逆向物流流量不确定闭环供应链鲁棒运作策略设计

考虑一类同时具有再分销,再制造和再利用的闭环供应链在逆向物流流量不确定环境下的运作问题.采用具有已知概率的离散情景描述逆向物流流量的不确定性,利用基于情景分析的鲁棒线性优化方法建立该闭环供应链的多目标运作模型.设计了一个数值算例,其结果验证了运作策略的鲁棒性.在该算例基础上,分析了逆向物流流量的大小对闭环供应链系统运作性能的影响.

     

一种三重接触架构下的退货物流优化方法研究

分析了闭环控制系统架构所涉及到的供应链逆向物流中,针对不确定的退货物流回收再制造的各个环节的特点,利用UML建模语言,从单一营销回收渠道和电子商务环境下的双源营销回收渠道两个方面对影响闭环回收再制造的众多因素进行了分析,建立了产品再制造过程的类图和状态图,给出了基于Arena仿真工具的一般回收再制造架构和双源渠道回收再制造架构,在此基础上利用三重生产架构实现了系统集成、科技创新和第三方外包的产品增值再制造,达到了对原来两种架构方案的优化。应用表明,该方案优化了整个闭环再制造供应链系统的运作。     

不同物流和回收状态下的供应链决策分析

基于第三方物流服务商在供应链管理中的重要性,构造了以第三方物流服务商为主导企业的供应链模型.运用博弈论思想,对制造商回收和零售商回收的两类闭环供应链模型进行求解比较,提出了制造商实施物流外包和废旧品回收的临界条件.最后通过数值算例,分析了动态时域上服务成本和回收成本与制造商决策的关系.     

生产厂商逆向供应链演化稳定策略分析

生产型企业通过实施逆向供应链策略取代传统供应链的单向运作模式,可以减少对环境的负面影响和对资源的过度依赖,对本企业生产的废旧产品回收再利用成为当前发展新趋势。将逆向供应链作为一类复杂适应系统,基于复制动态方程和演化稳定策略,构建了逆向供应链演化博弈模型,分析和讨论了逆向供应链中每一个供应商决策如何影响供应链绩效。在市场机制下,供应商的额外收益超出实施逆向供应链的成本,供应商才会采取实施逆向供应链策略,而在政府管制下,相应规制可以有效促进逆向供应链策略的实施。     

制造/再制造集成物流网络的优化设计研究

考虑到闭环物流系统中不同周期内各消费区域的产品需求数量、回收产品质量的不确定性,建立了多周期、有能力限制、正逆向物流集成运作的混合整数非线性规划模型。以总运营成本最少为目标,确定了制造/再制造集成物流系统中各种设施的数量、规模、位置以及各设施间的物流量分以配。通过算例验证了模型的有效性。     

分布式决策闭环供应链协调运作研究进展

在分析闭环供应链中存在的分布式决策问题及其产生原因的基础上,说明了分布式决策闭环供应链协调运作策略设计的重要性和研究意义,对闭环供应链的协调、运作以及运作过程中的不确定性和鲁棒性等问题的研究现状进行了综述,探讨了分布式决策闭环供应链协调运作研究的若干前瞻性问题.     

基于再制造的闭环供应链动态模型及其鲁棒H∞控制

研究基于再制造的闭环供应链运作问题，建立了具有再制造、废弃时滞和需求不确定性的闭环供应链动态模型．分析了闭环供应链的鲁棒运作，给出了针对再制造闭环供应链动态模型的鲁棒H∞控制策略和线性矩阵不等式算法，鲁棒H∞控制策略能够抑制闭环供应链运作过程中的不确定性影响，使闭环供应链系统运作达到理想总成本．最后通过仿真计算验证了所提出方法的有效性．     

TOPSIS定权的FCE法在物流供应商选择中的应用

根据汽车逆向物流供应链结构特点,构建了第三方汽车逆向物流供应商评价指标体系,创新性地提出了采用定量的TOPSIS法来确定二级指标的权重,采用定性的层次分析法(AHP)来确定一级指标的权重,构建了基于TOPSIS法与AHP法相结合定权的模糊综合评判法模型,给出了该模型的计算步骤。通过算例验证了该模型的可行性和该评价指标体系的实用性。     

物流服务供应链绩效动态评价研究

针对动态变化的物流服务供应链状态,设计改进型QFD模型,利用模糊评估法对不同历史时期物流服务供应链绩效进行评价;利用前期评价结果,应用Markov预测方法对未来物流服务供应链进行预测,以达到更好控制绩效的目的。最后,结合算例对该动态绩效评价模型进行验证分析。     

价格折扣契约下应对突发事件下的闭环供应链协调策略

针对MRCRM模式下的闭环供应链,在考虑随机性基础上,分析了价格折扣契约对闭环供应链的协调作用,对于突发事件可能造成闭环供应链协调被打破的问题,提出了具有抗突发事件性的价格折扣契约,使之能实现对突发事件的协调应对,最后应用一个算例对比加以说明。     

Development of RFID-based Reverse Logistics System

Reverse logistics, which is the management or return flow due to product recovery, goods return, or overstock, form a closed-loop supply chain. The success of the closed-loop supply chain depends on actions of both manufacturers and customers. Now, manufacturers require producing products which are easy for disassembly, reuse and remanufacturing owing to the law of environmental protection. On the other hand, the number of customers supporting environmental protection by delivering their used products to collection points is increasing. According to the findings, the total cost spent in reverse logistics is huge. In order to minimize the total reverse logistics cost and high utilization rate of collection points, selecting appropriate locations for collection points is critical in reverse logistics. This paper proposes a genetic algorithm to determine such locations in order to maximize the coverage of customers. Also, the use of RFID is suggested to count the quantities of collected items in collection points and send the signal to the central return center. This can facilitate the vehicle scheduling for transferring the items from collection points to the return center. The significance of this research is the proposal of RFID-based reverse logistics framework and optimization of locations of collection points which allow economically and ecologically reasonable recycling. Simulation results indicated that the genetic algorithm is able to produce good-quality solutions in terms of coverage of collection points by choosing suitable locations for collection points and RFID can help detect the quantity of returned products so as to increase efficiency of logistics operations.     

Pricing and return product collection decisions in a closed-loop supply chain with dual-channel in both forward and reverse logistics

This paper deals a closed-loop supply chain with two dual channels – forward dual-channel where a manufacturer sells a product to customers through traditional retail channel and e-tail (internet) channel, and reverse dual-channel where the used items are collected for remanufacturing through the traditional third party logistics and e-tail channel. We derive analytically the pricing and return product collection decisions for the supply chain under five different scenarios viz. centralized, decentralized (Nash game), and manufacturer-led, retailer-led and third party-led decentralized scenarios. We also demonstrate the proposed model through a series of data sets. It is observed from the numerical study that the retailer-led decentralized scenario provides more profit than other decentralized scenarios.     

A closed-loop logistic model with a spanning-tree based genetic algorithm

Due to the problem of global warming, the green supply chain management, in particular, closed-loop logistics, has drawn the attention of researchers. Although there were logistics models that were examined in the literatures, most of them were case based and not in a closed-loop. Therefore, they lacked generality and could not serve the purposes of recycling, reuse and recovery required in a green supply chain. In this study, the integration of forward and reverse logistics was investigated, and a generalized closed-loop model for the logistics planning was proposed by formulating a cyclic logistics network problem into an integer linear programming model. Moreover, the decisions for selecting the places of manufactories, distribution centers, and dismantlers with the respective operation units were supported with the minimum cost. A revised spanning-tree based genetic algorithm was also developed by using determinant encoding representation for solving this NP model. Numerical experiments were presented, and the results showed that the proposed model and algorithms were able to support the logistic decisions in a closed-loop supply chain efficiently and accurately.

Statement of scope and purposes

This study concerns with operations of 3R in the green supply chain logistics and the location selection optimization. Based on ‘cradle to cradle’ principle of a green product, a “closed-loop” structure of a network was proposed in order to integrate the environmental issues into a traditional logistic system. Due to NP-hard nature of the model, a Genetic Algorithm, which is based on spanning tree structure was developed. Test problems from the small size for accuracy to the large scale for efficiency have been demonstrated with comparison. The promising results have shown the applicability of the proposed model with the solution procedure.     

Enterprise modelling methodology for forward and reverse supply chain flows integration

The paper proposes a new enterprise modelling methodology called ERE-GIO applicable to supply chain reengineering and integration. It is based on two major phases: reverse engineering of the supply chain and then forward engineering. It takes advantages of previous enterprise modelling methodologies, especially CIMOSA. The application of the defined methodology on the logistics flow of an industrial company has allowed the integration of the reverse logistics flows in the traditional supply chain, thanks to the modelling of business processes and the flows related to it.     

An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing

With the development of a new generation of information technology, smart manufacturing has put forward higher requirements for supply chain. It is necessary to ensure the synchronization of the supply chain operation and maintain the reliability of the supply chain management, therefore the trust evaluation for the supply chain becomes extremely important. Traditional supply chain management has problems such as information flow is easy to be tampered with, logistics is difficult to trace, and capital flow is not true, which leads to increased opportunity costs due to the lack of trust among transaction entities in the supply chain. The emergence of blockchain technology provides an opportunity to improve the supply chain ecosystem. In this paper, an integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing is proposed to explain how to enhance trust management with the help of blockchain from the perspectives of information flow, logistics, and capital flow. An optimized trust management model is designed for better entities evaluation in supply chain. A coal mine equipment manufacturing industry scenario is presented to illustrate the effectiveness of the proposed framework.     

A particle swarm approach for optimizing a multi-stage closed loop supply chain for the solar cell industry

In order to implement sustainable strategies in a supply chain, enterprises should provide highly favorable and effective solutions for reducing carbon dioxide emissions, which brings out the issues of designing and managing a closed-loop supply chain (CLSC). This paper studies an integrated CLSC network design problem with cost and environmental concerns in the solar energy industry from sustainability perspectives. A multi-objective closed-loop supply chain design (MCSCD) model has been proposed, in consideration of many practical characteristics including flow conservation at each production/recycling unit of forward/reverse logistics (FL/RL), capacity expansion, and recycled components. A deterministic multi-objective mixed integer linear programming (MILP) model capturing the tradeoffs between the total cost and total CO₂ emissions was developed to address the multistage CSLC design problem. Subsequently, a multi-objective PSO (MOPSO) algorithm with crowding distance-based nondominated sorting approach is developed to search the near-optimal solution of the MCSCD model. The computational study shows that the proposed MOPSO algorithm is suitable and effective for solving large-scale complicated CLSC structure than the conventional branch-and-bound optimization approach. Analysis results show that an enterprise needs to apply an adequate recycling strategy or energy saving technology to achieve a better economic effectiveness if the carbon emission regulation is applied. Consequently, the Pareto optimal solution obtained from MOPSO algorithm may give the superior suggestions of CLSC design, such as factory location options, capacity expansion, technology selection, purchasing, and order fulfillment decisions in practice.     

多阶段煤炭供应链网络设计及其遗传算法

煤炭供应链网络设计旨在为大型煤炭集团选择合理的设施网络布局与最佳运量,以便提高效率并降低成本。考虑配煤加工与流量平衡等特有约束,建立了煤炭供应链网络混合整数规划模型,其优化目标是最小化固定设施成本、运输总成本与采购成本。考虑到模型求解的复杂度,设计了一种遗传算法,结合优先权与整数规则对染色体进行了编码与解码。实验算例表明所建立的模型能够真实地模拟煤炭供应链网络中设施布局与最佳运量的决策环境,其算法能够在允许的运算时间内获得稳定的满意解,随着算例规模的增大,其计算时间与优化结果均优于LINGO软件。     

Design and development of logistics workflow systems for demand management with RFID

This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today’s globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.