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Assessing the Conservation Value of Freshwaters. The Rivers of Wales. Water Science and Technology. Since the consumers are not experts, a detailed evaluation process cannot take place during the pricing procedure and trade-in programs are usually confined to categorizing the quality of the products into limited nominal levels. However, the proposed infrastructure can provide more accurate ranking systems. A numerical example has been provided to show the application of the model. Tables 3 and 4 illustrate the attribute values and the coefficients used in utility functions of DCA to model the consumers' decision structure.
Table 5 represents the global parameters of the simulation. The authors have modeled the consumers' EoL decision process under various scenarios previously in [ 25 ] in order to estimate the return stream focused solely on the collection process.
However, the current work provides insight about different direct and secondary effects that the buy-back price can have on the EoL recovery process. Here, the objective is to determine the optimal price to manipulate the quality distribution over the return stream in order to maximize the profit of the recovery process.
We incorporate the lessons learned from Ref. It should be noted that while consumers consider four options when discarding a used device store, sell, trash, and return , we only consider the information of the number of returns to the manufacturer and not the values of trash, sell and store. The simulation has been tested for extreme values, different number of agents and the presence or lack of different agent types, in order to evaluate the internal validity of the model.
In addition, in order to check the statistical integrity of the simulation, the sensitivity of the simulation to random seeds has been examined. One hundred simulations have been performed with the same input and different random seeds. If the results of the simulation are very sensitive to the seed of random, the robustness of the model is questionable. Figure 2 represents the distribution of the results in this case, number of returns.
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As can be seen, the desired Gaussian behavior is observed. Figure 3 represents the histogram of simulated revenues for each EoL recovery process refurbish, remanufacture, and recycle. The values have been drawn randomly during the experiment, based on the formulation presented in Table 3. The parameters of the recovery revenues are estimates based on the logic that usually, refurbishing provides more profit compared to remanufacturing and recycling if applied to a high quality product. On the other hand, recycling is more profitable for low-quality products, as the cost to remanufacture them or refurbish them is relatively higher.
As can be seen from the figure, moving from recycling to refurbishing, the mean of the distribution shifts slightly to the right. This indicates that, as expected, the revenue for refurbishing is slightly higher than remanufacturing and then recycling. Figure 4 illustrates the results of the simulation for four different values of bbp and the extent to which higher buy-back price increases the rate of return.
Increasing bbp, and consequently the buy-back price, increases the total number of returns. In other words, when higher prices are offered for the EoU products, more consumers would choose to return their products. In addition, increasing bbp means that the manufacturer would propose a better offer for lower quality products as well. In other words, if we increase the bbp to a sufficient extent, even the consumers that previously did not care about the monetary incentives or the consumers that have very low-quality products may consider returning or selling their products.
Thus, two different behaviors can be observed. First, increasing the buy-back price motivates the consumers who own high quality grade products to return them. Higher buy-back price would decrease the tendency to store the product for these consumers. Second, because bbp is a constant value in the buy-back price calculation formula, increasing it would increase the buy-back price offered for low-quality grades as well. As a result, both the number of high quality grade products and low-quality grade products will be increased in total number of returns. Combining these two effects prevents a great change in the quality of returns.
Figures 5 — 8 illustrate the distribution of quality grade of the products received by the manufacturer for each bbp. As can be seen, increasing the buy-back price only slightly increases the quality grades. Although increasing the buy-back price increases the total number of returns and revenue, it does not necessarily improve the profit.
There are two reasons behind this. First, increasing the buy-back price increases the cost and based on Eq. Second, increasing the buy-back price allows more low-quality products high obsolescence value products in the return stream, which creates less profit. Table 6 summarizes the profit for the four values of bbp in Fig. Table 6 indicates that, since the total number of returns increases, generally more products will be refurbished, remanufactured, and recycled.
This fact can also be verified in Figs. Figures 9 — 12 and Table 6 indicate that as the total number of returns increases, a bigger portion of products are remanufactured and recycled. Therefore, the average profit made per product decreases as bbp increases. However, the total profit decreases afterwards. This is due to the fact that from the manufacturer's perspective, lower quality grade products may not be profitable to be recovered. Thus, an optimum buy-back price should be defined in order to achieve a desirable return stream with a good quality distribution.
Therefore, we developed a simulation-based optimization model to find the best bbp value that maximizes the profit. Based on the results of the experiments presented in Table 6 , the lower band and upper band for bbp are defined. The simulation-based optimization has been conducted for iterations. The OptQuest engine has been used as the simulation-based optimization solver. As can be seen in Fig. Note that, the OptQuest solver uses Tabu Search, Neural Networks and Scatter Search in order to search the solution space for the global optima [ 64 ].
However, due to nonlinearity of the problem the global optima cannot be guaranteed. The closeness of the solution to the global optima can be tested via availability of external validation data and establishing a ground truth. However, further analysis of the solution quality is beyond the scope of this work. An application of the product life cycle information available through cloud has been discussed in this paper.
Selecting the best strategy to recover the EoL electronics, as well as understanding the consumer's choice structure about EoU electronics are necessary in order to improve the performance of recovery operations.
This paper used the ABS abilities to model manufactures decisions on the buy-back prices that motivate consumers toward on-time return of their devices. Sociodemographic properties of the consumers, as well as specific properties of the take-back programs have been considered to model consumers' utility.
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In addition, the remanufacturer's decision-making process about the best EoL strategy for products upon availability of the product identity data via cloud has been modeled. A numerical example of an electronic product take-back system is provided to illustrate the application of the model. This work has presented an application of the cloud-based remanufacturing infrastructure. However, while the emergence of cloud-based remanufacturing and ubiquitous information access may pave the way to appropriately handling the uncertainties associated with the recovery process, the level of implementation of such technologies is still debatable.
The manufacturers should be clear about why and to what extent they should share design and manufacturing information. It has been shown that in other domains, such as supply chain management, information sharing can actually be beneficial for different entities [ 65 ]. However, different aspects of adapting this concept, particularly intellectual property issues should be investigated further in the manufacturing context. This work can be improved in different ways. The provided results are used for a comparison between different scenarios and the specific values of attributes may not be translated to reality.
However, using real world data, the model can be calibrated, so that the results of the experiments can be used to predict real values of the attributes. Moreover, the rationale behind assigning values to the coefficients in the consumers' decision model is such that the final values of the model factors become comparable. This assumption is made without the loss of generality in order to compare different scenarios, but may be violated in real situations.
However, the paucity and scarcity of real world data make any further investigation for parameter estimation beyond the scope of this work. The product identity data considered in this work is in the form of product quality level. Other attributes, such as design features and event data can be also considered in the model, which were neglected in this work to avoid over complexity. Also, in addition to the pricing strategies, collection type and shipping method e.
Although environmental legislations can play a pivotal role in WEEE management, the current inconsistency among different rules and regulations on what they mandate and what they ban in different geographical locations makes it quite challenging to address them comprehensively in the model. However, future work aims to address the impact of various environmental policies on the economics of remanufacturing.
The discrepancy in the various types of collection options makes it challenging to come up with a standard index and introduce an accessibility index for other EoL options e. In this study, only the accessibility of the collection programs is considered in the model. However, in reality, selling the product to the secondhand market may or may not be more accessible, depending on the geographical location or the availability of waste recovery regulations at each location.
Further investigation of such factors should be a priority in the future work. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The buy-back price offered by the trade-in program for four different models of cellphones with different quality condition. Histogram of the number of returns after simulation days. This figure is based on simulation runs with different random seeds. Histogram of simulated revenue for each EoL process.
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The red line indicates the mean. Quality levels and their descriptions extracted from www. Value of attributes and local parameters extended from Ref. Value of coefficients used in utility functions of consumers modified from Ref. Global simulation parameters same parameters as Ref.
Detail results of the experiments profit, No. Sign In or Create an Account. Sign In. Advanced Search. Article Navigation. This Site. Google Scholar. Sara Behdad Sara Behdad. Jun Zhuang Jun Zhuang. Author and Article Information. Ardeshir Raihanian Mashhadi. Oct , 10 : 11 pages. Published Online: August 10, Article history Received:.
Standard View Views Icon Views. Issue Section:. The consumers should decide about the EoU of their products. When the usage cycle is over, the consumer should choose between one of the four available EoU options. These options are to store the product, sell it to the second hand market, return it to the manufacturer, or throw it away. Based on the rational utility theory, we have considered that the consumers choose the option that maximizes their utility. A linear utility function has been assigned to each consumer based on the DCA [ 55 ]. The manufacturer agent chooses the recovery option that maximizes its expected profit.
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ABS Framework. Numerical Example. Conclusion and Future Work. View large Download slide. Total number of returned products to the manufacturer per different initial buy-back prices. Download All Figures. Table 1. Share Give access Share full text access.
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