【導(dǎo)讀】逆向物流最近已逐漸成為重要的問題并分為兩種方法進行學(xué)。逆向物流就是規(guī)劃執(zhí)行和控制的效率和成本原料的有效流動，從消費點開。始在過程庫存，成品及相關(guān)信息處置適當(dāng)以奪回原點價值。責(zé)，這已成為公司刻不容緩的責(zé)任。許多以前的研究曾試圖制訂的逆向物流數(shù)學(xué)模?？藙谒怪Z和亨德里克森探索了產(chǎn)品的回報率和逆向物流的策略之間的關(guān)系。和塞蘭奇研究回收和價格貶值之間的聯(lián)系。雖然這些優(yōu)化模型提供部分逆向物流解。整個供應(yīng)鏈過程中討論隱藏價值的逆向物流。瑞奇等人在接受調(diào)查的逆向物流程序。越來越多的公司正在利用逆向物流作為一個商業(yè)策?；颦h(huán)境負責(zé)的行為還可能獲得顧客更高的滿意度。以維持其剩余的價值。這項研究的結(jié)果使這些逆向物流活動更加積極。先前的研究將逆向物流活動分為3組，即返回，修理和回收。轉(zhuǎn)讓可重復(fù)使用的材料給供應(yīng)商和制造商，這一過程被稱為逆向物流?！坝型亓Α钡男盘柺翘幚砥鞯囊庖娡ㄟ^客戶代表聯(lián)絡(luò)客戶以防止可

　　

【正文】 rs would require particular engineer skills, then a “serious” signal is flagged. The disposal agent remends appropriate treatments to the decision maker. For a “mode rat” signal, the disposal agent verifies the material stocks, and automatically schedules these repair requirements. For “influential” signals, the disposal agent remends material procurements to the decision maker. In this case, owing to the longer repair period, the disposal agent arranges a temporary replacement product for customers. For serious signals, the disposal agent schedules another engineer, or remends further training for engineers. After the treatment is pleted for certain period (say one month), the sensor agent follows up customer opinions, and gives feedback to the disposal agent for further improvement. The ITD of the SCM is higher under the proposed framework than in other systems, enabling the repair center to prepare for possible repairs to accelerate the repair time. Recycling Scenario According to the proposed framework, database triggers notify the sensor agent the possibilities for recycling when the product approaches the end its life. The product size and materials is checked. If the product materials are normal, then the sensor agent sends a moderate signal. If the products contain toxic or harmful materials, then the senor agent sends “serious” signal. The disposal agent then remends treatments to the decision maker. For a “moderate” signal, distributors are remended to conduct relationship marketing to their customers to express concerns about their product usage. Additionally, some notification messages could be transmitted automatically to the recycling collector to raise the ratio of recycled material. For serious signals, the disposal agent should report to the decision maker to ply with WEEE and RoHS requirements. The proposed framework raises the ITD of SCM. Moreover, the recycling ratio could be expected to increase if the recycling promotion bees more active. 5 Conclusions Reverse logistic activities have recently bee a critical issue for both consumer and producer sites, but present some dilemmas. (1) Businesses are increasingly adopting loose return policy as strategy. However, in practice, the returned products are stocked by distributors, cannot be processed quickly by manufacturers to regain economic value quickly. (2) As new environmental laws are increasingly being enforced, recycling activities are additional burdens to the manufacturer, but are also social and environment responsibilities. Additionally, the recycling behaviors are not necessary for customer, for whom the reward is limited. Therefore, recycling is difficult to implement well in practice. (3) Repair is inconvenient for both customers and repair centers. Customers cannot use their products during the repair period. Thus, decreasing the repair time could improve customer’s satisfaction. However, without proper information, repair centers cannot schedule the required resources to shorten the repair time. This study presents an agentbased framework to improve information transparency degree of these reverse activities. A sensor agent operates like an early warning system to detect possible reverse activities actively. A disposal agent operates like a consultant, remending treatments to decision maker, and even arouse related business processes automatically. The proposed framework is expected to increase the supply chain’s information transparency degree, and improve the performance of reverse supply chain activities. Future research will concentrate on implementing this framework, and on verifying its performance and effectiveness using realworld data and field studies.