Graduate Student Research Seminar Day ‑ Nov 16, 2022
You are cordially invited to the Graduate Student Research Seminar of the Department of Industrial Engineering.
Date: Wednesday, November 16, 2022
Time: 1:00 - 3:00 PM
Venue: In-person gathering: Room MA227, Sexton Campus
Schedule:
1:00-1:25 PM Wenhao Yi
Comparison of the physical internet and the traditional logistic system on carbon footprint
1:25-1:50 PM Benjamin Schulman
A production capacity investment decision making tool for the indoor vertical farming industry
1:50 -2:00 PM Break
2:00-2:25 PM Ana Carvalho Bianco
Prediction of changes in ship-generated noise in the Canadian Arctic
2:25-2:50 PM Dawne Skinner
Demand modelling for food produced via novel technologies: Cellular agriculture as a case study
Abstracts:
Comparison of the physical internet and the traditional logistic system on carbon footprint
Wenhao Yi, MASc Student
The Physical Internet is a concept that dramatically transforms how physical objects are designed, manufactures, and distributed. The idea behind PI is the distribution of modular containers from shipment centers called PI Hubs which assist in developing consolidation opportunities. Intuitively, this makes PI an interesting paradigm from both economic and environmental perspectives in comparison to the traditional distribution system.
The research is an extension of the 26 cities European example in Dr. Venkatadri’s IEEE paper for all nodes at once instead of separate two-way P2P models between hubs of heavy-duty trucks based on the shortest path. The research mainly focuses on shipment consolidation in PI by considering carbon emissions in addition to logistics costs considered in Dr. Venkatadri’s paper, such as inventory holding or shipment delay cost, vehicle dispatch cost, loading/unloading cost, etc. The calculation of carbon emissions by considering certain parameters such as distance covered, emission tax, emission limit etc. The costs related to emissions could be incorporated in the form of fuel costs, emission taxes, emission penalties etc. The model will be solved by Gurobi in AMPL. The research quest addresses whether the impact of PI on environmental sustainability is positive.
A production capacity investment decision making tool for the indoor vertical farming industry
Benjamin Schulman, MASc Student
Indoor vertical farming (VF) systems are a form of controlled environment agriculture, making use of modern technology to improve food availability and security. Commercially, it is a young industry with promise to disrupt the food supply chain status quo but is faced with a great deal of uncertainty and risk with respect to product demand and production technology efficacy. Currently funded largely by private investment, VF firms are challenged with properly developing production capacity for long-term profitability. This research presents a method developed for VF firms to facilitate decision making associated with allocation of capital resources to new production capacity considering demand and production uncertainty. The method in question makes use of two sequential MILP formulations to develop potential production capacity plans and presents an evaluation of how said plans perform according to preselected criteria. The method is extended to instruct VF firms on what to invest in reducing this uncertainty (expected value of perfect information). The author’s experience with GoodLeaf Farms, a Nova Scotia-based indoor VF company, was used as a basis for developing the method and a theoretical case study to prove its function.
Prediction of changes in ship-generated noise in the Canadian Arctic
Ana Carvalho Bianco, MASc Student
Marine shipping transports about 20% of Canadian exports and imports by dollar value. In 2015, marine trade was valued at $205 billion, with about 80% outside North America. Commercial marine shipping is one of the main sources of anthropogenic underwater noise, and its level has been increasing significantly with climate change effects around the globe. Sea ice loss, associated with economic and social factors such as community re-supply and oil and gas exploration, are resulting in a considerable increase of shipping traffic in the Arctic Ocean, which in turn caused an increase in underwater noise levels and impacts on marine life.
Within this situation, a need to understand these impacts on marine mammals has arisen in order to monitor and also analyse mitigation measures, as the low frequency noise generated by propellers and ship machinery overlaps with sound frequencies used by mammals to communicate, find prey, reproduce and navigate. Low frequency tones from a single large vessel can be heard as far away as 139 km. Therefore, this study aims to better assess the risks and impacts posed by vessel traffic and understand how all of these changes are going to determine the Arctic’s future in a range up to thirty years. With this purpose, two forecasting methods have been used: qualitative, using scenarios development, and quantitative, using time series and causal forecasting models. In this context, causal forecasting assumes that the variable to be predicted (vessel traffic and noise levels) has a cause-effect relationship with other variables or factors (population growth, economy measurements, ice retreat).
The software used for developing this quantitative statistical tool was Matlab and interviews were conducted with experts in shipping design, transportation methods and also economists, in order to develop different scenarios, which all have a low probability of occurrence and all factors and drivers are considered. The results of this study can be used to determine governance aspects and actions that are needed for a better protection and conservation of the Arctic ecosystem. Ultimately, this study provides benefits of using statistics and judgmental analysis for better employment of decision making methods.
Demand modelling for food produced via novel technologies: Cellular agriculture as a case study
Dawne Skinner, PhD Candidate
A variety of approaches to reducing the environmental impact of food production and consumption are being explored including technological solutions, such as genetically modified crops and biotechnological processes. However, the development of these technologies requires significant upfront investment and consumer acceptance is not guaranteed. The purpose of this research is to develop a demand forecasting model, under multiple marketing and quality scenarios, for foods produced via novel technologies, using cellular agriculture as a case study. The model considers consumer heterogeneity, product awareness, word of mouth marketing (WOM), instore marketing options, pricing options and product utility to estimate diffusion rates and market penetration. To our knowledge, there is no demand forecasting model available for food produced via novel technologies which relies on purchase intention data and incorporates all of these factors. Therefore, this research closes a critical gap for that industry. The modelling results suggest that, while the rate of diffusion was generally driven by marketing tactics, product utility and price determined the final demand for the product regardless of the marketing scenario. Market saturation appears to have been reached during the 32-week trial period for most scenarios however our model does not include ‘never-triers’ and is likely to overestimate demand.
Contact Person:
Prof. Dr. Floris Goerlandt
email: floris.goerlandt@dal.ca