CN103942609B - Product service supply chain optimization designing method - Google Patents
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Abstract
The invention provides a product service supply chain optimization designing method which comprises the steps of 1. product service supply chain strategic designing based on strategic design need frequency, 2. product service facility dynamic site selection, 3. product service provider selecting and 4. product service supply chain process designing. According to the basic feature of a product service supply chain, a product service network structure is optimized, the best cooperative partner is selected, a standard product service supply chain process is established, and then a product service supply chain is established on the basis of systematic optimization designing. Meanwhile, influence on product service facility site selection and product service provider selecting from human factors is avoided, and the designed product service supply chain better meets specific objective requirements.
Description
Technical Field
The invention belongs to the field of supply chain management, and particularly relates to a product service supply chain optimization design method, in particular to a technical method for product service supply chain strategic design matching strategy, product service facility site selection, product service provider selection and product service supply chain process design.
Background
The first background art is as follows: supply chain strategic design matching strategy
Supply chain strategic design matching strategies aim to divide products into functional and innovative products and then design efficient and reactive supply chains for the functional and innovative products.
The second background art is as follows: service facility site selection technology
The K-subset splitting problem, the P-center problem, the P-median problem, and the coverage problem are the three most classical infrastructure addressing problems, and since the assumed conditions and modeling methods of the latter three basic problems are similar, they can be simply classified into two categories:
(1) the basic characteristics of the K-subset splitting problem are: 1) the method comprises the steps of shape-setting splitting, namely, the number of service centers and demand points is certain; 2) ordered splitting, namely, a one-to-many relationship between a service center and a demand point, rather than a many-to-many service relationship, always has an optimal splitting strategy; 3) when k is equal to or greater than 3, the problem is NP-complete; 4) belonging to the discrete address selection problem.
(2) The P-center problem and the P-median problem and the coverage problem have the following basic characteristics: 1) the demand points and the service centers are generally in a many-to-many service relationship; 2) when P is more than or equal to 3, the problem is NP problem; 3) the goal is generally to optimize some (overall average) performance of the system, such as minimizing the distance of transportation, time, cost, or right-of-demand distance; 4) the attributes of demand, cost, etc. for different demand points are considered equivalent.
The third background technology is as follows: network analysis method
Network Analysis (ANP) is a method of determining the measure of a decision factor. ANP first divides system elements into two major parts: the first part, referred to as the control factor layer, includes problem objectives and decision criteria. All decision criteria are considered independent of each other and are only governed by the target element. There may be no decision criteria in the control factors, but at least one goal. The weight of each criterion in the control layer can be obtained by using an Analytic Hierarchy Process (AHP) method. The second part is a network layer which is composed of all element groups controlled by the control layer, the internal part of the network layer is an interactive network structure which is composed of all elements controlled by the control layer, the elements are interdependent and mutually controlled, the elements and the hierarchy are not independent, each criterion in the hierarchical structure is not a simple independent element but a interdependent and feedback network structure. The ANP method comprises the following basic steps:
(1) determining targets, criteria
(2) Building a network according to goals, criteria
(3) Constructing a weightless supermatrix
(4) Constructing a weight supermatrix
(5) Solving a limit over-matrix
(6) Extreme relative priority synthesis
(7) Alternative scheme ordering
The fourth background art is as follows: supply chain process design reference model
The current Supply Chain management reference models with relatively influences can be simply divided into two types, one is a product Supply Chain management model including an HP model, a GSCF model (Global Supply Chain format), an SCOR model (Supply-Chain reference) and a Smart SCOR model, and the other is a Service Supply Chain management model including an SSCM model (Service Supply-Chain management), an IUE-SSCM and an S2COR model (Service Supply-Chain reference), and an extended application of the above various management models. However:
(1) after the research and the enterprise practice of a large number of scholars, the product supply chain management model is mature in theory and application and has different applicable characteristics, but the product supply chain management model focuses on the logistics transfer and the management of related information, relationship and the like, is based on the structured supply chain organizational structure and process management, lacks the identification and consideration of services and the characteristics of the service supply chain, and therefore the existing product supply chain management model cannot be directly applied to the product service supply chain.
(2) Service supply chain management models have been proposed in recent years with the rise of the service industry, which focus on cross-organization service flow and its associated management, capabilities, etc. management based on the identification of service characteristics of the supply chain, but neglect product-to-service relationships and their impact on the service supply chain and do not take into account the structure and basic characteristics of the product-to-service supply chain because of over-emphasis on the basic properties of the service itself. More importantly, the representative service supply chain management models are oriented to the traditional service industries, such as tourism, bank, catering and the like
Currently, related research aiming at product service supply chain design is relatively lacked, and enterprises can only borrow basic theories or related methods and technologies in the field of product supply chains or service supply chains, so that the applicability and the effect of the related methods and technologies in the product service supply chain design are very limited. There is a need to develop and develop methods and techniques specifically designed for the product service supply chain.
Disclosure of Invention
The existing related methods and technologies for supply chain design cannot be directly applied to product service supply chain design optimization due to unique applicable conditions, and more importantly, a systematic solution cannot be formed among the methods to optimally design the product service supply chain. Aiming at the defects in the prior art, the invention provides a systematic method for optimally designing a product service supply chain, which is used for solving the design problem of the product service supply chain.
The invention solves the technical problems through the following technical scheme:
(1) product service supply chain strategic design based on product service demand frequency
(2) Dynamic addressing of product service facilities
(3) Product service provider selection
(4) Product service supply chain flow design
The product service supply chain optimization design method provided by the invention comprises the following steps:
step 1: designing a product service supply chain strategy based on the product service demand frequency;
designing a quick response type and high efficiency type product service supply chain, and a centralized service network and a distributed service network corresponding to the quick response type and high efficiency type product service supply chain according to the product service demand frequency;
step 2: dynamic site selection of product service facilities;
constructing a dynamic site selection model of the multi-target product service facility, and solving the model by adopting a multi-population coevolution genetic method;
and step 3: product service provider selection;
the consistency of the decision matrix is improved by introducing a trapezoidal fuzzy number and exponential scaling method, meanwhile, a new decision matrix consistency restoration method for avoiding decision information variation is adopted to carry out consistency restoration on the group decision matrix, and original decision information is retained to the maximum extent;
and 4, step 4: designing a product service supply chain process;
the product service supply chain processes are divided into two categories: one is an enabling flow including service requirement management, service resource and capability management and service delivery management, and the other is a functional flow including customer relationship management, outsourcing service provider and provider relationship management, service supply chain information technology management and application management thereof, and service supply chain network management.
Preferably, the dynamic location model of the multi-target product service facility is as follows:
an objective function:
object (1): minimum total weighted distance Z1 between demand point and corresponding product service center during planning period
Wherein,s is the maximum planning period of site selection of the product service facility, and n is the number of product service demand points;
object (2): total profit Z2 for product service during planning is maximized
Wherein, α0The product service index of the time period t is 0;
constraint conditions are as follows:
(1) product service center with limited service capability
(2) All demand points will be satisfied and each demand point will only obtain product services from one product service center for a period of time
(3) If one demand point provides product services for other demand points, a product service center must exist at the demand point
Stij≤xti，i∈I，j∈J，t∈T 4-15)
(4) Service level constraints, distance between product service center and demand point being less than a certain value
dijStij≤dmax，i∈I，j∈J，t∈T 4-16)
(5) Decision variable 0-1 constraint, each demand point can only establish one product service center at most
xti(1-xti)＝0，i∈I，t∈T 4-17)
(6) Decision variable 0-1 constraints
Stij(1-Stij)＝0，i∈I，j∈J，t∈T 4-18)
(7) If a demand point establishes a product service center, the demand point must be satisfied by the product service center established at the demand point
Stij＝1，i∈I，j∈J，i＝j (4-19)
(8) Once the product service center is established, the product service center is reserved all the time, and the subsequent product service center exists all the time
Wherein,
s: maximum planning period for site selection of product service facilities
k: time to establish a product service center
I: set of product service demand points, I ═ 1, 2, …, I, …, n }
J: set of product service centers, J ═ {1, 2, …, J, …, mt}
mt: total number of product service centers in t period
T: product service center planning period, T ═ 0, 1, T, …, s }
dij: distance between demand point i and demand point j
vΔti: product demand increment at demand point i at time t relative to time t-1
v0i: demand amount of demand point i at time t =0
αtIndex of product servitization at time t, reaction product servitization degree α∈ (0, 1)]
Beta: average profit per unit product service at product-to-service index of 1
csi: cost of building a product service center at demand point i
coi: the operating cost of providing a unit of product service to a product service center (if any) at a demand point i
Vmax: maximum service capacity of product service center
dmax: maximum distance allowed between product service center and demand point
xti: whether a product service center is established at a demand point i or not in the period t
Stij: and the demand point i in the period t is served by a product service center j.
Preferably, the multi-population coevolution genetic method specifically comprises the following steps:
step 1: initializing parameters;
each sub-population has a size of n, the maximum evolution algebra is G, and the cross probability of each sub-population is PCThe mutation probability is Pm；
Step 2: randomly generating n initial individuals meeting the constraint, and setting an initial evolution parameter g = 0;
and step 3: defining a fitness function F (X), and calculating the fitness function value F (X) of each individual in the sub-populationj) And ordering the individuals;
and 4, step 4: sorting according to the fitness value, selecting a certain equal number of excellent individuals from each current subgroup to form an n +1 th population, and forming an elite population;
and 5: copying individuals in the (n + 1) th elite population to an elite population buffer pool;
step 6: let g =1, perform the following for each sub-population and generate a new population:
(1) calculating an adaptive function value F (X) for each individual in the sub-populationj)；
(2) Selecting operation is carried out by adopting a championship selection method;
(3) performing cross mutation operation; randomly selecting number pairs of individuals from the ith sub-population according to cross probability pcPerforming fork operation and carrying out mutation probability p on the individualsmCarrying out variable operation;
and 7: step 4, updating the elite population;
and 8: if G is G or the total optimal individual in the (n + 1) th population meets the convergence condition, ending the evolution process and obtaining a global optimal solution; if G is less than or equal to G or the total optimal individuals in the (n + 1) th population do not satisfy the convergence condition:
-if g ≠ kxw, k being a positive integer, w being the population migration algebraic interval, g → g +1, the respective sub-population again executing the operation of step 6;
if g = kxw, namely after each group is independently evolved for w times, mixing the individuals in the elite population buffer pool with the individuals in each sub-population respectively, sorting according to the new fitness value, removing inferior solutions, and selecting the individuals with the same scale as the original population and better fitness from the inferior solutions to form a new population, thereby realizing the trans-generation migration operation based on the elite population;
and step 9: step 5, updating the elite population buffer pool;
step 10: g → g +1, the operation of step 6 is performed again for n sub-populations.
Preferably, the method for repairing consistency of the new decision matrix for avoiding variation of the decision information specifically includes the following steps:
the method comprises the following steps: calculating a characteristic vector omega of the original judgment matrix A, and carrying out normalization processing on omega;
step two: constructing a complete consistency positive and negative complementation judgment matrix B;
step three: by the original judgment matrix and the complete consistency positive and negative complementary judgment matrixConstructing a substitution by linear combination;
step four: and selecting an optimal alternative matrix by using the principle of minimum Euclidean distance of the feature vector.
Compared with the prior art, the invention has the following beneficial effects:
optimizing a product service network structure aiming at the basic characteristics of a product service supply chain, selecting an optimal partner and establishing a standardized product service supply chain flow, and further establishing the product service supply chain based on systematic optimization design. Meanwhile, the invention avoids and reduces the influence of human factors on product service facility site selection and product service provider selection through a technical method, so that the designed product service supply chain can serve specific objective requirements more.
Specifically, the technical means adopted by the invention comprises the following steps:
(1) a system engineering analysis method;
(2) 0-1 planning modeling techniques;
(3) a genetic algorithm design method;
(4) fuzzy theory;
(5) ANP network analysis;
the technical problems solved by the invention are as follows:
(1) matching problems of strategic design of a product service supply chain;
(2) the problem of dynamic site selection of product service facilities is modeled and the problem of algorithm design;
(3) the problem of artificial subjective influence selected by a product service provider;
(4) the standard problem of product service supply chain flow design.
More specifically, the technical effects obtained by the present invention are as follows:
(1) the method helps enterprises to correctly design proper supply chains according to the frequency of product service requirements;
(2) the enterprise is helped to optimize the product service facility site selection and the service relationship between the product service center and the customer;
(3) objectively extracting expert decision information to help enterprises select the most appropriate service provider;
(4) and establishing a perfect product service flow standard flow system by referring to a product service supply chain flow design reference model.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of a strategic design of a product service supply chain;
FIG. 2 is a schematic diagram of an adaptive multi-objective genetic method based on multi-population co-evolution;
FIG. 3 is a schematic view of a service provider selection evaluation flow;
FIG. 4 is a schematic diagram of a repair process for determining matrix consistency;
FIG. 5 is a schematic diagram of a product service supply chain process design reference model.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The product service supply chain optimization design method comprises the following steps:
step 1: and designing a product service supply chain strategy based on the frequency of product service demands.
According to the frequency of product service demands, a quick response type and high efficiency type product service supply chain, and a centralized service network and a distributed service network corresponding to the quick response type and high efficiency type product service supply chain are designed. (see FIG. 1 product service supply chain strategic design)
The design of the quick response type and high-efficiency product service supply chain comprises the following specific design processes: according to the demand frequency of product service, if the demand frequency is high, establishing an efficient service supply chain and establishing a decentralized service network; if the demand frequency is low, a quick response type service supply chain is established, and a centralized type service network is established.
The quick response type product service supply chain specifically refers to: the system embodies the physical function of a supply chain, converts product service resources into product service capacity with the lowest supply chain cost by establishing a high-efficiency service resource flow channel based on the principle of scale economy, and ensures the service quality through standardized process management.
The efficient product service supply chain specifically refers to: the system embodies the intermediary function of a supply chain, quickly responds to the abnormal product service requirement through process optimization, application of the latest technology and the like, and improves the response speed through flexible process management.
Centralized service network, specifically: the centralized service network has fewer intermediate levels, is often represented as a two-level or single-level structure, and improves the service resource benefit through centralized management of product service resources.
The decentralized service network specifically means: decentralized service networks are typically multi-level structures that increase the availability of resources by placing product service resources in areas closer to customers.
Step 2: and dynamically selecting the site of the product service facility.
And (3) constructing a dynamic site selection model of the multi-target product service facility, and solving the model by adopting a multi-population coevolution genetic method (shown in figure 2).
The dynamic site selection model of the product service facilities comprises the following concrete steps:
an objective function:
object (1): minimum total weighted distance Z1 between demand point and corresponding product service center during planning period
Wherein,s serving productsThe maximum planning period of the facility site selection, wherein n is the number of product service demand points;
object (2): total profit Z2 for product service during planning is maximized
Wherein, α0The product service index of the time period t is 0;
the first item in goal (2) is the total profit that the product service brings during the planning period; the second item is the construction cost of launching a new product service center; the third term is the total operating cost of the newly added product service center during its planned period, which is a function of the number of customers it serves.
Constraint conditions are as follows:
(1) product service center with limited service capabilities (capability constraint)
(2) All demand points will be satisfied and each demand point will only obtain product services from one product service center for a period of time
(3) One demand point may not be satisfied by another demand point. That is, if one demand point provides product services to other demand points, a product service center must exist at the demand point.
Stij≤xti，i∈I，j∈J，t∈T 4-25)
(4) Service level constraint (distance constraint), distance between product service center and demand point being less than a certain value
dijStij≤dmax，i∈I，j∈J，t∈T 4-26)
(5) Decision variable 0-1 constraint, each demand point can only establish one product service center at most
xti(1-xti)＝0，i∈I，t∈T 4-27)
(6) Decision variables 0-1 constrain that a demand point is either fully serviced by a production service center or is not serviced by the production service center
Stij(1-Stij)＝0，i∈I，j∈J，t∈T 4-28)
(7) If a demand point establishes a product service center, the demand point must be satisfied by the product service center established at the demand point
Stij＝1，i∈I，j∈J，i＝j (4-29)
(8) Once established, the product service center is always reserved, can not be cancelled or closed, namely, the subsequent product service center always exists
Wherein,
s: maximum planning period for site selection of product service facilities
k: time to establish a product service center
I: set of product service demand points, I ═ 1, 2, …, I, …, n }
J: set of product service centers, J ═ {1, 2, …, J, …, mt}
mt: total number of product service centers in t period
T: product service center planning period, T ═ 0, 1, T, …, s }
dij: distance between demand point i and demand point j
vΔti: product demand increment at demand point i at time t relative to time t-1
v0i: demand amount of demand point i at time t =0
αtIndex of product servitization at time t, reaction product servitization degree α∈ (0, 1)]
Beta: average profit per unit product service at product-to-service index of 1
csi: cost of building a product service center at demand point i
coi: the operating cost of providing a unit of product service to a product service center (if any) at a demand point i
Vmax: maximum service capacity of product service center
dmax: maximum distance allowed between product service center and demand point
xti: whether a product service center is established at a demand point i or not in the period t
Stij: the demand point i in the period t is served by a product service center j
The multi-population coevolution genetic method specifically comprises the following steps:
step 1: and initializing parameters. Each sub-population has a size of n, the maximum evolution algebra is G, and the cross probability of each sub-population is PCThe mutation probability is Pm。
Step 2: n initial individuals satisfying the constraint are randomly generated, and an initial evolution parameter g =0 is set.
And step 3: defining a fitness function F (X), and calculating the fitness function value F (X) of each individual in the sub-populationj) And rank the individuals.
And 4, step 4: and (3) selecting a certain (equal) number of excellent individuals from the current subgroups according to the fitness value sorting to form an n +1 th population so as to form an elite population.
And 5: individuals in the (n + 1) th population (elite population) are copied to the elite population buffer pool.
Step 6: let g =1, perform the following for each sub-population and generate a new population:
(1) calculating an adaptive function value F (X) for each individual in the sub-populationj)。
(2) And selecting by adopting a championship selection method.
(3) And (5) performing cross mutation operation. Randomly selecting number pairs of individuals from the ith sub-population according to cross probability pcPerforming fork operation and carrying out mutation probability p on the individualsmAnd performing a change operation.
And 7: step 4 is performed to update the elite population (i.e., the (n + 1) th population).
And 8: if G is equal to G or the total optimal individuals in the (n + 1) th population (namely the elite buffer pool) meet the convergence condition, the evolution process is finished, and a global optimal solution is obtained. If G is less than or equal to G or the total optimal individuals in the (n + 1) th population do not satisfy the convergence condition:
● if g ≠ k × w, k is a positive integer, w is the population migration algebra interval, g → g +1, the respective sub-population again performs the operation of step 6.
●, if g = kxw, namely after each group independently evolves for w times, mixing the individuals in the elite population buffer pool with the individuals in each sub-population respectively, sorting according to the new fitness value, removing inferior solutions, and selecting the individuals with the same scale as the original population and better fitness from the inferior solutions to form a new population, thereby realizing the operation of 'migration' across generations based on the elite population.
And step 9: and 5, executing the step 5, and updating the elite population buffer pool.
Step 10: g → g +1, the operation of step 6 is performed again for n sub-populations.
And step 3: and selecting a product service provider.
And (3) introducing a trapezoidal fuzzy number and exponential scaling method to improve the consistency of the judgment matrix, optimizing the evaluation process (see figure 3), and simultaneously adopting a new judgment matrix consistency restoration method for avoiding the decision information variation to restore the consistency of the group decision judgment matrix and furthest retaining the original decision information. (see FIG. 4)
The evaluation flow after introducing the trapezoidal fuzzy number and exponential scaling method specifically comprises the following steps:
the method comprises the following steps: defining problem and network model construction
Step two: establishing a judgment matrix and fuzzification
Step three: consistency check and repair for establishing group decision judgment matrix
Step four: decision judgment matrix
Step five: calculating per-element weights
Step six: evaluating end performance of a product service provider
The new method for repairing consistency of the judgment matrix for avoiding variation of the decision information specifically comprises the following steps:
the method comprises the following steps: and calculating a characteristic vector omega of the original judgment matrix A (conventional matrix), and normalizing omega.
A·ω＝γmax·ω
ω＝(ω1,ω2,ω3,…,ωn)T
Wherein, γmaxRepresents the maximum eigenvalue, ω, of the original decision matrix A1,ω2,ω3,…,ωnA value of a feature vector ω representing an original judgment matrix a (conventional matrix), n representing a dimension of the original judgment matrix a;
step two: constructing a complete consistency positive and negative complementary judgment matrix
Step three: by the original judgment matrix and the complete consistency positive and negative complementary judgment matrixIs combined to construct a surrogate matrix
Wherein, t is a parameter,representing an original judgment matrix (namely a fuzzy matrix before matrix A defuzzification);
however, different alternative matrices may be derived for different t' sAlternative matrices obtained if linear combinations are made for the same tJudging that the consistency of the matrix does not meet the requirement, and can orderCircularly using the above formula until the obtained judgment matrixUntil the consistency of the data meets the requirements. Thus, for any value t, the initial value of the matrix is replacedIs composed ofThe substitution matrix after k iterations isA series of alternative matrices meeting the consistency requirement can be obtainedThe expression is shown as the following formula,
where t is a linear combination coefficient; k is calculated for the same tThe number of cycles.
Step four: and selecting an optimal alternative matrix by using the principle of minimum Euclidean distance of the feature vector. First, a substitution matrix meeting the consistency requirement is calculatedCharacteristic vector omega oft,kThen calculate ω one by onet,kAnd the original judgment matrixOf the feature vectors of (a), ω having the smallest Euclidean distancet,kCorresponding toI.e. the optimal substitution matrix.
And 4, step 4: and designing a product service supply chain flow.
In order to design a product service supply chain in an operational level, a product service supply chain process design reference model needs to be established to provide a reference standard for a specific product service supply chain process design. Product service supply chain flows are divided into two categories here: one is an enabling flow including service requirement management, service resource and capability management and service delivery management, and the other is a functional flow including customer relationship management, outsourcing service provider and provider relationship management, service supply chain information technology management and application management thereof, and service supply chain network management. (see FIG. 5)
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (3)
1. A product service supply chain optimization design method is characterized by comprising the following steps:
step 1: designing a product service supply chain strategy based on the product service demand frequency;
designing a quick response type and high efficiency type product service supply chain, and a centralized service network and a distributed service network corresponding to the quick response type and high efficiency type product service supply chain according to the product service demand frequency;
step 2: dynamic site selection of product service facilities;
constructing a dynamic site selection model of the multi-target product service facility, and solving the model by adopting a multi-population coevolution genetic method;
and step 3: product service provider selection;
the consistency of the decision matrix is improved by introducing a trapezoidal fuzzy number and exponential scaling method, meanwhile, a new decision matrix consistency restoration method for avoiding decision information variation is adopted to carry out consistency restoration on the group decision matrix, and original decision information is retained to the maximum extent;
and 4, step 4: designing a product service supply chain process;
the product service supply chain processes are divided into two categories: one is an enabling flow, which comprises service requirement management, service resource and capability management and service delivery management, and the other is a functional flow, which comprises customer relation management, outsourcing service provider and supplier relation management, service supply chain information technology management and application management thereof and service supply chain network management;
the new method for repairing consistency of the judgment matrix for avoiding variation of the decision information specifically comprises the following steps:
the method comprises the following steps: calculating a characteristic vector omega of the original judgment matrix A, and carrying out normalization processing on omega;
step two: constructing a complete consistency positive and negative complementation judgment matrix B;
step three: by the original judgment matrix and the complete consistency positive and negative complementary judgment matrixConstructing a substitution by linear combination;
step four: and selecting an optimal alternative matrix by using the principle of minimum Euclidean distance of the feature vector.
2. The method of claim 1, wherein the multi-objective product service facility dynamic addressing model is as follows:
an objective function:
object (1): minimum total weighted distance Z1 between demand point and corresponding product service center during planning period
Wherein,s is the maximum planning period of site selection of the product service facility, and n is the number of product service demand points;
object (2): total profit Z2 for product service during planning is maximized
Wherein, α0The product service index of the time period t is 0;
constraint conditions are as follows:
(1) product service center with limited service capability
(2) All demand points will be satisfied and each demand point will only obtain product services from one product service center for a period of time
(3) If one demand point provides product services for other demand points, a product service center must exist at the demand point
Stij≤xti，i∈I，j∈J，t∈T (4-5)
(4) Service level constraints, distance between product service center and demand point being less than a certain value
dijStij≤dmax，i∈I，j∈J，t∈T (4-6)
(5) Decision variable 0-1 constraint, each demand point can only establish one product service center at most
xti(1-xti)＝0，i∈I，t∈T (4-7)
(6) Decision variable 0-1 constraints
Stij(1-Stij)＝0，i∈I，j∈J，t∈T (4-8)
(7) If a demand point establishes a product service center, the demand point must be satisfied by the product service center established at the demand point
Stij＝1，i∈I，j∈J，i＝j (4-9)
(8) Once the product service center is established, the product service center is reserved all the time, and the subsequent product service center exists all the time
Wherein,
s: maximum planning period for site selection of product service facilities
k: time to establish a product service center
I: set of product service demand points, I ═ 1, 2, …, I, …, n }
J: set of product service centers, J ═ {1, 2, …, J, …, mt}
mt: total number of product service centers in t period
T: product service center planning period, T ═ 0, 1, T, …, s }
dij: distance between demand point i and demand point j
vΔti: product demand increment at demand point i at time t relative to time t-1
v0i: demand amount of demand point i at time t-0
αtIndex of product servitization at time t, degree of reaction product servitization α∈ (0, 1)]
Beta: average profit per unit product service at product-to-service index of 1
csi: cost of building a product service center at demand point i
coi: operating cost of providing unit product service at product service center at demand point i
Vmax: maximum service capacity of product service center
dmax: maximum allowable between product service center and demand pointLarge distance
xti: whether a product service center is established at a demand point i or not in the period t
Stij: and the demand point i in the period t is served by a product service center j.
3. The product service supply chain optimization design method according to claim 1, wherein the multi-population coevolution genetic method specifically comprises the following steps:
step 1: initializing parameters;
each sub-population has a size of n, the maximum evolution algebra is G, and the cross probability of each sub-population is PCThe mutation probability is Pm；
Step 2: randomly generating n initial individuals meeting the constraint, and setting an initial evolution parameter g to be 0;
and step 3: defining a fitness function F (X), and calculating the fitness function value F (X) of each individual in the sub-populationj) And ordering the individuals;
and 4, step 4: sorting according to the fitness value, selecting a certain equal number of excellent individuals from each current subgroup to form an n +1 th population, and forming an elite population;
and 5: copying individuals in the (n + 1) th elite population to an elite population buffer pool;
step 6: let g be 1, the following is performed for each sub-population and a new population is generated:
(1) calculating an adaptive function value F (X) for each individual in the sub-populationj)；
(2) Selecting operation is carried out by adopting a championship selection method;
(3) performing cross mutation operation; randomly selecting number pairs of individuals from the ith sub-population according to cross probability pcPerforming fork operation and carrying out mutation probability p on the individualsmCarrying out variable operation;
and 7: step 4, updating the elite population;
and 8: if G is G or the total optimal individual in the (n + 1) th population meets the convergence condition, ending the evolution process and obtaining a global optimal solution; if G is less than or equal to G or the total optimal individuals in the (n + 1) th population do not satisfy the convergence condition:
-if g ≠ kxw, k being a positive integer, w being the population migration algebraic interval, g → g +1, the respective sub-population again executing the operation of step 6;
if g is k × w, namely after the various groups are independently evolved for w times, respectively mixing the individuals in the elite population buffer pool with the individuals in the sub-populations, sorting according to the new fitness value, removing inferior solutions, and selecting the individuals with the same scale as the original population and better fitness from the inferior solutions to form a new population, thereby realizing the trans-generation migration operation based on the elite population;
and step 9: step 5, updating the elite population buffer pool;
step 10: g → g +1, the operation of step 6 is performed again for n sub-populations.
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