Sara Behdad

Simultaneous selective disassembly and end-of-life decision making for multiple products that share disassembly operations

Environmental protection legislation, consumer interest in “green” products, a trend toward corporate responsibility and recognition of the potential profitability of salvaging operations, has resulted in increased interest in product take back. However, the cost effectiveness of product take-back operations is hampered by many factors, including the high cost of disassembly and a widely varying feedstock of dissimilar products. Two types of decisions must be made, how to carry out the disassembly process in the most efficient manner to “mine” the value-added that is still embedded in the product, and then how to best utilize that value-added once it is recovered. This paper presents a method for making those decisions. The concept of a transition matrix is integrated with mixed integer linear programming to determine the extent to which products should be disassembled and simultaneously determine the optimal end-of-life (EOL) strategy for each resultant component or subassembly. The main contribution of this paper is the simultaneous consideration of selective disassembly, multiple products, and the value added that remains in each component or subassembly. Shared disassembly operations and capacity limits are considered. An example using two cell phone products illustrates application of the model. The obtained results demonstrate the most economical level of disassembly for each cell phone and the best EOL options for each resultant module. In addition, the cell phone example shows that sharing disassembly operations between different products makes disassembly more cost effective compared with the case in which each product is disassembled separately.

E-Waste Stream Analysis and Design Implications

The quantity and age of the incoming stream of “feedstock” from product take-back systems are known as the major sources of the uncertainty that complicates the e-waste recovery. This paper presents the results of an analysis of data from an incoming stream for an e-waste collection center and analyzes the quantity and age of e-waste by product type and brand. The analysis results point out receiving of outdated products and processing of multiple generations, and brands of products at the same time are among main obstacles to the e-waste recovery. The potential role of product design in overcoming those obstacles is discussed with emphasis on design for upgrade, repurpose, and commonality.

Supply chain network optimization considering assembly line balancing and demand uncertainty

In supply chain optimisation problems, determining the location, number and capacity of facilities is concerned as strategic decisions, while mid-term and short-term decisions such as assembly policy, inventory levels and scheduling are considered as the tactical and operational decision levels. This paper addresses the optimisation of strategic and tactical decisions in the supply chain network design (SCND) under demand uncertainty. In this respect, a two-stage stochastic programming model is developed in which strategic location decisions are made in the first-stage, while the second-stage contains SCND problem and the assembly line balancing as a tactical decision. In the solution scheme, the combination of sample average approximation and Latin hypercube sampling methods is utilised to solve the developed two-stage mixed-integer stochastic programming model. Finally, computational experiments on randomly generated problem instances are presented to demonstrate the performance and power of developed model in handling uncertainty. Computational experiments showed that stochastic model yields better results compared with deterministic model in terms of objective function value, i.e. the sum of the first-stage costs and the expected second-stage costs. This issue proved that uncertainty would be a significant and fundamental element of developed model and improve the quality of solutions.

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

Immersive computing technology to investigate tradeoffs under uncertainty in disassembly sequence planning

The scientific and industrial communities have begun investigating the possibility of making product recovery economically viable. Disassembly sequence planning may be used to make end-of-life product take-back processes more cost effective. Much of the research involving disassembly sequence planning relies on mathematical optimization models. These models often require input data that is unavailable or can only be approximated with high uncertainty. In addition, there are few mathematical models that include consideration of the potential of product damage during disassembly operations. The emergence of Immersive Computing Technologies (ICT) enables designers to evaluate products without the need for physical prototypes. Utilizing unique 3D user interfaces, designers can investigate a multitude of potential disassembly operations without resorting to disassembly of actual products. The information obtained through immersive simulation can be used to determine the optimum disassembly sequence. The aim of this work is to apply a decision analytical approach in combination with immersive computing technology to optimize the disassembly sequence while considering trade-offs between two conflicting attributes: disassembly cost and damage estimation during disassembly operations. A wooden Burr puzzle is used as an example product test case. Immersive human computer interaction is used to determine input values for key variables in the mathematical model. The results demonstrate that the use of dynamic programming algorithms coupled with virtual disassembly simulation is an effective method for evaluating multiple attributes in disassembly sequence planning. This paper presents a decision analytical approach, combined with immersive computing techniques, to optimize the disassembly sequence. Future work will concentrate on creating better methods of estimating damage in virtual disassembly environments and using the immersive technology to further explore the feasible design space.

Leveraging virtual reality experiences with mixed-integer nonlinear programming visualization of disassembly sequence planning under uncertainty

Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in increased potential profitability of end-of-life salvaging operations. However, in the early design stage, determining the best disassembly sequence is challenging. First, the required information is not readily available and very time-consuming to gather. In addition, the best solution is sometimes counterintuitive, even to those with experience and expertise in disassembly procedures. Integrating analytical models with immersive computing technology (ICT) can help designers overcome these issues. A two-stage procedure for doing so is introduced in this paper. In the first stage, a stochastic programming model together with the information obtained through immersive simulation is applied to determine the optimal disassembly sequence, while considering uncertain outcomes, such as time, cost, and the probability of causing damage. In the second stage, ICT is applied as a tool to explore alternative disassembly sequence solutions in an intuitive way. The benefit of using this procedure is to determine the best disassembly sequence, not only by solving the analytic model but also by capturing human expertise. The designer can apply the obtained results from these two stages to analyze and modify the product design. An example of a Burr puzzle is used to illustrate the application of the method. Disciplines Applied Mechanics | Graphics and Human Computer Interfaces | Manufacturing | Systems Engineering and Multidisciplinary Design Optimization Comments This article is from Journal of Mechanical Design 136 (2014): 1, doi:10.1115/1.4026463. Posted with permission. This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/me_pubs/114

Disassembly sequence evaluation using graph visualization and immersive computing technologies

With the goal of making product recovery economically viable, disassembly sequence planning and evaluation can be used to influence product design features early in the product design process. Several researchers have investigated using optimization methods to determine disassembly sequences. One of the difficulties with using this approach is that because of the unique aspects of product disassembly at the end of life, input parameters for the optimization algorithms are commonly unavailable or estimated under high uncertainty. In practice, design engineers explore disassembly sequencing using either CAD software or manipulation of physical prototypes. These approaches produce solutions, but only intuitive solutions are explored and more optimal solutions may exist. To support decision making early in the design process, the research presented in this paper combines these two approaches within an immersive computing technology (ICT) application to aid in early product design with the goal of designing products with consideration of product recovery, reuse and recycle. The ICT application displays both 3D geometry of the product to be disassembled and an interactive graph visualization of the potential disassembly paths. The user can naturally interact with the geometric models and explore the potential paths indicated by the graph visualization. The optimal path can be indicated and the user can explore other potential paths. The result is an application that combines the strength of mathematical modeling with visualization and human interaction to provide an experience where the user can explore potential effects of design decisions. The initial application has been implemented in a 3 wall immersive projection environment and preliminary results show this approach proves to be an efficient method of evaluating and training potential disassembly sequences.

Disassembly process planning tradeoffs for product maintenance

The problem addressed in this paper is disassembly sequence planning for the purposes of maintenance or component upgrading, which is an integral part of the remanufacturing process. This involves disassembly, component repair or replacement, and reassembly. Each of these steps incurs cost as well as the probability of damage during the process. This paper presents a method for addressing these tradeoffs, as well as the uncertainty associated with them. A procedure for identifying the best sequence of disassembly operations for maintenance and/or component upgrade is presented. It considers both disassembly and reassembly costs and uncertainties. Graph-based integer linear programming combined with multiattribute utility analysis is employed to identify the best set of tradeoffs among (a) disassembly time (and resulting cost) under uncertainty, (b) the probability of not incurring damage during disassembly, (c) reassembly time (and resulting cost) and (d) the probability of not incurring damage during reassembly. An example of a solar heating system is used to illustrate the method.Copyright