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CN114708045A - Multi-cycle supply chain network design method and system based on consumer preference

Publication number

CN114708045A

CN114708045A CN202210618921.9A CN202210618921A CN114708045A CN 114708045 A CN114708045 A CN 114708045A CN 202210618921 A CN202210618921 A CN 202210618921A CN 114708045 A CN114708045 A CN 114708045A

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China

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product

supply chain

consumer

chain network

carbon

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2022-06-02

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技术领域technical field

本发明属于供应链网络设计领域，更具体地，涉及一种基于消费者偏好的多周期供应链网络设计方法及系统。The invention belongs to the field of supply chain network design, and more particularly relates to a multi-cycle supply chain network design method and system based on consumer preference.

背景技术Background technique

传统的供应链网络设计往往以最小化网络成本或者最大化网络利润为目标，然而，随着经济的飞速发展，资源消耗和环境破坏问题日益凸显，温室气体的排放是造成这些问题的主要原因之一。同时，消费者自身的环保意识也逐步提升，越来越多的人认可环境保护和可持续发展的概念，消费质量和消费结构随之发生改变。但现有的供应链网络设计方法往往只考虑了生产或运输过程中的碳排放，忽视了消费的需求变化，也并未综合考虑产品定价与环保技术投入的相关决策，无法保证供应链在现有市场的竞争力，导致企业需要投入更多的运营成本，难以实现总成本的最小化。The traditional supply chain network design is often aimed at minimizing network costs or maximizing network profits. However, with the rapid economic development, the problems of resource consumption and environmental damage have become increasingly prominent, and greenhouse gas emissions are the main cause of these problems. one. At the same time, consumers' own awareness of environmental protection has gradually improved, and more and more people recognize the concept of environmental protection and sustainable development, and the consumption quality and consumption structure have changed accordingly. However, the existing supply chain network design methods often only consider carbon emissions in the process of production or transportation, ignoring changes in consumption demand, and do not comprehensively consider decisions related to product pricing and environmental protection technology investment. With market competitiveness, enterprises need to invest more operating costs, and it is difficult to minimize the total cost.

发明内容SUMMARY OF THE INVENTION

针对现有技术的以上缺陷或改进需求，本发明提供了一种基于消费者偏好的多周期供应链网络设计方法及系统，由此解决基于现有的多周期供应链规划方法难以实现消费者偏好转变下的总成本最小化的问题。In view of the above defects or improvement needs of the prior art, the present invention provides a multi-cycle supply chain network design method and system based on consumer preference, thereby solving the difficulty in realizing consumer preference based on the existing multi-cycle supply chain planning method The problem of total cost minimization under transformation.

为实现上述目的，按照本发明的第一方面，提供了一种基于消费者偏好的多周期供应链网络设计方法，包括：In order to achieve the above object, according to the first aspect of the present invention, a multi-cycle supply chain network design method based on consumer preference is provided, including:

S1，以供应链利润最大化、供应链碳排放量最小化为优化目标，构建供应链网络设计模型；S1, build a supply chain network design model with the goal of maximizing supply chain profits and minimizing supply chain carbon emissions;

其中，所述供应链网络包括制造工厂、分销中心、消费者市场及第三方回收中心； 所述供应链利润为总销售额与固定成本、运输成本、可变成本的差值，总销售额

其中，

S2，确定所述供应链网络设计模型的约束条件，所述约束条件包括：产品分别在制造工厂及分销中心的数量均衡与容量限制约束，以及产品分别在消费者市场及第三方回收中心的数量均衡约束；S2, determine the constraints of the supply chain network design model, the constraints include: quantity equilibrium and capacity limit constraints of products in manufacturing plants and distribution centers, respectively, and the quantities of products in consumer markets and third-party recycling centers, respectively equilibrium constraints;

S3，求解所述供应链网络设计模型，得到基于消费者偏好的多周期供应链网络设计最优方案。S3: Solve the supply chain network design model to obtain an optimal solution for multi-cycle supply chain network design based on consumer preference.

按照本发明的第二方面，提供了一种基于消费者偏好的多周期供应链网络设计系统，包括：计算机可读存储介质和处理器；According to a second aspect of the present invention, there is provided a multi-cycle supply chain network design system based on consumer preference, comprising: a computer-readable storage medium and a processor;

所述计算机可读存储介质用于存储可执行指令；the computer-readable storage medium for storing executable instructions;

所述处理器用于读取所述计算机可读存储介质中存储的可执行指令，执行如第一方面所述的方法。The processor is configured to read the executable instructions stored in the computer-readable storage medium, and execute the method according to the first aspect.

总体而言，通过本发明所构思的以上技术方案与现有技术相比，能够取得下列有益效果：In general, compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

1、本发明提供的基于消费者偏好的多周期供应链网络设计方法及系统，考虑消费者偏好的绿色供应链，基于消费者偏好的绿色供应链网络设计方法展开研究，考虑产品的回收和再制造，用各环节的碳排放量作为环境目标，并结合消费者需求偏好的改变，同时优化供应链的利润与碳排放目标，为企业在消费者需求影响下的多周期绿色供应链网络设计提供参考，能够让企业应对不同消费者偏好影响下的市场；针对消费者需求转变的现状，考虑了消费者的低价偏好与低碳偏好对产品市场需求量的影响，以分段函数刻画了产品市场需求与产品价格和低碳水平的关系；同时，将产品定价与产品环保技术投入作为决策变量纳入到绿色供应链网络设计中，更加贴近实际市场状况，从而实现供应链网络在建设及销售、回收等运营过程中所需总成本的最小化，为企业的供应链管理提供更有效的决策。1. The multi-cycle supply chain network design method and system based on consumer preference provided by the present invention, consider the green supply chain preferred by consumers, conduct research on the network design method of green supply chain based on consumer preference, and consider the recycling and reuse of products. Manufacturing, using the carbon emissions of each link as the environmental goal, combined with the changes in consumer demand preferences, and optimizing the profit and carbon emissions goals of the supply chain, providing enterprises with multi-cycle green supply chain network design under the influence of consumer demand. For reference, it can allow enterprises to deal with the market under the influence of different consumer preferences; according to the current situation of consumer demand change, considering the impact of consumers' low-price preference and low-carbon preference on product market demand, the product is characterized by a piecewise function The relationship between market demand, product price and low-carbon level; at the same time, product pricing and product environmental protection technology input are included as decision variables into the design of green supply chain network, which is closer to the actual market situation, so as to realize the construction and sales of the supply chain network. The minimization of the total cost required in the operation process such as recycling provides more effective decision-making for the enterprise's supply chain management.

2、本发明提供的基于消费者偏好的多周期供应链网络设计方法及系统，在求解建立的复杂的多周期绿色供应链网络规划模型时，将多目标遗传算法和变邻域下降算法结合，并在每次迭代中引入K-means聚类算法来筛选帕累托前沿上的代表性解，提高了算法的局部寻优能力和计算效率，随着算法的局部寻优能力的提升，算法的整体寻优能力也得到提升，从而使模型的计算精度得到提高。2. The multi-cycle supply chain network design method and system based on consumer preference provided by the present invention combine multi-objective genetic algorithm and variable neighborhood descent algorithm when solving the established complex multi-cycle green supply chain network planning model. In each iteration, K-means clustering algorithm is introduced to filter the representative solutions on the Pareto frontier, which improves the local optimization ability and computational efficiency of the algorithm. The overall optimization ability is also improved, so that the computational accuracy of the model is improved.

附图说明Description of drawings

图1为本发明实施例提供的基于消费者偏好的多周期供应链网络设计方法流程示意图。FIG. 1 is a schematic flowchart of a method for designing a multi-cycle supply chain network based on consumer preference provided by an embodiment of the present invention.

图2为本发明实施例提供的多周期供应链网络结构图。FIG. 2 is a structural diagram of a multi-cycle supply chain network provided by an embodiment of the present invention.

图3为本发明实施例提供的结合多目标遗传算法与变邻域算法求解供应链网络设计模型的流程图。FIG. 3 is a flowchart of solving a supply chain network design model by combining a multi-objective genetic algorithm and a variable neighborhood algorithm according to an embodiment of the present invention.

图4为本发明实施例提供的结合多目标遗传算法与变邻域算法求解供应链网络设计模型的收敛图之一。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 an embodiment of the present invention.

图5为本发明实施例提供的结合多目标遗传算法与变邻域算法求解供应链网络设计模型的收敛图之二。FIG. 5 is the 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 an embodiment of the present invention.

具体实施方式Detailed ways

为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

采用传统的供应链网络设计方法得到的供应链网络忽视了消费者的需求偏好转变，其产品在市场中逐渐失去竞争优势，易出现缺货或产能过剩的情况，难以实现供应链网络在建设及销售、回收等运营过程中所需总成本的最小化，对此，本发明实施例提供一种考虑消费者偏好的绿色闭环供应链网络设计方法，如图1所示，包括：The supply chain network obtained by the traditional supply chain network design method ignores the change of consumer demand preferences, and its products gradually lose their competitive advantages in the market, and are prone to shortages or excess capacity. To minimize the total cost required in the operation process of sales, recycling, etc., the embodiment of the present invention provides a green closed-loop supply chain network design method considering consumer preference, as shown in FIG. 1, including:

S1，以供应链利润最大化、供应链碳排放量最小化为优化目标，构建供应链网络设计模型；S1, build a supply chain network design model with the goal of maximizing supply chain profits and minimizing supply chain carbon emissions;

其中，所述供应链网络包括制造工厂、分销中心、消费者市场及第三方回收中心； 所述供应链利润为总销售额与固定成本、运输成本、可变成本的差值，总销售额

其中，

如图2所示，绿色供应链网络由制造工厂I、分销中心J、消费者市场K和第三方回收中心M构成。制造工厂负责将外部提供的以及第三方回收商回收到的原材料生产为相应的产品。分销中心负责将制造工厂生产的产品运输到各消费者市场进行销售，同时分销中心具备一定的仓储能力，在其容量允许范围内，可以将产品存储下一周期再运输销售。第三方回收点负责回收消费者市场上的可回收产品，在检测后将可再利用的部分产品运输到制造工厂进行再制造。As shown in Figure 2, the green supply chain network consists of manufacturing plant I, distribution center J, consumer market K, and third-party recycling center M. The manufacturing plant is responsible for producing the corresponding products from the raw materials provided externally and recovered by third-party recyclers. The distribution center is responsible for transporting the products produced by the manufacturing plant to various consumer markets for sale. At the same time, the distribution center has a certain storage capacity. Within the allowable range of its capacity, the products can be stored for the next cycle and then transported and sold. Third-party recycling points are responsible for recycling recyclable products on the consumer market, and after testing, transport some of the reusable products to manufacturing plants for remanufacturing.

确定消费者偏好影响下产品需求量与产品属性的关系；所述消费者偏好包括：消费者低价偏好和消费者低碳偏好；所述产品属性包括：产品价格和产品低碳水平。其中，所述产品低碳水平由产品在生产和运输过程中的碳排放量决定。Determine the relationship between product demand and product attributes under the influence of consumer preferences; the consumer preferences include: consumer low-price preference and consumer low-carbon preference; the product attributes include: product price and product low-carbon level. Wherein, the low carbon level of the product is determined by the carbon emission of the product during production and transportation.

计算消费者偏好影响下产品需求量与产品属性的关系的具体步骤为：The specific steps to calculate the relationship between product demand and product attributes under the influence of consumer preference are:

（1）确定产品信息与市场上的消费者信息；所述产品信息包括产品价格区间和产品单位碳排放区间；所述市场上的消费者信息包括低价偏好消费者比例和低碳偏好消费者比例；(1) Determine product information and consumer information in the market; the product information includes the product price range and the product unit carbon emission range; the consumer information in the market includes the proportion of consumers who prefer low prices and consumers who prefer low carbon Proportion;

其中，低价偏好消费者为对产品价格敏感的消费者；低碳偏好消费者为对产品低碳水平敏感的消费者；Among them, low-price preference consumers are those who are sensitive to product prices; low-carbon preference consumers are consumers who are sensitive to the low carbon level of products;

（2）推导产品价格和产品低碳水平与产品需求量的函数关系；(2) Derive the functional relationship between product price and product low-carbon level and product demand;

其中，

（3）产品需求量函数离散化；(3) Discretization of product demand function;

其中，

以供应链利润最大化和碳排放最小化为优化目标，结合消费者偏好影响下产品需求量与产品属性的关系，构建有关制造工厂、分销中心、消费者市场和第三方回收中心的四层网络结构的供应链网络设计模型，并设立模型约束。With the optimization goals of maximizing profits in the supply chain and minimizing carbon emissions, combined with the relationship between product demand and product attributes under the influence of consumer preferences, build a four-layer network of manufacturing plants, distribution centers, consumer markets and third-party recycling centers Structural supply chain network design model and establish model constraints.

（4）构建利润目标函数

利润目标函数

其中，

优选地，固定成本

其中，如果候选制造工厂被选址并运营，则

运输成本TC的表达式为：The expression of transportation cost TC is:

其中，

可变成本AC的表达式为：The expression for variable cost AC is:

其中，

优选地，供应链碳排放量为固定碳排放量、运输碳排放量及生产碳排放量之和。Preferably, the carbon emissions of the supply chain are the sum of fixed carbon emissions, transportation carbon emissions and production carbon emissions.

也即，碳排放目标函数

其中，

其中，

运输碳排放量TC的表达式为：The expression of transportation carbon emissions TC is:

其中，

生产碳排放量PM的表达式为：The expression of production carbon emission PM is:

其中，

S2，确定所述供应链网络设计模型的约束条件，所述约束条件包括：产品分别在制造工厂及分销中心的数量均衡与容量限制约束，以及产品分别在消费者市场及第三方回收中心的数量均衡约束。S2, determine the constraints of the supply chain network design model, the constraints include: quantity equilibrium and capacity limit constraints of products in manufacturing plants and distribution centers, respectively, and the quantities of products in consumer markets and third-party recycling centers, respectively Equilibrium constraints.

具体地，设立模型约束：Specifically, set up model constraints:

为了满足产品在制造工厂的数量均衡与容量限制，定义约束如下：In order to meet the quantity balance and capacity constraints of products in the manufacturing plant, the constraints are defined as follows:

其中，

为了满足产品在分销中心的数量均衡与容量限制，定义约束如下：In order to meet the quantity balance and capacity constraints of products in the distribution center, the constraints are defined as follows:

其中，

为了保证产品在消费者市场的数量均衡，定义约束如下：In order to ensure the quantity equilibrium of the product in the consumer market, the constraints are defined as follows:

其中，

为了保证产品在第三方回收中心的数量均衡，定义约束如下：In order to ensure a balanced quantity of products in third-party recycling centers, the constraints are defined as follows:

其中，

相关决策变量的范围约束如下：The range constraints for the relevant decision variables are as follows:

S3，求解所述供应链网络设计模型，得到基于消费者偏好的多周期供应链网络设计最优方案。S3: Solve the supply chain network design model to obtain an optimal solution for multi-cycle supply chain network design based on consumer preference.

具体地，输出多周期供应链网络设计的最优方案集合，确定各方案下供应链的设施选址、产品定价、环保技术投入、产品生产、流量分配、库存管理与交通工具选择的结果，企业管理者可根据其实际规划从方案集合中选取方案。Specifically, output a set of optimal solutions for multi-cycle supply chain network design, and determine the results of facility location, product pricing, environmental protection technology investment, product production, flow distribution, inventory management, and vehicle selection in the supply chain under each solution. Managers can select a plan from the set of plans according to their actual plan.

由于多周期供应链网络设计的决策变量多，存在多个优化目标，且模型复杂，现有的优化方法求解速度慢，无法在大规模实例下快速求解；而常规的启发式算法在求解时无法保证得到高质量和高精度的求解方案，同样难以实现成本最小化。对此，优选地，采用多目标遗传算法结合变邻域算法求解所述供应链网络设计模型，如图2-3所示，具体包括：Due to the large number of decision variables in multi-cycle supply chain network design, the existence of multiple optimization objectives, and the complex model, the existing optimization methods are slow to solve and cannot be quickly solved in large-scale instances; and conventional heuristic algorithms cannot solve the problem. Guaranteed to obtain high-quality and high-precision solutions, it is also difficult to achieve cost minimization. In this regard, preferably, the multi-objective genetic algorithm combined with the variable neighborhood algorithm is used to solve the supply chain network design model, as shown in Figure 2-3, which specifically includes:

S31，参数初始化，设定迭代次数。S31, parameter initialization, setting the number of iterations.

具体地，对算法的参数进行初始化，并设定算法终止条件。Specifically, the parameters of the algorithm are initialized, and the algorithm termination condition is set.

S32，采用优先级编码方式产生初始种群，种群中个体的编码由五部分组成：第一部分定义制造工厂提供的产品（P）的优先级，第二部分、第三部分和第四部分分别表示候选分销中心（J），消费者市场（K）以及第三方回收中心（M）的优先级顺序，第五部分定义产品价格等级、产品低碳水平等级以及交通工具种类；编码序列中每一位的基因值的大小用来描述P种产品在制造工厂选择、候选分销中心选择、消费者市场选择或第三方回收中心选择的优先级以及产品价格等级、产品低碳水平等级以及交通工具种类。S32, using the priority coding method to generate the initial population, the coding of the individuals in the population consists of five parts: the first part defines the priority of the product (P) provided by the manufacturing plant, the second part, the third part and the fourth part respectively represent the candidate The priority order of distribution center (J), consumer market (K) and third-party recycling center (M), the fifth part defines product price level, product low carbon level level and vehicle type; the number of each bit in the coding sequence The size of the gene value is used to describe the priority of P products in the selection of manufacturing plants, candidate distribution centers, consumer markets or third-party recycling centers, as well as product price levels, product low-carbon level levels, and vehicle types.

其中，个体的编码序列为T行n列矩阵，T为总周期数，n列矩阵包括五个部分，前四个部分的列数即为制造工厂、分销中心、消费者市场及第三方回收中心的数量，第五部分包括3列，分别为产品价格等级、产品低碳水平等级以及交通工具种类。Among them, the coding sequence of the individual is a matrix of T rows and n columns, T is the total number of cycles, the n-column matrix includes five parts, and the columns of the first four parts are the manufacturing plants, distribution centers, consumer markets and third-party recycling centers. The fifth part includes 3 columns, which are the product price level, the product low carbon level level and the type of means of transportation.

具体地，种群中个体的编码包含选址信息、产品定价信息、产品低碳水平信息、流量分配信息和库存信息等。Specifically, the coding of individuals in the population includes location information, product pricing information, product low-carbon level information, flow distribution information, and inventory information.

相应地，当采用多目标遗传算法结合变邻域算法求解所述供应链网络设计模型 时，步骤S3中对基于上述算法对模型求解得到的编码结果进行解码以获取方案的过程包 括，按照周期顺序分别进行解码：首先对第一周期的第五部分编码进行解码，明确产品价格 等级、低碳水平等级和运输时的交通工具种类。接着，根据第一部分编码中的最高优先级及 产品生产的优先顺序，结合工厂的生产成本函数

S33，对种群中个体进行解码，并根据适应度函数，计算种群中个体适应度值；S33, decode the individuals in the population, and calculate the fitness value of the individuals in the population according to the fitness function;

S34，进行选择、交叉及变异等遗传操作，对种群更新，得到帕累托最优解集；S34, perform genetic operations such as selection, crossover and mutation, update the population, and obtain a Pareto optimal solution set;

其中，帕累托最优解集指多目标优化中所有帕累托最优解组成的集合，帕累托最优解指不被解空间中任一解支配的解。Among them, the Pareto optimal solution set refers to the set composed of all Pareto optimal solutions in the multi-objective optimization, and the Pareto optimal solution refers to the solutions that are not dominated by any solution in the solution space.

S35，对所述帕累托最优解集进行K-means聚类，选出

S36，对所述

S37，判断是否达到迭代次数，若是，停止迭代，输出结果，若否，返回S33。S37, determine whether the number of iterations has been reached, if so, stop the iteration, and output the result, if not, return to S33.

也即，判断是否达到终止条件，若是，停止迭代，输出结果，若否，转步骤S33。That is, it is judged whether the termination condition is reached, if so, the iteration is stopped and the result is output, if not, go to step S33.

优选地，步骤S31中，采用响应曲面法对算法的参数进行初始化，具体包括：Preferably, in step S31, the parameters of the algorithm are initialized by using the response surface method, which specifically includes:

S311，确定作为控制因子的算法参数，包括最大迭代次数，交叉概率和变异概率；选择将采用的响应曲面实验设计（如中心复合设计或Box-Behnken设计），如中心复合设计或Box-Behnken设计；根据实验设计进行实验，获取各组算法参数下的算法性能指标（例如：超体积、平均理想距离、最大分散度等）；S311, determine the algorithm parameters as control factors, including the maximum number of iterations, crossover probability and mutation probability; select the response surface experimental design (such as central composite design or Box-Behnken design) to be used, such as central composite design or Box-Behnken design ; Carry out experiments according to the experimental design to obtain algorithm performance indicators under each group of algorithm parameters (for example: hypervolume, average ideal distance, maximum dispersion, etc.);

S312，用二阶多项式模型来分析算法参数的取值和算法性能指标之间的函数关系；S312, use a second-order polynomial model to analyze the functional relationship between the values of the algorithm parameters and the performance indicators of the algorithm;

S313，根据得到的算法参数与算法性能指标的函数关系，计算极值点，确定算法的最优参数组合。S313, according to the obtained functional relationship between the algorithm parameters and the algorithm performance index, calculate the extreme point, and determine the optimal parameter combination of the algorithm.

优选地，步骤S36具体包括：Preferably, step S36 specifically includes:

S361，给定初始解

S362，根据邻域结构来对解搜索，当在

S363，若遍历当前邻域结构

S364，若

采用本发明实施例提供的结合多目标遗传算法与变邻域算法求解供应链网络设计模型的收敛情况如图4-5所示，可以看出，随着迭代次数的增加，逐步收敛，最后趋于稳定。Figure 4-5 shows the convergence situation of solving the supply chain network design model using the combination of the multi-objective genetic algorithm and the variable neighborhood algorithm provided by the embodiment of the present invention. It can be seen that with the increase of the number of iterations, it gradually converges and finally tends to in stability.

本发明实施例提供一种基于消费者偏好的多周期供应链网络设计系统，包括：计算机可读存储介质和处理器；Embodiments of the present invention provide a multi-cycle supply chain network design system based on consumer preference, including: a computer-readable storage medium and a processor;

所述计算机可读存储介质用于存储可执行指令；the computer-readable storage medium for storing executable instructions;

所述处理器用于读取所述计算机可读存储介质中存储的可执行指令，执行如上述任一实施例所述的方法。The processor is configured to read the executable instructions stored in the computer-readable storage medium, and execute the method described in any of the foregoing embodiments.

本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.

1. A method for designing a multi-cycle supply chain network 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,total sales volume

；

Wherein,

respectively a consumer market number set, a product type number set and a cycle number set,

respectively corresponding indexes;

is a product

At the price level

And low carbon level rating

Price of hour, if product

Selecting price classes

And low carbon level rating

Then, then

Is 1, otherwise

Is 0;

is period of

Product(s)

In the consumer market

The number of out-of-stock items,

is period of

Bottom product

At the price level

And low carbon level rating

Is in the consumer market

The required amount of (a) to be used,

，

is the product price grade

The corresponding price is set to the corresponding price,

low carbon level grade for the product

The corresponding low carbon level is achieved by the method,

is period of

The 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 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;

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.

2. The method of claim 1, wherein the method further comprises the step of applying a voltage to the substrateFixed cost of

；

Wherein if the candidate manufacturing plant

Is located and operated, then

Is 1, otherwise

Is 0; if candidate distribution center

Is located and operated, then

Is 1, otherwise

Is 0;

to build candidate manufacturing plants

Fixed cost of (2);

to build candidate distribution centers

Fixed cost of (2);

is low carbon level grade

The product of (1)

The investment cost of the environmental protection technology is reduced;

cost of transportationTCThe expression of (a) is:

wherein,

is a product

In the period

By means of a vehicle

Slave facility

Transport to a facility

The amount of the product(s) of (c),

;

as a facility node

And a facility node

The distance of (a) to (b),

，

；

to select a vehicle

Transporting products

Unit transportation cost of (a);

variable costACThe expression of (a) is:

wherein,

is a product

In the period

At a manufacturing plant

The amount of production;

is a product

In the period

Manufacturing plant

Unit production cost of (2);

is period of

At the end of the run, the product

In a distribution center

The inventory quantity of (c);

is a product

In a distribution center

The unit warehousing cost of;

is a product

Unit stock out loss cost;

is a product

The unit recovery cost of (2);

for the recovered product

At a manufacturing plant

The unit of remanufacturing saves cost.

3. The method of claim 1 or 2, wherein the supply chain carbon emissions are the sum of fixed carbon emissions, transport carbon emissions, and production carbon emissions;

fixed carbon emissions

Wherein,

to build candidate manufacturing plants

Carbon emissions of (d);

for buildingSorting pin center

Carbon emissions of (d);

transport carbon emissionsTCThe expression of (a) is:

wherein,

to select a vehicle

Transporting products

Unit carbon emission of (c);

carbon emission in productionPMThe expression of (c) is:

wherein,

is low carbon level grade

The product of (1)

At a manufacturing plant

Unit carbon emissions of production of (a);

is low carbon level grade

The product of (1)

At a manufacturing plant

Remanufactured unit carbon emissions.

4. The method of claim 1, wherein the quantity balance and capacity limit constraints of the product at the manufacturing facility are:

wherein,

is a product

The storage coefficient of (2);

for candidate manufacturing plants

Of the facility；

The quantity balance and capacity limit constraints of the product at the distribution center are:

wherein,

as candidate distribution centers

The facility capacity of (a);

the quantity balance constraint of products in the consumer market is:

wherein,

is a product

In the consumer market

The recovery rate is lower;

the quantity balance constraint of the product in the third-party recycling center is as follows:

wherein,

for recycled products

The reusability of (c); if in the period

Selecting a vehicle class

Then, then

Is 1, otherwise

Is 0;

is int32 maximum.

5. The method of claim 1, wherein solving the supply chain network design model using a multi-objective genetic algorithm in combination with a variable neighborhood algorithm comprises:

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 genetic operations such as selection, crossing and mutation, and updating the population to obtain a pareto optimal solution set;

s35, performing K-means clustering on the pareto optimal solution set to select

A representative optimal solution;

s36, for the

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.

6. The method according to claim 5, wherein in step S31, initializing the parameters of the algorithm by using a response surface method specifically comprises:

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 a functional relation 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.

7. The method according to claim 5, wherein step S36 specifically comprises:

s361, giving an initial solution

Let us order

Definition of

A neighborhood of

(

) Respectively 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

To search for the solution when in

In a ratio found in

Better solution

When it is used, order

,

；

S363, if the current neighborhood structure is traversed

Can not be found

Better solution, order

；

S364, if

Go to step S362, otherwise, output the optimal solution.

8. 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 1-7.

• 2022
  • 2022-06-02 CN CN202210618921.9A patent/CN114708045B/en active Active

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