CN114708045B - Multi-cycle supply chain network design method and system based on consumer preference - Google Patents

Multi-cycle supply chain network design method and system based on consumer preference Download PDF

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CN114708045B
CN114708045B CN202210618921.9A CN202210618921A CN114708045B CN 114708045 B CN114708045 B CN 114708045B CN 202210618921 A CN202210618921 A CN 202210618921A CN 114708045 B CN114708045 B CN 114708045B
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王剑
万迁
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Abstract

The invention discloses a multi-cycle supply chain network design method and a multi-cycle supply chain network design system based on consumer preference, wherein the consumer preference and the recovery and remanufacturing of products are considered, the carbon emission of each link is taken as an environmental target, the change of the consumer demand preference is combined, and meanwhile, the profit and the carbon emission target of a supply chain are optimized; aiming at the current situation of consumer demand transition, the influence of low-price preference and low-carbon preference of consumers on the product market demand is considered, and the relation between the product market demand and the product price and the low-carbon level is described by a piecewise function. Meanwhile, product pricing and product environmental protection technology investment are taken as decision variables and incorporated into the design of a green supply chain network, and the method is closer to the actual market condition, so that the total cost required by the supply chain network in the operation processes of construction, sale, recovery and the like is minimized, and a more effective decision is provided for the supply chain management of enterprises.

Description

Multi-period supply chain network design method and system based on consumer preference
Technical Field
The invention belongs to the field of supply chain network design, and particularly relates to a multi-cycle supply chain network design method and system based on consumer preference.
Background
Traditional supply chain network designs tend to target minimizing network costs or maximizing network profit, however, with the rapid development of economy, resource consumption and environmental destruction problems become increasingly prominent, and greenhouse gas emissions are one of the main causes of these problems. Meanwhile, the environmental awareness of consumers is gradually improved, more and more people can recognize the concepts of environmental protection and sustainable development, and the consumption quality and the consumption structure are changed accordingly. However, the existing supply chain network design method usually only considers carbon emission in the production or transportation process, neglects consumption demand change, does not comprehensively consider relevant decisions of product pricing and environmental protection technology investment, cannot guarantee the competitiveness of the supply chain in the existing market, causes that enterprises need to invest more operation cost, and is difficult to realize the minimization of the total cost.
Disclosure of Invention
In view of the above drawbacks or needs for improvement in the prior art, the present invention provides a method and system for designing a multi-cycle supply chain network based on consumer preferences, so as to solve the problem that it is difficult to minimize the total cost of consumer preference transition based on the existing multi-cycle supply chain planning method.
To achieve the above object, according to a first aspect of the present invention, there is provided a multi-cycle supply chain network design method based on consumer preferences, comprising:
s1, constructing a supply chain network design model by taking the maximization of supply chain profit and the minimization of supply chain carbon emission as optimization targets;
wherein the supply chain network comprises a manufacturing plant, a distribution center, a consumer market, and a third party recycling center; the supply chain profit is the difference between the total sales and the fixed, transportation, variable costs, the total sales
Figure 230755DEST_PATH_IMAGE001
；
Wherein,
Figure 489174DEST_PATH_IMAGE002
respectively a consumer market number set, a product type number set and a cycle number set,
Figure 737752DEST_PATH_IMAGE003
respectively corresponding indexes;
Figure 992016DEST_PATH_IMAGE004
is a product
Figure 275230DEST_PATH_IMAGE005
At the price level
Figure 832113DEST_PATH_IMAGE006
And low carbon level rating
Figure 443354DEST_PATH_IMAGE007
Price of hour, if product
Figure 376675DEST_PATH_IMAGE005
Selecting price classes
Figure 639029DEST_PATH_IMAGE006
And low carbon level rating
Figure 101235DEST_PATH_IMAGE007
Then, then
Figure 589985DEST_PATH_IMAGE008
Is 1, otherwise
Figure 936784DEST_PATH_IMAGE008
Is 0;
Figure 929010DEST_PATH_IMAGE009
is period of
Figure 93275DEST_PATH_IMAGE010
Product(s)
Figure 193955DEST_PATH_IMAGE005
In the consumer market
Figure 937921DEST_PATH_IMAGE011
The number of out-of-stock items,
Figure 315812DEST_PATH_IMAGE012
is a period of
Figure 995186DEST_PATH_IMAGE013
Bottom product
Figure 192950DEST_PATH_IMAGE005
At the price level
Figure 865239DEST_PATH_IMAGE006
And low carbon level rating
Figure 97638DEST_PATH_IMAGE007
Is in the consumer market
Figure 210563DEST_PATH_IMAGE011
The required amount of (a) to be used,
Figure 895622DEST_PATH_IMAGE014
，
Figure 246969DEST_PATH_IMAGE015
is the product price grade
Figure 458507DEST_PATH_IMAGE006
The corresponding price is set to the corresponding price,
Figure 604318DEST_PATH_IMAGE016
low carbon level grade for the product
Figure 386460DEST_PATH_IMAGE007
The corresponding low carbon level is achieved by the method,
Figure 541498DEST_PATH_IMAGE017
is a period oftThe largest size of the consumer market is,
Figure 607543DEST_PATH_IMAGE018
a market proportion that favors consumers for low prices;
Figure 455413DEST_PATH_IMAGE019
an upper threshold value for product prices approved by the consumer;
Figure 849485DEST_PATH_IMAGE020
a lower threshold value for product prices approved by the consumer;
Figure 683580DEST_PATH_IMAGE021
an upper threshold for a low carbon level approved by the consumer;
Figure 213919DEST_PATH_IMAGE022
a lower threshold of low carbon level approved by the consumer;
s2, determining the constraint conditions of the supply chain network design model, wherein the constraint conditions comprise: the quantity balance and capacity limit constraints of the products in a manufacturing plant and a distribution center respectively, and the quantity balance constraints of the products in a consumer market and a third-party recovery center respectively;
and S3, solving the supply chain network design model to obtain the multi-period supply chain network design optimal scheme based on the preference of the consumer.
According to a second aspect of the present invention there is provided a multi-cycle supply chain network design system based on consumer preferences, comprising: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method according to the first aspect.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. according to the multi-period supply chain network design method and system based on the preference of the consumer, the green supply chain preferred by the consumer is considered, the green supply chain network design method based on the preference of the consumer is researched, the recovery and remanufacturing of products are considered, the carbon emission of each link is taken as an environmental target, the change of the preference of the demand of the consumer is combined, meanwhile, the profit and the carbon emission target of the supply chain are optimized, a reference is provided for the multi-period green supply chain network design of an enterprise under the influence of the demand of the consumer, and the enterprise can cope with markets under the influence of different preferences of the consumer; aiming at the current situation of consumer demand transition, the influence of low-price preference and low-carbon preference of consumers on product market demand is considered, and the relation between the product market demand and the product price and the low-carbon level is described by a piecewise function; meanwhile, product pricing and product environmental protection technology investment are taken as decision variables and incorporated into the design of the green supply chain network, and the method is closer to the actual market condition, so that the minimization of the total cost required by the supply chain network in the operation processes of construction, sale, recovery and the like is realized, and a more effective decision is provided for the supply chain management of enterprises.
2. According to the multi-period supply chain network design method and system based on consumer preference, when the established complex multi-period green supply chain network planning model is solved, the multi-target genetic algorithm and the variable neighborhood descent algorithm are combined, the K-means clustering algorithm is introduced in each iteration to screen the representative solution on the pareto frontier, the local optimization searching capability and the calculation efficiency of the algorithm are improved, the overall optimization searching capability of the algorithm is improved along with the improvement of the local optimization searching capability of the algorithm, and therefore the calculation accuracy of the model is improved.
Drawings
Fig. 1 is a schematic flow chart of a multi-cycle supply chain network design method based on consumer preferences according to an embodiment of the present invention.
Fig. 2 is a diagram of a multi-cycle supply chain network structure according to an embodiment of the present invention.
Fig. 3 is a flowchart for solving a supply chain network design model by combining a multi-target genetic algorithm and a variable neighborhood algorithm according to an embodiment of the present invention.
Fig. 4 is one of the convergence diagrams for solving the supply chain network design model by combining the multi-objective genetic algorithm and the variable neighborhood algorithm according to the embodiment of the present invention.
Fig. 5 is a second convergence diagram for solving the supply chain network design model by combining the multi-objective genetic algorithm and the variable neighborhood algorithm according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention. In addition, the technical features involved in the respective embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The supply chain network obtained by adopting the traditional supply chain network design method neglects the requirement preference transition of consumers, the product gradually loses competitive advantages in the market, the conditions of shortage or excess capacity are easy to occur, and the minimization of the total cost required by the supply chain network in the operation processes of construction, sale, recovery and the like is difficult to realize, therefore, the embodiment of the invention provides a green closed-loop supply chain network design method considering the preference of the consumers, as shown in fig. 1, comprising the following steps:
s1, constructing a supply chain network design model by taking the maximization of supply chain profit and the minimization of supply chain carbon emission as optimization targets;
wherein the supply chain network comprises a manufacturing plant, a distribution center, a consumer market, and a third party recycling center; the supply chain profit is the difference between the total sales and the fixed, transportation, variable costs, the total sales
Figure 232690DEST_PATH_IMAGE001
；
Wherein,
Figure 504272DEST_PATH_IMAGE023
numbering a set for the consumer market, indexed by
Figure 1112DEST_PATH_IMAGE011
；
Figure 917116DEST_PATH_IMAGE024
For a product category set, index is
Figure 450996DEST_PATH_IMAGE005
(ii) a For a periodically numbered set, the index is
Figure 85240DEST_PATH_IMAGE013
(ii) a If the product is
Figure 385771DEST_PATH_IMAGE005
Selecting price classes
Figure 546494DEST_PATH_IMAGE006
And low carbon level rating
Figure 641489DEST_PATH_IMAGE007
Then, then
Figure 497450DEST_PATH_IMAGE008
Is 1, otherwise
Figure 474108DEST_PATH_IMAGE008
Is 0;
Figure 99125DEST_PATH_IMAGE009
is period of
Figure 365021DEST_PATH_IMAGE013
Product(s)
Figure 832912DEST_PATH_IMAGE005
In the consumer market
Figure 740825DEST_PATH_IMAGE011
The number of out-of-stock items,
Figure 485927DEST_PATH_IMAGE012
is period of
Figure 188304DEST_PATH_IMAGE013
Bottom product
Figure 894223DEST_PATH_IMAGE005
At the price level
Figure 340247DEST_PATH_IMAGE006
And low carbon level rating
Figure 939856DEST_PATH_IMAGE007
Hour in the consumer market
Figure 672189DEST_PATH_IMAGE011
The required amount of (a) to be used,
Figure 990038DEST_PATH_IMAGE025
，
Figure 708595DEST_PATH_IMAGE026
is the product price gradelThe corresponding price is set to the corresponding price,
Figure 303655DEST_PATH_IMAGE027
low carbon level grade for the producteThe corresponding low carbon level is achieved by the method,
Figure 347835DEST_PATH_IMAGE017
is period oftThe largest size of the consumer market.
As shown in FIG. 2, the green supply chain network is comprised of a manufacturing plant I, a distribution center J, a consumer market K, and a third party recycling center M. The manufacturing plant is responsible for producing raw materials provided externally and recovered by third-party recyclers into corresponding products. The distribution center is responsible for transporting products produced by a manufacturing plant to various consumer markets for sale, and meanwhile, the distribution center has certain storage capacity, and can store the products within the allowable range of the capacity for transportation and sale in the next period. The third party recycling point is responsible for recycling the recyclable products on the consumer market and transporting the recyclable part of the products to the manufacturing plant for remanufacturing after inspection.
Determining a relationship between product demand and product attributes under the influence of consumer preferences; the consumer preferences include: a low price preference and a low carbon preference of the consumer; the product attributes include: product price and low carbon level of the product. Wherein the low carbon level of the product is determined by the carbon emission of the product in the production and transportation processes.
The specific steps of calculating the relationship between the product demand and the product attributes under the influence of the preference of the consumer are as follows:
(1) determining product information and consumer information on the market; the product information comprises a product price interval and a product unit carbon emission interval; the consumer information on the market comprises a low-price preferred consumer proportion and a low-carbon preferred consumer proportion;
wherein the low price preference consumer is a consumer sensitive to the product price; low carbon preference consumers are consumers sensitive to the low carbon level of the product;
(2) deducing a functional relation between the product price and the low carbon level of the product and the product demand;
Figure 152980DEST_PATH_IMAGE028
wherein,
Figure 940807DEST_PATH_IMAGE029
is the product demand;
Figure 639642DEST_PATH_IMAGE018
a market proportion that favors consumers for low prices;
Figure 589143DEST_PATH_IMAGE019
an upper threshold value for product prices approved by the consumer;
Figure 616005DEST_PATH_IMAGE020
a lower threshold value for product prices approved by the consumer;
Figure 82890DEST_PATH_IMAGE021
an upper threshold for a low carbon level approved by the consumer;
Figure 511597DEST_PATH_IMAGE022
a lower threshold of low carbon level approved by the consumer;
Figure 632000DEST_PATH_IMAGE030
for the price of the product, the range is
Figure 536371DEST_PATH_IMAGE031
；
Figure 666001DEST_PATH_IMAGE032
The product is low-carbon level in the range
Figure 949214DEST_PATH_IMAGE033
，
Figure 771677DEST_PATH_IMAGE034
Is the largest size of the consumer market;
(3) discretizing a product demand function;
Figure 403426DEST_PATH_IMAGE035
wherein,
Figure 336747DEST_PATH_IMAGE036
as a product price rating
Figure 474467DEST_PATH_IMAGE006
And low carbon level grade of the product
Figure 592465DEST_PATH_IMAGE007
The demand for the next;
Figure 815636DEST_PATH_IMAGE015
as a price grade
Figure 287068DEST_PATH_IMAGE006
A corresponding price;
Figure 420240DEST_PATH_IMAGE016
low carbon level grade for the product
Figure 318926DEST_PATH_IMAGE006
Corresponding low carbon level.
And constructing a supply chain network design model of a four-layer network structure related to a manufacturing plant, a distribution center, a consumer market and a third-party recycling center by taking the maximization of supply chain profit and the minimization of carbon emission as optimization targets and combining the relationship between product demand and product attributes under the influence of consumer preference, and establishing model constraints.
(4) Constructing a profit objective function
Figure 29393DEST_PATH_IMAGE037
And carbon emission objective function
Figure 429151DEST_PATH_IMAGE038
；
Profit objective function
Figure 807042DEST_PATH_IMAGE039
；
Wherein,
Figure 611050DEST_PATH_IMAGE040
is the total sales volume;
Figure 949759DEST_PATH_IMAGE041
for fixed costs;
Figure 762994DEST_PATH_IMAGE042
for transportation costs;
Figure 995392DEST_PATH_IMAGE043
variable cost;
preferably, the cost is fixed
Figure 235881DEST_PATH_IMAGE044
；
Wherein if the candidate manufacturing plant is located and operated
Figure 45574DEST_PATH_IMAGE045
Is 1, otherwise
Figure 662500DEST_PATH_IMAGE045
Is 0; if candidate distribution center
Figure 749405DEST_PATH_IMAGE046
Is located and operated, then
Figure 301740DEST_PATH_IMAGE047
Is 1, otherwise
Figure 474095DEST_PATH_IMAGE047
Is 0;
Figure 629133DEST_PATH_IMAGE048
to build candidate manufacturing plants
Figure 836123DEST_PATH_IMAGE049
Fixed cost of (2);
Figure 277469DEST_PATH_IMAGE050
to build candidate distribution centers
Figure 937120DEST_PATH_IMAGE046
Fixed cost of (2);
Figure 895849DEST_PATH_IMAGE051
is low carbon level grade
Figure 564203DEST_PATH_IMAGE007
The product of (1)
Figure 582975DEST_PATH_IMAGE005
The investment cost of the environmental protection technology is reduced;
transportation costTCThe expression of (a) is:
Figure 729923DEST_PATH_IMAGE052
Figure 85818DEST_PATH_IMAGE053
wherein,
Figure 1821DEST_PATH_IMAGE054
is a product
Figure 191494DEST_PATH_IMAGE005
In the period
Figure 701104DEST_PATH_IMAGE013
By means of a vehicle
Figure 1635DEST_PATH_IMAGE055
Slave facility
Figure 506566DEST_PATH_IMAGE056
Transport to a facility
Figure 991774DEST_PATH_IMAGE057
The number of the products of (a) is,
Figure 113313DEST_PATH_IMAGE058
;
Figure 686377DEST_PATH_IMAGE059
as a facility node
Figure 452339DEST_PATH_IMAGE056
And a facility node
Figure 983815DEST_PATH_IMAGE057
The distance of (a) to (b),
Figure 327071DEST_PATH_IMAGE060
，
Figure 94039DEST_PATH_IMAGE061
；
Figure 839141DEST_PATH_IMAGE062
to select a vehicle
Figure 541518DEST_PATH_IMAGE055
Transporting products
Figure 372071DEST_PATH_IMAGE005
Unit transportation cost of (a);
variable costACThe expression of (a) is:
Figure 427882DEST_PATH_IMAGE063
Figure 27491DEST_PATH_IMAGE064
wherein,
Figure 900769DEST_PATH_IMAGE065
is a product
Figure 343252DEST_PATH_IMAGE005
In the period of
Figure 327388DEST_PATH_IMAGE013
At a manufacturing plant
Figure 781503DEST_PATH_IMAGE066
The amount of production;
Figure 698119DEST_PATH_IMAGE067
is a product
Figure 503264DEST_PATH_IMAGE005
In the period
Figure 25512DEST_PATH_IMAGE013
Manufacturing plant
Figure 724347DEST_PATH_IMAGE066
Unit production cost of (2);
Figure 939428DEST_PATH_IMAGE068
is period of
Figure 966290DEST_PATH_IMAGE013
At the end of the run, the product
Figure 167595DEST_PATH_IMAGE005
The number of stocks in the distribution center;
Figure 596302DEST_PATH_IMAGE069
is a product
Figure 982284DEST_PATH_IMAGE005
In a distribution center
Figure 621076DEST_PATH_IMAGE046
The unit warehousing cost of (a);
Figure 750706DEST_PATH_IMAGE070
is a product
Figure 33920DEST_PATH_IMAGE071
Unit stock out loss cost;
Figure 466169DEST_PATH_IMAGE072
is a product
Figure 467623DEST_PATH_IMAGE005
The unit recovery cost of (2);
Figure 135365DEST_PATH_IMAGE073
for the recovered product
Figure 538665DEST_PATH_IMAGE005
At a manufacturing plant
Figure 656662DEST_PATH_IMAGE066
The unit of remanufacturing saves cost.
Preferably, the supply chain carbon emissions are the sum of fixed carbon emissions, transport carbon emissions, and production carbon emissions.
That is, the carbon emission objective function
Figure 614254DEST_PATH_IMAGE074
；
Wherein,
Figure 351266DEST_PATH_IMAGE075
fixed carbon emissions;
Figure 484438DEST_PATH_IMAGE076
to produce carbon emissions;
Figure 117545DEST_PATH_IMAGE077
for transporting carbon emissions;
Figure 93591DEST_PATH_IMAGE078
wherein,
Figure 493348DEST_PATH_IMAGE079
to build candidate manufacturing plants
Figure 340081DEST_PATH_IMAGE066
Carbon emissions of (c);
Figure 675248DEST_PATH_IMAGE080
to build candidate distribution centers
Figure 745447DEST_PATH_IMAGE046
Carbon emissions of (d);
transport carbon emissionsTCThe expression of (a) is:
Figure 558683DEST_PATH_IMAGE081
Figure 791081DEST_PATH_IMAGE082
wherein,
Figure 156203DEST_PATH_IMAGE083
to select a vehicle
Figure 106842DEST_PATH_IMAGE055
Transporting products
Figure 723768DEST_PATH_IMAGE005
Unit of (2)Carbon emissions;
carbon emission in productionPMThe expression of (a) is:
Figure 810672DEST_PATH_IMAGE084
wherein,
Figure 831849DEST_PATH_IMAGE085
is low carbon level grade
Figure 738625DEST_PATH_IMAGE007
The product of (1)
Figure 283876DEST_PATH_IMAGE005
At a manufacturing plant
Figure 225287DEST_PATH_IMAGE066
Unit carbon emissions of production of (a);
Figure 807578DEST_PATH_IMAGE086
is low carbon level grade
Figure 342596DEST_PATH_IMAGE007
The product of (1)
Figure 301325DEST_PATH_IMAGE005
At a manufacturing plant
Figure 362822DEST_PATH_IMAGE066
Remanufactured unit carbon emissions.
S2, determining the constraint conditions of the supply chain network design model, wherein the constraint conditions comprise: the quantity balance and capacity limit constraints of the products at the manufacturing plant and distribution center, respectively, and the quantity balance constraints of the products at the consumer market and third party recycling center, respectively.
Specifically, model constraints are established:
to meet the quantity balance and capacity limitation of products at the manufacturing plant, the constraints are defined as follows:
Figure 240648DEST_PATH_IMAGE087
Figure 856437DEST_PATH_IMAGE088
wherein,
Figure 618857DEST_PATH_IMAGE089
is a product
Figure 410226DEST_PATH_IMAGE005
The storage coefficient of (a);
Figure 334320DEST_PATH_IMAGE090
for candidate manufacturing plants
Figure 968564DEST_PATH_IMAGE049
The facility capacity of (a);
to meet the quantity balance and capacity limitation of products in distribution centers, the constraints are defined as follows:
Figure 393729DEST_PATH_IMAGE091
Figure 164239DEST_PATH_IMAGE092
Figure 524813DEST_PATH_IMAGE093
wherein,
Figure 380773DEST_PATH_IMAGE094
as candidate distribution center
Figure 357432DEST_PATH_IMAGE046
The facility capacity of (a);
to ensure a balanced number of products in the consumer market, the constraints are defined as follows:
Figure 248028DEST_PATH_IMAGE095
Figure 513924DEST_PATH_IMAGE096
wherein,
Figure 981814DEST_PATH_IMAGE097
is a product
Figure 624148DEST_PATH_IMAGE005
In the consumer market
Figure 369250DEST_PATH_IMAGE011
The recovery rate is lower;
to ensure a balanced number of products in a third party recycling center, constraints are defined as follows:
Figure 71627DEST_PATH_IMAGE098
Figure 777546DEST_PATH_IMAGE099
Figure 957992DEST_PATH_IMAGE100
wherein,
Figure 823180DEST_PATH_IMAGE101
for recycled products
Figure 555512DEST_PATH_IMAGE005
The reusability of (c); if in the period
Figure 607782DEST_PATH_IMAGE013
Selecting a vehicle class
Figure 857498DEST_PATH_IMAGE055
Then, then
Figure 452558DEST_PATH_IMAGE102
Is 1, otherwise
Figure 496738DEST_PATH_IMAGE102
Is 0;
Figure 770724DEST_PATH_IMAGE103
is an extremely large number, e.g., int32 maximum: 2147483647.
the range of the relevant decision variables is constrained as follows:
Figure 683185DEST_PATH_IMAGE104
；
Figure 257386DEST_PATH_IMAGE105
；
Figure 472467DEST_PATH_IMAGE106
；
Figure 764908DEST_PATH_IMAGE107
。
and S3, solving the supply chain network design model to obtain the optimal scheme of the multi-period supply chain network design based on the preference of the consumer.
Specifically, an optimal scheme set of a multi-cycle supply chain network design is output, results of facility site selection, product pricing, environmental protection technology investment, product production, flow distribution, inventory management and vehicle selection of a supply chain under each scheme are determined, and an enterprise manager can select the scheme from the scheme set according to actual planning of the enterprise manager.
Because the multi-period supply chain network design has a plurality of decision variables, a plurality of optimization targets exist, the model is complex, the existing optimization method has low solving speed and cannot quickly solve under a large-scale instance; and the conventional heuristic algorithm can not ensure to obtain a high-quality and high-precision solving scheme during solving, and the cost minimization is difficult to realize. In this regard, preferably, the method for solving the supply chain network design model by using a multi-objective genetic algorithm in combination with a variable neighborhood algorithm, as shown in fig. 2 to 3, specifically includes:
and S31, initializing parameters and setting iteration times.
Specifically, parameters of the algorithm are initialized, and an algorithm termination condition is set.
S32, generating an initial population by adopting a priority coding mode, wherein the codes of individuals in the population consist of five parts: the first part defines the priority of products (P) provided by a manufacturing plant, the second part, the third part and the fourth part respectively represent the priority order of the candidate distribution center (J), the consumer market (K) and the third-party recycling center (M), and the fifth part defines the product price level, the product low carbon level and the vehicle type; the size of the gene value of each digit in the coding sequence is used for describing the priority of P products in selection of manufacturing plants, candidate distribution centers, consumer market or third-party recycling centers, the price level of the products, the low carbon level of the products and the types of vehicles.
The coding sequence of each individual is a matrix with T rows and n columns, T is the total cycle number, the matrix with n columns comprises five parts, the number of the columns of the first four parts is the number of a manufacturing factory, a distribution center, a consumer market and a third-party recycling center, and the fifth part comprises 3 columns which are respectively a product price grade, a product low-carbon level grade and a vehicle type.
Specifically, the codes of the individuals in the population include address selection information, product pricing information, low carbon level information of the product, flow distribution information, inventory information and the like.
Accordingly, when the supply chain network design model is solved by combining the multi-objective genetic algorithm and the variable neighborhood algorithm, the model is solved based on the algorithm in step S3The process of decoding the decoded coding result to obtain the scheme comprises the following steps of respectively decoding according to a periodic sequence: the fifth part of the first cycle is first decoded to clarify the product price level, low carbon level and vehicle type at the time of transport. Then, according to the highest priority in the first part of codes and the priority of product production, combining the production cost function of the factory
Figure 966213DEST_PATH_IMAGE067
The production allocation of P products in the manufacturing plant is determined, that is, the priority of the code not only represents the priority of the production of the product types, but also determines which manufacturing plant will produce which type of product and the number of products produced by the manufacturing plant in each period. In the second part of codes, the distribution center with the highest priority is found, and the transportation cost function is combined according to the priority sequence
Figure 394921DEST_PATH_IMAGE108
And calculating the transportation amount and the inventory amount of each distribution center. Then, based on the demand of the consumer market, according to the third partial code value
Figure 780903DEST_PATH_IMAGE109
And calculating the flow direction and the flow rate of the products between each distribution center and the consumer market. Finally, coding according to the fourth part, based on the transportation cost function
Figure 419694DEST_PATH_IMAGE110
And determining a consumer market and a manufacturing factory corresponding to the third-party recycling center, and calculating a flow distribution result of the product recycled from the consumer market.
S33, decoding the individuals in the population, and calculating the fitness value of the individuals in the population according to the fitness function;
s34, carrying out genetic operations such as selection, crossing, variation and the like, and updating the population to obtain a pareto optimal solution set;
the pareto optimal solution set refers to a set formed by all pareto optimal solutions in multi-objective optimization, and the pareto optimal solution refers to a solution which is not dominated by any solution in a solution space.
S35, performing K-means clustering on the pareto optimal solution set to select
Figure 549324DEST_PATH_IMAGE023
A representative optimal solution;
s36, for the
Figure 98117DEST_PATH_IMAGE023
Local search is carried out on the representative optimal solution by using a variable neighborhood descent algorithm;
and S37, judging whether the iteration times are reached, if so, stopping iteration and outputting a result, otherwise, returning to S33.
That is, it is determined whether the termination condition is reached, if so, the iteration is stopped, and the result is output, otherwise, the process goes to step S33.
Preferably, in step S31, initializing parameters of the algorithm by using a response surface method, specifically including:
s311, determining algorithm parameters serving as control factors, including maximum iteration times, cross probability and mutation probability; selecting a response surface experimental design (such as a center composite design or a Box-Behnken design) to be adopted, such as a center composite design or a Box-Behnken design; performing experiments according to the experimental design to obtain algorithm performance indexes (such as ultra-volume, average ideal distance, maximum dispersion degree and the like) under each group of algorithm parameters;
s312, analyzing the functional relationship between the value of the algorithm parameter and the algorithm performance index by using a second-order polynomial model;
and S313, calculating extreme points according to the obtained functional relation between the algorithm parameters and the algorithm performance indexes, and determining the optimal parameter combination of the algorithm.
Preferably, step S36 specifically includes:
s361, giving an initial solution
Figure 655001DEST_PATH_IMAGE111
Let us order
Figure 263312DEST_PATH_IMAGE112
Definition of
Figure 196633DEST_PATH_IMAGE113
A neighborhood of
Figure 599932DEST_PATH_IMAGE114
(
Figure 452351DEST_PATH_IMAGE115
) Respectively is as follows: changing the product price or low carbon level grade, changing the selection of vehicles, and randomly exchanging the coding sequence of the manufacturing factory (i.e. the first part of codes) and the coding sequence of the individual;
s362, searching the solution according to the neighborhood structure
Figure 675522DEST_PATH_IMAGE116
In which a ratio is found
Figure 412534DEST_PATH_IMAGE111
Better solution
Figure 545706DEST_PATH_IMAGE117
When it is used, make
Figure 444392DEST_PATH_IMAGE118
，
Figure 154859DEST_PATH_IMAGE112
；
S363, if the current neighborhood structure is traversed
Figure 554616DEST_PATH_IMAGE119
Can not be found as before
Figure 666928DEST_PATH_IMAGE111
Better solution, order
Figure 2095DEST_PATH_IMAGE120
；
S364，If it is
Figure 199858DEST_PATH_IMAGE121
Go to step S362, otherwise, output the optimal solution.
The convergence situation of the supply chain network design model solved by combining the multi-target genetic algorithm and the variable neighborhood algorithm provided by the embodiment of the invention is shown in fig. 4-5, and it can be seen that the convergence situation gradually becomes stable as the iteration times increase.
The embodiment of the invention provides a multi-cycle supply chain network design system based on consumer preference, which comprises: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is used for reading the executable instructions stored in the computer readable storage medium and executing the method according to any one of the above embodiments.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (4)

1. A method for multi-cycle supply chain network design based on consumer preferences, comprising:
s1, constructing a supply chain network design model by taking the maximization of supply chain profit and the minimization of supply chain carbon emission as optimization targets;
wherein the supply chain network comprises a manufacturing plant, a distribution center, a consumer market, and a third party recycling center; the supply chain profit is the difference between the total sales and the fixed, transportation, variable costs, the total sales
Figure 844301DEST_PATH_IMAGE001
；
Wherein,
Figure 432409DEST_PATH_IMAGE002
respectively a consumer market number set, a product type number set and a cycle number set,
Figure 90923DEST_PATH_IMAGE003
are respectively corresponding indexes;
Figure 256325DEST_PATH_IMAGE004
is a product
Figure 261584DEST_PATH_IMAGE005
At the price level
Figure 500935DEST_PATH_IMAGE006
And low carbon level rating
Figure 268034DEST_PATH_IMAGE007
Price of hour, if product
Figure 858415DEST_PATH_IMAGE005
Selecting price classes
Figure 228217DEST_PATH_IMAGE006
And low carbon level rating
Figure 86189DEST_PATH_IMAGE007
Then, then
Figure 24189DEST_PATH_IMAGE008
Is 1, otherwise
Figure 570708DEST_PATH_IMAGE008
Is 0;
Figure 947463DEST_PATH_IMAGE009
is period of
Figure 223724DEST_PATH_IMAGE010
Product(s)
Figure 896407DEST_PATH_IMAGE005
In the consumer market
Figure 133484DEST_PATH_IMAGE011
The number of out-of-stock items,
Figure 782771DEST_PATH_IMAGE012
is period of
Figure 179118DEST_PATH_IMAGE013
Bottom product
Figure 521237DEST_PATH_IMAGE005
At the price level
Figure 744146DEST_PATH_IMAGE006
And low carbon level rating
Figure 197124DEST_PATH_IMAGE007
Hour in the consumer market
Figure 120081DEST_PATH_IMAGE011
The amount of the required amount of (c) is,
Figure 695418DEST_PATH_IMAGE014
，
Figure 969405DEST_PATH_IMAGE015
is the product price grade
Figure 489441DEST_PATH_IMAGE006
The corresponding price is set to the corresponding price,
Figure 532483DEST_PATH_IMAGE016
low carbon level grade for product
Figure 278722DEST_PATH_IMAGE007
The corresponding low carbon level is achieved by the method,
Figure 508846DEST_PATH_IMAGE017
is period oftThe largest size of the consumer market is,
Figure 303627DEST_PATH_IMAGE018
a market proportion that favors consumers for low prices;
Figure 699711DEST_PATH_IMAGE019
an upper threshold value for product prices approved by the consumer;
Figure 288955DEST_PATH_IMAGE020
a lower threshold value for product prices approved by the consumer;
Figure 475217DEST_PATH_IMAGE021
an upper threshold for a low carbon level approved by the consumer;
Figure 73689DEST_PATH_IMAGE022
a lower threshold of low carbon levels approved by the consumer;
s2, determining the constraint conditions of the supply chain network design model, wherein the constraint conditions comprise: the quantity balance and the capacity limit constraint of the products in a manufacturing plant and a distribution center respectively, and the quantity balance constraint of the products in a consumer market and a third-party recycling center respectively;
s3, solving the supply chain network design model to obtain a multi-period supply chain network design optimal scheme based on consumer preference;
the quantity balance and capacity limit constraints of a product at a manufacturing plant are:
Figure 888061DEST_PATH_IMAGE023
Figure 415251DEST_PATH_IMAGE024
wherein,
Figure 885546DEST_PATH_IMAGE025
is a product
Figure 959813DEST_PATH_IMAGE005
The storage coefficient of (2);
Figure 566374DEST_PATH_IMAGE026
for candidate manufacturing plants
Figure 356476DEST_PATH_IMAGE027
The facility capacity of (a);
the quantity balance and capacity limit constraints of the product at the distribution center are:
Figure 547024DEST_PATH_IMAGE028
Figure 487298DEST_PATH_IMAGE029
Figure 151628DEST_PATH_IMAGE030
wherein,
Figure 253577DEST_PATH_IMAGE031
as candidate distribution centers
Figure 199929DEST_PATH_IMAGE032
The facility capacity of (a);
the quantity balance constraint of products in the consumer market is:
Figure 6211DEST_PATH_IMAGE033
Figure 587365DEST_PATH_IMAGE034
wherein,
Figure 329056DEST_PATH_IMAGE035
is a product
Figure 995661DEST_PATH_IMAGE005
In the consumer market
Figure 776273DEST_PATH_IMAGE011
The recovery rate is lower;
the quantity balance constraint of the product in the third-party recycling center is as follows:
Figure 539830DEST_PATH_IMAGE036
Figure 249160DEST_PATH_IMAGE037
Figure 606323DEST_PATH_IMAGE038
wherein,
Figure 692091DEST_PATH_IMAGE039
for the recovered product
Figure 310154DEST_PATH_IMAGE005
The reusability of (c); if in the period
Figure 691850DEST_PATH_IMAGE013
Selecting a vehicle category
Figure 270730DEST_PATH_IMAGE040
Then, then
Figure 160188DEST_PATH_IMAGE041
Is 1, otherwise
Figure 304862DEST_PATH_IMAGE041
Is 0;
Figure 683891DEST_PATH_IMAGE042
int32 maximum;
the method for solving the supply chain network design model by adopting the multi-target genetic algorithm and combining the variable neighborhood algorithm specifically comprises the following steps:
s31, initializing parameters and setting iteration times;
s32, generating an initial population by adopting a priority coding mode, wherein the size of each gene value in the coding sequence of an individual in the population is used for describing the priority of P products in the selection of a manufacturing plant, the selection of a candidate distribution center, the selection of a consumer market or the selection of a third-party recovery center, the price level of the products, the low-carbon level of the products and the types of vehicles;
s33, decoding the individuals in the population, and calculating the fitness value of the individuals in the population according to the fitness function;
s34, carrying out selection, crossover and mutation genetic operations, and updating the population to obtain a pareto optimal solution set;
s35, performing K-means clustering on the pareto optimal solution set to select
Figure 310919DEST_PATH_IMAGE043
A representative optimal solution;
s36, for the
Figure 472910DEST_PATH_IMAGE043
Local search is carried out on the representative optimal solution by using a variable neighborhood descent algorithm;
s37, judging whether the iteration times are reached, if so, stopping iteration and outputting a result, otherwise, returning to S33;
the carbon emission of the supply chain is the sum of the fixed carbon emission, the transport carbon emission and the production carbon emission;
fixed carbon emissions
Figure 940931DEST_PATH_IMAGE044
Wherein,
Figure 428545DEST_PATH_IMAGE045
to build candidate manufacturing plants
Figure 841071DEST_PATH_IMAGE027
Carbon emissions of (d);
Figure 562079DEST_PATH_IMAGE046
to build candidate distribution centers
Figure 681344DEST_PATH_IMAGE032
Carbon emissions of (d);
transport carbon emissionsTCThe expression of (a) is:
Figure 277542DEST_PATH_IMAGE047
Figure 380627DEST_PATH_IMAGE048
wherein,
Figure 382956DEST_PATH_IMAGE049
to select a vehicle
Figure 684624DEST_PATH_IMAGE040
Transporting products
Figure 514040DEST_PATH_IMAGE005
Unit carbon emission of (c);
carbon emission in productionPMThe expression of (a) is:
Figure 776525DEST_PATH_IMAGE050
wherein,
Figure 677485DEST_PATH_IMAGE051
is low carbon level grade
Figure 771343DEST_PATH_IMAGE007
The product of (1)
Figure 273125DEST_PATH_IMAGE005
At a manufacturing plant
Figure 22906DEST_PATH_IMAGE027
Unit carbon emissions of production of (a);
Figure 134082DEST_PATH_IMAGE052
is low carbon level grade
Figure 82446DEST_PATH_IMAGE007
The product of (1)
Figure 315982DEST_PATH_IMAGE005
At a manufacturing plant
Figure 113911DEST_PATH_IMAGE027
Remanufactured unit carbon emissions;
fixed cost
Figure 763198DEST_PATH_IMAGE053
；
Wherein if the candidate manufacturing plant
Figure 34911DEST_PATH_IMAGE027
Is located and operated, then
Figure 377030DEST_PATH_IMAGE054
Is 1, otherwise
Figure 960458DEST_PATH_IMAGE054
Is 0; if candidate distribution center
Figure 180480DEST_PATH_IMAGE032
Is located and operated, then
Figure 306699DEST_PATH_IMAGE055
Is 1, otherwise
Figure 554141DEST_PATH_IMAGE055
Is 0;
Figure 562548DEST_PATH_IMAGE056
to build candidate manufacturing plants
Figure 881534DEST_PATH_IMAGE027
Fixed cost of (2);
Figure 423112DEST_PATH_IMAGE057
to build candidate distribution centers
Figure 841455DEST_PATH_IMAGE032
Fixed cost of (2);
Figure 540421DEST_PATH_IMAGE058
is low carbon level grade
Figure 335201DEST_PATH_IMAGE007
The product of (1)
Figure 560646DEST_PATH_IMAGE005
The investment cost of the environmental protection technology is reduced;
cost of transportationTCThe expression of (a) is:
Figure 651355DEST_PATH_IMAGE059
Figure 837617DEST_PATH_IMAGE060
wherein,
Figure 170510DEST_PATH_IMAGE061
is a product
Figure 188144DEST_PATH_IMAGE005
In the period
Figure 276186DEST_PATH_IMAGE013
By means of a vehicle
Figure 245017DEST_PATH_IMAGE040
Slave facility
Figure 584862DEST_PATH_IMAGE062
Transport to a facility
Figure 457003DEST_PATH_IMAGE063
The number of the products of (a) is,
Figure 388050DEST_PATH_IMAGE064
;
Figure 407959DEST_PATH_IMAGE065
is provided withConstruction node
Figure 861417DEST_PATH_IMAGE062
And a facility node
Figure 791326DEST_PATH_IMAGE063
The distance of (a) to (b),
Figure 158854DEST_PATH_IMAGE066
，
Figure 338162DEST_PATH_IMAGE067
；
Figure 144444DEST_PATH_IMAGE068
to select a vehicle
Figure 224134DEST_PATH_IMAGE040
Transporting products
Figure 762562DEST_PATH_IMAGE005
Unit transportation cost of (a);
variable costACThe expression of (a) is:
Figure 366850DEST_PATH_IMAGE069
Figure 914506DEST_PATH_IMAGE070
wherein,
Figure 412484DEST_PATH_IMAGE071
is a product
Figure 888858DEST_PATH_IMAGE005
In the period
Figure 980442DEST_PATH_IMAGE013
At a manufacturing plant
Figure 66209DEST_PATH_IMAGE027
The amount of production;
Figure 356376DEST_PATH_IMAGE072
is a product
Figure 564504DEST_PATH_IMAGE005
In the period
Figure 704236DEST_PATH_IMAGE013
Manufacturing plant
Figure 531378DEST_PATH_IMAGE027
Unit production cost of (2);
Figure 941630DEST_PATH_IMAGE073
is period of
Figure 992763DEST_PATH_IMAGE013
At the end of the process, the product
Figure 183573DEST_PATH_IMAGE005
In a distribution center
Figure 456816DEST_PATH_IMAGE032
The inventory quantity of (2);
Figure 49471DEST_PATH_IMAGE074
is a product
Figure 271505DEST_PATH_IMAGE005
In a distribution center
Figure 589092DEST_PATH_IMAGE032
The unit warehousing cost of;
Figure 882670DEST_PATH_IMAGE075
is a product
Figure 1935DEST_PATH_IMAGE076
Unit stock out loss cost;
Figure 660450DEST_PATH_IMAGE077
is a product
Figure 435639DEST_PATH_IMAGE005
The unit recovery cost of (2);
Figure 440897DEST_PATH_IMAGE078
for recycled products
Figure 945828DEST_PATH_IMAGE005
At a manufacturing plant
Figure 837561DEST_PATH_IMAGE027
The unit of remanufacturing saves cost.
2. The method according to claim 1, wherein in step S31, initializing the parameters of the algorithm by using a response surface method specifically includes:
s311, determining algorithm parameters serving as control factors, including maximum iteration times, cross probability and variation probability; carrying out experiments according to the response surface experiment design to obtain algorithm performance indexes under each group of algorithm parameters;
s312, analyzing the functional relationship between the value of the algorithm parameter and the algorithm performance index by using a second-order polynomial model;
and S313, calculating extreme points according to the obtained functional relation between the algorithm parameters and the algorithm performance indexes, and determining the optimal parameter combination of the algorithm.
3. The method according to claim 1, wherein step S36 specifically comprises:
s361, giving an initial solution
Figure 162363DEST_PATH_IMAGE079
Let us order
Figure 673110DEST_PATH_IMAGE080
Definition of
Figure 360443DEST_PATH_IMAGE081
A neighborhood of
Figure 593716DEST_PATH_IMAGE082
(
Figure 405814DEST_PATH_IMAGE083
) Respectively is as follows: changing the product price or the low-carbon level grade, changing the selection of vehicles, and randomly exchanging the coding sequence of manufacturing plants in the individuals and the coding sequence of the individuals;
s362, according to the neighborhood structure
Figure 454673DEST_PATH_IMAGE084
To search for a solution when in
Figure 668616DEST_PATH_IMAGE084
In which a ratio is found
Figure 902152DEST_PATH_IMAGE079
Better solution
Figure 691292DEST_PATH_IMAGE085
When it is used, order
Figure 809421DEST_PATH_IMAGE086
,
Figure 612292DEST_PATH_IMAGE080
；
S363, if go throughAnterior neighborhood structure
Figure 16728DEST_PATH_IMAGE087
Can not be found
Figure 537839DEST_PATH_IMAGE079
Better solution, order
Figure 958194DEST_PATH_IMAGE088
；
S364, if
Figure 881151DEST_PATH_IMAGE089
Go to step S362, otherwise, output the optimal solution.
4. A multi-cycle supply chain network design system based on consumer preferences, comprising: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1-3.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108549234A (en) * 2018-05-11 2018-09-18 江南大学 A kind of multiobjective optimization control method based on dynamic variate
CN109711908A (en) * 2018-11-09 2019-05-03 上海大学 Modeling method, system and the electronic equipment of supply chain network
CN110619395A (en) * 2019-08-27 2019-12-27 武汉科技大学 Product configuration method for reusing recovered product
CN110852667A (en) * 2019-09-18 2020-02-28 浙江工商大学 Two-stage scheduling method for multi-period multi-product evanescent product supply chain network design
CN114548702A (en) * 2022-01-28 2022-05-27 中国科学技术大学 Supply chain modeling method and system based on platform operation mode and carbon transaction policy

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090177505A1 (en) * 2008-01-04 2009-07-09 Dietrich Brenda L Supply and Distribution Method and System Which Considers Environmental or "Green" Practices
WO2018185635A1 (en) * 2017-04-03 2018-10-11 Muthusamy Rajasekar Product chain based derivation of future product cost using cascading effect of the product chain
US20210350442A1 (en) * 2018-07-22 2021-11-11 Scott Amron Distributed inventory system
CN113935142A (en) * 2020-07-14 2022-01-14 武汉理工大学 Supply chain carbon emission reduction cost subsidy contract method based on low carbon preference of consumers
CN111882355A (en) * 2020-07-27 2020-11-03 哈尔滨商业大学 Analysis method of double-recovery-channel closed-loop supply chain
CN113674017A (en) * 2021-07-21 2021-11-19 扬州大学 Method and system for calculating and analyzing optimal proportion of cost sharing of closed-loop supply chain of household appliance
CN114154674A (en) * 2021-10-11 2022-03-08 浙江大学 Multi-stage optimization configuration method for product value chain in uncertain environment

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108549234A (en) * 2018-05-11 2018-09-18 江南大学 A kind of multiobjective optimization control method based on dynamic variate
CN109711908A (en) * 2018-11-09 2019-05-03 上海大学 Modeling method, system and the electronic equipment of supply chain network
CN110619395A (en) * 2019-08-27 2019-12-27 武汉科技大学 Product configuration method for reusing recovered product
CN110852667A (en) * 2019-09-18 2020-02-28 浙江工商大学 Two-stage scheduling method for multi-period multi-product evanescent product supply chain network design
CN114548702A (en) * 2022-01-28 2022-05-27 中国科学技术大学 Supply chain modeling method and system based on platform operation mode and carbon transaction policy

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A multi-period multi-product green supply network design problem with price and greenness dependent demands under uncertainty;Jian Wang,Qian Wan;《Applied Soft Computing》;20220131;第114卷;第1-17页 *
Multi-period planning of closed-loop supply chain with carbon policies under uncertainty;Fareeduddin Mohammed等;《Transportation Research Part D》;20170331;第51卷;第146-172页 *

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