Integrated Optimization of Port Rotation Direction and Fleet Deployment for Container Liner Shipping Routes

This paper provides an integrated planning methodology for the optimization of port rotation direction and fleet deployment for container liner shipping routes with consideration of demand uncertainty. We first consider a special case that demand is deterministic. A multicommodity flow network model is developed via minimizing the total network-wide cost. Its decisions are the selection of port rotation direction and fleet deployment and container routings in the shipping network. Afterward, we address the generic case that uncertain demand is considered, which is represented by potentially realizable demand scenarios. We develop a minimax regret model to procure the least maximum regret across all the demand scenarios. The proposed models are applied to an Asia-Europe-Oceania liner shipping network with 46 ports and 12 ship routes. Results could provide the liner company with a comprehensive decision tool to simultaneously determine port rotation direction and fleet deployment when tackling uncertain demand.

Socio-Economic Impact of the Covid-19 Pandemic in the U.S

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.

15. The general ‘economic’ torts

This chapter explains the five general economic torts: inducing breach of contract, causing loss by unlawful means (after OBG v Allan), lawful means conspiracy, unlawful means conspiracy (after Total Network v Revenue and Customs Commissioners as well as JSC BTA Bank v Ablyazov), and intimidation. The chapter notes that what binds these torts together is their focus on the element of intention and causation of loss, as well as the tendency of the courts to apply them in business and other financial settings. Otherwise, the torts are quite disparate in their operation. It is argued that some of the torts should be seen as applicable outside the dominant setting, in part where they reflect general principles of responsibility for harm causation.

Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay

The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.

A Robust Self-Organizing Tree-Based Routing Protocol for Wireless Sensor Networks

For a wireless sensor network (WSN), routing protocols not only affect the reliability and real-time data transmission but also affect the energy consumption of communication and the survival time of the entire network. In this paper, a routing protocol is proposed that combines virtual potential energy with local density of nodes and sleep-wake-up mechanism. The proposed routing protocol is self-organizing and robust. The routing protocol allows nodes to dynamically join or leave the network and enables nodes to automatically transmit information along the shortest path to reach a corresponding sink. The protocol can be applied in scenarios of single sink, multiple sinks, and three-dimensional WSNs. At the same time, the protocol takes a sleep-wake-up mechanism into account, makes a significant decrease in total network traffic, and achieves energy-saving as much as possible. Examples are given in detail to illustrate the effectiveness of the proposed routing protocol.

Pemetaan Informasi Sebaran Bidang Ilmu Pada Skripsi Fakultas Matematika dan Ilmu Pengetahuan Alam Lulusan IPB University Tahun 2015 - 2019 Berdasarkan Universal Decimal Classification

Pendahuluan. IPB-University, perguruan tinggi terkemuka di Indonesia senantiasa menghasilkan banyak penelitian dan inovasi. Karya tulis akademik berupa skripsi suatu bukti nyatanya. Namun demikian belum ada suatu kajian yang pernah memetakan hasil-hasil penelitian tersebut. Penelitian ini dilakukan untuk mengetahui: Sebaran topik penelitian, Membuat peta (create map) bidang ilmu berdasarkan standar Universal Decimal Classification, dan Memvisualisasikan peta (map) kata kunci, serta Produktivitas dosen pembimbing pada skripsi Fakultas Matematika dan Ilmu Pengetahuan Alam IPB University. Metode Penelitian. Penelitian ini merupakan suatu kajian bibliometrik dengan metode penelitian kuantitatif, guna mendeskripsikan pemetaan informasi sebaran bidang ilmu pada skripsi Fakultas Matematika dan Ilmu Pengetahuan Alam IPB University. Pengolahan Data Penelitian. Obyek penelitian ini adalah data skripsi Fakultas Matematika dan Ilmu Pengetahuan Alam pada pangkalan data Perpustakaan IPB University. Data diolah menggunakan Program M.S. Excel dan dikelompokkan berdasarkan standar Universal Decimal Classification. Data kata kunci dibuat File RIS (RIS File) dan dianalisis menggunakan Program VosViewer-Visualizing Scientific Landscapes 1.6.1.5 yang dikembangkan oleh Nees Jan Van Eck dan Ludo Waltman. Hasil Penelitian. Membuktikan, topik penelitian skripsi Fakultas Matematika dan Ilmu Pengetahuan Alam IPB University lulusan tahun 2015-2019 sebanyak 3.550 judul. Peta bidang ilmu berdasarkan Universal Decimal Classification menunjukkan topik penelitian paling banyak kelas 004: “Ilmu dan Teknologi Komputer, Komputasi dan Pemrosesan data” 719 (20,25%) judul, topik penelitian paling sedikit kelas 590: “Zoologi” 43 (1,21%). Analisis kata kunci atau co-word menggunakan Vosviewer 1.6.1.5 pada kemunculan kata kunci minimum 5 berhasil memvisualisasikan 683 kata kunci dalam 12 gugus (cluster). Kata kunci paling banyak muncul terjadi yaitu “Ilmu dan Teknologi Komputer” 153 kali. Visualisasi Jaringan (Network Visualization) menunjukan kecenderungan (trend) topik penelitain paling banyak diminati: “Ilmu dan Teknologi Komputer” ada pada gugus (cluster) 5 dengan kekuatan tautan total (total network link) 397. Visualisasi Hamparan (Overlay Visualization) menunjukan kecenderungan (trend) tahun penelitian pernah dilakukan yaitu antara tahun 2016-2018. Visualisasi Kepadatan (Density Visualization) menunjukan kecenderungan (trend) kepadatan volume penelitian masing-masing kata kunci tersebut berpariasi. Produktivitas dosen pembimbing skripsi paling tinggi sebagai pembimbing utama ialah Imas Sukaesih Sitanggang sebanyak 79 bimbingan, sedangkan sebagai pembimbing anggota ialah Ali Kusnanto dan Budi Waryanto, jumlah bimbingan masing-masing sebanyak 42 dan 8 bimbingan skripsi.

Performance Analysis of Cache Based on Popularity and Class in Named Data Network

The communication network is growing with some unique characteristics, such as consumers repeatedly request the same content to the server, similarity in local demand trend, and dynamic changes to requests within a specific period. Therefore, a different network paradigm is needed to replace the IP network, namely Named Data Network (NDN). The content store, which acts as a crucial component in the NDN nodes is a limited resource. In addition, a cache mechanism is needed to optimize the router’s content store by exploiting the different content services characters in the network. This paper proposes a new caching algorithm called Cache Based on Popularity and Class (CAPIC) with dynamic mechanism, and the detail explanation about the static method also presented. The goal of Static-CAPIC was to enhance the total cache hit ratio on the network by pre-determining the cache proportion for each content class. However, this technique is not appropriate to control the cache hit ratio for priority class. Therefore, the Dynamic-CAPIC is used to provide flexibility to change the cache proportion based on the frequency of requests in real-time. The formula involves considering the consumers’ request all the time. It gives a higher cache hit ratio for the priority content class. This method outperforms Static-CAPIC, and the LCD+sharing scheme in the total network cache hit ratio parameter and channels it to the priority class.

Inventory Share Policy Designs for a Sustainable Omni-Chanel E-Commerce Network

This paper studies inventory share policies for sustainable omni-channel e-commerce supply network design by seeking for a good integration policy of online and offline retailers so that the overall supply network reduce its cost, environmental negative impacts by the decreased number of shipments from the main depot, and increase its responsiveness. By the recent advancement in information technologies and internet use, e-commerce practice gained popularity also to keep up with the competitive environment. The increased competitive supply chain environment has revealed the business-to-business (B2B) concepts enabling business applications between companies. Strategic alliance is a partnership concept realized between two or more organizations ensuring that stages are managed with consideration of the welfare of the others in the whole network. By considering that there are inventory share policies between stages, we accept the existence of strategic alliance implementation in the network, aiming to increase total network flexibility and profitability as well as sustainability in the network. In the study, we research inventory share policies towards strategic alliance concept to have a network design with a decreased negative effect of demand uncertainty and increased profitability in the network. By inventory share policies, businesses share their current inventories with the others so that transportation cost and CO2 emission caused by traffic intensity is decreased in the network. We propose six inventory share policy combinations and optimize the (s, S) inventory levels under those policies by minimizing total network cost. We utilize the simulation modeling approach for the modeling purpose. We compare the policy results based on the total network cost, the total number of shipments completed from the main warehouse, and total lost sale cost, etc., at the optimal levels and suggest the best policy design.

Performance Analysis of IoT-Based Health and Environment WSN Deployment

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

A Distributionally Robust Chance-Constrained Approach for Modeling Demand Uncertainty in Green Port-Hinterland Transportation Network Optimization

This paper discusses a bi-objective programming of the port-hinterland freight transportation system based on intermodal transportation with the consideration of uncertain transportation demand for green concern. Economic and environmental aspects are integrated in order to obtain green flow distribution solutions for the proposed port-hinterland network. A distributionally robust chance constraint optimization model is then established for the uncertainty of transportation demand, in which the chance constraint is described such that transportation demand is satisfied under the worst-case distribution based on the partial information of the mean and variance. The trade-offs among different objectives and the uncertainty theory applied in the modeling both involve the notion of symmetry. Taking the actual port-hinterland transportation network of the Yangtze River Economic Belt as an example, the results reveal that the railway-road intermodal transport is promoted and the change in total network CO2 emissions is contrary to that in total network costs. Additionally, both network costs and network emissions increase significantly with the growth of the lower bound of probability for chance constraint. The higher the probability level grows, the greater the trade-offs between two objectives are influenced, which indicates that the operation capacity of inland intermodal terminals should be increased to meet the high probability level. These findings can help provide decision supports for the green development strategy of the port-hinterland container transportation network, which meanwhile faces a dynamic planning problem caused by stochastic demands in real life.