Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports

Developing Models and Algorithms to Design a Robust Inland Waterway Transportation Network Under Uncertainty

This dissertation develops mathematical models to efficiently manage the inland waterway port operations while minimizing the overall supply chain cost. In the first part, a capacitated, multi-commodity, multi-period mixed-integer linear programming model is proposed capturing diversified inland waterway transportation network related properties. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. The next study develops a two-stage stochastic mixed-integer nonlinear programming model to manage congestion in an inland waterway transportation network under stochastic commodity supply and water-level fluctuation scenarios. The model also jointly optimizes trip-wise towboat and barge assignment decisions and different supply chain decisions (e.g., inventory management, transportation decisions) in such a way that the overall system cost can be minimized. We develop a parallelized hybrid decomposition algorithm, combining Constraint Generation algorithm, Sample Average Approximation (SAA), and an enhanced variant of the L-shaped algorithm, to effectively solve our proposed optimization model in a timely fashion. While the first two parts develop models from the supply chain network design viewpoint, the next two parts propose mathematical models to emphasize the port and waterway transportation related operations. Two two-stage, stochastic, mixed-integer linear programming (MILP) models are proposed under stochastic commodity supply and water level fluctuations scenarios. The last one puts the specific focus in modeling perishable inventories. To solve the third model we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines SAA with an enhanced Progressive Hedging and Nested Decomposition algorithm. Similarly, to solve the last mathematical model we propose a hybrid decomposition algorithm combining the enhanced Benders decomposition algorithm and SAA to solve the large size of test instances of this complex, NP-hard problem. Both proposed approaches are highly efficient in solving the real-life test instances of the model to desired quality within a reasonable time frame. All the four developed models are validated a real-life case study focusing on the inland waterway transportation network along the Mississippi river. A number of managerial insights are drawn for different key input parameters that impact port operations. These insights will essentially help decisions makers to effectively and efficiently manage an inland waterway-based transportation network

A Bayesian Network Based Approach For Modeling and Assessing Resilience: A Case Study of a Full Service Deep Water Port

Ports are an integral part of the transportation system and are often susceptible to a diverse range of risks, including natural disasters, malicious cyber-attacks, technological factors, organizational factors, economic factors, and human error. To address the challenges triggered by these diverse risks, this research identifies the basic factors that could enhance the resilience of the port system. After these factors are identified and expressed as different resilience capacities, they are used to quantify the resilience of the port infrastructure by applying a Bayesian network. Quantification of resilience is further analyzed based on different advanced techniques such as forward propagation, backward propagation, sensitivity analysis, and information theory. The formal interpretation of these analyses indicates that maintenance, alternate routing, and manpower restoration are the leading factors contributing to enhancing the resilience of a port infrastructure system under disruptive conditions

Selecting a Biomass Pelleting Processing Depot Using a Data Driven Decision-Making Approach

Bioenergy is one of the potential solutions to satisfy the extensive demand for energy and reduce fossil fuel dependency. For biomass to be an efficient source of bioenergy, it must be converted to a usable form, one of which is pellets. This study compares three commonly used methods to produce pellets in a biomass depot and presents a framework to select the most effective and economic pelleting processes. The comparison is performed using a data driven decision-making method called the Preference Index Selection Method (PSI). We considered three main pelletization technologies and compared four of their most critical attributes. The three popular biomass pellet processing methods used for this study are the conventional pelleting process (CPP), the high moisture pelleting process (HMPP), and the ammonia fiber expansion (AFEX). These processes were evaluated from both economic and environmental perspectives. We used the state of Mississippi as a testing ground for our analyses. The results obtained through the PSI method were validated with the Grey relational analysis (GRA) method. The results revealed that of the three available pelleting processes, the conventional pelleting process and the high moisture pelleting process were the most economic and environmentally friendly

Traditional Fish Farming Based on Indigenous Knowledge in Homestead Pond Can Uplift Socioeconomic Status of Coastal Rural People and Sustainability

In a time of environmental degradation and increasing demand for safe food production, traditional fish farming is gaining global attention. Utilizing indigenous agricultural methods founded on traditional knowledge contributes to sustainability by safeguarding the ecosystem and preserving biodiversity. However, it is still less studied whether traditional farming systems based on indigenous knowledge currently in place are improving the socioeconomic conditions of farmers. Hence, this study was conducted with the following objectives: (i) to reveal the present status of traditional fish farming systems, (ii) to define the socioeconomic profile of the farmers, (iii) to identify the problems associated with traditional fish farming, and (iv) to show the inter-relationship between fish farming and socioeconomic development. For achieving these objectives, data were collected from 100 small-scale fish farmers from a rural coastal area of Bangladesh through a well-structured questionnaire, focus group discussion, and cross-check interviews. Our findings showed that most of the homestead ponds were small-sized (44%) and shallow (61%) where a polyculture system was prevalent (91%). The majority of the ponds (77%) were found to be perennial, 60% of which had single ownership. Socioeconomic data revealed that the highest number of farmers (42%) earned 1000.00 to 1500.00 USD annually, and 62% of the respondents took fish farming as their secondary occupation. Among the farmers, 62% had primary education, whereas 7% had no education, and only 26% of the farmers had official training in fish farming, indicating that culture management was mainly based on indigenous knowledge. A total of 55% of the farmers had 5 to 10 family members, and 80% of them lived in joint families. Furthermore, 40% of the farmers owned tin shed houses, whereas the maximum (60%) utilized katcha toilets. However, almost half of the farmers (57%) utilized their own funds for fish farming, and the majority (90%) had access to their own tube well. The study found that the biggest obstacles to fish farming were pressure from large families, a lack of education and training, a lack of quality seed and feed, outbreaks of fish diseases, an inadequate supply of water during the dry season, and a lack of adequate funding. However, Pearson correlation showed that there was a significant positive association between age and experience (r = 0.908, p p < 0.01). Multiple regression analyses also demonstrated that age and experience in fish farming played a significant role in increased annual income. In conclusion, 94% of the respondents claimed that fish farming had improved their socioeconomic situation. Homestead pond fish farming through indigenous knowledge increased household fish consumption with a source of protein and micronutrients, improved dietary diversity, and generated extra household income, which inferred their better sustenance

Knowledge and Attitude towards COVID-19: A Cross Sectional Study in Bangladesh through Phone and Online Survey

Aim: This study investigated the knowledge and attitudes towards COVID-19 in Bangladeshi adults by online and phone survey methods during the early stage of its spread. Methods: Data were collected through phone calls (April 14-23, 2020) and online survey (April 18-19, 2020) in Bangladesh. The questionnaire had 20 knowledge questions with each correct response getting one point and incorrect/don’t know response getting no point (maximum total knowledge score 20). Participants scoring &gt;17 were categorized as having good knowledge. Results: The percentages of good knowledge holders were 57.6%, 75.1%, and 95.8% in the phone, online non-medical, and online medical participants, respectively. Most of the phone and online participants had good knowledge of the preventive practices of COVID-19. However, among the non-medical participants (both phone and online), the correct response rates were lower than 80% for the knowledge questions asking about the facts that - some patients may have no symptoms, diarrhea is a symptom of this disease and that it cannot be prevented by any currently available medication. Male gender, higher education, living in town/urban areas, good financial condition, and use of internet were positively associated with higher knowledge score among the non-medical participants. However, higher knowledge score was associated with having less confidence in the final control of COVID-19. Conclusion: Our study identified some COVID-19 information that were less known among the participants and the potential factors that were associated with having good versus poor knowledge. Besides, this study sheds light on the attitude of Bangladeshi adults towards COVID-19