废旧机械产品再制造的资源环境评价*

再制造不仅延续了产品的寿命周期,而且具有巨大的资源环境效益.通过简式寿命周期评价矩阵中有关元素的分析、钢铁原材料生产过程的环境负荷分析、机械产品（零部件）原始制造与再制造中的环境负荷分析,表明再制造产品的资源环境优势集中体现在其原料获取和产品生产阶段.零件再制造主要使用各种表面技术对局部失效表面进行修复与强化,免去了其原始制造中金属材料生产、毛坯生产的全部及后续切削加工、材料处理中的大部分资源、能源消耗和废弃物排放,其相应总量比原始制造一般低1～2个数量级,整个产品再制造的资源环境效益随新品零件替换率的提高而降低.     

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.     

Effects of an Inaccurate Sorting Procedure on Optimal Procurement and Production Decisions in a Remanufacturing System

Under the uncertainty of market demand and quality of returns, sorting prior to disassembly is effective for timely obtaining information about the remanufacturability of used products. In this article, we assume that the remanufacturable fraction of used products is a random variable and introduce an inaccurate sorting procedure of used products prior to disassembly. Then, three two-stage optimization models are formulated to maximize the expected profits of a remanufacturer in a single period with used products and/or new parts as inputs to meet the stochastic market demand. Moreover, the article provides a case study to explore the optimal decisions under different scenarios and analyzes the effects of parameters, such as the unit disassembly cost, unit sorting cost, and proportion of sorting errors. Finally, the results indicate that whether remanufacturing with sorting is more profitable than that without sorting mainly depends on the sorting accuracy and the relative value between disassembly cost and sorting cost. When considering or not considering a long lead time of new parts, the effects of sorting errors on procurement policies are different. A long lead time will result in lower expected profits. Finally, the diverse types of classification errors have different influences on procurement policies and corresponding expected profit.     

Theory and methodology on the global optimal solution to a General Reverse Logistics Inventory Model for deteriorating items and time-varying rates

In this paper, we present a unified general inventory model for integrated production of new items and remanufacturing of returned items for an infinite planning horizon. Our model considers a production environment that consists of three shops. The first shop is for remanufacturing returned items, the second shop is for manufacturing new items, while the third shop is for collecting returned items to be remanufactured in the first shop. The system is subject to joint production and remanufacturing options, the first one is to produce new items while the second one is to reproduce/recycle the returned items “as-good-as new”. Items deteriorate while they are in storage, and production, remanufacturing, demand, return, and deterioration rates are arbitrary functions of time. A closed form for the total relevant costs as well as a rigorous mathematical proof, which shows the global optimality of the solution to the underlying inventory system are introduced. Illustrative examples, which explain the application of the theoretical results as well as their numerical verifications, are also given.     

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.     

Multi-product economic lot scheduling problem with manufacturing and remanufacturing using a basic period policy

In this research we study the multi-product Economic Lot Scheduling Problem (ELSP) with manufacturing and remanufacturing opportunities. Manufacturing and remanufacturing operations are performed on the same production line. Both manufactured and remanufactured products have the same quality thus they fulfil the same demand stream. Tang and Teunter (2006) firstly studied this type of Economic Lot Scheduling Problem with Returns (ELSPR) and presented a complex algorithm for the optimal solution. More recently Teunter, Tang, and Kaparis (2009) proposed several heuristics to deal with the same problem using more computational efficient approaches. However, both studies have limited the attention to the common cycle policy with the assumption that a single (re)manufacturing lot is used for each item in each cycle. Relaxing the constraint of common cycle time and a single (re)manufacturing lot for each item in each cycle, we propose a simple, easy to implement algorithm, based on Segerstedt (1999), to solve the model using a basic period policy. Several numerical examples show the applicability of the algorithm and the cost savings.     

A strategic framework for the design and implementation of remanufacturing operations in reverse logistics

Reverse logistics (RL) is a systematic process that manages the flow of products/parts from the point of consumption back to the point of manufacturing for possible recycling, remanufacturing, or disposal. Although the concept of RL is well-known in logistics and supply chain management, the available holistic literature and theory of RL are scarce. This paper surveys current RL literature and identifies the present state of theory in RL by formulating the propositions for strategic factors. The approach used is grounded theory development. The strategic factors are in turn delineated and evaluated in terms of specific sub-factors associated with each factor by the use of interview protocol and within the context of an in-depth analysis of two companies in different industries that are engaged in remanufacturing/recycling operations within RL systems. The analysis of these case studies, using the grounded theory approach, resulted in insights regarding their RL practices. Based on these insights and strategic factors and sub-factors, a framework for effective design and implementation of remanufacturing/recycling operations in RL is provided. This framework allows for the determination of the viability of returned products/parts in the RL system. The findings outline how our RL theory is enhanced and how our understanding of RL practices with respect to remanufacturing/recycling operations is improved. In conclusion, managerial implications and future research directions are presented.     

Network and contract optimization for maintenance services with remanufacturing

Implementing comprehensive service contracts and sustainable supply chains are two recent trends that create the opportunity to develop maintenance contracts with an uptime guarantee for the customer and a remanufacturing process for removed parts. This involves management decisions on the design of the contract (price, uptime guarantee and overhaul interval) and the logistics network (facility locations, capacities and inventories with given service level). These two decision levels are interrelated: the number of contracts is a function of price and machine uptime, while this uptime is affected by the overhaul interval and network responsiveness. Steady-state queueing equations explicitly model the stochastic nature of the problem, e.g. the impact of resource utilization levels on lead time of the remanufacturing process. This approach results in a non-linear mixed integer model, which is solved by a differential evolution algorithm to find the maximum profit that simultaneously optimizes both problems. A real-life application reveals that price sensitivity is a critical determinant and that measures must be taken to tackle the problem of moral hazard.     

Determining the prices of remanufactured products,capacity of internal workstations and the contracting strategy within queuing framework

This paper studies a remanufacturing facility with several types of incoming nonconforming products and different independent remanufacturing workstations. The workstations have limited capacities so that an outsourcing strategy can be practiced. Each workstation is modeled with an M/M/1/k queuing system considering k as a decision variable. Additionally, a binary decision variable is taken into account to determine the contracting strategy along with some decision variables for the prices of remanufactured products. Thus, a bi-objective mixed-integer nonlinear programming is built to obtain optimal values of the decision variables. The first objective attempts to maximize the total profit and the second minimizes the average length of queuing at workstations. To solve the complex bi-objective mixed-integer nonlinear programming problem, the best out of six multi-objective decision-making (MODM) methods is selected in order to make the bi-objective optimization problem a single-objective one. Afterward, a genetic algorithm (GA) is developed to find a near-optimum solution of the single-objective problem. Besides, all of the important parameters of the algorithm are calibrated using regression analysis. To validate the results obtained, the solutions of some test problems are compared to the ones obtained by the GAMS software. The applicability of the proposed model and the solution procedure are shown with an illustrative example.     

不确定度评定法预测再制造机床定位精度及其工程应用

根据不确定度B类评定的知识,提出一种简单、实用的方法,预测再制造机床定位精度。并以单轴伺服进给机械系统为研究对象,给出了预测模型的计算公式。以此为基础开发了定位精度的预测软件,该软件可方便地得到计算结果并直观地显示各因素对定位精度的影响程度。最后结合生产实例进行计算分析并与验收结果进行比较,表明该方法是可行的。