TY - JOUR
T1 - A heuristic approach for allocation of data to RFID tags
T2 - A data allocation knapsack problem (DAKP)
AU - Davis, Lauren
AU - Samanlioglu, Funda
AU - Jiang, Xiaochun
AU - Mota, Daniel
AU - Stanfield, Paul
PY - 2012/1
Y1 - 2012/1
N2 - Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the unexplained data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.
AB - Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the unexplained data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.
KW - Data allocation
KW - Knapsack problem
KW - RFID tags
UR - https://www.scopus.com/pages/publications/79957583708
U2 - 10.1016/j.cor.2011.01.019
DO - 10.1016/j.cor.2011.01.019
M3 - Article
SN - 0305-0548
VL - 39
SP - 93
EP - 104
JO - Computers and Operations Research
JF - Computers and Operations Research
IS - 1
ER -