1,250 research outputs found

    Self-Organizing Maps Infusion with Data Envelopment Analysis

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    Benchmarking Environmental Efficiency of Ports Using Data Mining and RDEA: The Case of a U.S. Container Ports

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    This study provides step-wise benchmarking practices of each port to enhance the environmental performance using a joint application of the data-mining technique referred to as Kohonen’s self-organizing map (KSOM) and recursive data envelopment analysis (RDEA) to address the limitation of the conventional data envelopment analysis. A sample of 20 container ports in the U.S.A. were selected, and data on input variables (number of quay crane, acres, berth and depth) and output variables (number of calls, throughput and deadweight tonnage, and CO2 emissions) are used for data analysis. Among the selected samples, eight container ports are found to be environmentally inefficient. However, there appears to be a high potential to become environmentally efficient ports. In conclusion, it can be inferred that the step-wise benchmarking process using two combined methodologies substantiates that a more applicable benchmarking target set of decision-making units is be projected, which consider the similarity of the physical and operational characteristics of homogenous ports for improving environmental efficiency

    The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

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    Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

    Disruption Response Support For Inland Waterway Transportation

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    Motivated by the critical role of the inland waterways in the United States\u27 transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA\u27s population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions

    An evaluation of energy consumption and emissions from intermodal freight operations on the eastern seaboard: A GIS network analysis approach

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    As global trade continues to increase, the energy and environmental impacts of freight movement in the US have become more of a concern. As such, the freight transport system needs to consider opportunities to meet customer objectives, while also meeting social goals. In the US there has been legislation enacted to address the growing impact that freight movement has on the environment, but there are limited tools to assist in the implementation of those polices. This research sets forth a process for creating a geospatial intermodal freight transportation (GIFT) model within ArcGIS that can be used to analyze freight movement under different economic and environmental scenarios. The GIFT model uses an intermodal network that connects various modes (rail, truck, and ship) via intermodal terminals. ArcGIS Network Analyst is used to create the intermodal network and conduct optimal route analysis for various network attributes. Routes along the network are characterized not only by temporal and distance attributes, but also by cost, energy, and emissions attributes. Decision makers can use the model to explore tradeoffs among alternative route selection across different modal combinations, and to identify optimal routes for objectives that feature energy and environmental parameters (e.g., least carbon dioxide intensive route). The research illustrates the use of this network using a case study that analyzes freight traffic along the US Eastern Seaboard

    Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks

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    Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates

    Freight transportation and the environment: Using geographic information systems to inform goods movement policy

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    The freight transportation sector is a major emitter of the greenhouse gas carbon dioxide (CO2) which has been recognized by numerous experts and science organizations as a significant contributor to climate change. The purpose of this thesis is to develop a a framework for obtaining the freight flows for containerized goods movement through the U.S. marine, highway, and rail systems and to estimate CO2 emissions associated with the freight traffic along interstate corridors that serve the three major U.S. ports on the West Coast, namely the port of Los Angeles and Long Beach, the Port of Oakland and the Port of Seattle. This thesis utilizes the Geospatial Intermodal Freight Transportation (GIFT) model, which is a Geographic Information Systems (GIS) based model that links the U.S. and Canadian water, rail, and road transportation networks through intermodal transfer facilities, The inclusion of environmental attributes of transportation modes (trucks, locomotives, vessels) traversing the network is what makes GIFT a unique tool to aid policy analysts and decision makers to understand the environmental, economic, and energy impacts of intermodal freight transportation. In this research, GIFT is used to model the volumes of freight flowing between multiple origins and destinations, and demonstrate the potential of system improvements in addressing environmental issues related to freight transport. Overall, this thesis demonstrates how the GIFT model, configured with California-specific freight data, can be used to improve understanding and decision-making associated with freight transport at regional scales

    Benchmarking Sustainability Performance of Ports

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    Sustainable development agendas are challenging the world and ports, in particular, to find ways to become more efficient while meeting economic, social and environmental objectives. Although there has been a considerable body of documentation on green port practices and performance in Europe and America, there is limited synthesis about evaluation of sustainable practices in the Canadian ports context. This research aims to provide a modeling framework for benchmarking the sustainability performance of ports and to identify targets for improvement. A two-step approach is proposed. First, a review of literature and initiatives employed by global port authorities is conducted to identify major sustainability performance indicators. Second, data envelopment analysis (DEA) is applied to evaluate port performance while taking into account the dimensions of sustainable development. The DEA models evaluate both undesirable and desirable outputs for ports. Three categories of models are proposed namely; ignoring undesirable output, treating undesirable output as input, and directional distance function under variable and constant returns to scale. A case study for 13 North American ports is conducted. The results indicate that performance evaluations vary with economic and social criteria. The indicators and methodology undertaken can be used by ports and other industrial service sectors for improving green performance
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