ارزیابی عملکرد پویای الگوریتم ژنتیک برای مدیریت لجستیک معکوس RFID
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1396||2010||7 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 11, November 2010, Pages 7329–7335
Environmental awareness, green directives, liberal return policies, and recycling of materials are globally accepted by industry and the general public as an integral part of the product life cycle. Reverse logistics reflects the acceptance of new policies by analyzing the processes associated with the flow of products, components and materials from end users to re-users consisting of second markets and remanufacturing. The components may be widely dispersed during reverse logistics. Radio frequency identification (RFID) complying with the EPCglobal (2004) Network architecture, i.e., a hardware- and software-integrated cross-platform IT framework, is adopted to better enable data collection and transmission in reverse logistic management. This research develops a hybrid qualitative and quantitative approach, using fuzzy cognitive maps and genetic algorithms, to model and evaluate the performance of RFID-enabled reverse logistic operations (The framework revisited here was published as “Using fuzzy cognitive map for evaluation of RFID-based reverse logistics services”, Proceedings of the 2009 international conference on systems, man, and cybernetics (Paper No. 741), October 11–14, 2009, San Antonio, Texas, USA.). Fuzzy cognitive maps provide an advantage to linguistically express the causal relationships between reverse logistic parameters. Inference analysis using genetic algorithms contributes to the performance forecasting and decision support for improving reverse logistic efficiency.
Enterprises are applying reverse logistics as a means for fulfilling different market regions’ recycling requirements. The European Union has a waste electrical and electronics equipment (WEEE) directive and the United States uses state and federal laws for enforcing recycling programs. Reverse logistic processes help enterprises fulfill their social responsibility and build their reputation by providing systems and processes for customers to return products and components either for repair, reuse, or disposal. Traditionally, supply chains without return and recycling processes are modeled as linear structures with a one way flow of goods from suppliers, manufacturers, wholesalers, retailers, and finally to consumers. Modern distribution channels that include repair, recycling, and responsible waste disposal must accommodate bi-directional flows or reverse logistics flows. Reverse distribution channels include direct returns to manufacturers, indirect returns to repair facilities, individualized returns with small quantities, extended order cycles associated with product exchanges, and a variety of disposition options (e.g., repair versus exchange). The complexity of processes makes the modeling and implementation of reverse logistics a challenging task. In addition, it is difficult to measure the impact of product return and recycling on profitability and customer loyalty. An underlying cause for the measurement difficulties is that most enterprises are unable to trace the reverse logistics processes in real-time. Radio frequency identification (RFID) technology enables enterprises to gather and track reverse logistics process data in real-time. RFID uses tags that can be automatically detected by readers without manual scanning, a major advantage over bar code readers. RFID uses radio frequency as a means to transmit data from tags affixed to physical objects such as products, boxes, or shipping containers. Data related to physical objects can be identified, stored, traced and monitored during transportation through the entire product life cycle. RFID also makes it possible to simultaneously detect and identify multiple items. For example, a list of goods packed in a sealed box can be automatically identified using a RFID reader without opening the box. Tags with memory can also be dynamically modified, inventory modifications can be batch processed, and stock keeping unit (SKU) data are readily transferred across enterprise systems. As a result, RFID technology enables precise tracking and real-time monitoring of each tagged item with minimal effort. In this research, fuzzy cognitive maps (FCM) are used to construct a reverse logistics network decision model. RFID technology provides the mechanism for real-time monitoring of the reverse logistics processes. The FCM decision model, using data collected by RFID technology, provides two critical functions, i.e., inference analysis and decision analysis. Inference analysis is applied to forecast future states of the reverse logistic operations. If sudden changes occur, the information system sends a warning message to alert the manager. The manager also receives decision support to improve logistic performance. In this research, a case is used to demonstrate and evaluate the implementation of fuzzy cognitive maps and genetic algorithms for managing the RFID-enabled reverse logistics of a cold storage chain.
نتیجه گیری انگلیسی
This paper proposes a fuzzy cognitive map model for improving reverse logistic process decision support. Given the dynamic and complex features of the reverse logistics network, the FCM is used to construct a reverse logistics network that incorporates RFID technology to collect real-time data from daily operations. The model is integrated with the RFID module to provide data for network performance forecasting and decision support. Finally, a cold storage container management case is presented. The inference analysis and decision analysis is used to forecast the container logistics chain response and adjust the operation parameters to better control the system performance according to managements established operating processes. The management of uncertainty is a critical task for forward and reverse logistic operations. This study provides a method to predict future logistic operation states and to constructs a decision support model to manage system performance based on the forecast. The results show the potential of the proposed methodology for enhancing competitiveness and efficiency of complex and dynamic reverse logistic chains.