A simulation modeling and analysis for RFID-enabled mixed-product loading strategy for outbound logistics: A case study☆
Highlights
► Study the adoption of RFID technology. ► Propose loading strategies. ► Measure the performance.
Introduction
This paper is motivated by a giant printing and paper bag manufacturer with two manufacturing units in a single site. Each area has an independent warehouse and a loading facility. Both have equal finished goods (FG) production capacities, and have similar arrival patterns of FG each working day. The manufacturer signs long term contracts with its clients to produce finished goods according to a rough shipping schedule, and to arrange shipment after receiving the exact shipping order.
Since all clients are abroad, all FG have to be first transported from the manufacturing site to different ports by truck, from where the goods are shipped to their final destination required by the client. The current problem is that trucks spend a great deal of time waiting in the loading dock. It is because usually products have not been completely finished and a portion of products is still being on the production line when trucks arrive at the loading dock.
Worse still, due to the limited capacity of the loading bay, other trucks whose goods have already been completely finished may be blocked. During the peak season, trucks occupy much space outside the loading area, inside and outside the manufacturing area. It seriously affects normal operations of the plant and the traffic, and further weakens loading efficiency. The purpose of this study is to design several loading strategies, which use information provided by RFID (radio-frequency identification) technology. Since simulation is widely used as a decision support to help decision maker to solve difficult problems in design, control and analyze of complex system, and to identify the impact of changing parameters on system performance (Cho, 2005, Zhou et al., 2010). Moreover, different performance measures can be derived from the simulation models, using which the decision maker is able to fine-tune the suggested strategies (Mendes, Ramos, Simaria, & Vilarinho, 2005). Therefore, in this paper, simulation models will be built to compare the current loading process with the proposed RFID-enabled ones to test which perform better in terms of average time consumed in loading, throughput of trucks and percentage of trucks missing their due dates.
The organization of this paper is as follows. After this introductory section, the relevant literature of RFID applications in various fields, especially logistics and supply chain management has been reviewed. In Section 3, the difference of RFID-enabled and non-RFID-enabled loading processes has been presented. Different loading strategies based on RFID information are described in detail in Section 4. Section 5 discusses the experimental design of the simulation study, including independent variables, dependent variables, and simulation assumptions. In Section 6, comparison and analysis of results will be conducted. Conclusions are given in Section 7.
Section snippets
Literature review
Much past research has addressed applications of RFID technology, especially in the area of logistics and supply chain management. Kim, Tang, Kumara, Yee, and Tew (2008) designed a simulation framework to analyze the value of wireless location information provided by RFID technology for logistics operations during vehicle deployment and load makeup planning (DLMP) in an automotive shipping yard. The results showed that application of RFID driven by intelligent algorithms can greatly reduce the
Comparison of RFID-enabled and non-RFID-enabled loading processes
We firstly introduce the current loading process of trucks without using RFID technology in the plant. Then, we present the proposed loading process of trucks with RFID technology. According to the delivery date of various products for customers, the transportation department arranges trucks to go the plant for loading. The truck would arrive at the production plant at any time before the deadline. Currently two deadlines (10:00 am and 16:00 pm) have been set. When a truck arrives at the plant,
Different loading dispatching rules
In the following part, some notations, which are used in our strategies, are introduced first.Dij: Number of pallets of product j to be loaded on truck i σ: Time when dispatching decision is made FGijσ: Finished number of pallets of product j to be loaded on truck i at the time σ PRj: Production rate of product j RPTij: Remaining production time of product j to be loaded on truck i RPTi: Remaining processing time of truck i, which is equal to the maximum value of RPTij Pj: Priority value of truck i DDi: Due
Independent and decision variables
In our model, two kinds of variables impact the experiment results: one is independent variables, which are related with time (such as the operating time of different activities), and the other is decision variables, which are related with capacities of different resources. Table 1 describes the detailed information of independent and decision variables.
Dependent variables
We use three key performance indicators (KPIs) to evaluate and analyze the performance under different strategies. Since trucks’ waiting time
Results and discussion
Performances of the four rules with respect to average operating time in the system, throughput of trucks, and percentage of tardy trucks are discussed as follows. All these results are obtained by taking the average of 30 replications. Since point estimate just calculates the mean and standard deviation of the replication sample, it cannot accurately estimate the true value of the performance indicators. Therefore, we adopt the confidence interval estimate, which can provide a range within
Conclusion
In this paper, we design three different loading strategies and compare them with the original FIFO rule. Simulation models, according to different loading strategies, are built to conduct the comparison.
The results indicate that these strategies have different performances with respect to different criteria. In terms of mean time in the system and throughput, the dynamic SRT rule performs better than other rules since it selects trucks with the shortest remaining production time for loading,
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This manuscript was processed by area editor Paul Savory.