier's subsidy cost per unit; wherein the supplier's emergency material storage quantity is less than or equal to the supplier's maximum reserve quantity, and the supplier's emergency material storage quantity is less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario; According to the emergency warehouse activation cost function, the emergency material procurement cost function, the emergency material reserve cost function, the supplier activation cost function and the supplier subsidy cost function, the expected pre-disaster reserve cost is obtained.

4. The method for collaborative intelligent reconstruction of emergency supply chain according to claim 3 is characterized in that: Before obtaining the expected performance and actual performance variance based on the probability of occurrence of any scenario, the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost, it also includes: For any scenario, an emergency warehouse material transportation cost function is constructed according to the emergency warehouse, the first amount of emergency materials transported from any of the emergency warehouses to any demand point, the first distance between the emergency warehouse and the corresponding demand point, and the transportation cost between the emergency warehouse and the corresponding demand point; wherein the total amount of the first emergency materials transported from the emergency warehouse to all demand points is less than or equal to the emergency material storage quantity of the emergency warehouse, the first amount of emergency materials is greater than or equal to 0, and the first amount of emergency materials transported from the emergency warehouse to any demand point is less than the product of a preset positive number and a scenario parameter of whether the emergency warehouse can supply emergency materials in the scenario; A supplier material transportation cost function is constructed based on the supplier, the second emergency material quantity transported from any of the suppliers to any demand point, the second distance between the supplier and the corresponding demand point, and the transportation cost between the supplier and the corresponding demand point; wherein the second emergency material quantity is greater than or equal to 0, and the second emergency material quantity transported from the supplier to any demand point is less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in the scenario; A supplier material procurement cost function is constructed according to a first procurement cost, a first purchase quantity purchased from the supplier at the first procurement cost, a second procurement cost, and a second purchase quantity purchased from the supplier at the second procurement cost; wherein the first procurement cost is the procurement cost per unit of emergency materials within the emergency material reserve quantity range agreed upon when determining the supplier, the second procurement cost is the procurement cost per unit of emergency materials outside the emergency material reserve quantity range agreed upon when determining the supplier, the second emergency material quantity is the sum of the first procurement quantity and the second procurement quantity, the first procurement quantity is less than or equal to the emergency material storage quantity of the supplier, the sum of the first procurement quantity and the second procurement quantity is less than the maximum reserve quantity of the supplier, the first procurement quantity is greater than or equal to 0, and is less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in a scenario; the second procurement quantity is greater than or equal to 0, and is less than the product of the preset positive number and the scenario parameter of whether the supplier can supply emergency materials in a scenario; According to the emergency warehouse material transportation cost function, the supplier material transportation cost function and the supplier material procurement cost function, the expected post-disaster emergency supply cost is obtained.

5. The method for collaborative intelligent reconstruction of emergency supply chain according to claim 4 is characterized in that: According to the demand point information, the scenario robust model constructed previously is used to obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transportation volume, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to the demand point, including: Inputting the demand point information into a previously constructed scenario robust model to generate an initial emergency warehouse reserve and an initial supplier reserve based on the pre-disaster reserve cost expectation; According to the initial emergency warehouse reserve and the initial supplier reserve, based on the expected post-disaster emergency supply cost, the initial emergency warehouse transportation volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material volume transported from suppliers to demand points are obtained; The initial emergency warehouse reserves and the initial supplier reserves are optimized by using a genetic algorithm to obtain optimized emergency warehouse reserves and supplier reserves, as well as the emergency warehouse transportation volume corresponding to the optimized emergency warehouse reserves and supplier reserves, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points; wherein the chromosomes of the genetic algorithm include the emergency warehouse reserves and the supplier reserves.

6. The method for collaborative intelligent reconstruction of emergency supply chain according to claim 5 is characterized in that: The initial emergency warehouse reserve and the initial supplier reserve are optimized by using a genetic algorithm to obtain the optimized emergency warehouse reserve and supplier reserve, as well as the emergency warehouse transportation volume corresponding to the optimized emergency warehouse reserve and supplier reserve, the amount of emergency materials purchased from suppliers at different prices, and the amount of materials transported from suppliers to demand points, including: The initial emergency warehouse reserve and the initial supplier reserve as chromosomes of the genetic algorithm are evaluated for fitness, and the selection probability or weight is allocated according to the obtained fitness; According to the assigned selection probability or weight, the corresponding chromosome is eliminated; Select any two chromosomes from the retained chromosomes to perform crossover and mutation operations, and use the mutation operation results to update the chromosomes; Using the updated chromosome, based on the expected post-disaster emergency supply cost, the initial emergency warehouse transportation volume, the initial emergency material volume purchased from suppliers at different prices, and the initial material volume transported from suppliers to demand points are obtained again; The updated chromosomes are re-evaluated for fitness, selected for probability or weight distribution, eliminated, crossover and mutation operations until the fitness becomes stable or the number of iterations meets the preset number. The optimized emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, emergency supplies purchased from suppliers at different prices and the quantity of supplies transported from suppliers to demand points are obtained.

7. An emergency supply chain collaborative intelligent reconstruction device, characterized in that: include: Demand acquisition module, to obtain demand point information; The intelligent reconstruction module uses the previously constructed scenario robust model based on the demand point information to obtain the emergency warehouse reserve, supplier reserve, emergency warehouse transportation, the emergency material quantity purchased from suppliers at different prices, and the material quantity transported from suppliers to the demand point; wherein the scenario robust model is constructed based on the expected performance and actual performance variance obtained based on the expected pre-disaster reserve cost and the expected post-disaster emergency supply cost in any scenario; The supply chain construction module reconstructs the emergency supply chain based on the emergency warehouse reserves, supplier reserves, emergency warehouse transfer quantities, the quantity of emergency materials purchased from suppliers at different prices, and the quantity of materials transported from suppliers to demand points.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the emergency supply chain collaborative intelligent reconstruction method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the emergency supply chain collaborative intelligent reconstruction method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for collaborative intelligent reconstruction of an emergency supply chain as described in any one of claims 1 to 6 is implemented.

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