Behzad Esmaeilian

Uncertainty Management in Remanufacturing Decisions: A Consideration of Uncertainties in Market Demand, Quantity, and Quality of Returns

As market demand for remanufactured products increases and environmental legislation puts further enforcement on original equipment manufacturers (OEMs), remanufacturing is becoming an important business. However, profitability of salvaging operations is still a challenge in remanufacturing industry. Several factors influence the cost effectiveness of remanufacturing operations, including uncertainties in the quantity of return flows and market demand as well as variability in the quality of received items. The objective of this paper is to develop a stochastic optimization model based on chance-constrained programming to deal with 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 maximizing profit. An example of personal computer is provided to show the application of the method. This article is available in the ASME Digital Collection at http://...

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

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

Multi-Purpose Disassembly Sequence Planning

Efficient disassembly operation is considered a promising approach toward waste reduction and End-of-Use (EOU) product recovery. However, many kinds of uncertainty exist during the product lifecycle which make disassembly decision a complicated process. The optimum disassembly sequence may vary at different milestones depending on the purpose of disassembly (repair, maintenance, reuse and recovery), product quality conditions and external factors such as consumer preference, and the market value of EOU components. A disassembly sequence which is optimum for one purpose may not be optimum in future life cycles or other purposes. Therefore, there is a need for incorporating the requirements of the entire product life-cycle when obtaining the optimum disassembly sequence. This paper applies a fuzzy method to quantify the probability that each feasible disassembly transition will be needed during the entire product lifecycle. Further, the probability values have been used in an optimization model to find the disassembly sequence with maximum likelihood. An example of vacuum cleaner is used to show how the proposed method can be applied to quantify different users’ evaluation on the relative importance of disassembly selection criteria as well as the probability of each disassembly operation.Copyright

The future of waste management in smart and sustainable cities: A review and concept paper

The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product life-cycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed.

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

Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA, however the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit for the period of 2014–2019.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.