Activity routing in a distributed supply chain: Performance evaluation with two inputs

In this paper, an industrial system is represented as a 2-input, three-stage queuing network. The two input queuing network receives orders from clients, and the orders are waiting to be served. Each order comprises (i) time of occurrence of the orders and (ii) quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the queuing network. After getting serviced by the last node (a queue and its server) in each stage of the queuing network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the 2-input network are derived and plotted. Closed-form expressions for the equivalent service rate, equivalent average queue lengths, and equivalent response and waiting times of a single queue with a single server representing the 2-input queuing network are also derived and plotted.     

An agent-based distributed computational experiment framework for virtual supply chain network development

This paper focuses on research on virtual supply chain networks instead of real supply chain networks by making use of agent technology and computational experiment method. However, the recent research is inefficient in computational experiment modeling and lack of a related methodological framework. This paper proposes an agent-based distributed computational experiment framework with in-depth study of material flow, information flow and time flow modeling in supply chain networks. In this framework, a matrix-based formal representation method for material flow, a task-centered representation method for information flow and an agent-based time synchronization mechanism for time flow are proposed to aid building a high quality computational experiment model for a multi-layer supply chain network. In order to conduct the model, a computational experiment architecture for virtual supply chain networks is proposed. In this architecture, coordination mechanisms among agents based on material flow, information flow and time flow as well as consistency check methods for computational experiment models are discussed. Finally, an implementation architecture of the framework is given and a case of virtual supply chain network is developed to illustrate the application of the framework. The computational experiment results of the case show that the proposed framework, not only feasible but correct, has sound advantages in virtual supply chain network development, computational experiment modeling and implementation.     

多Agent的供应链分布式仿真系统设计与实现

供应链是由供应商、制造商、仓库、配送中心和渠道商等构成的物流网络。并且供应链已经为社会带来了巨大的经济效益,本文就多Agent的供应链分布式仿真系统设计做了详细的研究,以便其能够很快的得到实现。     

A comprehensive clustering algorithm for strategic analysis of supply chain networks

Inventory management in supply chain networks involves keeping track of hundreds of items spread across multiple locations with complex interrelationships between them. However, it is not computationally feasible to consider each item individually during the decision making process. The use of clusters of items is preferred for the evaluation of these decisions. In addition, the use of groups of items provides management with more effective methods for characterizing and controlling system performance and results in cost savings such as group discounts. In this research, we introduce a comprehensive clustering methodology for supporting inventory management in supply chain networks. All product characteristics which have a significant impact on the performance of the supply chain are taken into account. The nodes in the network are split into subnodes prior to clustering to reduce the complexity. The average linkage clustering algorithm and the Calinski and Harabasz index are used to identify clusters of similar items. In addition, a set of heuristics is used to capture the relationships between items as specified in the bill of materials for the products. Examples are presented to demonstrate the effectiveness of the clustering methodology as well as the performance of the heuristics, by comparing the results obtained with the optimal solution.     

Supply Chain Network Designs Developed for Deteriorating Items Under Conditions of Trade Credit and Partial Backordering

Deteriorating items, trade credit, and partial backordering are common in today’s business. However, no previous study on supply chain network design has considered these business aspects together. In this paper, we present supply chain networks designed for deteriorating items under trade credit and two conditions: (a) no shortage and (b) partial backordering of goods. We also present 2 algorithms based on nonlinear optimization that were developed in order to optimize the influence area and the joint replenishment-cycle time in the no-shortage case, and to identify the optimal shortage level in the partial-backordering case. The numerical examples presented herein illustrate how the solution procedure works. The effects of various values of the tested parameters on decisions and costs are also discussed. Our results could be used as a reference by managers when making business decisions.     

Processing skyline queries in incomplete distributed databases

Due to its great benefits over many database applications, skyline queries have received formidable concern in the last decades. Skyline queries attempt to assist users by identifying the set of data items which represents the best results that meet the conditions of a given query. Most of the existing skyline techniques concentrate on identifying skylines over a single relation. However, in distributed databases, the process of skyline queries required accessing multiple relations which might be located at different sites. Consequently, data items from these multiple relations need to be joined and thus transferring these data items from one site to another is unavoidable. Moreover, the previous techniques also assume that the values of dimensions for every data item are presented (complete) which is not always true as some values may be missing. In this paper, we proposed an approach for processing skyline queries in incomplete distributed databases. The approach derives skylines from multiple relations where dominated data items are removed before joining the relations to reduce the processing time and the network cost. The experimental results illustrate that our proposed approach outperforms the previous approaches in terms of processing time and network cost.     

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.     

基于位置吸引力的加权复杂供应链网络局域世界演化模型研究

在一般局域世界演化模型的基础上,文章使得企业节点的初始位置值呈现幂率分布,以体现节点企业的不同角色。受万有引力定律的启发,用位置值的大小和远近值来定义节点企业间位置吸引力的概念,并应用吸引力规则确定每一个新加入节点的局域世界。新节点与局域世界中的老节点之间采用节点度与节点强度的复合优先连接方式,弥补了优先连接仅仅依赖节点度值的缺陷,从而构建基于位置吸引力的加权复杂供应链网络局域世界演化模型。实验模拟了该复杂网络的生长、边的退出和节点的退出等动态演化过程,通过计算与统计整体度分布、平均路径长度和聚集系数等复杂供应链网络的重要参数发现:该复杂供应链网络的度分布呈现出一定的幂率分布形式,能够保证大多数节点的度较低和少数节点的度较高的重尾特征,同时具有较大的集聚系数和较小的平均路径长度的小世界特征。该研究工作为供应链企业在实践中构建供应链网络提供了理论依据,有助于更好地分析现实供应链网络的相关特性并识别重要节点以便对供应链网络进行保护。     

Economic selection of process mean and lot‐sizing decisions for multi‐newsvendor supply chain

Two related problems are integrated in this paper, the first is the targeting problem and another is production/inventory decisions in a supply chain. The supply chain under consideration consists of a supplier of raw material, a single producer, and multiple newsvendors. The producer can adjust the process mean before starting the production run. Once set to a certain target value, the process mean is not changed until the production lot is completed. At the end of a production run, the producer screens the lot and uses specification limits to evaluate the quality of the item. Nonconforming items are scrapped with no salvage value; however, conforming items are delivered to newsvendors who are subjected to random demand. If demand on a newsvendor in a season is lower than anticipated, surplus items will be returned to the producer at a certain transportation cost. We first develop a mathematical model that maximizes the expected total profit of the supply chain. Then, a table for two special functions is devised to simplify the solution method and is used to find the optimal solution of the proposed model. We also study the significance of this integration by comparing the performance of the proposed model with an independent model where the process mean selection and lot‐sizing decisions are found separately.     

Two inventory review policies on supply chain configuration problem

With increasing business competition and complexity, supply chain provides opportunity to increase business competitiveness. Supply chain configuration is an important strategy to enhance business advantage. It is a vital approach to develop new products and manage dynamic supply chain. In this paper, two inventory review policies, continuous replenishment and periodic replenishment, are modeled in the supply chain configuration problem. Harmony search is used to solve the problem. Numerical example is given to illustrate how the models work. Using three different scenarios of various average on hand inventory rate and work in process rate, both review policies are tested. The proposed model shows that the average on hand inventory plays a more significant role when compared with the work in process.