Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

The tremendous advancement in technology, productivity and improved standard of living has come at the cost of environmental deterioration, increased energy and raw material consumption. In this regard, remanufacturing is viable option to reduce energy usage, carbon footprint and raw material usage. In this manuscript, using computational intelligence techniques we try to determine the feasibility of remanufacturing in case of roller bearings. We collected used N308 bearings from 5 different Indian cities. Using Fuzzy-TOPSIS, we found that the roundness, surface roughness and weight play a vital role in design for remanufacturing of roller bearings. Change in diameter, change in thickness and change in width showed minimal influence.  We also used Taguchi analysis to reassess the problem. The roundness of inner and outer race was found to be the most influential parameters in deciding the selection of bearing for remanufacturing. The results suggest the bearing designer to design the bearing in such a way that roundness of both races will be taken cared while manufacturing a bearing. However, using Taguchi the weight of the rollers was found to be of least influence. Overall, the predictions of Taguchi analysis were found to be similar to Fuzzy-TOPSIS analysis

Supply planning for a closed loop system with uncertain demand and return

This proposed model considers a supply network consisting of a manufacturer, its external suppliers, and a remanufacturing facility. The manufacturer, facing an uncertain market demand and return, has two options for supplying parts: either ordering the required parts to external suppliers or remanufacturing used products and bringing those back to \u27as new\u27 conditions. We propose a general framework for this multi product, closed loop system and develop a non-linear programming (NLP) model to maximize the total expected profit by optimally deciding quantity of parts to be remanufactured and quantity of parts to be purchased from external suppliers. We solved the mathematical model using two different solution techniques to find optimal or near optimal solution values. With a numerical example we compared the results from both solution techniques and introduced sensitivity analysis to illustrate the interacting effects among critical parameters in the model

Optimal Decision Making for Capacitated Reverse Logistics Networks with Quality Variations

Increasing concerns about the environmental impact of production, product take-back laws and dwindling natural resources have heightened the need to address the impact of disposing end-of-life (EOL) products. To cope this challenge, manufacturers have integrated reverse logistics into their supply chain or chosen to outsource product recovery activities to third party firms. The uncertain quality of returns as well as uncertainty in return flow limit the effectiveness of planning, control and monitoring of reverse logistics networks. In addition, there are different recovery routes for each returned product such as reuse, repair, disassembling, remanufacturing and recycling. To determine the most profitable option for EOL product management, remanufacturers must consider the quality of returns and other limitations such as inventory size, demand and quantity of returns. The work in this dissertation addresses these pertinent aspects using two models that have been motivated by two remanufacturing facilities whereby there are uncertainties in the quality and quantity of return and capacitated inventories. In the first case, a disposition decision making model is developed for a remanufacturing process in which the inventory capacity of recoverable returns is limited and where there\u27s a constant demand to be met, for remanufactured products that meet a minimum quality threshold. It is assumed that the quality of returns is uncertain and remanufacturing cost is dependent on the quality grade. In this model, remanufacturing takes place when there is demand for remanufactured products. Accepted returns that meet the minimum quality threshold undergo the remanufacturing processes, and any unacceptable returns are salvaged. A continuous time Markov chain (CTMC) is presented as the modeling approach. The Matrix-Geometric solution methodology is applied to evaluate several key performance metrics for this system, to result in the optimal disposition policy. The numerical study shows an intricate trade-off between the acceptable quality threshold value and the recoverable product inventory capacity. Particularly, there are periodic system starvation whenever there is a mis-match between these two system metrics. In addition, the sensitivity analysis indicates that changes to the demand rate for remanufactured products necessitates the need to re-evaluate the existing system configuration. In the second case, a general framework is presented for a third party remanufacturer, where the remanufacturer has the alternative of salvaging EOL products and supplying parts to external suppliers, or remanufacture the disassembled parts to \u27as new\u27 conditions. The remanufacturing processes of reusable products and parts is studied in the context of other process variables such as the cost and demand of remanufactured products and parts. The goal of this model is to determine the return quality thresholds for a multi-product, multi-period remanufacturing setting. The problem is formulated as a mixed integer non-linear programming (MINLP) problem, which involves a discretization technique that turns the problem turns into a quadratic mixed integer programming (QMIP) problem. Finally, a numerical analysis using a personal computer (PC) remanufacturing facility data is used to test the extent to which the minimum acceptance quality threshold is dependent on the inventory level capacities of the EOL product management sites, varying operational costs and the upper bound of disposal rate

A Simulation-based Methodology to Compare Reverse Logistics System Configuration Considering Uncertainty

With increasing environmental concerns, recovery of used products through various options has gained significant attention. In order to collect, categorize and reprocess used products in a cost and time efficient manner, a pre-evaluated network infrastructure is needed in addition to existing traditional forward logistics networks, in most cases. However, such networks, which are referred to as reverse logistics networks, impose inherent uncertainty in returned product supply and challenges additional to forward networks. Incorporating uncertainty in long term decisions with regards to network planning is significant especially in RL networks, since such decisions are difficult and costly to adjust later on. Uncertainty in product returns, dynamic and complex behavior of the system can be modeled as a queueing model, using a discrete event simulation methodology. In this work, a simulation based tool is developed which can be used as a platform for evaluating and comparing reverse logistics network configurations. In addition to defining system parameters, the tool provides experimentation with the number of collection, sorting, and processing centers, as well as the standard deviation of the return rate distribution. Various types of experiments are used in order to illustrate the use and goal of the tool, where the trade-offs within and across scenarios are addressed. Experiments are divided into three main parts; verification, pairwise detailed and a final more holistic scenario which illustrates the usage of the tool. A user interface is developed via Microsoft Excel for convenient specification of operational system parameters and scenario values. Upon running the simulation with specified experimental factors, the tool automatically computes and displays the total weighted score of each scenario, which is an indicator of the scenario quality

A MULTI-STAGE DECISION SUPPORT MODEL FOR COORDINATED SUSTAINABLE PRODUCT AND SUPPLY CHAIN DESIGN

In this research, a decision support model for coordinating sustainable product and supply chain design decisions is developed using a multi-stage hierarchical approach. The model evaluates alternate product designs and their corresponding supply chain configurations to identify the best product design and the corresponding supply chain configuration that maximizes the economic, environmental and societal benefits. The model considers a total life-cycle approach and incorporates closed-loop flow among multiple product lifecycles. In the first stage, a mixed integer linear programming model is developed to select for each product design an optimal supply chain configuration that maximizes the profit. In the subsequent stages, the economic, environmental and societal multiple life-cycle analysis models are developed which assess the economic, environment and the societal performance of each product design and its optimal supply chain configuration to identify the best product design with highest sustainability benefits. The decision support model is applied for an example problem to illustrate the procedure for identifying the best sustainable design. Later, the model is applied for a real-time refrigerator case to identify the best refrigerator design that maximizes economic, environmental and societal benefits. Further, sensitivity analysis is performed on the optimization model to study the closed-loop supply chain behavior under various situations. The results indicated that both product and supply chain design criteria significantly influence the performance of the supply chain. The results provided insights into closed-loop supply chain models and their behavior under various situations. Decision support models such as above can help a company identify the best designs that bring highest sustainability benefits, can provide a manager with holistic view and the impact of their design decisions on the supply chain performance and also provide areas for improvement

A model for the economic assessment of disassembly-line integration in traditional manufacturing processes

Abstract Managing End-of-Life (EoL) products and reintroducing materials and components within the production loop become crucial for guaranteeing the Circular Economy business model. In such a way, the proper management of disassembly process for recovering components and materials from returned EoL products is essential as well as strategic: disassembly is the main gateway of information and can ensure economic returns. This paper aims to provide a model for the economic assessment of the introduction of a manual disassembly line in a traditional and already operating assembly line of manufacturing industries. Therefore, recovered components and materials could directly feed the assembly lines and the recycling processes. The model takes in input probabilistic factors, as products' characteristics, and provides the operating times and component recovery indicators, as well as allows the sizing of the right number of operators needed in the new disassembly line through the optimisation of the industrial cost. An interesting natural evolution of this study is the development of a model-based simulator, with the aim of providing a user-friendly tool to industrial practitioners to estimate the economic feasibility and convenience of introducing a disassembly line