Performance Evaluation of Remanufacturing Systems

Implementation of new environmental legislation and public awareness has increased the responsibility on manufacturers. These responsibilities have forced manufacturers to begin remanufacturing and recycling of their goods after they are disposed or returned by customers. Ever since the introduction of remanufacturing, it has been applied in many industries and sectors. The remanufacturing process involves many uncertainties like time, quantity, and quality of returned products. Returned products are time sensitive products and their value drops with time. Thus, the returned products need to be remanufactured quickly to generate the maximum revenue. Every year millions of electronic products return to the manufacturer. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like failure rate of the servers, buffer capacity and inappropriate preventive maintenance policy would be highly responsible the delays in remanufacturing. In this thesis, a simulation based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line, which will help to reduce the cycle time and increase the profit of the firm. Moreover, an estimated relationship between preventive maintenance frequency and MTBF (Mean Time Between Failure) is presented to determine the best preventive maintenance frequency for any industry. The solution approach is applied to a computer remanufacturing and a cell phone remanufacturing industry. Analysis of variance and regression analysis are performed to denote the influential factors in the remanufacturing line, and optimization is done by using the regression techniques and ANOVA results

Performance evaluation of the remanufacturing system prone to random failure and repair

Implementation of new environmental legislation and public awareness has increased the responsibility of manufacturers. Remanufacturing has been applied in many industries and sectors since its introduction. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like high failure rates of the servers, buffer capacities, and inappropriate preventive maintenance policies would be responsible for most of the delays in remanufacturing operations. In this paper, a simulation-based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line. Moreover, an estimated relationship between preventive maintenance frequency and Mean Time Between Failure (MTBF), is presented to determine the best preventive maintenance frequency. The solution approach is applied to computer remanufacturing industry. Analysis of variance (ANOVA), and regression analysis are performed to denote the most influential factors to remanufacturing cycle time (performance measures). A case study is used to show the applicability of the modelling approach in assessing and improving the cycle time, and the profit of a remanufacturing line . Managerial insights are highlighted to support managers and decision-makers in their quest for more efficient and smooth operation of the remanufacturing system

Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases

Coping with disassembly yield uncertainty in remanufacturing using sensor embedded products

© 2011, Ilgin et al; licensee Springer.This paper proposes and investigates the use of embedding sensors in products when designing and manufacturing them to improve the efficiency during their end-of-life (EOL) processing. First, separate design of experiments studies based on orthogonal arrays are carried out for conventional products (CPs) and sensor embedded products (SEPs). In order to calculate the response values for each experiment, detailed discrete event simulation models of both cases are developed considering the precedence relationships among the components together with the routing of different appliance types through the disassembly line. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. The results showed that sensor embedded products improve revenue and profit while achieving significant reductions in backorder, disassembly, disposal, holding, testing and transportation costs. While the paper addresses the EOL processing of dish washers and dryers, the approach provided could be extended to any other industrial product

A hybrid meta-heuristic approach for buffer allocation in remanufacturing environment

Remanufacturing system is complicated due to its stochastic nature. Random customer demand, return product rate and system unreliability contribute to this complexity. Remanufacturing systems with unreliable machines usually contain intermediate buffers which are used to decouple the machines, thereby, reducing mutual interference due to machine breakdowns. Intermediate buffers should be optimized to eliminate waste of resources and avoid loss of throughput. The Buffer Allocation Problem (BAP) deals with allocating optimally fixed amount of available buffers to workstations located in manufacturing or remanufacturing systems to achieve specific objectives. Optimal buffer allocation in manufacturing and remanufacturing systems not only minimizes holding cost and stock space, but also makes facilities planning and remanufacturing decisions to be effectively coordinated. BAP in a non-deterministic environment is certainly one of the most difficult optimization problems. Therefore, a mathematical framework is provided to model the dependence of throughput on buffer capacities. Obviously, based on the survey undertaken, not only there exists no algebraic relation between the objective function and buffer size but the current literature does not offer analytical results for buffer capacity design in remanufacturing environment. Decomposition principle, expansion method for evaluating system performance and an efficient hybrid Meta-heuristic search algorithm are implemented to find an optimal buffer allocation for remanufacturing system. The proposed hybrid Simulated Annealing (SA) with Genetic Algorithm (GA) is compared to pure SA and GA. The computational experiments show better quality, more accurate, efficient and reliable solutions obtained by the proposed hybrid algorithm. The improvement obtained is more than 4.18 %. Finally, the proposed method is applied on toner cartridge remanufacturing company as a case study, and the numerical results from hybrid algorithm are presented and compared with results from SA and GA

Studying the impact of merged and divided storage policies on the profitability of a remanufacturing system with deteriorating revenues

Peer ReviewedMerging capacity for a remanufacturing system is studied in this paper. In the system under study, there are two streams for returns and each stream has its dedicated processing line. However, the storage space is merged between the streams. Two strategies are investigated and compared in this paper. The first strategy is to divide the storage space between the two streams in the way that each type of return has its predetermined space in the storage area (divided capacity). In the second strategy, storage space is not split between the two streams and each unit of return, independent of its type, is admitted if there is vacant space (merged capacity). In both strategies, the value of remanufactured products decreases over time by a known factor called the decay rate. Mathematical models to maximize the total profit in each strategy is presented and also verified by a simulation model. From a practical point of view, selecting the correct strategy is an important decision for the remanufacturers because choosing the wrong policy leads to lost profits. Numerical experiments reveal that neither of the scenarios is always preferred to the other one and the choice of the optimal strategy depends on the parameters' values and product types. For instance, increasing the remanufacturing cost of the superior product, or increasing the sale price of the inferior product make the merged storage strategy more desirable. On the contrary, increasing the remanufacturing cost of the inferior product, or increasing the sale price of the superior product make the divided storage policy more appealing

Deep learning based vision inspection system for remanufacturing application

Deep Learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, localisation, natural language processing, prediction and forecasting systems. With significant applicability, Deep Learning is continually seeking other new fronts of applications for these techniques. This research is the first to apply Deep Learning algorithm to inspection in remanufacturing. Inspection is a key process in remanufacturing, which is currently an expensive manual operation in the remanufacturing process that depends on human operator expertise, in most cases. This research further proposes an automation framework based on Deep Learning algorithm for automating this inspection process. The proposed technique offers the potential to eliminate human factors in inspection, save cost, increase throughput and improve precision. This paper presents a novel vision-based inspection system on Deep Convolution Neural Network (DCNN) for three types of defects, namely pitting, surface abrasion and cracks by distinguishing between these surface defected parts. The materials used for this feasibility study were 100cm x 150cm mild steel plate material, purchased locally, and captured using a web webcam USB camera of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies, especially for accuracy and speed. This preliminary study demonstrates that Deep Learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of Deep Learning algorithms to remanufacturing

System Reconfiguration for Reverse Logistics: A case study

With climate change becoming very concerning in the world today, reverse logistics has become important for several companies with respect to regaining the value of their products and/ or for proper disposal or recycling of returned products. The reverse logistics framework in this paper is inspired by research papers in the literature; reverse logistics plays an important role in the return and the remanufacturing processes of products to meet specific demands. Manufacturers face an increased flow of returned products in their system; hence, they need to effectively address the flow of returned products in their system and the challenges that occur in the remanufacturing processes due to the uncertainty related to the quantities and quality of the returned components. This research focuses on identifying the challenges encountered in a remanufacturing Reverse Logistics (RL) system and are illustrated with an automotive study. Lean Six-sigma techniques are used to find these issues encountered in the RL process or system, and a linear programming approach is taken to reconfigure the system to improve system throughput. The results obtained from the analysis and improvements done show that the facility should invest in additional machine-operator for process improvements and to increase the throughput of the system. After adjusting the various parameters, cost and production strategies, it is observed that the solutions are very similar