Ardeshir Raihanian Mashhadi

Reusability Assessment of Lithium-Ion Laptop Batteries Based on Consumers Actual Usage Behavior

In this paper, a data set of Lithium-ion (Li-ion) laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the life cycle of these batteries and improving the recycling process is becoming increasingly important. The reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their life cycle. After capturing the utilization behavior, the stability time of batteries is approximately derived. The stability time represents the interval that a battery works normally without any significant drop in performance. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities. [DOI: 10.1115/1.4031654]

Impact of Additive Manufacturing Adoption on Future of Supply Chains

Although separation of product design from manufacturing capabilities is a major advantage of Additive Manufacturing (AM), the impact of AM is not only limited to the design and manufacturing stages. In addition to the freedom of design such as elimination of design constraints, material saving, and free complexity, AM offers other potential benefits to the manufacturing industry as well. One of the most immediate potentials of AM is the possibility of more efficient logistics. This paper aims at describing the characteristics and requirement of a Supply Chain (SC) as well as the changes AM will bring into the current structure of supply chain. Insights are provided on the transformative effects of AM on traditional businesses, and how these changes impact the configuration of a supply chain. The potential for using simulation tools to evaluate AM supply chain have been discussed. Further, two examples of Agent Based Simulation (ABS) and System Dynamics (SD) have been provided to show the application of simulation models. The ABS results show the possibility of lead time reduction in AM based supply chain. In addition, the SD model illustrates the potential for less ‘pipeline’ effect in AM compared to traditional supply chain.Copyright

Environmental Impact Assessment of the Heterogeneity in Consumers’ Usage Behavior: An Agent‐Based Modeling Approach

The aim of this study is to develop a framework for understanding the heterogeneity and uncertainties present in the usage phase of the product life cycle through utilizing the capabilities of an agent‐based modeling (ABM) technique. An ABM framework is presented to model consumers’ daily product usage decisions and to assess the corresponding electricity consumption patterns. The theory of planned behavior (TPB), with the addition of the habit construct, is used to model agents’ decision‐making criteria. A case study is presented on the power management behavior of personal computer users and the possible benefits of using smart metering and feedback systems. The results of the simulation demonstrate that the utilization of smart metering and feedback systems can promote the energy conservation behaviors and reduce the total PC electricity consumption of households by 20%.

Modeling Consumer Decisions on Returning End-of-Use Products Considering Design Features and Consumer Interactions: An Agent Based Simulation Approach

As electronic waste (e-waste) becomes one of the fastest growing environmental concerns, remanufacturing is considered as a promising solution. However, the profitability of take back systems is hampered by several factors including the lack of information on the quantity and timing of to-be-returned used products to a remanufacturing facility. Product design features, consumers’ awareness of recycling opportunities, socio-demographic information, peer pressure, and the tendency of customer to keep used items in storage are among contributing factors in increasing uncertainties in the waste stream. Predicting customer choice decisions on returning back used products, including both the time in which the customer will stop using the product and the end-of-use decisions (e.g. storage, resell, through away, and return to the waste stream) could help manufacturers have a better estimation of the return trend. The objective of this paper is to develop an Agent Based Simulation (ABS) model integrated with Discrete Choice Analysis (DCA) technique to predict consumer decisions on the End-of-Use (EOU) products. The proposed simulation tool aims at investigating the impact of design features, interaction among individual consumers and socio-demographic characteristics of end users on the number of returns. A numerical example of cellphone take-back system has been provided to show the application of the model.Copyright

Agent Based Simulation Optimization of Waste Electrical and Electronics Equipment Recovery

The profitability of electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in the quantity, quality, and timing of returns originating from consumers’ behavior. The cloud-based remanufacturing concept, data collection, and information tracking technologies seem promising solutions toward the proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an agent based simulation (ABS) framework to model the overall product takeback and recovery system based on the product identity data available through cloudbased remanufacturing infrastructure. Sociodemographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process, and product life cycle information have all been considered to capture the optimum buy-back price (bbp) proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model. [DOI: 10.1115/1.4034159]

Assessment of Products Future Reusability Based on Consumers Usage Behavior: Implications for Lithium-Ion Laptop Batteries

Product reuse is a recommended action toward sustainability. However, the profitable reusability of End-of-Use or End-of-Life (EoU/L) products depends on how consumers have used them over the initial lifecycles and what are their EoU conditions. In addition to consumers’ behavior, product design features such as product durability has an impact on the future reusability. In this paper, a data set of Lithium-ion laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the lifetime of these batteries and improving the recycling process is becoming important. In this paper, the reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their lifetimes. After capturing the utilization behavior, the performance-based stability time of batteries is approximately derived. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.Copyright

Impact of additive manufacturing adoption on future of supply chains

Although separation of product design from manufacturing capabilities is a major advantage of Additive Manufacturing (AM), the impact of AM is not only limited to the design and manufacturing stages. In addition to the freedom of design such as elimination of design constraints, material saving, and free complexity, AM offers other potential benefits to the manufacturing industry as well. One of the most immediate potentials of AM is the possibility of more efficient logistics. This paper aims at describing the characteristics and requirement of a Supply Chain (SC) as well as the changes AM will bring into the current structure of supply chain. Insights are provided on the transformative effects of AM on traditional businesses, and how these changes impact the configuration of a supply chain. The potential for using simulation tools to evaluate AM supply chain have been discussed. Further, two examples of Agent Based Simulation (ABS) and System Dynamics (SD) have been provided to show the application of simulation models. The ABS results show the possibility of lead time reduction in AM based supply chain. In addition, the SD model illustrates the potential for less ‘pipeline’ effect in AM compared to traditional supply chain.

Upgrade Planning of Used Products Considering Uncertainty in Market Demand, Quantity and Variability in Quality of Returns

Remanufacturing is becoming an important business as market demand for remanufactured products increases and environmental legislation puts further enforcement on Original Equipment Manufacturers (OEMs). However, profitability of salvaging operations is still a challenge in remanufacturing industry. Several factors influence the cost effectiveness of remanufacturing operations, including variability in the quality of received used items and uncertainty in the quantity of return flows and the market demand for those products. Remanufacturing companies need to handle these sources of uncertainties in order to improve their profitability. The current paper develops a Stochastic Optimization method to model these sources of uncertainties in take-back and inventory planning systems. The main purpose of the model is to determine the best upgrade level for a received product with certain quality level with the aim of profit maximization. An example of personal computer is provided to show the application of the method.Copyright