基于食品药品供应链质量协同的社会共治实现机制

食品药品安全社会共治本质上是一项制度安排的体制变革。在社会共治中,如何解决好多主体参与的个体理性与集体理性之间的矛盾是一个难题。本文根据文献研究元分析方法,从供应链质量协同视角考察食品药品安全社会共治的实现机制,由此探讨食品药品供应链质量协同中的协同契约设计与全供应链信息披露机制,认为不同权力结构下多主体协同管理需求及其激励机制设计,是构建食品药品供应链质量协同契约设计的前提。同时,建设基于云计算环境的食品药品安全社会共治信息共享机制,是构建食品药品供应链质量协同的运作环境,这是推动食品药品供应链质量协同社会共治制度实现机制的两个关键举措。     

连锁零售供应链多级库存协同决策研究

针对连锁零售供应链多级库存资源的动态优化配置问题,提出了在上层对库存策略和下层对物流分配方案协同寻优的多级库存双层规划模型。借鉴细粒度模型遗传算法的遗传操作具有局部性的特点,模拟微观群体交互作用的局部性,基于细粒度模型遗传算法的Agent群体行为优化算法和基于复杂适应系统涌现机理的协同决策机制,进行连锁零售供应链多级库存协同决策研究。通过算例实验对模型的有效性进行了验证。仿真实验结果表明,通过连锁零售供应链微观个体Agent的群体行为优化,从系统工程的角度,实现了连锁零售供应链多级库存的动态资源优化配置和信息共享,降低了多级库存管理与运营的总成本。     

基于区块链技术和遗传算法的物流供应链优化

为了增强货物可达性和物流信息安全性、提高供应链效率、降低运输成本,对物联网背景下基于区块链技术和遗传算法的物流供应链优化进行研究。首先,介绍区块链基本技术、供应链优化及遗传算法等理论,分析区块链对物流信息协同管理的优化。然后,基于遗传算法建立物流供应链网络优化模型,同时分析了优化结果。优化结果表明,区块链技术被应用到物流信息协同管理系统中,提升了物流供应链整体效益;利用遗传算法,降低了物流配送成本。从增加物流信息安全性和可达性方面出发,研究经由区块链技术、遗传算法优化物流供应链中的物流信息协同管理系统,以及物流进行配送的路径,体现了创新性。实例研究表明,通过区块链技术和遗传算法加强优化物流供应链,提高了工作效率、降低了成本、提高了企业管理水平和经济效益,提升了企业的竞争力。     

基于区块链技术的农产品供应链演化博弈研究

为确保加入区块链的农产品供应链主体遵循协议共享初始真实信息,针对农产品供应链信息不对称、缺乏信任等问题,搭建了区块链技术的农产品供应链框架.对采用区块链技术的农产品供应链参与主体是否守约构建演化博弈模型,分析各主体遵循区块链节点共享初始真实信息的影响因素.使用MATLAB进行仿真,结果表明,当加入区块链的信任程度、协同收益、激励和惩罚提高、风险系数变低,农产品供应链参与主体更倾向于遵守区块链协议守约,这些因素影响区块链节点下参与的主体策略选择.明确区块链技术运用在农产品供应链的可行性,可为各参与方提供对策,提高农产品供应链的整体效率.     

多平台海上协同搜索与路径优化策略研究

针对传统海上搜索研究中搜索平台与行为单一、缺乏协同的问题,建立多平台海上协同搜索路径优化模型,研究协同搜索与路径优化对策.在协同搜索策略方面,给出同构协同与异构协同搜索策略的定义,并设计不同规模的协同仿真场景;在路径优化策略方面,根据海上搜索平台的搜索特点,设计基于行为和基于智能算法的路径优化策略.在此基础上,对比研究不同规模的海上协同搜索策略与路径优化策略效果.结果表明:基于混合智能算法的路径优化效果普遍优于基于行为的搜索策略与单一算法,异构协同搜索策略能够充分发挥各平台优势、取长补短,取得了优于单平台和同构协同搜索的收益效果.     

智能仓储货位规划与AGV路径规划协同优化算法

智能仓储的优化一般分为货架优化和路径优化两部分.货架优化针对货物与货架两者的关系,对货物摆放位置进行优化;而路径优化主要寻找自动引导小车（Automated Guided Vehicle,AGV）的最优路径规划.目前,大多的智能仓储优化仅对这两部分进行独立研究,在实际仓储应用中只能以线性叠加的方式解决问题,导致问题的求解易陷入局部最优中.本文通过对智能仓储环节中各部分的关系进行耦合分析,提出了货位和AGV路径协同优化数学模型,将货架优化和路径规划归为一个整体;此外,提出了智能仓储协同优化框架的求解算法,包括货品相似度求解算法和改进的路径规划算法;并在以上两种算法的基础上,使用改进的遗传算法,实现了货位路径协同优化.实验结果验证了本文提出的智能仓储协同优化算法的有效性和稳定性.通过使用该算法可有效提高仓储的出货效率,降低运输成本.     

基于物联网技术的电网物资供应优化模型建立

电网物资供应链受到环境因素的干扰存在波动性,为提高电网物资供应的稳定性,设计新的电网物资供应优化模型。借助物联网中组网部署供应链中的相应节点,采用线性规划方法部署电网物资供应网络的最优节点,采用空间自适应调度模型规划电网物资供应网络的最优路径,提取电网物资供应物联网传输路径统计特征量,采用描述性统计分析重构电网物资供应网络的子空间,通过自适应寻优方法,实现电网物资供应链网络的决策和规划设计,完成电网物资供应优化模型构建。结果表明,采用该方法优化电网物资供应,提高物资供应的吞吐水平,降低时间开销。     

基于生物地理学优化的多UCAV协同航迹规划

研究多无人机协同路径规划问题,为了获取从起始点到达目标点,能够避开各种阻碍的最优运动路径,提出了一种基于BBO的多UCAV协同航迹规划方法.通过对地形环境、航迹表示方式进行描述,将生物地理学优化算法运用于多UCAV协同航迹规划,对约束条件及威胁进行分析,建立了UCAV航迹规划模型及多UCAV协同航迹规划模型;对BBO算法适宜度向量编码及迁徙模型进行了分析,设计了相应的优化算子;最后,构建了多UCAV协同航迹规划的求解框架,并结合BBO算法开展了相应的仿真.仿真结果表明,改进方法较好地实现了多UCAV协同航迹规划的优化.     

混合粒子群算法和多Agent系统的协同生产调度及其组合优化

为了从整体角度优化调度供应链网络的各个环节,研究了在供应链环境下核心制造商与制造商的协同生产调度方案,考虑在满足产品生产时间节点的最少加工时间。建立了多Agent的供应链环境下的协同生产调度模型,针对此模型设计了协同混合粒子群优化算法并进行求解。通过实例研究表明,供应链环境下制造商的协同优化对生产的计划与实行起到了关键的作用。     

JIT供应链物料采购协同优化及其粒子群算法

在供应链环境中,传统的物料需求计划没有考虑供应商的供货能力与经济利益,不利于供应链上下游企业的长期合作。研究了一种基于JIT的供应链物料采购协同优化问题,以单一制造商和多供应商构成的二级供应链为研究对象,遵循产品生产的BOM约束和MRP制定原理,以最小化供应链上下游企业的库存、运输、缺货、赶工等总成本为目标,构建了以MRP为引导的供应链订购批量协同优化模型;通过调整制造商的主生产计划变更采购计划,以获得供应商供货方案,据此设计了基于整数编码和带有交叉操作的改进离散粒子群优化算法进行模型求解;结合实例对模型可行性进行了验证,通过算法结果分析及比较,证明了算法的有效性。     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.     

Coordinating a two-supplier and one-retailer supply chain with forecast updating

We consider the component-purchasing problem for a supply chain consisting of one retailer and two complementary suppliers with different lead-times. The retailer purchases a specific component from each supplier for assembling into a fashionable product. After ordering from the long-lead-time supplier (Supplier 1) and before ordering from the short-lead-time supplier (Supplier 2), the retailer can update its demand forecast for the product. The retailer can partially cancel its order from Supplier 1 after forecast updating. By formulating the problem as a dynamic optimization problem, we explore the measures that can be deployed to coordinate the retailer’s ordering decisions with forecast updating. We analytically show that the supply chain can be coordinated if both suppliers offer a returns policy and Supplier 1 charges an order-cancelation penalty to the retailer. We find that the coordination mechanism is independent of demand distribution and the forecast updating process. We further show that it is easier for the suppliers to coordinate the supply chain if market observation indicates the future market demand is sufficiently large. We also study the case where demand is price-dependent and propose a generalized revenue-sharing contract to coordinate the supply chain. We discuss the academic and managerial implications of the theoretical findings.     

整合供应商选择的多层级选址-库存模型

针对多级供应链网络设计中选址和库存一体化决策问题,基于梯级库存策略,建立了整合供应商选择的多层级选址-库存模型。模型以网络中供应商的选择成本、工厂和配送中心的打开成本、层级之间的运输成本、库存成本、采购成本和生产成本之和最小为目标,同时对供应商的选择、工厂和配送中心的选址、配送中心对顾客的分配、层级之间的运输量、工厂和配送中心的订货批量进行决策。为了求解所建立的模型,设计了基于部分编码的粒子群优化算法。20个不同规模的算例测试表明：所建立的模型是有效的,能用于多层级供应链网络的设计;所设计的算法无论是在求解精度,还是在运算速度上都明显优于数学优化软件Lingo 9.0,尤其是当供应链网络中总节点数较大时。     

Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective

This paper examines contingent rerouting strategy for enhancing supply chain resilience taking a supplier's point of view. We consider a supply chain with multiple suppliers at each stage and establish a mathematical model for product allocation behavior among different suppliers. The allocation model is based on each supplier's production capacity, product quality, production cost, as well as possible decision maker's preferences. As a performance measure for rerouting strategy, we use the total outflow of the supply chain. We propose an optimization model and its solution determines the rerouting strategy for product flow through the supply chain under disruptions. Numerical examples demonstrate the effect of the rerouting strategy and show the resilience of the supply chain.     

考虑供应商选择的选址一库存一路径的联合优化

针对装配型制造企业供应链集成优化问题,建立了随机需求情形下整合供应商选择和各层级之间运输方式选择的多层级选址—库存模型。该模型通过对供应商的选择,装配厂和分销中心的选址,相邻两层级之间的分配服务关系及运输方式的确定,实现整体供应链网络成本最小化。为求解此混合整数非线性规划模型,设计了一种矩阵编码的改进自适应遗传算法。仿真实验表明,该算法的解的寻优能力明显优于标准遗传算法,得出了供应链总成本与装配厂的最大提前期存在一定规律性的结论。     

Modeling and optimization of two-stage procurement in dual-channel supply chain

An online marketing platform should be designed to fairly take the benefits of buyers and suppliers into consideration based on their risk preferences and business strategies. In this paper, the dual-channel supply chain models are developed to implement the risk-averse strategy for buyers and risk-neutral strategy for suppliers, respectively. The buyers under the consideration are the manufacturers who acquire raw materials, parts, or components to make their final products. The major factors in the developed models include the risk preferences of buyers and suppliers, random price fluctuations of goods, and varying demands of final products. To reflect the purchasing practice of a manufacturer, (1) a supply chain is considered to have two supply channels, i.e., contract-based purchase with a lead-time before the goods are used and a direct purchase from online spot markets when the goods are used; (2) the time factor on decision making is specially taken into account, and the procurements are divided into the contract stage of purchase and online stage of purchase. Gaming analysis is conducted to develop the supply chain models for manufactures and suppliers to implement their purchasing or pricing strategies. The simulation is conducted and the result has shown that two-stage purchases in a dual-channel supply chain have improved the performances of suppliers and manufacturers in terms of the profits they can make, their supply–demand relations, and their adjustability to uncertainties in globalized and segmented markets. The proposed model has its significance for manufacturers to better control the price risk of goods and the demand risk of final products; on the other hand, suppliers can benefit from adjusting dynamic sales using online spot markets.     

Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.     

基于HCPN的BTO供应链的动态构建与仿真

采用层次着色Petri网（HCPN）对企业从接到用户订单、编制供应链计划,直到选定上游供应商的BTO供应链网络的动态构建过程进行建模和仿真分析,其结果可以评价企业的供应链战略联盟响应用户个性化需求的能力。为企业管理者动态构建BTO供应链网络和评估供应链战略联盟响应用户个性化需求的能力提供了一种有效的实用方法,从而有助于改进整个供应链的绩效。