A hybrid method of blockchain and case-based reasoning for remanufacturing process planning

Remanufacturing plays a vital role in promoting the development of circular economy for its great advantages in energy saving, material saving and emission reduction. Remanufacturing process planning (RPP), which affects the performance of remanufacturing greatly, becomes increasingly important to the remanufacturing enterprises. In general, RPP is knowledge dependent. Some remanufacturing enterprises, especially small and middle-sized remanufacturing enterprises (SMREs) may have inadequate remanufacturing knowledge, which makes it difficult to implement a proper RPP. Therefore, how to share and make full use of the knowledge in different remanufacturing enterprises for RPP has become a bottleneck. To this end, a hybrid method integrating blockchain (BC) and case-based reasoning (CBR) for RPP, which can take full advantage of the remanufacturing knowledge by cross enterprises knowledge sharing, is presented in this paper. In this proposed method, a BC network was utilized to record the remanufacturing knowledge and its associated transactions to guarantee the security and reliability of knowledge sharing, and CBR was employed to retrieve and reuse the most suitable solution by analyzing the similarity between previous remanufacturing cases and new case with the nearest neighbor algorithm. Finally, a used lathe guideway was set as a case study to verify the feasibility and superiority of the proposed approach. The hybrid method has been applied in a prototype system written in HTML and JavaScript. The results indicated that the proposed approach can effectively help SMREs to obtain optimum solutions for RPP with comprehensive economic, environmental and social benefits.

     

Development of design for remanufacturing guidelines to support sustainable manufacturing

Developing sustainable approaches to manufacture is a critical global concern. Key measures towards this include practicing design for environment (ecodesign), for example by improving remanufacturing efficiency and effectiveness. Remanufacturing is a process of bringing used products to a “like-new” functional state with warranty to match. Its significance is that it can be both profitable and less harmful to the environment in comparison to conventional manufacturing. Remanufacturing has a low profile in world economies and is poorly understood because of its relative novelty in research terms. However, environmental and competitive pressures are changing the global and business environment and this is fuelling interest in the practice. This paper provides the background to remanufacturing together with the findings from workshops recently undertaken in the UK as part of research into design and manufacturing approaches to facilitate remanufacturing.     

Heuristics for demand-driven disassembly planning

Remanufacturing of used products has become accepted as an advantageous disposition option within the field of reverse logistics. Remanufacturing, where a firm takes returned products at the end of their life and disassembles them to obtain parts which are reassembled into “good as new” products, may require so-called demand-driven- disassembly, where a specific amount of returned products must be disassembled to yield parts which are either demanded externally, or used in the remanufacturing operation. While in its simplest form, the solution can be merely calculated, more realistically complex product structures require a more powerful solution method. The first choice, integer programming (IP) can be used to arrive at an optimal solution, with the disadvantage that the time required to solve the problem explodes with increasingly complex product structures and longer time horizons. Another possible method, heuristics which were presented in a previous work on this problem [Taleb and Gupta (Computers & Industrial Engineering 1997; 32(4): 949–61], offer a faster, easier solution with the disadvantage that it is not necessarily optimal, and under certain circumstances may deliver an infeasible result. In this work, this problem was corrected and the heuristic was extended in several important ways to deal with holding costs and external procurement of items. The methodology is illustrated by an example. Another advantage of the heuristic is that it can be programmed into code and executed via spreadsheet application, which will facilitate its application. A performance study reveals that the new heuristic performs quite well for a wide spectrum of randomly generated problem test instances.     

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.     

两周期双寡头竞争环境中再制造影响

针对两周期双寡头竞争市场,分别构建单个企业实施再制造和两个企业实施再制造的产量决策模型,探讨再制造对双寡头竞争市场的产品供应、产品价格、企业利润、消费者福利以及环境污染的影响.研究表明:1)当且仅当再制造补贴与再制造成本节约之和满足一定条件时,双寡头才会进入再制造市场.2)再制造可增加企业利润、提高消费者剩余,并在一定条件下降低环境污染;当再制造成本优势明显时,再制造企业会扩大第1周期的新产品供给.3)若只有一个企业具备再制造条件,则再制造能帮助企业提高市场份额,当再制造成本优势明显时,再制造企业可在第1周期实现对新产品的完全垄断,从而改变市场竞争格局.     

A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.     

Remanufacturing and pricing decisions with random yield and random demand

Remanufacturing is creating considerable benefit to industry and community, but the uncertainties in both supply and demand sides bring significant difficulty to the production and marketing management of remanufactured products. This paper considers the remanufacturing and pricing decisions when both the remanufacturing yield and the demand for remanufactured products are random. Two typical sequential decision strategies are explored: First-Remanufacturing-Then-Pricing (FRTP) and First-Pricing-Then-Remanufacturing (FPTR). The optimal remanufacturing quantity and selling price of the remanufactured product are developed for each strategy, under negligible salvage value and shortage penalty. We also conduct a numerical study to investigate the scenario where the salvage value and shortage penalty are non-negligible, and explore the parameter sensitivity of the systems. We find that FRTP strategy is more preferable for low remanufacturing cost, market-price sensitivity, and variance of demand randomness, and for high shortage penalty, salvage value, and variance of remanufacturing yield; while FRTP strategy is more preferable for the complementary situation.     

A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling

Remanufacturing has attracted growing attention in recent years because of its energy-saving and emission-reduction potential. Process planning and scheduling play important roles in the organization of remanufacturing activities and directly affect the overall performance of a remanufacturing system. However, the existing research on remanufacturing process planning and scheduling is very limited due to the difficulty and complexity brought about by various uncertainties in remanufacturing processes. We address the problem by adopting a simulation-based optimization framework. In the proposed genetic algorithm, a solution represents the selected process routes for the jobs to be remanufactured, and the quality of a solution is evaluated through Monte Carlo simulation, in which a production schedule is generated following the specified process routes. The studied problem includes two objective functions to be optimized simultaneously (one concerned with process planning and the other concerned with scheduling), and therefore, Pareto-based optimization principles are applied. The proposed solution approach is comprehensively tested and is shown to outperform a standard multi-objective optimization algorithm.     

再制造率随机的闭环供应链产品差别定价策略

考虑闭环供应链的差别定价问题, 制造商回收废旧产品, 并生产新产品和再制造产品, 再制造率随机. 采用Stackelberg 博弈方法, 研究集中决策和分散决策下相应产品的最优定价, 给出供应链各成员的最优利润. 结果表明:若制造商对产品延迟定价, 则集中决策下回收价格高于分散决策, 而销售价格均低于分散决策; 当零售商的保守利润不低于一定值时, 集中决策下总利润不低于分散决策, 制造商可以采用改进的两部定价契约协调供应链.

     

Service Platform for Robotic Disassembly Planning in Remanufacturing

Remanufacturing is being paid much attention due to environmental protection and resource re-utilization. Disassembly is an inevitable step of remanufacturing and it is always finished by the manual labor. Robotic disassembly helps to improve the automation of disassembly process, while robotic disassembly planning helps to improve the efficiency of robotic disassembly. However, the existing research on robotic disassembly planning seldom integrates the physical industrial robots and then provides convenient services for the users. In this paper, the framework of service platform for robotic disassembly planning in remanufacturing is studied. The database of the service platform is designed, and the data communication method between the software system and KUKA industrial robots is studied. After that, based on the physical facilities, this service platform is implemented using MyEclipse software. The developed service platform is convenient for the users to access the methods of robotic disassembly planning and the connected industrial robots can also execute the optimal disassembly solutions through the data communication method after the optimal disassembly solutions are obtained. Finally, case study based on an idler shaft is conducted to verify the developed service platform for robotic disassembly planning.     

Remanufacturability evaluation method and application for used engineering machinery parts based on fuzzy-EAHP

Remanufacturing is an important mode to promote the sustainable development of manufacturing industry. Remanufacturing mechanical components at the best recycling time can effectively reduce the remanufacturing cost and improve the remanufacturing efficiency. Therefore, the decision-making model of optimal recycling time based on full lifecycle assessment and full lifecycle cost method was firstly established to analyze and model the energy consumption, cost and emission of components in the original manufacturing, usage, remanufacturing and reuse stages. Then, a remanufacturing evaluation method based on fuzzy extension analytic hierarchy process was proposed. Considering a variety of uncertainties of waste components, a qualitative and quantitative evaluation index system was established, and the weight of each evaluation index was determined by expert evaluation and fuzzy comprehensive evaluation method. Finally, a boom cylinder of a concrete pump truck was studied. The boom cylinder recovered by the method proposed has a high recovery value, and its comprehensive score reaches 70, which verifies the effectiveness and superiority of the model.     

Investigating remanufacturing competition with yield uncertainty on market share,profit, and consumer surplus

Remanufacturing has been recognized both in literature and practice because of its greenness and cost saving. Since the reproduction processes could be affected by uncertain factors, remanufacturing is much more complex than producing new products. To investigate how yield uncertainty and market competition affect remanufacturing decisions, we compare the results under three classical market scenarios with random yield, that is, (a) Nash–Cournot, (b) monopoly, and (c) Stackelberg–Cournot. On comparing the expected remanufacturing scale, expected profit, and consumer surplus, it shows that as the number of remanufacturers increases, the planned collection quantity of a single remanufacturer decreases, while the total expected market output increases. Furthermore, we find that the largest expected profit and expected consumer surplus brought by market scenarios depend on a threshold remanufacturer number. We also relax the assumptions and extend our model to consider the scenario of m leaders and followers in a Stackelberg game. It is interesting to find that the first‐mover advantage in profit and market share of the leader remanufacturer would be challenged when the number of leader remanufacturers is no less than 2. The results can be potentially useful for managerial decisions and environmental administrations.     

考虑政府补贴及环境设计的再制造闭环供应链生产决策

在外包及授权再制造模式下,研究考虑政府补贴及制造商环境设计的再制造闭环供应链生产决策问题.构建4种闭环供应链决策模型,分析政府补贴及环境设计水平对制造商与再制造商竞合关系、闭环供应链绩效以及环境的影响.研究发现,制造商可以通过调节单位再制造外包费或专利许可费实现与再制造商共享政府补贴,因此政府不同补贴策略不会对闭环供应链最优生产决策及绩效产生影响.政府补贴与消费者对再制造产品的认知程度,并不总是有利于提高制造商环境设计水平,但均有助于促进再制造产品销售.制造商总是有动机进行环境设计,而再制造商只有在环境设计能为再制造产品带来更多成本节约时,才有动力接受制造商的环境设计方案.相比于授权再制造,制造商及再制造商均在外包再制造下获得更多的利润.另外,政府补贴与环境设计并不一定总能起到减少产品环境总影响程度的作用.     

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.     

Issues in environmentally conscious manufacturing and product recovery: a survey

Environmentally Conscious Manufacturing and Product Recovery (ECMPRO) has become an obligation to the environment and to the society itself, enforced primarily by governmental regulations and customer perspective on environmental issues. This is mainly driven by the escalating deterioration of the environment, e.g. diminishing raw material resources, overflowing waste sites and increasing levels of pollution. ECMPRO involves integrating environmental thinking into new product development including design, material selection, manufacturing processes and delivery of the product to the consumers, plus the end-of-life management of the product after its useful life. ECMPRO related issues have found a large following in industry and academia who aim to find solutions to the problems that arise in this newly emerged research area. Problems are widespread including the ones related to life cycle of products, disassembly, material recovery, remanufacturing and pollution prevention. In this paper, we present the development of research in ECMPRO and provide a state-of-the-art survey of published work.     

考虑广告投入影响的再制造闭环供应链优化决策博弈模型研究

针对制造/再制造产品的市场竞争与合作问题, 考虑广告投入对产品消费者效用的增长效应及消费者环境偏好的影响, 在构建制造产品和再制造产品的市场需求函数基础上, 应用博弈方法比较分析合作博弈、纳什均衡博弈、Stackelberg主从博弈三种决策模式下制造/再制造产品的最优定价和广告投入策略, 并针对非合作博弈下的效率损失设计了闭环供应链中制造和再制造过程的利益协调机制。数值算例分析表明, 合作博弈决策下供应链总利润最优、制造和再制造产品市场销售价格最高, 而合作博弈和Stackelberg主从博弈都会以牺牲再制造产品利润为代价获得最优利润, 因此再制造部门会偏好纳什均衡博弈, 采取以自身利益最优为目标的竞争策略, 没有动机成为制造部门的跟随者。     

A production–inventory model with remanufacturing for defective and usable items in fuzzy-environment

This paper investigates a production–remanufacturing system for a single product over a known-finite time horizon. Here the production system produces some defective units which are continuously transferred to the remanufacturing unit and the constant demand is satisfied by the perfect items from production and remanufactured units. Remanufacturing unit uses the defective items from production unit and the collected used-products from the customers and later items are remanufactured for reuse as fresh items. Some of the used items in the remanufacturing unit are disposed off which are not repairable. The remanufactured units are treated as perfect items. Normally, rate of defectiveness varies in a production system and may be approximated by a constant or fuzzy parameter. Hence, two models are formulated separately with constant and fuzzy defective productions. When defective rate is imprecise, optimistic and pessimistic equivalent of fuzzy objective function is obtained by using credibility measure of fuzzy event by taking fuzzy expectation. Here, it is assumed that remanufacturing system starts from the second production cycle and after that both production and remanufacturing units continue simultaneously. The models are formulated for maximum total profit out of the whole system. Here the decision variables are the total number of cycles in the time horizon, the duration for which the defective items are collected and the cycle length after the first cycle. Genetic Algorithm is developed with Roulette wheel selection, Arithmetic crossover, Random mutation and applied to evaluate the maximum total profit and the corresponding optimum decision variables. The models are illustrated with some numerical data. Results of some particular cases are also presented.     

A system dynamics framework for integrated reverse supply chain with three way recovery and product exchange policy

With the technological advancements and rapid changes in demand pattern, diverse ranges of products are entering into the market with reduced lifecycle which leads to the environmental disasters. The awareness of product take-back and recovery has been increasing in various supply chains not only due to the obligation imposed by legislation but also competitive economics worldwide. In this paper, we develop a system dynamics framework for a closed-loop supply chain network with product exchange and three way recovery policy, namely; product remanufacturing, component reuse and remanufacturing, and raw material recovery. In the simulation study, we investigate the significance of various factors including product exchange, collection and remanufacturing; their interactions and the type of their impact on bullwhip and profitability through sensitivity and statistical analysis. Our results suggest that the inclusion of three way recovery in reverse channel and product exchange policy in the forward channel reduce the order variation and bullwhip effect at both retailer and distributor level and increases the profitability of RL operations. In addition, we redefine the reverse logistics framework with “open-loop” in which the remanufactured products are redistributed only in the secondary market and compare the performance of open-loop model with that of closed-loop. Extended numerical investigation provides insights to the decision makers regarding the actions which can lead to better performance of the system.     

Acquisition and pricing strategies in hybrid manufacturing-remanufacturing systems

There are two typical cases of acquisition and remanufacturing in a closed-loop supply chain: The third party remanufacturer (3PR) and the original equipment manufacturer (OEM) separately acquire and remanufacture used products. The used products have different qualities, and they can be classified for acquisition in reality. In this paper, under the grading acquisition mode, we compare the acquisition strategies of used products and the pricing strategies of new products and remanufactured products in different manufacturing-remanufacturing systems. Then we further study the impact of the timing of remanufacturing on the profit and the environment. From an environmental perspective, the 3PR is willing to acquire and remanufacture used products at the best grade, whereas the OEM may choose more grades. The latter can reduce environmental impact. From an economic perspective, as long as the total cost of acquiring and remanufacturing used products does not exceed the threshold, remanufacturing can bring more profit for the OEM than only selling new products. Moreover, when consumers’ preference for remanufactured products is relatively high, and the total cost of acquisition and remanufacturing does not exceed the threshold, the OEM can engage in remanufacturing business before the new products exit from the market.     

Stochastic model predictive control for remanufacturing system management

Uncertainties in the quality, quantity, and operational time of used products pose a challenge to the management of remanufacturing systems. In addition, it becomes a necessity to optimize the operation of the remanufacturing system to balance the quality of products, remanufacturing efficiency, and service level. In this study, a stochastic discrete-time dynamical model is proposed to represent a remanufacturing system, where the relationship between the market satisfaction, inventory status, and operational actions is explicitly modeled. This includes production and inventory planning, resource allocation and acquisition. To handle uncertainties, a stochastic model predictive control approach is proposed to plan the actions that optimize the remanufacturing efficiency. Our results in the simulation examples show that: (a) without supplies, the remanufacturing system has better stability and robustness than a conventional manufacturing system with the same initial stocks; and (b) with insufficient initial stocks, the remanufacturing system demands fewer and more gradual supplies, thereby keeping the system stable. Finally, a sensitivity analysis is conducted for testing the performance of the remanufacturing system. By changing the operational action capacity, different state equilibria are discovered, which correspond to distinct system response characteristics. The study reveals notable managerial insights and effects of product commonality, demand patterns, and operational actions scheduling on the efficiency of the remanufacturing system.