外文翻译---综合框架逆向物流(编辑修改稿)

外文翻译---综合框架逆向物流(编辑修改稿)内容摘要：

ent, the disposal agent remends treatments by casebased reasoning (CBR) [1], and reference supplementary rules if necessary. The case base stores successful cases from previous experience. The rule base includes some supplementary heuristics from domain experts. As revealed in Figure 2, a warning signal consists of three parts {urgent degree(UF), signal flag (SF), trigger features}. SF could be “return”, “recycle”, or “repair”.UF indicates degrees of impact. In Figure 3, depending on the different UF, the system would have different actions. It pares {SF, trigger features} to those {CF, case features} of cases in case base, and retrieve the treatments of the fittest case to decision maker. It might refer to supplementary rules for detailed suggestions or other suggestions (if no suitable case could be found). Then, disposal agent might perform treatments automatically or suggest to decisionmakers. It would cooperate with other systems, ., programs of scheduling, inventory management or quality checking. If the response of this problem solving is good, then the experiences may be annotated by human experts, and then retained in the case base as further references. Therefore, the disposal agent could have learning capability to improve its performance next time. The Integrated System Framework The framework has three stages. At stage I, the sensor agent monitors the data。 predicts the possibilities of reverse activities, and transmits different warning signals to the disposal agent. The rule base es from heuristics, and is periodically updated by data mining techniques (., clustering and association analyses). At stage II, the disposal agent remends feasible treatments from past cases and referencing rules. At stage III, for possible serious effects, disposal might further remend or automatically initiate some related business process preparations (., scheduling). Additionally, the disposal agent 6 should notify the sensor agent of its treatment, and ask for a necessary followup. For instance, if a sensor agent discovers that the frequencies of customer plaint phones have risen, and predicts that the possible return rate is likely to increase, then the disposal agent remends employing customer specialists to listen to customer concerns. After the treatment has been pleted, the sensor agent performs a customer satisfaction survey to check whether the problems have been solved. The sensor agent also gives the disposal agent the evaluation feedback concerning the effectiveness of the treatment. Based on the feedback, the disposal agent adds annotations to the original case base, and remends further treatment if needed. 4 Business Illustrative Scenarios To understand the proposed framework clearly, the three classes of reverse logistic activity are described as follows. Return Scenario According to the proposed framework, the sensor agent monitors the data, which are gathered from the consumer site and shared data center。 performs weekly cross analyses to diagnose the return probability, and transmits alarm signals. For instance, assume customer is making an increasing number of plaints, and that her (his) profile (Gender, Education)=(Female, High) matches one return pattern in Table 3. The sensor agent verifies the warrant period of the related transaction. If the guarantee period has expired, then a “moderate” signal is sent. Conversely, if the product is still under guarantee, then an “influential” signal is sent, while if the original transaction amount was also large, then a “serious” signal is flagged. The disposal agent then remends appropriate treatments. For moderate signals, the disposal agent automatically sends an to a customer acknowledging the customer’s concerns. For “influential” signals, the disposal agent advises a customer specialist to contact the customer in order to prevent possible return. For serious signals, the disposal agent remends performing related business processes such as preparing return stocklocation. After the treatment is pleted, the sensor agent should follow up the customer satisfaction and give feedback to the disposal agent. The proposed framework could provide an early warning to the manufacturer about possible returns, and additionally could summarize the top 10 return reasons for product redesign. The ITD 7 would increase under this framework. Repair Scenario Based on the proposed framework, the sensor agent would analyze the plaints from consumers monthly, and calculate the repair possibilities. For instance, suppose that some customers of electronic products live in the moist area, matching a rule in Table 2. The sensor agent judges, according to the past data, that some parts of these products might malfunction later. If these parts are normal materials, then a“ mode rate” signal is transmitted. If these parts contain special materials, then an “influent。