考虑提前期压缩的Newsvendor型产品供应链协调模型

在假定制造商的单位生产成本是关于提前期的减函数的前提下,将提前期视为可控决策变量,建立了带有提前期压缩的Newsvendor型产品供应链协调模型,讨论了提前期压缩对供应链及其成员收益的影响,并利用由回购和回扣/惩罚所组成的联合契约实现了供应链的协调.数值仿真分析结果验证了结论的正确性.     

基于不同交货期策略的供应链库存优化与协调模型

根据供应链的实际运作情况建立了交货策略1,即基于时间窗的协议交货期模型,分析了以制造商为主的Stackelberg对策博弈中各方的最优决策;接着建立了交货策略2,即基于费用分担的可控交货期模型,分析了以分销商为主的Stackelberg对策博弈中各方的最优决策;然后对交货策略1和策略2进行了比较,给出了成本分担系数范围,提出了一种基于改进理想点的Nash协商交货期费用分担模型.数值分析比较了两种策略,表明费用分担模型是有效的.     

基于动态批量和提前期的MRP研究

MRP是ERP的关键。论文分析了基于固定批量和提前期来确定计划数量和计划时间的传统MRP算法存在的问题,提出了利用日产量对批量和提前期进行动态调整的方法,并给出了相应的MRP算法。     

订货提前期对服务水平决策的影响研究

在服务水平的研究中,考虑了顾客面临缺货时的选择行为,引入顾客忍耐值,并度量了与顾客等待时间相关的损失,使建立的服务水平模型较传统模型更能真实地反映现实的购买行为.在该模型基础上,分析了订货提前期的变化对服务水平决策的影响,得出了有别于传统模型的结论,为企业在基于时间的竞争中作出恰当的服务水平决策提供了新的思路.算例分析进一步验证了该模型的有效性.     

考虑不确定性的供应链计划模型

对供应链计划建立了考虑不确定性的战略层模型和运作层模型。以一啤酒厂进行了实例研究,对所建模型和确定性的供应链计划模型进行了比较,并对柔性参数进行了灵敏度分析。结果表明,该模型能帮助设计具有柔性的供应链系统,通过灵敏度分析,可以更好地理解供应链的特性。     

考虑风险规避和谈判权力的农产品供应链绿色投资机制

研究风险规避的农户和风险中性的销售商组成的农产品供应链,在集中式决策和分散式决策下绿色投资的最优决策问题.考虑两类成本共担契约,分别为一般成本共担契约和考虑谈判权力的讨价还价的成本共担契约;同时,分析风险规避和谈判权力对相关决策变量的影响.结果表明:在分散式供应链中,风险规避与绿色投资水平负相关,而在两类契约中,两者正相关.两类契约都有利于供应链绿色投资水平和期望效用的提高.在双方谈判的成本共担契约中,成本分担比例和绿色投资水平分别与谈判权力和风险规避正相关;在满足一定条件时,供应链绿色投资水平高于集中式供应链的相应值;供应链绿色投资水平和期望效用可同时高于一般成本共担契约中的相应值.投资成本系数对供应链绿色投资水平和期望效用有负向影响.     

时尚产业敏捷供应链研究

时尚产业是一个很宽泛的名词术语,通常包括以生命周期短为特征的任何产品和市场。法国时尚学院(IFM)和巴黎HEC商学院定义时尚产业为以下行业:纺织品、服装、皮草、香水、化妆品、珠宝、钟表、眼镜和餐具等行业。时尚产业市场很早就引起了国外研究者的兴趣。但是,他们通常集中在时尚产业的心理学和社会学以及     

考虑产品替代的供应链协调问题研究

替代品大大增加了企业间的竞争,使企业难以达到所期望的盈利水平.对供应链进行合理有效的管理可最大限度地促进企业适应当今多变的市场环境.人们对于产品的需求不仅仅取决于产品本身的价格,也取决于互补产品的价格.对此,构建一个以产品替代的供应链协调问题为研究目标,以两种供应链管理模式(集中供应链(CSC)和分散供应链(DSC))...     

随机需求下提前期可控的生产-库存联合优化模型

考虑单供应商和单采购商的生产-库存联合优化问题.假设采购商面临正态需求,供应商的提前期可以控制,并基于此建立供应商与采购商联合期望总成本最小化模型.在所建立的模型中允许采购商缺货,且部分缺货可延期交付,部分缺货发生销售损失;同时考虑运输成本,并假设运输成本依赖于订货量和提前期.给出了求解最优生产批量、最优提前期、最优再订货点和订货量的算法,并通过数值算例进行了说明.     

线性时变需求的二级供应链协调模型

考虑由一个供应商和一个零售商构成的二级供应链系统,在线性时变需求、产品的零售价格与其存储时间有关、货栈容量有限且不允许缺货的情形下,研究了供应商如何利用全数量折扣计划来激励零售商合作从而实现自身利润最大化和供应链系统完美协调的问题。从理论上证明和分析了模型最优解的存在性,同时提供了求解最优解的简单方法,并通过算例对上述结论进行佐证。     

考虑质量价值水平的复杂产品供应链质量成本优化方法

优化复杂产品供应链质量成本是提高供应链质量管控能力、降低供应链质量风险的重要方法.综合考虑产品质量水平和产品研制成本,提出复杂产品质量价值概念,并详细分析以供应商网络为基础的复杂产品质量价值形成机理.建立复杂产品质量价值GERT网络模型,实现复杂产品质量价值水平的量化计算,并以此为基础,通过识别网络中的关键供应商建立优化模型,提出考虑质量价值水平的复杂产品供应链质量成本优化方法.最后通过算例验证模型方法的科学性和有效性.     

Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels

The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.     

Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm

In this paper, a design of the supply chain distributer–retailer network for a seasonal multiple-product location allocation-inventory control problem in a planning horizon consisting of multiple periods is modeled. The distance between the distributers and retailers are assumed to be Euclidean and square Euclidean while retailers purchase the products from the distributers under all-unit and incremental quantity discount policies. Furthermore, the products are delivered in packets of known size of items and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the distributers store the manufactured products in their warehouses before delivering them to the retailers since the total warehouse spaces and the total available budget are limited. Capacity constraints are also taken into account when planning inventory levels. It is considered that the distributers manufacture the products under some production limitations. The aim of the problem is to find the optimal number of packets of the products purchased by the retailers from the distributers in different periods and determine the coordinates of the distributers’ locations to minimize the total inventory cost. As the mixed integer nonlinear model of the problem is complicated to solve using exact methods, a modified fruit fly optimization algorithm (MFOA) is proposed to find the optimal solution. Due to the nonlinear nature of the original formulation and noticing that there is no benchmark available in the literature to justify and validate the results, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are represented as well. Some numerical examples are generated to show the performance and application of the algorithms for both Euclidean and square Euclidean distances where the MFOA has a better performance than the PSO and SA.     

A study on inventory replenishment policies in a two-echelon supply chain system

Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.     

两级供应链减排与促销的合作策略研究

首先假设在由零售商主导的供应链中, 制造商的低碳声誉和零售商的促销都能增加产品的需求, 减排对制造商的低碳声誉有正面影响; 然后运用微分博弈理论, 比较了不合作、成本分担契约以及合作3 个契约对供应链成员的影响. 研究发现, 制造商和零售商的促销及减排水平在合作契约下最高, 但供应链的总价值在合作契约下最大是有条件的. 当存在合作价值时, 为了使制造商和零售商获得更多的价值设计了利润共享契约.     

利润-CVaR准则下的二级供应链定价与订货策略研究

传统的定价与订货策略研究多是建立在风险中性的假设之上,近来也有利用风险度量CVaR研究风险厌恶对库存的影响.因此,以期望利润和CVaR的加权平均为目标函数,研究零售商的订货策略,并在此基础上研究上游供应商的定价策略.这样的利润-CVaR目标既反映了决策者追求高利润的愿望,又反映了其对潜在风险的控制.     

A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network

In this study, the design of a two-echelon distribution supply chain network for the seasonal products with multiple vendors (manufacturers) and buyers (retailers), and a set of warehouses for each vendor are considered. The locations of the buyers are known and the capacity of the warehouses is restricted while the buyers purchase different products from the vendors under all unit discount policy. The main objective of this research is to find out the optimal locations of the potential vendors in addition to the quantity ordered (allocation) by the buyers so that the total inventory cost including ordering (transportation), holding and the purchasing costs is minimized. Besides, the distance from the buyers to the vendors is considered as the Euclidean distance. The total budget to buy the products is limited and the production capacity of each vendor is also restricted. To solve the problem, a modified particle swarm optimization (MPSO) algorithm is applied where the results are validated using a genetic algorithm (GA). Finally, some computational examples are generated to assess the algorithms’ performance where MPSO shows a better efficiency in comparison with the GA.     

Inventory management in a two-echelon closed-loop supply chain with correlated demands and returns

Reverse logistics or closed-loop supply chains where product returns are integrated with traditional forward supply chains have been one of the major topics of research since about the last one and a half decades. In this paper, we address the inventory management issue in closed-loop supply chains, and develop deterministic and stochastic models for a two-echelon system with correlated demands and returns under generalized cost structures. In particular, we address the following questions – Do closed-loop supply chains cost more than traditional forward supply chains? Does a higher rate of return always translate into lower demand variability and hence lower expected costs? What is the relationship between expected costs and correlations between demands and returns? Models developed and numerical examples shown in the paper reveal that although a higher rate of return and a higher correlation between demand and return reduce the variability of net demand, it may not necessarily lead to cost savings; rather the movement of costs will depend on the values of system parameters. We also quantify the cost savings in case the actual demand and return information is available at the time of decision-making. We conclude the paper by providing managerial implications and directions for future research.     

Optimal decisions for a two-echelon supply chain with capacity and demand information

Due to the applications of Internet of Things and big data in the Industry 4.0 context, more information in and out of a smart factory can be collected and shared between manufacturers and retailers. In this study, we consider two types of information that can be available in a supply chain consisting of a manufacturer and a retailer in Industry 4.0: the capacity information for the later rush production and the demand information shared between the retailer and manufacturer. In the supply chain, the manufacturer provides two orders with maximum limits by using a capacitated normal production and two capacitated rush production modes. To study the effects of the information, we investigate the optimal decisions and profits for the supply chain with and without the capacity information and demand information sharing. In addition, we propose a coordination mechanism for the supply chain with both the capacity information and demand information sharing. The coordination mechanism does not only rely on cost parameters, but also on the capacity and demand information. The numerical examples show that the supply chain profit can be improved by as large as 16.76% in the coordinated system, compared with the original system without the capacity information and demand information sharing.