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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- product
- supply chain
- consumer
- chain network
- algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013461 design Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 38
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 88
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 79
- 230000007613 environmental effect Effects 0.000 claims abstract description 11
- 238000011084 recovery Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims abstract description 8
- 238000005516 engineering process Methods 0.000 claims abstract description 6
- 238000004519 manufacturing process Methods 0.000 claims description 58
- 238000009826 distribution Methods 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 12
- 230000002068 genetic effect Effects 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 10
- 108091026890 Coding region Proteins 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 4
- 238000002474 experimental method Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 2
- 108090000623 proteins and genes Proteins 0.000 claims description 2
- 230000007704 transition Effects 0.000 abstract description 4
- 230000008859 change Effects 0.000 abstract description 3
- 238000010276 construction Methods 0.000 abstract description 3
- 238000013068 supply chain management Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000013439 planning Methods 0.000 description 3
- 238000004064 recycling Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000013401 experimental design Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/30—Administration of product recycling or disposal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- Finance (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Biophysics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Quality & Reliability (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Sustainable Development (AREA)
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
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;
Wherein,respectively a consumer market number set, a product type number set and a cycle number set,respectively corresponding indexes;is a productAt the price levelAnd low carbon level ratingPrice of hour, if productSelecting price classesAnd low carbon level ratingThen, thenIs 1, otherwiseIs 0;is period ofProduct(s)In the consumer marketThe number of out-of-stock items,is a period ofBottom productAt the price levelAnd low carbon level ratingIs in the consumer marketThe required amount of (a) to be used,,is the product price gradeThe corresponding price is set to the corresponding price,low carbon level grade for the productThe corresponding low carbon level is achieved by the method,is a period oftThe largest size of the consumer market is,a market proportion that favors consumers for low prices;an upper threshold value for product prices approved by the consumer;a lower threshold value for product prices approved by the consumer;an upper threshold for a low carbon level approved by the consumer;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;
Wherein,numbering a set for the consumer market, indexed by;For a product category set, index is(ii) a For a periodically numbered set, the index is(ii) a If the product isSelecting price classesAnd low carbon level ratingThen, thenIs 1, otherwiseIs 0;is period ofProduct(s)In the consumer marketThe number of out-of-stock items,is period ofBottom productAt the price levelAnd low carbon level ratingHour in the consumer marketThe required amount of (a) to be used,,is the product price gradelThe corresponding price is set to the corresponding price,low carbon level grade for the producteThe corresponding low carbon level is achieved by the method,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;
wherein,is the product demand;a market proportion that favors consumers for low prices;an upper threshold value for product prices approved by the consumer;a lower threshold value for product prices approved by the consumer;an upper threshold for a low carbon level approved by the consumer;a lower threshold of low carbon level approved by the consumer;for the price of the product, the range is;The product is low-carbon level in the range,Is the largest size of the consumer market;
(3) discretizing a product demand function;
wherein,as a product price ratingAnd low carbon level grade of the productThe demand for the next;as a price gradeA corresponding price;low carbon level grade for the productCorresponding 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.
Wherein if the candidate manufacturing plant is located and operatedIs 1, otherwiseIs 0; if candidate distribution centerIs located and operated, thenIs 1, otherwiseIs 0;to build candidate manufacturing plantsFixed cost of (2);to build candidate distribution centersFixed cost of (2);is low carbon level gradeThe product of (1)The investment cost of the environmental protection technology is reduced;
transportation costTCThe expression of (a) is:
wherein,is a productIn the periodBy means of a vehicleSlave facilityTransport to a facilityThe number of the products of (a) is,;as a facility nodeAnd a facility nodeThe distance of (a) to (b),,;to select a vehicleTransporting productsUnit transportation cost of (a);
variable costACThe expression of (a) is:
wherein,is a productIn the period ofAt a manufacturing plantThe amount of production;is a productIn the periodManufacturing plantUnit production cost of (2);is period ofAt the end of the run, the productThe number of stocks in the distribution center;is a productIn a distribution centerThe unit warehousing cost of (a);is a productUnit stock out loss cost;is a productThe unit recovery cost of (2);for the recovered productAt a manufacturing plantThe 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.
wherein,to build candidate manufacturing plantsCarbon emissions of (c);to build candidate distribution centersCarbon emissions of (d);
transport carbon emissionsTCThe expression of (a) is:
carbon emission in productionPMThe expression of (a) is:
wherein,is low carbon level gradeThe product of (1)At a manufacturing plantUnit carbon emissions of production of (a);is low carbon level gradeThe product of (1)At a manufacturing plantRemanufactured 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:
wherein,is a productThe storage coefficient of (a);for candidate manufacturing plantsThe facility capacity of (a);
to meet the quantity balance and capacity limitation of products in distribution centers, the constraints are defined as follows:
to ensure a balanced number of products in the consumer market, the constraints are defined as follows:
to ensure a balanced number of products in a third party recycling center, constraints are defined as follows:
wherein,for recycled productsThe reusability of (c); if in the periodSelecting a vehicle classThen, thenIs 1, otherwiseIs 0;is an extremely large number, e.g., int32 maximum: 2147483647.
the range of the relevant decision variables is constrained as follows:
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 factoryThe 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 sequenceAnd 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 valueAnd 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 functionAnd 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 selectA representative optimal solution;
s36, for theLocal 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 solutionLet us orderDefinition ofA neighborhood of() 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 structureIn which a ratio is foundBetter solutionWhen it is used, make,;
S363, if the current neighborhood structure is traversedCan not be found as beforeBetter solution, order;
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;
Wherein,respectively a consumer market number set, a product type number set and a cycle number set,are respectively corresponding indexes;is a productAt the price levelAnd low carbon level ratingPrice of hour, if productSelecting price classesAnd low carbon level ratingThen, thenIs 1, otherwiseIs 0;is period ofProduct(s)In the consumer marketThe number of out-of-stock items,is period ofBottom productAt the price levelAnd low carbon level ratingHour in the consumer marketThe amount of the required amount of (c) is,,is the product price gradeThe corresponding price is set to the corresponding price,low carbon level grade for productThe corresponding low carbon level is achieved by the method,is period oftThe largest size of the consumer market is,a market proportion that favors consumers for low prices;an upper threshold value for product prices approved by the consumer;a lower threshold value for product prices approved by the consumer;an upper threshold for a low carbon level approved by the consumer;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:
wherein,is a productThe storage coefficient of (2);for candidate manufacturing plantsThe facility capacity of (a);
the quantity balance and capacity limit constraints of the product at the distribution center are:
the quantity balance constraint of products in the consumer market is:
the quantity balance constraint of the product in the third-party recycling center is as follows:
wherein,for the recovered productThe reusability of (c); if in the periodSelecting a vehicle categoryThen, thenIs 1, otherwiseIs 0;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 selectA representative optimal solution;
s36, for theLocal 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;
Wherein,to build candidate manufacturing plantsCarbon emissions of (d);to build candidate distribution centersCarbon emissions of (d);
transport carbon emissionsTCThe expression of (a) is:
carbon emission in productionPMThe expression of (a) is:
wherein,is low carbon level gradeThe product of (1)At a manufacturing plantUnit carbon emissions of production of (a);is low carbon level gradeThe product of (1)At a manufacturing plantRemanufactured unit carbon emissions;
Wherein if the candidate manufacturing plantIs located and operated, thenIs 1, otherwiseIs 0; if candidate distribution centerIs located and operated, thenIs 1, otherwiseIs 0;to build candidate manufacturing plantsFixed cost of (2);to build candidate distribution centersFixed cost of (2);is low carbon level gradeThe product of (1)The investment cost of the environmental protection technology is reduced;
cost of transportationTCThe expression of (a) is:
wherein,is a productIn the periodBy means of a vehicleSlave facilityTransport to a facilityThe number of the products of (a) is,;is provided withConstruction nodeAnd a facility nodeThe distance of (a) to (b),,;to select a vehicleTransporting productsUnit transportation cost of (a);
variable costACThe expression of (a) is:
wherein,is a productIn the periodAt a manufacturing plantThe amount of production;is a productIn the periodManufacturing plantUnit production cost of (2);is period ofAt the end of the process, the productIn a distribution centerThe inventory quantity of (2);is a productIn a distribution centerThe unit warehousing cost of;is a productUnit stock out loss cost;is a productThe unit recovery cost of (2);for recycled productsAt a manufacturing plantThe 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 solutionLet us orderDefinition ofA neighborhood of () 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 structureTo search for a solution when inIn which a ratio is foundBetter solutionWhen it is used, order, ;
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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210618921.9A CN114708045B (en) | 2022-06-02 | 2022-06-02 | Multi-cycle supply chain network design method and system based on consumer preference |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210618921.9A CN114708045B (en) | 2022-06-02 | 2022-06-02 | Multi-cycle supply chain network design method and system based on consumer preference |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114708045A CN114708045A (en) | 2022-07-05 |
CN114708045B true CN114708045B (en) | 2022-09-13 |
Family
ID=82177564
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210618921.9A Active CN114708045B (en) | 2022-06-02 | 2022-06-02 | Multi-cycle supply chain network design method and system based on consumer preference |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114708045B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116258419B (en) * | 2023-05-10 | 2023-09-08 | 吉林大学 | Method for discriminating hierarchical carbon reduction paths of automobile supply chain based on structural hole theory |
Citations (5)
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)
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 |
-
2022
- 2022-06-02 CN CN202210618921.9A patent/CN114708045B/en active Active
Patent Citations (5)
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)
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 * |
Also Published As
Publication number | Publication date |
---|---|
CN114708045A (en) | 2022-07-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kadambala et al. | Closed loop supply chain networks: Designs for energy and time value efficiency | |
Mohammed et al. | A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks | |
Kannan et al. | A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling | |
Afrouzy et al. | A genetic algorithm for supply chain configuration with new product development | |
Lee et al. | A multi-objective hybrid genetic algorithm to minimize the total cost and delivery tardiness in a reverse logistics | |
Aksoy et al. | Dynamic strategic supplier selection system with fuzzy logic | |
CN114708045B (en) | Multi-cycle supply chain network design method and system based on consumer preference | |
Guo et al. | Intelligent optimization for project scheduling of the first mining face in coal mining | |
Karimi-Nasab et al. | A random search heuristic for a multi-objective production planning | |
Karimi et al. | Multi-objective multi-facility green manufacturing closed-loop supply chain under uncertain environment | |
CN115495859B (en) | Warehouse net planning method based on genetic algorithm | |
Zhang et al. | Production mode and pricing coordination strategy of sustainable products considering consumers’ preference | |
Wang et al. | Optimal operation and subsidies/penalties strategies of a multi-period hybrid system with uncertain return under cap-and-trade policy | |
Király et al. | Minimization of off-grade production in multi-site multi-product plants by solving multiple traveling salesman problem | |
Lam et al. | A hybrid case-GA-based decision support model for warehouse operation in fulfilling cross-border orders | |
Yuan et al. | How Government Regulations and Consumer Behavior Influence Manufacturers’ Product Green Degree Decision‐Making: An Agent‐Based Model | |
Afshar et al. | Developing multi-objective mathematical model of sustainable multi-commodity, multi-level closed-loop supply chain network considering disruption risk under uncertainty | |
Zhou et al. | [Retracted] Research on Intelligent Solution of Service Industry Supply Chain Network Optimization Based on Genetic Algorithm | |
Chen et al. | [Retracted] A Supply Chain Model Based on Data‐Driven Demand Uncertainty under the Influence of Carbon Tax Policy | |
CN109919688B (en) | Electronic cigarette product line planning method considering market factors | |
Mehri Charvadeh et al. | Presenting a Management Model for a Multiobjective Sustainable Supply Chain in the Cellulosic Industry and Its Implementation by the NSGA‐II Meta‐Heuristic Algorithm | |
Wang et al. | Self-remanufacturing or outsourcing? Hybrid manufacturing system with remanufacturing options under yield uncertainty | |
CN111078203A (en) | 4M1T innovation system supporting incubation of creators and small micro-enterprises | |
CN113052418A (en) | Production planning method and system based on multi-dimensional data structure | |
CN111563659B (en) | Multi-ant colony system-based multi-target supply chain configuration method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |