基于第三方物流的多周期多目标产品回收网络设计

鉴于多周期回收网络比单周期更能诠释现实回收网络的复杂性,多目标回收网络比单目标更能兼顾各供应链成员的利益,建立了基于第三方物流的多周期多目标产品回收网络模型.在建模过程中,考虑了消费者对最近收集中心的偏好,通过回收中心的库存对各周期的废旧品流进行联结.利用LINGO对算例进行了求解,并通过目标规划法对第三方物流供应商利益目标和原始设备制造商满意度目标两者之间的关系进行了探讨,研究了一方发生改变对另一方的影响.     

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。     

loss-averse测度的低碳闭环供应链最优减排研究

以低碳产品销售为不确定市场环境为背景,以Stackelberg博弈理论为方法,以loss-averse为工具,构建了双第三方回收低碳风险再制造闭环供应链模型,研究了制造商低碳减排能力、碳排放征税政策、销售商风险厌恶程度和废旧低碳产品回收市场竞争特性等因素的影响作用机理;揭示非合作博弈和协调运作下,订单数量、交易价格、低碳废旧产品回收价格等重要变量和节点企业收益、期望效用及整个链条收益的变化规律及隐含的经济意义与管理理念。设计正向渠道收益共享、逆向渠道风险共担契约,克服风险规避效应和双重边际效应的不利影响,实现低碳再制造闭环供应链的协调;通过数值仿真研究和灵敏度分析,验证了低碳风险再制造闭环供应链模型的有效性及实用性,为供应链上下游企业开展低碳减排合作提供了理论依据和政策建议。     

基于回收产品质量水平的闭环供应链渠道选择模型

基于回收努力和回收产品质量水平的考虑,应用Stackelberg博弈模型理论,构建分别由制造商回收、零售 商回收和第三方回收的3种再制造闭环供应链决策模型,考察不同渠道下参与主体最优决策差异和基于回收产品 质量水平的回收渠道选择问题.结果表明,第三方回收为下策均衡,制造商回收或零售商回收取决 于产品再制造成本的节约水平,制造商需衡量其再制造成本,从制造商回收和零售商回收中选择最优的回收渠道.     

不确定环境下的再制造闭环物流网络优化

考虑废旧产品回收数量、回收质量、再生产品需求量的不确定性以及废弃处理中心的选址等多重因素,构建单产品、多周期的再制造闭环物流网络优化设计模型,运用云遗传算法来确定物流网络中各设施的数量、位置、规模以及各设施间的合理物流分配量,使得在整个运营周期的净收益最大。通过算例来验证该模型的有效性。     

逆向物流多产品两阶段再生产网络鲁棒优化

考虑不同周期内逆向物流回收数量的不确定性和再生产设备的生产能力约束,将逆向物流回收数量、再生产成本和市场需求作为不确定参数,以成本最小为目标,建立了包含生产成本、设备运作成本、库存成本在内的多周期多产品两阶段逆向物流网络鲁棒优化模型。通过算例验证了模型的有效性。     

废旧产品回收再制造物流网络的优化设计模型

以总运作成本最小和对环境的影响最小为目标,建立了一个包含再制造工厂、回收中心和消费区域的三层逆向物流网络优化设计模型。并确定了物流网络中各种设施的位置和数量,以及由此构成的各条物流路径上的物流量分配。通过算例验证了模型的有效性。     

随机需求和奖惩机制下的闭环供应链决策模型

研究新产品和再制造产品需求均为不确定下的闭环供应链系统。通过比较两种产品的消费者WTP(willing to pay)差异性,构建三种政府奖惩机制条件下的闭环供应链模型。深入分析不同制造商主导的博弈模型及其数值实验。结果表明:无论是基于回收率还是回收量的政府奖惩机制,第三方回收模式下回收率最高,制造商利润最高,制造商回收模式下零售商利润最高;消费者对再制造产品WTP差异增加时,制造商利润、零售商利润及渠道总利润先减少后增加;政府奖惩机制变化时,第三方回收模式的回收率随政府奖惩的增加而增幅最大。     

再制造/制造系统集成物流网络模糊机会约束规划模型

在再制造/制造(R/M)系统集成物流网络中,回收产品的数量具有不确定性.根据这一特点,将各消费区域废旧产品的回收数量看成是模糊参数,提出了该集成物流网络的模糊机会约束规划模型.通过把模型中模糊机会约束清晰化,将模型转化为确定性的混合整数规划模型.利用实例数据,针对不同的置信水平对模型进行分析,其结果为该集成物流网络的设计提供了依据.     

基于博弈论的再制造逆向物流定价决策

针对再制造逆向物流定价决策问题,探讨在垄断回收模式和竞争回收模式下,受多因素(行业竞争、规模经济、政府补贴、废旧品再制造等)影响的由两个制造商和两个回收商组成的逆向物流系统;建立基于Stackelberg博弈的再制造逆向物流定价决策模型;求解关于决策变量和目标函数的子博弈精炼Nash均衡解,并进行模型参数约束分析、影响因素灵敏度分析、两种回收模式的比较分析以及数值分析.研究结果表明,竞争回收模式比垄断回收模式对各方参与者更为有利,提高废旧品的回收率、再制造率以及政府补贴有利于逆向物流系统的发展.     

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

Network model and optimization of reverse logistics by hybrid genetic algorithm

The interest about recovery of used products and materials have been increased. Therefore, reverse logistics network problem (rLNP) will be powerful and get a great potential for winning consumers in a more competitive context in the future.We formulate a mathematical model of remanufacturing system as three-stage logistics network model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of the disassembly centers and processing centers. And we consider a multi-stage, multi-product and some attach condition for disassembly centers and processing centers, respectively.For solving this problem, we propose a genetic algorithm (GA) with priority-based encoding method consisting of 1st and 2nd stages combined a new crossover operator called as weight mapping crossover (WMX). A heuristic approach is applied in the 3rd stage to transportation of parts from processing center to manufacturer. Numerical experiments with various scales of rLNP models show the effectiveness and efficiency of our approach by comparing the recent researches.     

Reverse logistics network design for product reuse,remanufacturing, recycling and refurbishing under uncertainty

With the improvement of the quality of life in human society, the need to use more natural resources is felt more than ever. In this regard, much research has been done on restoring depreciated and consumed products to the supply chain; many factors, including the quality of returned products, can significantly impact how the reverse logistics network will be used. The two-stage stochastic mixed-integer programming model proposed in this paper considers various processes of recovering recyclable products, including reuse, refurbishing, remanufacturing, recycling, and selling spare parts. Also, considering uncertainty on quality and quantity of returned products, product variety, and bill of material are model features. Due to the computational complexity of large-scale problems, such problems require considerable time to solve. To tackle this issue, a hybrid algorithm constructed by a genetic algorithm and branch and cut algorithm (with CPLEX solver) has been introduced, which can significantly reduce the solution time. Finally, the algorithm is applied to a real-world problem to design a reverse logistics network for a small-size laboratory equipment manufacturer.     

预售策略下的快速时尚品物流分销网络双层规划模型

为解决预售策略下的快速时尚品物流分销网络的选址与分配问题,将最小化网络运作成本作为决策目标,构建了反映网络销售商与顾客决策行为的双层规划模型。模型充分考虑了决策双方的共同利益,并利用交互式模糊算法来确定中央仓库和第三方物流企业的选择、分配及服务方案。通过实例分析,既验证了算法的可行性,同时也表明了中央仓库的选址应尽量靠近需求点密集的区域,网络销售商应提供少量第三方物流服务企业供顾客选择。     

Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider

In this study, we consider a manufacturer that has strategically decided to outsource the company specific reverse logistics (RL) activities to a third-party logistics (3PL) service provider. Given the locations of the collection centers and reprocessing facilities, the RL network design of the 3PL involves finding the number and places of the test centers under supply uncertainty associated with the quantity of the returns. Hybrid simulation-analytical modeling, which iteratively uses mixed integer programming models and simulation, is a suitable framework for handling the uncertainties in the stochastic RL network design problem. We present two hybrid simulation-analytical modeling approaches for the RL network design of the 3PL. The first one is an adaptation of a problem-specific approach proposed in the literature for the design of a distribution network design of a 3PL. The second one involves the development of a generic approach based on a recently proposed novel solution methodology. In the generic approach instead of exchanging problem-specific parameters between the analytical and simulation model, the interaction is governed by reflecting the impact of uncertainty obtained via simulation to the objective function of the analytical model. The results obtained from the two approaches under different scenario and parameter settings are discussed.     

再制造供应链的回收责任转移模型

考虑由一个制造商( ),一个第三方回收商(TPL)组成的二级再制造供应链系统,构建 TPL 通过转移支付将回收责任转移给 的6种模型,得到各模型的均衡解,并分析了各模型的环保效果和意义.分析结果表明:责任转移因子 的主导者不同和转移因子 的上限不同均会影响双方的博弈结果;由 TPL 决定转移因子 时的回收效果较好;权力极端化的两种模型( - 模型和 - 模型)下, 起到的良性作用优于 TPL.     

Location based treatment activities for end of life products network design under uncertainty by a robust multi-objective memetic-based heuristic approach

Rapid growth in world population and recourse limitations necessitate remanufacturing of products and their parts/modules. Managing these processes requires special activities such as inspection, disassembly, and sorting activities known as treatment activities. This paper proposes a capacitated multi-echelon, multi-product reverse logistic network design with fuzzy returned products in which both locations of the treatment activities and facilities are decision variables. As the obtained nonlinear mixed integer programming model is a combinatorial problem, a memetic-based heuristic approach is presented to solve the resulted model. To validate the proposed memetic-based heuristic method, the obtained results are compared with the results of the linear approximation of the model, which is obtained by a commercial optimization package. Moreover, due to inherent uncertainty in return products, demands of these products are considered as uncertain parameters and therefore a fuzzy approach is employed to tackle this matter. In order to deal with the uncertainty, a stochastic simulation approach is employed to defuzzify the demands, where extra costs due to opening new centers or extra transportation costs may be imposed to the system. These costs are considered as penalty in the objective function. To minimize the resulting penalties during simulation's iterations, the average of penalties is added to the objective function of the deterministic model considered as the primary objective function and variance of penalties are considered as the secondary objective function to make a robust solution. The resulted bi-objective model is solved through goal programming method to minimizing the objectives, simultaneously.