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"Search and classification using multiple autonomous vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. "

"Efficiency energy and stream data mining on Wireless Sensor Networks (WSNs) are a very interesting issue to be discussed. Routing protocols technology and resource-aware can be done to improve energy efficiency. In this paper we try to merge routing protocol technology using routing Distance Vector and Resource-Aware (RA) framework on heterogeneity wireless sensor networks by combining sun-SPOT and Imote2 platform wireless sensor networks. RA perform resource monitoring process of the battery, memory and CPU load more optimally and efficiently. The process uses Light-Weight Clustering (LWC) and Light Weight Frequent Item (LWF). The results obtained that by adapting Resource-Aware in wireless sensor networks, the lifetime of wireless sensor improve up to ± 16.62%.

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Efisiensi energi dan stream data mining pada Wireless Sensor Networks (WSN) adalah masalah yang sangat menarik untuk dibahas. Teknologi Routing Protocol dan Resource-Aware dapat dilakukan untuk meningkatkan efisiensi energi. Dalam penelitian ini peneliti mencoba untuk menggabungkan teknologi Routing Protocol menggunakan routing Distance Vector dan Resource-Aware (RA) framework pada Wireless Sensor Networks heterogen dengan menggabungkan sun-SPOT dan platform Imote2 Wireless Sensor Networks. RA melakukan proses pemantauan sumber daya dari memori, baterai, dan beban CPU lebih optimal dan efisien. Proses ini menggunakan Light-Weight Clustering (LWC) dan Light Weight Frequent Item (LWF). Hasil yang diperoleh bahwa dengan mengadaptasi Resource-Aware dalam Wireless Sensor Networks, masa pakai wireless sensor meningkatkan sampai ± 16,62%."

"Penelitian ini dilakukan untuk mengukur kinerja rantai pasok pada perusahaan alat kesehatan di Indonesia. Metode yang digunakan dalam penelitian ini adalah Supply Chain Operations Reference (SCOR) sebagai kerangka pengukuran kinerja dan metode Analytical Hierarchy Process (AHP) untuk menentukan bobot masing-masing indikator kinerja. Tujuan dari penelitian ini adalah untuk mengukur kinerja rantai pasokan perusahaan pada tahun 2021 dan mendapatkan strategi perbaikan untuk meningkatkan kinerja rantai pasokan perusahaan secara keseluruhan. 29 indikator kinerja diukur dan dibagi ke dalam perspektif model SCOR untuk setiap level. Hasil pengukuran menunjukkan kinerja rantai pasok pada tahun 2021 sebesar 63,35% yang menunjukkan kinerja perusahaan berada pada kategori average. Kemudian, setiap KPI akan dipetakan ke kuadran Importance Performance Analysis (IPA) untuk mendapatkan indikator mana yang perlu ditingkatkan. Strategi yang diusulkan dianalisis dengan melakukan focus group discussion dengan perwakilan perusahaan. Terdapat enam usulan strategi yang dipilih untuk meningkatkan lima KPI berdasarkan Importance Performance Analysis (IPA) yang diharapkan dapat memberikan dampak yang baik terhadap peningkatan kinerja rantai pasok perusahaan secara keseluruhan.

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This research was conducted to measure supply chain performance in medical device companies in Indonesia. The method used in this research is Supply Chain Operations Reference (SCOR) as a performance measurement framework and Analytical Hierarchy Process (AHP) method to determine the weight of each performance indicator. The objective of this research is to measure the supply chain performance of the company in 2021 and obtain improvement strategies to improve the overall supply chain performance of the company. 29 performance indicators are measured and divided into the SCOR model’s perspectives for each level. The result of the measurement indicates that the performance of the supply chain in 2021 is 63,35% which shows the company's performance is in an average category. Then, each KPI will be mapped to the Importance Performance Analysis (IPA) quadrant to get which indicators that need to be improved. The proposed strategy is analyzed by conducting focus group discussion with the company's representative. There are six proposed action plan strategies chosen to improve five KPIs based on the Importance Performance Analysis (IPA) which is expected to give a good impact on the company’s overall supply chain performance improvement.

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