基于可信性理论的模糊环境下再制造生产计划

针对再制造系统生产计划问题,分析了再制造系统的特点。以一般再制造系统为原型,在可回收产品数量、单位产品拆卸时间、单位零部件再制造加工时间以及客户需求不确定情况下,考虑回收、拆卸、再制造加工、重新装配的能力限制,以总成本最小化为目标,构建了模糊再制造系统混合整数规划模型。利用可信性理论给出了模糊模型的清晰等价形式。最后,通过算例分析了不同置信水平选取对再制造成本的影响,验证了模型对再制造企业生产计划制定的适用性。     

制造与再制造决策的优化

产品回收再利用越来越受到重视，许多原产商都积极地进行回收再制造。再制造与制造过程通常会共用一些相同的生产资源，由此出现的问题就是如何协调这两者的作业，使之达到最优。本文假定制造与再制造产品可以互相替换，着重探讨了当制造提前时间小于或等于再制造提前时间时，制造与再制造的最优策略。     

基于可信性理论的闭环供应链生产计划

针对生产、库存和拆卸能力有限情况下,考虑可回收旧产品数量、分销商需求量、生产成本、外包成本、单位旧产品拆卸时间、拆卸成本和单位产品生产时间为模糊不确定数,以供应链总成本最小化为目标,采用期望值规划和机会约束规划相结合的形式,建立不确定环境下供应链生产计划模型。基于可信性规划方法,将不确定模型转化为清晰确定形式。最后,设计算例,通过求解给出在供应链总成本最小下,企业各个计划期内的最优生产量、零部件采购量以及制造商和回收中心对物料的运输计划;并分析了可信性水平、再制造率和回收率变动对供应链总成本的影响,为企业制定生产计划提供参考依据。     

基于生态效益的制造商再制造竞争决策模型

以生态效益最大化为决策准则,研究了两家制造商回收再制造废旧产品的竞争决策,研究发现:无论回收再制造本身是否经济,回收再制造废旧产品都对企业有利,先实施回收再制造的企业获利更多。随着环境成本的增加,回收率升高,制造商收益下降。产品价格与再制造成本优势有关,成本优势高时,随着环境成本的增加,产品价格降低,对消费者有利;成本优势低时,制造商将环境成本转嫁给消费者,产品价格上升到最高点后下降。随着再制造成本优势的增加,产品价格下降,回收率和制造商收益升高,制造商和消费者均获益。再制造成本优势不同的两家制造商回收再制造时,成本优势高的制造商产品价格低,回收率和企业收益高。     

再制造逆向物流网络的机会约束目标规划模型

应用随机规划方法,将废旧产品回收数量作为机会约束,以生态环境利益、顾客服务水平、固定投资成本及平均变动成本为目标体系,建立了机会约束目标规划模型。通过模型算例,对多种回收不确定性及目标结构下的最优决策进行对比分析,检验了多目标及回收数量不确定性对再制造逆向物流网络结构及成本收益的影响。     

需求和提前期不确定下的两级供应链系统研究

由一个部件提供商和一个装配商组成的两级组装供应链系统,部件提供商提供两种不同的部件给装配商进行装配;分析在单销售周期内,系统上同时存在部件的生产提前期和装配提前期的不确定,以及需求的不确定时,系统的特性.采用博弈论方法,装配商确定装配计划提前期,以及向部件提供商提交的订购量;而部件提供商确定部件生产计划提前期,以及部件批发价格.得出,首先,不确定提前期导致供应链系统订购量减小;其次,在分散控制的组装供应链系统中,系统的总生产、装配计划提前期,要大于集中控制系统中的,并且装配计划提前期与部件生产计划提前期无关;再者,过大的提前期不确定性对组装供应链系统的影响要比需求不确定对组装供应链的影响大.     

模糊环境下考虑供应中断的再制造闭环供应链研究

为研究需求和回收品质量模糊不确定环境下供应中断对再制造闭环供应链的影响,建立了系统总成本模型。运用模糊机会约束规划方法解决模型中不确定性问题,并采用遗传算法和粒子群算法来寻找算例的最优解。结果表明：在不同需求量和回收品质量的水平下企业成本有不同的变化;随着供应中断概率的增加,平均总成本也随之增加。因此,再制造企业在面临需求量和回收品质量模糊不确定的环境时,应尽量避免供应中断,制定符合企业发展的运作计划。     

不同缺货策略下非线性再制造供应链系统动态行为

为研究非线性约束下有缺货和无缺货两种策略对再制造供应链系统动态行为的影响,建立了不可退货、供货受制于当前供货服务水平且回收产量受最大回收能力限制的更符合实际情况的非线性系统模型.依据混沌动力学原理和方法,借助相图对两种缺货策略下非线性系统的动力学行为进行了仿真分析;进一步采用分岔图和最大李亚普诺夫指数对比了不同缺货策略下逆向环节参数对系统行为的影响.数值模拟结果表明,无论系统中有缺货还是无缺货,非线性约束都不是导致系统产生复杂混沌行为的充分条件;此外,除再制造提前期外,其它参数处于不同范围时,有缺货和无缺货策略各有优劣,需要根据管理目标和参数取值,选择适当的缺货策略.     

再制造生产率和市场需求不确定情况下考虑资金时间价值的制造/再制造生产决策优化北大核心CSSCI

在再制造生产率和市场需求不确定情况下,研究考虑了资金时间价值的企业制造/再制造生产决策优化。结合应收账款回收期,建立了考虑资金时间价值的制造/再制造生产决策模型,通过两阶段随机过程分析得到了回收品最优采购量和最大利润。算例分析表明:阶段一的期望利润随着回收品采购量的增加先增加后减少;阶段二的期望利润随着计划产量的增加呈先增加后减少趋势;随着贴现率的增加,在每个贴现率下的利润曲线随着应收账款回收期的增加,利润下降速度加快。     

再制造生产率和市场需求不确定情况下考虑资金时间价值的制造/再制造生产决策优化

在再制造生产率和市场需求不确定情况下,研究考虑了资金时间价值的企业制造/再制造生产决策优化。结合应收账款回收期,建立了考虑资金时间价值的制造/再制造生产决策模型,通过两阶段随机过程分析得到了回收品最优采购量和最大利润。算例分析表明:阶段一的期望利润随着回收品采购量的增加先增加后减少;阶段二的期望利润随着计划产量的增加呈先增加后减少趋势;随着贴现率的增加,在每个贴现率下的利润曲线随着应收账款回收期的增加,利润下降速度加快。     

Joint hybrid repair and remanufacturing systems and supply control

The control of a stochastic manufacturing system that executes capital asset repairs and remanufacturing in an integrated system is examined. The remanufacturing resources respond to planned returns of worn-out equipment at the end of their expected life and unplanned returns triggered by major equipment failures. Remanufacturing operations for planned demand can be executed at different rates and costs corresponding to different replacement and repair modes. The replacement components inventory is provided by an upstream supply with random lead times. The objective is to determine a control policy for both the supply and remanufacturing activities that minimises the average repair/replacement, acquisition and inventory/shortage total cost over an infinite horizon. We propose a suboptimal joint remanufacturing and supply control policy, composed of a multi-hedging point policy (MHPP) for the remanufacturing stage and an (s, Q) policy for the replacement parts supply. The MHPP is based on two inventory thresholds that trigger the use of predefined remanufacturing modes. Control policy parameters are obtained combining analytical modelling, simulation experiments and response surface methodology. The effects of the distribution, mean and variability of the lead time are tested and a sensitivity analysis of cost parameters is conducted to validate the proposed control policy. We also show that our policy leads to a significant cost reduction as compared to a combination of a hedging point policy (HPP) and an (s, Q) policy.     

Optimal acquisition policy in remanufacturing under general core quality distributions

The quality of acquirable used products (cores) is highly variable, which has made production planning and control of remanufacturing systems difficult. This paper studies an acquisition problem in presence of uncertain core quality. In order to derive optimal acquisition policy, the problem is formulated as a non-linear integer programming model in the framework of order statistics. The model is a strictly discrete convex problem with a unique global minimal solution. Then, a single bisection method is developed to obtain the optimal solution under a general continuous quality distribution. Moreover, the expressions of the optimal solution in some frequently used quality distributions are derived. Furthermore, the model is extended to the case of a general remanufacturing cost function, and corresponding results are presented. Finally, numerical experiments are conducted to test the effects of quality distribution, cost relationships of acquirable cores and remanufacturing cost function.     

Integrated production planning and quality control for linear production systems under uncertainties of cycle time and finished product quality

This work considers serial production systems with several process steps and a possible quality control at final step. It deals with the problem of optimising planned lead time when the real lead time for each process is stochastic and the finished product quality is uncertain unless it is inspected. Three analytical models are proposed aiming to minimise the expected total cost, which is composed of the inventory and backlogging costs for the finished product and quality costs associated with inspection and non-conformities. These models correspond to three quality control policies: (i) without quality control, (ii) with quality control but without taking into account the inspection duration when optimising the planned lead time and (iii) with quality control and with considering the inspection duration when optimising the planned lead time. Based on the results, it can be highlighted the economic advantage of integrating quality control at the early stage of supply and production planning decisions for some cost parameters conditions. The robustness of the proposed models is also analysed regarding the variance of the probability distributions of the lead times.     

Effect of limited capacity on optimal planning parameters for a multi-item production system with setup times and advance demand information

An analytical production/inventory model to optimise the planning parameters lot-size, safety stock and planned lead time is developed for a stochastic single-stage production system with multiple items and limited capacity. Based on queuing analysis, the influence of item specific lot-sizes on the production lead time distribution is modelled. Applying stochastic advance demand information, the expected values for finished-goods-inventory, backorders and service level are explicitly stated. Numerical optimisation is applied to solve the respective cost minimisation problem and a solution heuristic is developed to support this approach. A numerical study provides managerial insights concerning capacity limitation effects on the optimal planning parameters. Higher shop loads, i.e. tighter capacity constraints, are found to significantly increase optimal lot-size and optimal safety stock. Safety stock and planned lead time are substitutes, an increase of both leads to higher FGI and lower backorders, however, the specific trade-off depends on the demand information quality. A sensitivity analysis investigating other (non-) financial system parameters is conducted as well. The main contribution of this paper is that the interaction of different planning parameters, i.e. lot-size, safety stock and planned lead time, for different items is simultaneously studied for a capacity constrained production/inventory system.     

Optimal policies in hybrid manufacturing/remanufacturing systems with random price-sensitive product returns

We study a production planning problem in which a manufacturer aims to meet a random market demand by manufacturing new product and remanufacturing returned product. The product returns are random and price-sensitive. To maximise his profit, the manufacturer needs to optimally decide on the acquisition price for the returned product as well as the quantities of the new product to be manufactured and the returned product to be remanufactured. We investigate two cases based on the relative lengths of the manufacturing and remanufacturing lead times. (1) In the case with a shorter manufacturing lead time, the manufacturer first decides his acquisition price for the returned product before the production starts, and then decides the quantities of manufacturing/remanufacturing after the product returns are realised. (2) In the case with a shorter remanufacturing lead time, the manufacturer first decides the manufacturing quantity and the acquisition price simultaneously, and then the remanufacturing quantity based on the quantity of manufactured and returned products. Each case is formulated as a two-stage stochastic optimisation problem, and the corresponding optimal polices of both are characterised and derived.     

Effects of carbon emission regulations on remanufacturing decisions with limited information of demand distribution

Policy-makers are developing regulation policies to drive down carbon emissions from industries. Independent remanufacturers (IRs), which remanufacture recycled products/components/parts, must manage and evaluate economic costs generated by the production under future carbon emission regulations. We present three optimisation models to determine the remanufacturing quantity that maximises the total profits under three common carbon emission regulation policies: (a) mandatory carbon emissions capacity, (b) carbon tax and (c) cap and trade. These models include sales revenue, remanufacturing cost, disposal cost, inventory holding cost, shortage cost and carbon emission cost. The max–min approach is used to solve the models, which assume limited information on demand distribution. We investigate how the three regulation policies affect remanufacturing decision-making for IRs and we also solve some numerical examples where we vary the magnitudes of incentives, penalties and stringency of constraints to provide implications to policy-makers. The results indicate that remanufacturers should aim to improve yield rate to maximise the profit irrespective of the implemented carbon emissions policy. Policy-makers should prefer the carbon tax policy, if any of the other two policies must be performed, a remanufacturing discount such as a higher carbon emission cap or lower penalty should be implemented to better promote the development of remanufacturers.     

基于消费者异质性需求的闭环供应链再制造授权模式研究

在消费者异质性需求的市场中,为了探讨最佳的再制造授权模式,基于原始设备制造商(original equipment manufacturer, OEM)与第三方再制造商((third party remanufacturer, TPR)同时进行再制造的闭环供应链(closed loop supply chain, CLSC),采用博弈论分别讨论TPR独立再制造和OEM专利授权再制造(包括单位费用授权和固定费用授权)的CLSC决策模型,对比3种模型的均衡解及最优利润,并分析TPR再制品市场接受度、再制造成本节约对均衡解及企业利润的影响。研究表明,授权增加了OEM利润,故OEM更倾向于对TPR进行授权,并根据固定授权费的大小来决定授权模式;CLSC总利润在固定费用授权模式下最大,且该模式下消费者的购买成本最低;TPR再制品市场接受度及再制造成本节约的增加可提高TPR利润,但会加大对OEM再制品的挤兑程度,最终导致OEM提高授权费来弥补利润损失。     

Minimising earliness and tardiness by integrating production scheduling with shipping information

Minimisation of earliness and tardiness is known to be critical to manufacturing companies because it may induce numerous tangible and intangible problems, i.e. extra storage cost, spacing, risk of damages, penalty, etc. In literature, earliness and tardiness is usually determined based on order due date, generally regarded as the time of delivering the finished products to the customers. In many papers, delivery time and cost required are usually simplified during the production scheduling. They usually assume that transportation is always available and unlimited. However, transportation usually constructs a critical portion of the total lead time and total cost in practice. Ignoring that will lead to an unreliable schedule. This is especially significant for electronic household appliances manufacturing companies as the studied company in this paper. In general, they usually rely on sea-freight transportation because of the economic reasons. As different sea-freight forwarders have different shipments to the same destination but with different shipping lead time, cost and available time. Adequately considering this shipping information with the production scheduling as an integrated model can minimise the costs induced by earliness and tardiness and the reliability of the schedule planned. In this paper, a two-level genetic algorithm (TLGA) is proposed, which is capable of simultaneously determining production schedule with shipping information. The optimisation reliability of the proposed TLGA is tested by comparing with a simple genetic algorithm. The results indicated that the proposed TLGA can obtain a better solution with lesser number of evolutions. In addition, a number of numerical experiments are carried out. The results demonstrate that the proposed integrated approach can reduce the tardiness, the storage cost, and shipping cost.