Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Mar 2019)
The forest sector in Canada has been losing its competitiveness due to globalization and rapid change in technology. Partnership is one of the strategies that could help companies remain competitive; however, partnership is costly and has a high failure rate, according to the literature. Therefore, it is essential to monitor the performance of a partnership and evaluate the factors that affect its performance. Previous studies reveal that the performance of an ongoing partnership is influenced directly by a number of components, which are joint decision-making, information sharing, risk/reward sharing and relationship-specific assets. However, there is a gap for a comprehensive study that investigates partnerships and their components in the forest industry. In this study, first a survey is conducted from the forest companies in British Columbia, Canada, to investigate existing and potential partnerships and the factors that influence the performance of existing ones. The respondents are asked to subjectively evaluate partnership performance and the influencing factors using the Likert scale. The results of regression analysis indicate the degree of joint decision-making, relationship-specific assets, and risk/reward sharing as the best predictors of the performance of the surveyed companies. Then, two multi-criteria decision support models are developed to evaluate partnership performance and components quantitatively. Multiple quantitative criteria are used in the models. Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP) are used in order to address the interdependency and the importance of criteria, respectively. Fuzzy Logic (FL) is used to capture the uncertainty in the criteria for evaluating partnership performance. The outputs of these two models are the importance of the criteria and two single numbers for the overall partnership performance and components in each period, named as Partnership Performance Index (PPI) and Partnership Component Index (PCI). The proposed models are applied to a partnership between a logging company and a sawmill in Canada, to find PPIs and PCIs in three different periods. The rankings of the criteria from the models are compared to the ones estimated by the managers, and the results show the rankings are compatible. The results are assessed by sensitivity analysis and validated by the managers.
Utilization of forest-based biomass for bioenergy and biofuels production could generate additional revenue streams, reduce greenhouse gas (GHG) emissions and generate development opportunities for forest-dependent communities. Barriers such as the capital intensity of conversion technologies, complexity of biomass procurement logistics, and the need to establish sustainable supply chains must be overcome. Mathematical modeling has supported the optimal design of biomass supply chains for bioenergy or biofuels production separately, mostly from an economic perspective. Some studies incorporated environmental and/or social criteria in the optimal supply chain design. However, no study modeled forest-based biomass supply chains for the simultaneous bioenergy and biofuels production, considering economic, environmental and social benefits. The development of such model is the objective of this thesis. First, an optimization model is developed that determines the optimal network design and the optimal yearly flows of raw materials and products that maximize the net present value (NPV) of the supply chain. The model considers the flow of energy among co-located conversion technologies and is applied to a case study in Canada. Second, a life cycle environmental analysis is developed to analyze the environmental impacts of the supply chain alternatives in the case study. Third, the optimization model is reformulated as bi-objective with an environmental objective that maximizes the GHG emission savings associated with the supply chain. These savings are estimated by comparing the emissions of the forest-based biomass supply chain system, versus those of the baseline system where unused biomass is disposed with current methods and energy demands are satisfied with currently available sources. Finally, a multi-objective optimization model is generated that integrates a social objective. The social objective is quantified by a social benefit indicator that assigns different levels of impact of job creation based on the type and location of the jobs. The bi-objective and multi-objective optimization models are applied to the case study and solved using a Pareto-generating solution method. Results indicate a trade-off between the NPV of the supply chain and the other two objectives, and a positive correlation between the generation of high impact jobs in the region, and the overall GHG emission savings.
The efficient management of a diverse portfolio of resources is vital for sustainable economic growth in the bioenergy and biofuel sector. Considerable complexities and inherent uncertainties in supply and demand, and ever evolving technology for the utilization of biomass necessitate careful design and management of supply chains. Supply chain modelling is commonly implemented to develop “decision support tools” required in the planning of highly integrated, multi-faceted value-adding processes. This thesis demonstrates an object-oriented approach to simulate the supply chain of forest biomass to biofuel and bioenergy in three case studies in British Columbia, Canada. Three main sources of complexity, namely uncertainties, interdependencies, and resource constraints, are considered in system parameterization and model development. After verification and validation, the models are used as a representation of the system to conduct model-based analysis. The supply chain of forest biomass for large-scale power generation is considered in the first case study. Different harvesting systems are considered that are employed based on the limitations on the annual harvest volume, characteristics of the stand, and intended products. Reliability of feedstock supply over the project’s lifespan, and the delivered costs were subject of the analysis. Demand fulfilment at the power plant and the cost of raw materials depend on the realized harvest volume, dictated by the practice of primary wood processing facilities. The delivered cost to the plant shows an ascending trend during the planning horizon, further complicating the investment. The second case study concentrates on the wood pellets production and distribution supply chains; modifications in an existing system are evaluated through simulation, and assessment of integrating torrefaction into the chain is carried out. Torrefaction technology promises an opportunity to reduce the distribution cost of wood pellets in the presented case study, contingent on the market readiness and fluctuating prices. Combined heat and power generation is considered in the third case study where modifications to an existing supply chain are evaluated. Realization of the vast bioenergy and biofuel potentials in BC requires coordinated planning across the forest biomass supply chains, and simulation modeling provides valuable decision support tools to facilitate future investments.
Mathematical modeling has been employed to improve the cost competitiveness of forest bioenergy supply chains. Most of the studies done in this area are at the strategic level, focus on one part of the supply chain and ignore uncertainties. The objective of this thesis is to optimize the value generated in a forest biomass power plant at the tactical level considering uncertainties. To achieve this, first the supply chain configuration of a power plant is presented and a nonlinear model is developed and solved to maximize its overall value. The model considers procurement, storage, production and ash management in an integrated framework and is applied to a real case study in Canada. The optimum solution forecasts $1.74M lower procurement cost compared to the actual cost of the power plant. Sensitivity analysis and Monte Carlo simulation are performed to identify important uncertain parameters and evaluate their impacts on the solution. The model is reformulated into a linear programming model to facilitate incorporating uncertainty in the decision making process. To include uncertainty in the biomass availability, biomass quality and both of them simultaneously, a two-stage stochastic programming model, a robust optimization model and a hybrid stochastic programming-robust optimization model are developed, respectively. The results show that including uncertainty in the optimization model provides a solution which is feasible for all realization of uncertain parameters within the defined scenario sets or uncertainty ranges, with a lower profit compared to the deterministic model. Including uncertainty in biomass availability using the stochastic model decreases the profit by $0.2M. In the robust optimization model, there is a trade-off between the profit and the selected range of biomass quality. Profit decreases by up to $3.67M when there are ±13% variation in moisture content and ±5% change in higher heating value. The hybrid model takes advantage of both modeling approaches and balances the profit and model tractability. With the cost of only $30,000, an implementable solution is provided by the hybrid model with unique first stage decision variables. These models could help managers of a biomass power plant to achieve higher profit by better managing their supply chains.
The overall objective of this dissertation is to design and schedule a highly constrained agricultural biomass supply chain to meet the daily biomass demand of a commercial-sized cellulosic ethanol plant at the minimum delivery cost possible. To this end, an integrated simulation/optimization model is developed. The developed simulation model plans and schedules a flow of multi-biomass in the supply chain to meet the daily demand subject to the dynamics and stochasticity of the supply chain. The developed optimization model is used to meet the annual demand at the minimum delivery cost by prescribing the design of the supply chain. The design includes the selection of farms, the location of storage sites, and the assignment of the farms to the storage sites. It also determines the flow of biomass between farms, storage sites and the plant. The integration of the models is made via an iterative procedure. In this procedure, the design is used in the simulation model to manage the flow of biomass in the supply chain. On the other hand, the outputs of the simulation model are used as the inputs of the optimization model to adjust the design. The iterative procedure continues until no improvement can be made in the design. The integrated model is applied to a proposed ethanol plant in Prince Albert, Saskatchewan. The numbers of selected farms and the established storage sites in the integrated model are reduced by 6% and 10%, respectively, compared to the optimization model. Compared to the simulation model, the integrated model leads to the reduction in number of farms (15%), number of storage sites (57%), amount of purchased biomass from farmers (7%), harvested area (13%), supply radius (13%), number of maximum trucks (2 trucks), supply costs (6-12%), energy input (19%), and emitted CO₂ (12%). The results of the sensitivity analysis reveal that the most influential parameter on the design is biomass yield. In addition, bale bulk density and in-field and road transportation operations have the highest impacts on the total supply cost compared to other input parameters.
No abstract available.
Master's Student Supervision (2010-2017)
This thesis analyzes a forest-based biomass supply chain network considering uncertainties and variations. It is based on the Williams Lake Timber Supply Area (TSA) located in British Columbia, Canada. The network includes: five conversion facilities distributed in three locations, two types of forest-based biomass, sourced from 337 cutblocks, and two types of sawmill residues sourced from three local sawmills. The main objective of this research is to evaluate the supply chain of forest-based residues for bioenergy and biofuel production considering uncertainties and variations. The specific objectives of this research are to: 1) Develop a simulation model to evaluate a forest-based biomass supply chain for bioenergy and biofuel production considering uncertainties and variations; and 2) apply the simulation model to a case study. To achieve the objectives, a discrete-event simulation model is developed using the commercial software Anylogic 7® (Anylogic 7, 2000). Evaluating a network with various supply and demand points, with various biomass types, and a hybrid push-pull biomass flow management distinguishes this work from previous research. The results show the demand is fulfilled to at least 95%, requiring 23 to 24 trucks during the peak season. Furthermore, the cost and CO₂ equivalent emissions vary per location, from $56.52 to $87.36 and from 19.66 to 72.61 (kg/odt), respectively. Long transportation distances and transportation cycle times greatly affected the number of required resources, and consequently the final cost per oven dry tonne. This results in higher costs than similar studies performed in less remote areas. Finally, a sensitivity analysis is performed to evaluate the effect of changes in moisture content and in supply and demand. Extreme changes in biomass supply and demand affected dramatically the demand fulfillment. By increasing the biomass demand 20% while simultaneously decreasing the biomass supply 20%, reduced the demand fulfillment by 23.18%. Finally, this model can be improved in several ways, one of them being by including the possibility of routing between different cutblocks to consolidate biomass pick-ups, therefore increasing the demand fulfillment of the supply chain and possibly reducing costs.
This research investigates the feasibility of exploiting local forest biomass for district heat generation in Williams Lake, BC. The objectives of this research are (1) to examine the economic viability of delivering forest biomass to the gate of a potential heating plant, and (2) to find a cost-optimized supply chain for delivering biomass to the plant. Considering the impact of biomass availability on the design of the supply chain and the required logistics in the system makes this study distinctive from the previous research. To achieve the first objective, the annual total delivery cost of biomass to the plant, namely the material, handling, processing, and transportation costs, was calculated for supply chain options with and without terminal storages. The results of the feasibility study showed that depending on the distance of source points to the plant, the delivery cost of woodchips to the plant ranged from $2.19 GJ⁻¹ to $2.87 GJ⁻¹. However, the gap between supply and demand in some months indicated that the direct flow of woodchips from source points to the plant would not be always possible. To meet the demand in months with biomass shortage, forest biomass should be stored in a terminal storage although this could increase the total annual cost to $6.59 GJ⁻¹. At the same time, transferring all the plant’s demand via terminal storage would not seem economical since in the months with more supply than demand and also with good accessibility to the collection areas, the direct flow is possible. Using a mix of direct and indirect flows might provide the opportunity to deliver forest biomass to the plant at a lower cost. A linear programming model was used to minimize the total annual cost and to determine the optimal flow of biomass to the heating plant. The optimization results revealed that the optimal flow of biomass would cost $2.62 GJ⁻¹, which is less expensive than the current delivery cost of natural gas to the plant ($6.39 GJ⁻¹). Therefore, the use of forest biomass for energy generation might be economical depending upon the capital and operating costs of the energy conversion facility.
Manufacturing is the single largest sector of the Canadian economy, accounting for 12.7% of the nation’s GDP in 2009 (Statistics Canada 2011). Over the past decade, this sector has faced numerous challenges such as a stronger Canadian dollar, increased foreign competition, and the recent decline of the US economy. One of the ways Canadian manufacturers have responded to these challenges is through increased information technology (IT) investments (Baldwin & Sabourin 2004). Wood products industries, though, generally invest much less in IT than the sector as a whole (Atrostic & Gates 2001). When wood manufacturers do invest in IT, it is often at a very basic level (Hewitt et al. 2011). Consequently, more intensive and sophisticated use of IT presents an opportunity for the cabinet industry to improve their competitive position.The first research objective was to determine the types of software products currently available to the cabinet industry and their associated functionalities. This was done using simple proportions, cluster analysis, and association rule learning. Next, a strategic analysis of which types of software applications are most important for the industry’s future competitiveness was done using the analytic network process. Lastly, any large gaps between what is currently represented in the industry and what is important for future competitiveness were identified.Operations & Engineering functionalities were found in 65.7% of all observed functionalities, whereas Content, Collaborative, and CRM functionalities were found in less than 10% each. Operations & Engineering and ERM software were determined to be the most important for future competitiveness because of their contribution to the Quality strategy. While Operations & Engineering software is important for the industry, they may be overrepresented because the current market is highly saturated with these functionalities. ERM, Collaborative, and CRM software are underrepresented as their future priority is higher than their current presence. The sensitivity analysis shows that the final priorities of software applications are most sensitive to the weighting of the Customer Service strategy. If an individual firm places a high emphasis on customer service and marketing, then CRM and Collaborative software become most critical for success.
Recent Tri-Agency Grants
The following is a selection of grants for which the faculty member was principal investigator or co-investigator. Currently, the list only covers Canadian Tri-Agency grants from years 2013/14-2016/17 and excludes grants from any other agencies.
- Operational level transportation, inventory, and processing optimization of the forest biomass supply chain - Natural Sciences and Engineering Research Council of Canada (NSERC) - Engage Grants Program (2016/2017)
- Process flow improvement at a Parallam mill using simulation modeling - Mathematics of Information Technology and Complex Systems (MITACS) - Networks of Centres of Excellence (NCE) - Internship Funds (2014/2015)
- Integrated and sustainable forest biomass supply chain planning - Natural Sciences and Engineering Research Council of Canada (NSERC) - Discovery Grants Program - Individual (2014/2015)
- 2-3.26 Fostering innovation along the solid wood chainL wood product/market development in Canada - Natural Sciences and Engineering Research Council of Canada (NSERC) - Strategic Network Grant (2013/2014)
- Optimization of forest biomass supply chain - incorporating uncertainties - Natural Sciences and Engineering Research Council of Canada (NSERC) - Discovery Grants Program - Individual (2013/2014)