Name
Abstract | ||
|---|---|---|
At the port-of-entry, containers are inspected through a specific sequence of sensor stations to detect the presence of radioactive materials, biological and chemical agents, and other illegal cargo. The inspection policy, which includes the sequence in which sensors are applied and the threshold levels used at the inspection stations, affects the probability of misclassifying a container as well as the cost and time spent in inspection. This work is an extension of a paper by Elsayed et al., which considers an inspection system operating with a Boolean decision function combining station results. In this paper, we present a multiobjective optimization approach to determine the optimal sensor arrangement and threshold levels, while considering cost and time. The total cost includes cost incurred by misclassification errors and the total expected cost of inspection, while the time represents the total expected time a container spends in the inspection system. Examples which apply the approach in various systems are presented. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TASE.2009.2022172 | Automation Science and Engineering, IEEE Transactions |
Keywords | Field | DocType |
Boolean functions,containers,decision theory,inspection,optimisation,Boolean decision function combining station,container misclassification,multiobjective optimization,optimal sensor arrangement,port of entry inspection policy,sensor station,total expected cost,total expected time,Boolean function,multiobjective,probability of false accept,probability of false reject,sensor threshold levels | Boolean function,Customs duty,Computer science,Decision function,Operations research,Multi-objective optimization,Decision theory,Expected cost,Total cost,Port of entry | Journal |
Volume | Issue | ISSN |
7 | 2 | 1545-5955 |
Citations | PageRank | References |
5 | 0.45 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christina M. Young | 1 | 10 | 1.81 |
| Mingyu Li | 2 | 5 | 0.79 |
| Yada Zhu | 3 | 39 | 10.49 |
| Minge Xie | 4 | 18 | 3.46 |
| Elsayed A. Elsayed | 5 | 148 | 12.69 |
| Tsvetan Asamov | 6 | 24 | 2.78 |