Taraneh Sowlati
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Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Forest harvesting consists of multiple sequential activities to convert trees into logs ready for delivery to mills: felling, processing, yarding and loading. The forest harvesting cost contributes significantly to the delivered cost of logs. Therefore, it is important to optimize the schedule of forest harvesting activities to minimize costs. Previous studies have developed mathematical programming models to optimize the forest harvesting scheduling at the operational level. However, these studies did not consider the precedence relationship between forest harvesting activities, multiple machine assignment decisions, and the use of multi-task machines. The goal of this dissertation is to optimize the scheduling of harvesting activities at the operational level considering the mentioned research gaps. To achieve this goal, three mathematical programming models are developed in this work. In the first model, the precedence relationship between harvesting activities and the movement of individual machines is considered. In the second model, the multiple machine assignment decisions are incorporated in addition to those conditions considered in the first model. Also, the precedence relationship based on the slope of cut blocks is included in the second model. In the last model, the use of multi-task machines is incorporated in addition to other considerations in the second model. In this model, the scheduling of activities related to road construction within a cut block is also included.All models determine the start time and end time of each harvesting activity at each cut block, and where the machine should move after completing its operation in one cut block. In addition, the second and third models also determine the number of machines to be assigned at each cut block for each activity. All the models are applied to the harvesting operations of a real case study in the coast of British Columbia. The results indicate that the harvesting cost from the models is (at most) 4.5% higher than that of the defined ideal cost benchmark. All the developed models can be easily applied to other cases and regions by modifying the sets of succeeding and preceding activities in the input data according to the requirement of the harvesting system.
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Log logistics activities, such as transporting, loading/unloading, and processing of logs, are essential elements of forest operations and account for a significant portion of the total costs of delivered logs. Although previous studies investigated log logistics, they mainly emphasized on economic aspects and did not address some practical aspects of the logistics problem. The main goal of this dissertation is to optimize log logistics at the operational level incorporating practical considerations and complexities. In order to achieve this goal, a decomposition framework is employed that divides the log logistics problem into two phases. In the first phase, a bi-objective optimization model is developed in which sorting decisions, trucking contractors, and compatibility requirements at supply and demand locations are considered. The first objective of the bi-objective model is to minimize the total transportation costs, while the second objective addresses the social aspects by balancing the workload of contractors. The outputs of the first phase are used as the inputs of the second phase, for which an optimization model is developed to determine the daily routing and scheduling of heterogeneous trucks using continuous time representation. The model enables synchronization of log loaders and trucks and generates a detailed schedule of activities. In addition, a solution approach based on the simulated annealing algorithm is developed to solve the large-sized daily routing and scheduling problem. The Taguchi method is used to enhance the quality of the solutions by calibrating the input parameters of the algorithm. The framework, optimization models, and the solution approach are applied to a case study of a large Canadian forest company where logs are transported from cut blocks to sort yards for further processing. Results show that the framework can generate balanced workloads for all contractors with less than 1% increase in transportation costs and can determine the daily schedule of log trucks considering practical operational considerations. It is concluded that assigning overtime to trucks instead of dispatching a new truck can generate cost savings. The proposed models and the solution approach can be applied to other cases and regions by modifying the input data.
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Producing energy from forest-based biomass could aid the transitions in energy and forest sectors to replace fossil fuels; reduce emissions and wastes; and diversify product portfolios and revenue streams. Optimizing the economic and environmental performance of biomass supply chains can help realize these benefits. Besides optimizing the supply chain, investigating other factors, e.g., government incentives and carbon and energy pricing, which contribute to success or failure of forest-based biomass projects, is beneficial. The supply chain of forest-based biomass includes different activities that are cost- and emission-intensive and involve uncertainties. Unlike the majority of studies on forest-based biomass supply chain planning that focused on long-term and deterministic optimization, this dissertation aims to optimize the supply chain of syngas production at tactical level considering uncertainties. Thus, a multi-period bi-objective robust optimization model is developed and applied to the case of a British Columbia Kraft pulp mill that would produce syngas to fuel their lime kiln. The objectives are to minimize costs and emissions while optimizing the monthly biomass procurement, storage, and preprocessing decisions. Robust optimization with an adjustable budget of uncertainty is used to model the uncertainties in biomass supply and cost. Robust Pareto-optimal solutions are obtained that demonstrate a trade-off between objectives. The cost and emissions of robust solutions are on average 68% and 41% higher than those of the deterministic solutions. However, unlike the robust solution, the deterministic solution becomes infeasible in 98% of simulated future scenarios.Besides logistics costs, other factors (e.g., capital investment, government funding, fuel prices, and carbon tax) determine the financial viability of biomass projects. A previous techno-economic feasibility study for the same case revealed that despite a short payback period and positive net present value, the syngas price and carbon tax would result in negative annual cashflows halfway through project’s lifespan. Thus, the pulp mill would be reluctant to invest in this project despite all its potential, especially emission savings. To improve the financial attractiveness of this project for the pulp mill, an optimization model is developed that shows an optimal syngas price of 27 $/GJ, increasing by 0.49¢ annually, would make all cashflows positive.
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Logistics cost and emissions are important factors affecting the utilization of forest-based biomass. While numerous studies optimized biomass logistics at the tactical level, those at the operational level are limited. The focus of most of the previous studies was on cost minimization, while emission reduction from biomass logistics received less attention. Few recent studies analyzed the impact of carbon pricing policies on the optimum cost and emissions of biomass logistics. However, due to the focus on specific case studies, the results obtained in these studies may not be generalized. Moreover, these studies combined the cost and the emissions into one objective function resulting in the loss of information about the trade-off between the two objectives. The overall goal of this dissertation is to optimize biomass logistics at the operational level considering biomass storage, pre-processing and transportation decisions, and to analyze the impacts of carbon pricing policies on biomass logistics optimization models independent of the underlying case study. First, optimization models are developed to minimize the total cost of biomass logistics considering all logistics operations. The models are applied to the case study of a large logistics company. The results indicated a potential to save up to 12% of the total cost compared to the actual plans implemented by the company. Next, several properties of the optimal cost and emissions of case-independent logistics models under different carbon pricing policies are proposed and proved mathematically. The properties are numerically verified using the case study of a biomass-fed district heating plant. The results indicate that the carbon tax and the carbon cap-and-trade models result in equal emissions for equal carbon prices. The carbon cap-and-trade model is more cost-effective than the carbon tax model only if the carbon price is more than the price of initial allowances. Finally, the optimization model developed for the biomass-fed district heating plant is extended to incorporate cost and emissions as two separate objectives. A new algorithm is proposed to solve bi-objective optimization models considering carbon pricing policies to overcome the computational complexity involved in solving these models. Being case-independent, the algorithm can be applied to other cases.
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Biomass has emerged as an attractive renewable source of energy to shift away from fossil fuels. However, the high cost of biomass feedstock and variations, such as those in biomass supply and demand, impact the competitiveness of biomass and restrict bioenergy and biofuel developments. Therefore, supply chain planning is essential in improving the efficiency of biomass supply chains. In the literature, supply chain planning often has been carried out at strategic, tactical, and operational levels hierarchically by developing distinct models. Hierarchical planning may result in inconsistent and even infeasible solutions of higher planning levels at the lower levels because the details and variations at the lower levels are not considered at the higher levels. Hence, integrating the three different planning levels, while capturing the variations at the lower planning levels, could assure that plans from higher levels (e.g. strategic) are attainable at lower planning levels (tactical and operational). However, an integrated optimization model could require an enormous computational effort for solving. Therefore, proper solution approaches that can overcome this problem should be used. The main goal of this dissertation is to develop an integrated strategic, tactical, and operational planning model considering variations and details of lower planning levels, and employ a suitable solution approach to solve it.Herein, first, an optimization model that integrates the strategic and tactical decisions of forest-based biomass supply chains is developed to optimize the design of the supply chain considering variations at the tactical level. Then, three common decision rules, representing optimistic, moderate pessimistic, and pessimistic perspectives, are used to optimize the design of the supply chain considering the decision maker’s perspective towards risk. Next, a discrete event simulation model is developed to incorporate the operational level variations and its aspects. Finally, a hybrid scheme is proposed in which a linkage between the optimization and the simulation models is constructed to integrate different planning levels while incorporating variations at tactical and operational levels. The hybrid model is applied to a case study. The results of this research indicate that ignoring the tactical and operational level variations could result in sub-optimal and even infeasible solutions.
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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.
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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.
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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.
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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.
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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.
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The hypothesis that this thesis investigates is that “the wood pellet is a competitive primary energy source option for generating district heat in Vancouver, BC”. The competitiveness of the wood pellet as an energy source is evaluated by investigating a major district heating project in Vancouver, BC in which the wood pellet option was compared with natural gas, sewer heat, and geothermal heat. It is observed that in addition to technical and economic factors, environmental and social acceptability criteria play an important role in the selection of the energy source for district heating systems. These include stakeholders’ concerns regarding global warming impacts associated with production and transportation of the wood pellets, as well as particulate matter emissions from wood pellet combustion at the facility. In order to investigate the hypothesis, detailed study of: (a) particle emissions formation and levels, (b) techno-economic performance, and (c) upstream and life-cycle environmental impacts when using wood pellets at the district heating centre, has been carried out. This thesis accepts the hypothesis in that: 1. Particulate emission levels from wood pellet combustion when an electrostatic precipitator flue gas cleaning system is used is below the 18 mg/m³ (20ºC, 101.3 kPa, 8% O₂) regulatory limits in Vancouver, BC,2. The cost of heat generation (CAD/MWhth) from the wood pellet option (19.08~23.66) is comparable to that of the natural gas option (17.38) and well below those of the heat pump options (26.34~30.71),3. Based on the upstream environmental impacts of the energy options, a single energy option, which outperforms others when all the impact categories at the same time are considered, cannot be identified. However, it was shown that the impact of upstream production and transportation activities for the wood pellet option does not offset the global warming mitigation advantage of this option. The greenhouse gas equivalent of upstream emissions from the wood pellet option is in the same order of magnitude as the renewable heat pump options, and has remarkably lower (less than 200 kgeq of GHG emissions per MWh of produced district heat) than that of the natural gas option.
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Gasification is one of the processing technologies to convert biomass into syngas and renewable natural gas (RNG). The economic feasibility and amount of emission reduction are important factors affecting the investment decisions related to biomass gasification. Uncertainty and variability in parameters impact the economics and emissions of biomass gasification; however, they were not considered in evaluating gasification options in previous studies. The first objective of this research is to evaluate three biomass gasification alternatives with different capacities for syngas/RNG production at a Canadian Kraft pulp mill. The alternatives are evaluated based on the mean value and the risk associated with the net present value and emission reduction. After identifying the best gasification alternative for investment, it is important to minimize the costs and emissions of the biomass supply chain since the supply chain costs can be as high as 50% of the total gasification cost and emissions resulted from the supply chain activities can offset the emissions avoided by replacing fossil fuels with biofuels. Therefore, the second objective of this thesis is to develop a bi-objective optimization model for tactical planning of the forest-based biomass supply chains in order to analyze the trade-offs between the costs and emissions.To evaluate the best investment alternative under uncertainty, Monte-Carlo simulation is first performed to derive the mean value and Value-at-Risk associated with the NPV and emission reduction of each capacity alternative. Next, using the outputs of the Monte-Carlo simulation as the evaluation criteria, the alternatives are ranked based on the multi-criteria decision-making method. According to the weights identified by the pulp mill for the criteria, the small-scale biomass gasification with 38 MW syngas production capacity is the most appropriate alternative for investment. The developed optimization model determines the optimal monthly biomass quantities to be transported, stored, and preprocessed. The case of a 38 MW biomass gasification for the same Kraft pulp mill was considered to apply the supply chain optimization model. The results indicate a maximum of 24% (217 t of CO₂eq.) emissions reduction is possible if the supply chain cost is allowed to increase by 1.3% ($32,734).
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Forest-based biomass is an important renewable source for generating bio-energy and bio-fuels, while it has high feedstock costs and a complex supply chain. Therefore, many previous studies focused on optimizing forest-based biomass supply chains to improve its competitiveness. The main question after optimization is the allocation of benefits among supply chain entities. Allocation based on game theory methods can be useful and has been used on collaboration in transportation activities in forestry, but allocation of benefits to individual participants in forest-based biomass supply chains has not been done before. This thesis addresses this gap using concepts of game theory. A case involving three bio-product conversion plants (denoted as plant A,B,C) in British Columbia is studied, and collaboration among plants is defined as the exchange of sawmill residues. An optimization model is presented to determine biomass flow and technology type at each plant, with the goal of maximizing the net present value of the total profit. The results indicate the collaboration would generate $61 million, which is more profitable than plants operating individually. To distribute the total profit, a number of allocation methods are investigated, including the Shapley value, the nucleolus, proportional methods, methods based on separable and non-separable costs (ECM, ACAM, CGM), and the equal profit method (EPM). The comparison of methods reveals that the Shapley value, the nucleolus, ACAM, and CGM generate similar stable results in which plant A, B, and C could save 0.2%, 3.7%, and 620%, respectively, while EPM gives a different stable allocation, where the relative saving reduces to 7% for plant C, and increases to 0.4% and 7% for plant A and B. The relative saving obtained by plants is also investigated through revenue and cost break-down analysis, which shows plant A and C make the largest portion of profit by selling bio-fuel, and plant B is highly dependent on the sales of sawmill residues. Furthermore, a sensitivity analysis is conducted to evaluate the impact of changes in biomass availability, biomass costs, bio-product demand, bio-product prices, and discount rate. It is observed the profitability of collaboration is closely related to the market situation of bio-oil.
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Engineered wood products are manufactured by adhering small pieces of wood together with a bonding agent. They have many benefits. They allow the logs to be used more completely and more efficiently. They can increase the structural efficiency of wood frame construction, and natural wood defects can be dispersed in the product, which increases the uniformity of the mechanical and physical properties. Parallam® is one of these engineered wood products. It is manufactured in only two facilities in the world – Delta, British Columbia, Canada, and Buckhannon, West Virginia, United States. Parallam is manufactured from a grade of veneer that is not suitable for other products using Douglas Fir at the Canadian plant, and various species of pine at the American plant. The veneer is cut into strands, which are then adhered into long billets and are cut into the desired sizes. The Canadian plant was experiencing limitations in their total throughput, and was interested in exploring solutions to improve it. Since production operations are complex and subject to a variety of uncertainties and complexities, discrete-event simulation modelling was used to analyze the processes and evaluate potential improvement scenarios. Two projects were conducted in this research where simulation models were developed to analyze different scenarios for possible alternative plant configurations or policies. The first project analyzed the replacement of a machine, changing the policy of order customization, and the flow of quality assurance pieces. The main finding was that the machine replacement had no positive impact on the throughput and should not be done. In addition, it was determined that a decrease in the amount of customization could increase the throughput by 20%. The second project analyzed the worker-machine interactions within the entire mill and the automation of an outfeed conveyor. The main finding was that the addition of one worker to the packaging station and the automation of the conveyor could result in a 22% increase in throughput. Further research should be conducted to assess the impact of quality assurance pieces through the mill, or to assess the impact of different workers’ schedules instead of just their assignments.
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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.
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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.
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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.
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