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。     

基于第三方回收再制造逆向物流网络设计

针对再制造逆向物流网络设计问题, 在考虑产品回收量和消费市场需求量不确定的条件下, 以第三方物流企业收益最大化和制造商收益最大化为目标建立了基于第三方回收多周期再制造逆向物流网络模型. 利用该模型可以确定每个周期制造商购买第三方物流企业产品的最优价格, 可以确定网络中回收中心和检测/拆卸中心的开设数量以及各设施间的物流量分配. 将不确定参数用三角模糊数表示, 借助模糊机会约束规划方法将该模型转化为确定性等价模型. 利用算例验证了模型的有效性, 并用目标规划法分析了两个目标之间的关系.     

再制造混合物流网络的多周期多目标优化设计

考虑到产品生产和运输过程中对环境的影响以及产品的单位生产成本和生产批量之间的关联,以多周期内的总运营成本最少和对环境的有害影响最少为设计目标,并考虑到物流设施的能力限制和正逆向物流的集成运作,构建了制造/再制造集成物流网络的多周期优化设计模型。通过求解模型,确定了制造/再制造集成物流网络中各种设施的数量、位置、规模以及各设施间的物流量分配。最后,用算例验证了模型的有效性。     

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。     

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.     

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。     

再生资源回收物流网络优化模型与算法研究

再生资源回收利用是发展循环经济的有机组成部分,再生资源回收物流网络规划问题的研究是再生资源回收利用系统运行的前提与基础。在考虑再生资源回收量和需求量波动性的基础上,以最小化回收物流总成本为优化目标,构建具有回收点、回收中心、集散市场和深加工中心或客户的四层级、多品种单周期再生资源回收物流网络规划模型。采用基于回收主体综合处理能力及映射关系的解改进优化策略,设计两阶段启发式算法求解模型。算例表明,提出的模型与算法能有效制定再生资源回收物流网络布局方案,且可推广用于单品种单周期、单品种多周期、多品种多周期的再生资源回收物流网络优化问题。     

不确定环境下逆向物流动态选址问题的研究

由于回收的废旧物资和再处理"新产品"在回收数量、质量和供求等方面的不确定性,给逆向物流网络设施选址造成了一定程度的困难。该文以多周期内的逆向物流网络总成本最小化为优化目标,对多决策周期内逆向物流网络的动态选址进行了优化设计,并通过具体的算例分析,验证了不确定性条件下模型的有效性和可靠性。     

基于遗传模拟退火算法的多层设施选址方法

逆向物流网络是逆向物流系统高效运作的基础和前提,而设施的选址定位是逆向物流网络设计的核心问题.为此,提出一个多层设施选址模型,旨在构建由回收点、回收中心和生产点相结合的最佳逆向物流回收网络.根据模型特点,提出基于遗传模拟退火算法的求解方法,个体采用二进制十进制混合编码;提出基于Metropolis准则的特定遗传进化操作;设计顾客对回收点、回收点对回收中心的两个子分配算法保证所有约束的满足性.最后通过仿真实验,得到满意的设施选址方案.可见,选址模型和算法是一种有效的设施选址方法,具有一定的应用前景.     

Optimal production planning for a multi-product closed loop system with uncertain demand and return

We study the production planning problem for a multi-product closed loop system, in which the manufacturer has two channels for supplying products: producing brand-new products and remanufacturing returns into as-new ones. In the remanufacturing process, used products are bought back and remanufactured into as-new products which are sold together with the brand-new ones. The demands for all the products are uncertain, and their returns are uncertain and price-sensitive. The problem is to maximize the manufacturer's expected profit by jointly determining the production quantities of brand-new products, the quantities of remanufactured products and the acquisition prices of the used products, subject to a capacity constraint. A mathematical model is presented to formulate the problem and a Lagrangian relaxation based approach is developed to solve the problem. Numerical examples are presented to illustrate the model and test the solution approach. Computational results show that the proposed approach is highly promising for solving the problems. The sensitivity analysis is also conducted to generate managerial insights.     

考虑政府补贴的新产品与再制造产品定价模型

在有限财政预算下,政府对再制造产品进行补贴,以促进再制造产品的生产和销售.基于此,首先建立政府对制造商进行生产补贴的三层决策模型,该模型中制造商与新产品销售商和再制造产品销售商进行Stackelberg博弈,同时两个销售商之间通过Bertrand博弈确定各自的销售价,通过对解的分析,揭示生产补贴对批发价、销售价以及销售量的影响,并通过与无补贴销售量的比较,进一步给出政府补贴提升再制造产品销量的根本原因;然后给出对再制造产品销售商进行销售补贴下的三层模型,证明销售补贴与生产补贴在影响销售价格、销售量方面的等价性;接着讨论集中决策供应链中新产品与再制造产品销售的二层决策模型,比较集中决策供应链与分散决策供应链再制造产品的销售量高低并分析其原因,指出政府倾向于优先补贴的供应链类型;最后给出一个算例,分析不同类型供应链中再制造产品销售量对参数的敏感性.     

基于期权的供应链数量柔性契约决策模型

针对单个制造商和单个销售商组成的多周期单产品两级供应链协调问题, 建立了期权与数量柔性契约相结合的决策模型; 需求的不确定性通过有限的情境描述, 采用逐步对冲算法对基于情境的随机规划模型进行求解, 给出了销售商最优的期权购买量和初始订货量以及制造商最优的原材料采购量和产品生产量; 最后通过数值仿真实验验证了所提出模型的有效性.

     

A stochastic programming model for multi-product oriented multi-channel component replenishment

Assemble-to-order (ATO) manufacturing companies face decision problems on components replenishment and outsourcing channels adoption. This paper proposes a stochastic programming approach on component replenishment decisions for an ATO contract manufacturer who faces a single period stochastic demand for multiple products made by multiple components. The first stage of the proposed model is to decide the quantities for pre-stocking all types of components. After the confirmation of the demands, the second stage is to decide the quantities of outsourcing components and the outsourcing channels among several candidates for each component. In addition, the assignment of components to the productions of different products, and the decreasing trends of products׳ prices with respect to their delivery time are considered. For the problem, this paper proposes a stochastic programming model. The nonlinear objective is linearized by some approximation. For solving the model, a local branching based solution method is suggested. Some numerical experiments are performed to investigate the efficiency of the proposed solution method and the effectiveness of the proposed model.     

Determining the capacity and its level of utilization in make-to-order manufacturing: A simple deterministic model for single-machine multiple-product case

A simple deterministic model is developed to determine the capacity and its level of utilization for a single specific machine producing multiple products in make-to-order manufacturing plants. It integrates the variables of processing time, set-up time, product defective rate, and maintenance downtime. The number of product types and the number of lots per product are critical determinants. The model first computes the net production quantities per unit of time for each product type and for the product mixtures, and then, capacity utilization figures are derived based on the net capacity inverse coefficients and net product quantities per planned time unit for each product type and product mixture.     

Adaptive model predictive inventory controller for multiproduct warehouse system

An inventory control system has been developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations.     

An advanced order acceptance model for hybrid production strategy

Competitors stand out through commitment to providing excellent customer service. Many organizations strive to promote their order promising system to generate more reliable quotes with quantity and due date constraints. This paper develops a Capable-to-Promise (CTP) based Order Acceptance Model (OAM) for a Hybrid Production Strategy (HPS) based on the positioning of the Customer Order Decoupling Points. The proposed model allocates the uncommitted availability and planned production receipts to current and anticipation of future needs. A Mixed-Integer Linear Programming (MILP) is developed to determine the optimal order quantities based on the resource availability. The proposed model efficiently mitigates the risk of not being reliable in the commitments due to discrepancies between the real and unused quantities. The CTP based OAM is compatible with a HPS with both make-to-order (MTO) and make-to-stock (MTS). The presented model encompasses four steps. First, demands for MTO products are collected in batches on daily basis and a forecasting model is applied to predict orders for MTS products. Secondly, the quantity-based Revenue Management approach is used to prioritize orders. Afterwards, the optimization model assesses the availability of resources in order to produce collected orders. The final step is to accept valuable orders based on the resource availability. In order to illustrate the applications of the modeling approach, two case studies are provided.     

The Traveling Purchaser Problem with time-dependent quantities

The deterministic Traveling Purchaser Problem looks for a tour visiting a subset of markets in order to satisfy a positive discrete demand for some products at minimum traveling and purchasing costs. In this paper, we assume that the quantities available in the markets for all the products are time-varying decreasing at a constant rate. We propose a compact mixed integer formulation for the problem, and strengthen it with the introduction of connectivity constraints. A new branching strategy and a primal heuristic enforcing the bounding operations have been embedded into a branch-and-cut framework. The branching rule exploits a simple valid inequality and the potential presence of necessary markets. The resulting method outperforms CPLEX 12.6 when used to solve the proposed model. The algorithms have been tested on standard TSPLIB instances, modified to include products and quantities that decrease at different rates of consumption.     

Performance analysis of a hybrid system under quality impact of returns

In this paper we analyze a hybrid system that meets the demand with remanufactured or new products. In the remanufacturing stage there are uncertainties in the quality of remanufactured products, return rates and return times of returned products. These uncertainties affect raw material order quantities, processing times and material recovery rates. In the study returned products are classified by considering quality uncertainties. According to this classification remanufacturing processing times, material recovery rates, remanufacturing costs and disposal costs are determined. In order to analyze the effect of uncertainties in return quality a simulation model is constructed by using the ARENA simulation program. Our analysis denotes that under different cost scenarios quality based classification of returned products brings significant cost savings. The numerical analysis indicates that a cost improvement of more than 8% is achieved when return rates are high.