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As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions. [ABSTRACT FROM AUTHOR]

This study addresses a new electric vehicle routing problem with time windows and recharging strategies (EVRPTW-RS), where two recharging policies (i.e., full or partial recharging) and three recharging technologies (i.e., normal, rapid, and ultra-rapid) are considered. For this problem, we first develop a mixed-integer linear programming model defined in a series of vertices including a depot, a series of recharging stations, and a set of customers. Due to the strong NP-hardness of EVRPTW-RS, a tailored adaptive large neighborhood search heuristic (ALNS) which contains a number of advanced efficient procedures tailored to handle the proposed problem is developed. Numerical experiments for benchmark instances generated based on the Greater Toronto Area and Ontario in Canada are conducted to evaluate the performance of the proposed model and ALNS. Computational results demonstrate that the ALNS is highly effective in solving EVRPTW-RS and outperforms commercial solver CPLEX. Moreover, the advantages of the proposed recharging strategies are illustrated and some recommendations are provided for stakeholders when using electric vehicles for delivery. [ABSTRACT FROM AUTHOR]

• Developing a new optimization model for designing a tire closed-loop supply chain. • Presenting a Spherical fuzzy logic method to calculate the importance of suppliers. • Discussing the application of the model in Greater Toronto Area, Canada. • Solving the multi-objective model using the augmented ε -constraint method. • Performing extensive sensitivity analyses and analyzing the results. For tire manufacturers to remain profitable while fulfilling environmental and social obligations such as producer responsibility, the opportunity lies in designing a tire Closed-Loop Supply Chain (CLSC) which combines forward and reverse supply chains. In this paper, a new multi-objective mixed-integer linear programming model is proposed to configure and optimize a multi-echelon, multi-product, multi-period tire CLSC network based on multiple recovery options and markets. For one of the objectives of the model, the weighting factors (importance) of suppliers are determined according to a unique framework of qualitative criteria. In this respect, a novel decision-making method based on Spherical fuzzy logic is developed. Finally, the solution approach is devised based on the formulation of the augmented ε -constraint method for finding efficient solutions. The application of the model is illustrated focusing on the region of Greater Toronto Area in Ontario, Canada. The optimal quantities for the flows of products, and number and locations of open facilities of the network are computed. The results show that the selected suppliers and allocated orders from them are impacted by considering multiple objectives. [ABSTRACT FROM AUTHOR]

The biodiversity and climate change crises have led countries—including Canada—to commit to protect more land and inland waters and to stabilize greenhouse gas concentrations. Canada is also obligated to recover populations of at-risk species, including boreal caribou. Canada has the opportunity to expand its protected areas network to protect hotspots of high value for biodiversity and climate mitigation. However, co-occurrence of hotspots is rare. Here we ask: is it possible to expand the network to simultaneously protect areas important for boreal caribou, other species at risk, climate refugia, and carbon stores? We used linear programming to prioritize areas for protection based on these conservation objectives, and assessed how prioritization for multiple, competing objectives affected the outcome for each individual objective. Our multi-objective approach produced reasonably strong representation of value across objectives. Although trade-offs were required, the multi-objective outcome was almost always better than when we ignored one objective to maximize value for another, highlighting the risk of assuming that a plan based on one objective will also result in strong outcomes for others. Multi-objective optimization approaches could be used to plan for protected areas networks that address biodiversity and climate change objectives, even when hotspots do not co-occur. [ABSTRACT FROM AUTHOR]

Subscription-based online newspapers usually offer non-subscribed users a certain number of free articles in a period of time, and then directs them to a page (called paywall) asking for subscription. This approach (also known as metered or fixed paywall) does not consider the user's reading history nor the articles that the user may read in the future, and consequently, it may disengage many potential subscribers. To that end, we propose adaptive paywall mechanisms to make optimal paywall decisions (i.e., showing the article or the paywall) by balancing the benefit of showing the article against that of presenting the paywall. We define the notions of utility and cost which are used to define an objective function for the optimal paywall decision problem. We propose the Lookahead policy (LAP) and QPaywall policy (QP) as two data-driven approaches to solve the adaptive paywall problem. While the LAP method makes paywall decisions on the fly by simulating trajectories of article requests using Monte Carlo sampling, the QP approach is based on reinforcement learning and learns a neural network-based action-value (Q) function for this purpose. We compare advantages of the proposed approaches and discuss the practical considerations of using them in a real environment. Empirical studies on a real dataset from a major newspaper in Canada show that the proposed methods outperform several baseline approaches in terms of various business objectives. [ABSTRACT FROM AUTHOR]

Tower-mounted camera-based wildfire detection systems provide an effective means of early forest fire detection. Historically, tower sites have been identified by foresters and locals with intimate knowledge of the terrain and without the aid of computational optimisation tools. When moving into vast new territories and without the aid of local knowledge, this process becomes cumbersome and daunting. In such instances, the optimisation of final site layouts may be streamlined if a suitable strategy is employed to limit the candidate sites to landforms which offer superior system visibility. A framework for the exploitation of landforms for these purposes is proposed. The landform classifications at 165 existing tower sites from wildfire detection systems in South Africa, Canada and the USA are analysed using the geomorphon technique, and it is noted that towers are located at or near certain landform types. A metaheuristic and integer linear programming approach is then employed to search for optimal tower sites in a large area currently monitored by the ForestWatch wildfire detection system, and these sites are then classified according to landforms. The results support the observations made for the existing towers in terms of noteworthy landforms, and the optimisation process is repeated by limiting the candidate sites to selected landforms. This leads to solutions with improved system coverage, achieved within reduced computation times. The presented framework may be replicated for use in similar applications, such as site-selection for military equipment, cellular transmitters, and weather radar. [ABSTRACT FROM AUTHOR]

The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programing (EILP) solvers. Using a case study in BC, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12–30% cheaper than plans using SA, due to EILP’s ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1,071 times faster than the SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process. [ABSTRACT FROM AUTHOR]

In western Canada, decades of oil-and-gas exploration have fragmented boreal landscapes with a dense network of linear forest disturbances (seismic lines). These seismic lines are implicated in the decline in wildlife populations that are adapted to function in unfragmented forest landscapes. In particular, anthropogenic disturbances have led to a decline of woodland caribou populations due to increasing predator access to core caribou habitat. Restoration of seismic lines aims to reduce the landscape fragmentation and stop the decline of caribou populations. However, planning restoration in complex landscapes can be challenging because it must account for a multitude of diverse aspects. To assist with restoration planning, we present a spatial network optimization approach that selects restoration locations in a fragmented landscape while addressing key environmental and logistical constraints. We applied the model to develop restoration scenarios in the Redrock-Prairie Creek caribou range in northwestern Alberta, Canada, which includes a combination of caribou habitat and active oil-and-gas and timber extraction areas. Our study applies network optimization at two distinct scales to address both the broad-scale restoration policy planning and project-level constraints at the level of individual forest sites. We first delineated a contiguous set of coarse-scale regions where restoration is most cost-effective and used this solution to solve a fine-scale network optimization model that addresses environmental and logistical planning constraints at the level of forest patches. Our two-tiered approach helps address the challenges of fine-scale spatial optimization of restoration activities. An additional coarse-scale optimization step finds a feasible starting solution for the fine-scale restoration problem, which serves to reduce the time to find an optimal solution. The added coarse-scale spatial constraints also make the fine-scale restoration solution align with the coarse-scale landscape features, which helps address the broad-scale restoration policies. The approach is generalizable and applicable to assist restoration planning in other regions fragmented by oil-and-gas activities. • The proposed methodology can assist restoration of linear disturbances in forest landscapes. • Optimization model supports restoration decisions by accounting for both operational and wildlife conservation objectives. • The two-tier approach addresses both the broad-scale policy goals and practical planning at the level of individual sites. • The approach is applicable in areas with presence of both resource extraction activities and endangered species populations. [ABSTRACT FROM AUTHOR]

In this study, a linear programming (l1-norm) sparse spike inversion (LPSSI) technique is used to estimate acoustic impedance distribution in the subsurface of the Blackfoot Field, Alberta, Canada. The aim of study is to determine high-resolution subsurface rock properties from the low-resolution seismic data and characterise the clastic Glauconitic channel. There are many traditional post-stack seismic inversion techniques available to estimate rock properties from seismic data, but LPSSI is a relatively simple and quick to compute subsurface model that can be used for qualitative as well as quantitative interpretation. The technique is applied in two steps; first, composite traces near to well locations are extracted and inverted for acoustic impedance, and comparison with well log impedance is used to optimise the LPSSI parameters. Analysis of the composite traces indicates that the algorithm has good performance with high correlation (0.97). In the second step, LPSSI is applied to the Blackfoot seismic data to estimate the distribution of acoustic impedance in the subsurface. Analysis of inverted acoustic impedance shows a low impedance anomaly ranging from 6500 to 8500 m/s*g/cc at the 1060–1075 ms time interval, which is characterised as a clastic Glauconitic sand channel. Thereafter, to confirm the sand channel, another important rock property, porosity, is estimated in the inter-well region using multi-attribute analysis. Analysis of the porosity shows the presence of a high porosity (15–22%) zone in the 1060–1075 ms time interval which coincides with the low impedance zone and confirms the presence of the sand channel. [ABSTRACT FROM AUTHOR]

We present a forest harvest scheduling model that meets timber harvest targets while maximizing a proxy measure of woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)) habitat based on the configuration of preferred habitat on the landscape. Woodland caribou within the boreal forest region in Canada tend to prefer mature jack pine forest stands, which tend to be rich in their preferred resource, lichen, and also reduce predation pressure. This can create conflict with industrial wood supply needs. We designed a model that can be used to identify good harvest scheduling plans given these competing objectives. Our approach is to use a series of sequential linear programming models that are solved within a replanning framework. Specifically, each individual linear programming model seeks to produce a solution that will meet timber harvest targets while minimizing the harvest of high-quality woodland caribou habitat stands. Stands are assessed with respect to their suitability as woodland caribou habitat based on their contribution to the overall landscape equivalent connected area (ECA), a combined spatial measure of preferred habitat amount and its connectivity. We used our model for a case study of the Trout Lake Forest in northwestern Ontario, Canada, and found that our model creates approximately 10% more caribou habitat than an earlier heuristic procedure and 30% more caribou habitat than the prevailing woodland caribou habitat forest management plan in the Trout Lake Forest while meeting the same timber harvest targets. [ABSTRACT FROM AUTHOR]

We present a tactical wood flow model that appears in the context of the Canadian forestry industry, and describe the implementation of a decision support system created for use by an industrial partner. In this problem, mill demands and harvested volumes of a heterogeneous set of log types are given over a multi-period planning horizon. Wood can be stored at the forest roadside before delivery at a financial cost. Rather than solve this as a network linear programme on the basis of out-and-back deliveries, we choose to model this problem as a generalization of a log-truck scheduling problem. By routing and scheduling the trucks in the resolution, this allows us to both anticipate potential backhaul opportunities for cost and fuel savings, and also minimize queuing times at log-loaders, management of which is a major concern in the industry. We model this problem as a mixed integer linear programme and solve it via column generation. The methodology is tested on several case studies. [ABSTRACT FROM AUTHOR]

Background Well-organized administrative data with large numbers of cases (building on linked files from several government departments) and a population registry facilitate new studies of population health and child development. Analyses of family relationships and a number of outcomes—educational achievement, health, teen pregnancy, and receipt of income assistance—are relatively easy to conduct using several birth cohorts. Looking both at means/proportions and at sibling correlations enriches our study of opportunity and wellbeing in late adolescence. With observational research possibly exaggerating the causal effects of risk factors, sibling comparisons involving individuals sharing both many family characteristics and many genes help deal with such criticisms. Methods This paper uses a rich dataset from one Canadian province (Manitoba) covering a wide range of geographical areas (cities to rural regions). Influences on opportunity and well-being are analyzed looking at both means/proportions and sibling correlations. We measure a variety of outcomes that may reflect different causal influences. A creative application of linear programming advances the use of data on residential location. Results Predicting educational achievement using available variables was much easier than predicting adolescent health status (R-square of .200 versus R-square of .043). Low levels of educational achievement, high levels of teenage pregnancy, and high sibling correlations outside Winnipeg and within Winnipeg's lower income areas highlight inequalities across socioeconomic and geographic backgrounds. Stratifying our analyses by different variables, such as income quintiles, reveals differences in means and correlations within outcomes and across groups. Particular events—changes in mother's marital status and in place of residence—were associated with less favorable outcomes in late adolescence. Conclusion Our findings suggest a paradox: Canadian developmental outcomes through late adolescence appear quite similar to those in the United States, even though intergenerational mobility in Canada is closer to mobility in the Nordic countries than to that in the United States. [ABSTRACT FROM AUTHOR]

This work is devoted to bridging the gap between large-area, economically driven macromodels such as the Canadian Regional Agriculture Model ( CRAM) and small-area biophysically based process models used in environmental assessments through the development of a Land Use Allocation Model ( LUAM). LUAM is designed to enable environmental assessments of economic scenarios to be conducted by allocating crop area changes predicted for large areas by CRAM to much smaller Soil Landscapes of Canada ( SLC) polygons through an optimization method based on land capability, relative crop productivity and current land use. To develop the procedures, we used linear programming to optimize crop production for large areas under current commodity prices and land productivity ratings and then allocated the results to much smaller soil-landscape polygons based on land capability. To assess the validity of our prototype LUAM, we compared the predicted crop areas with actual crop data from the Census of Agriculture using the method of cumulative residuals ( MCR). We concluded that this version of the LUAM model can predict the location of land use to some extent, but requires further refinement. The potential for further development of LUAM using the Land Suitability Rating System ( LSRS) is discussed. [ABSTRACT FROM AUTHOR]

We look at the environmental impact of formulating diets for an animal production system, namely the case of growing pigs. The classic approach in animal production is to use a growth model based on the least-cost diet formulation. This optimal diet is generally established by linear programming. Such an approach can lead to adverse environmental effects in the form of nitrogen and phosphorus excretions. Multi-criteria (two and three criteria) models are proposed with the aim of addressing both economic and environmental considerations. We apply the models to two real-life contexts: Québec (Canada) and France, and make some comparisons. We show that important reductions in nitrogen and phosphorus excretions can be achieved at relatively low costs in both contexts. [ABSTRACT FROM AUTHOR]

The Canadian Forces (CF) is seeking to establish permanent and temporary operational support hubs at strategic locations around the globe to improve its logistics support effectiveness and responsiveness for deployed operations. This paper addresses two logistics problems associated with the hub-based support concept, namely, hub location optimization and aircraft routing problems. A discrete facility location model was developed to analyse the hub-based support effectiveness and to determine the optimal hub locations. An aircraft routing model was also developed to determine optimal aircraft routes for the movement of cargo and supplies from various support hubs to a theatre of operation. Both models were formulated using mixed integer nonlinear programming. Historical CF deployment and sustainment data were used to conduct the analysis. and to illustrate the methodology. The study indicates that the hub-based support approach would offer potential cost avoidance on sustainment lift and could be an effective strategy for improvement of the CF's support capability. It also indicates that potential lift costs could be avoided through optimal routing of sustainment flights. [ABSTRACT FROM AUTHOR]

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada. [Copyright &y& Elsevier]

This paper discusses decomposition of a multiregional economic equilibrium model that is characterized by a cost minimizing, linear programming (LP) model of the supply side and a vector-valued function that gives demand prices as functions of the quantities demanded. Our motivation is to ease model development and maintenance by a solution method that links separately developed regional models only when a consistent multiregion solution is desired. A heuristic strategy is described to extend any existing (LP) decomposition principle to a procedure for decomposing an equilibrium model by region. This strategy is applied to extend Dantzig-Wolfe decomposition to the multiregional economic equilibrium model, and several theoretical results are derived for the resulting algorithm. The central result is a proof of asymptotic convergence, under usefully general conditions. The extended Dantzig-Wolfe procedure is illustrated with an existing, two-region model of Canadian energy supplies and demands. [ABSTRACT FROM AUTHOR]

Efficiency scores are determined for Canadian universities using both data envelopment analysis and stochastic frontier methods for selected specifications. The outcomes are compared. There is considerable divergence in the efficiency scores and their rankings among methods and specifications. An analysis of rankings, however, reveals that the relative positions of individual universities across sets of several efficiency rankings (e.g., all the data envelopment analysis and stochastic frontier outcomes) demonstrate an underlying consistency. High-efficiency and low-efficiency groups are evidenced but the rank for most universities is not significantly different from that of many others. The results emphasize the need for caution when employing efficiency scores for management and policy purposes, and they recommend looking for confirmation across viable alternatives. [ABSTRACT FROM AUTHOR]

We developed a model of interrelated timber markets in the U.S. West to assess the impacts of large-scale fuel reduction programs on these markets, and concomitant effects of the market on the fuel reduction programs. The linear programming spatial equilibrium model allows interstate and international trade with western Canada and the rest of the world, while accounting for price effects of introducing softwood logs to the market. The model maximizes area treated, given fire regime-condition class priorities, maximum increases in softwood processing capacity, maximum rates of annual treatments, prohibitions on exports of U.S. and Canadian softwood logs from public lands and a fixed annual treatment budget. Results show that the loss to U.S. private timber producers is less than the gains for timber consumers (mills). States receiving more treatments when spending is not constrained by state proportions include Idaho, Montana, New Mexico and Oregon. When only the wildland-urban interface is treated, California, Oregon and Washington receive more treatments. Utah and Colorado receive more treatments when low risk stands are included. [ABSTRACT FROM AUTHOR]

For some investors their own personal investment counsellors address their investment strategy; for others automated means are used. To protect investors, the Canadian Government has enacted the "Know Your Client" Act requiring that all investment dealers and vendors of securities must know their clients and advise them on the appropriate investment strategy. This paper uses Data Envelopment Analysis (DEA) in a novel manner by applying it to a large data set of answers to a number of psychological questions. A Slacks Based Model was used to estimate investor risk tolerance. The model analyses the risk profile of the investor and can be used as a guide to match the risk rating of the investment vehicles for the client. Statistical comparisons were also carried out to show how risk tolerance relates to various demographic variables. Finally, the DEA results were validated through comparisons with the commercial system already in use. [ABSTRACT FROM AUTHOR]

Following the excess commuting literature, we develop a benchmark measure of the minimum required commute, conditional on the layout of the specific urban area. Through segmentation of the commuters into those that can be relocated and those that cannot, we try to explain the difference between the minimum required and actual commute. To determine the segmentation of the commuters we make use of a micro-level explanatory model of commuting distance. This micro-level modeling exercise informs a number of excess commuting simulations that address the contribution of certain groups of commuters to total commuting. Findings from the micro-analysis reaffirm similar findings in the literature for other urban areas. The simulation exercise suggests that varied levels of commute savings may result from policy directed at particular commuter groups. [Copyright &y& Elsevier]

Discusses how Quebec Urban Community (QUC) has selected Global Predictive Real-Time Control (GP-RTC) as the most efficient approach to achieve environmental objectives defined by the Ministry of Environment. Goals to reduce the combined sewer overflows (CSOs) frequency to the St. Lawrence river; Consideration of two events per summer period in order to reclaim the use of Jacques-Cartier Beach for recreational activities and sports of primary contact; How QUC's control scheme is based on the Certainty Equivalent Control Open Loop Feedback (CEOLF) strategy; Use of a non-linear programming package to find the flow set points that minimise a multi-objective function; Implementation of GP-RTC on QUC's westerly network; Optimal use of two existing tunnels as retention facilities.

The research investigates the potential for the inclusion of lupins (Lupinus spp.) in the crop mix of a typical dairy farmer in Southwestern Quebec. This crop is an alternative legume crop to soybean but it contributes to a greater reduction in soil loss and more nitrogen being fixed for future crops than soybean. Results of a linear programming approach to the problem indicate that the crop can be profitably included in the crop mix with environmental benefits. [ABSTRACT FROM AUTHOR]

The problem of real time scheduling of vessels through the Welland Canal is discussed. A mathematical programming scheduling algorithm is presented. The problem is formulated as a master schedule selection problem and a schedule evaluation subproblem. The schedule evaluation subproblem is a linear programming ,model, which, due to special structure, can be solved using an efficient dynamic programming algorithm. The schedule selection algorithm is a heuristic that employs optimal dynamic programming submodels for scheduling the individual locks. Sensitivity information from the schedule evaluation model is used in a greedy type of algorithm to fine tune the schedule. An example of a schedule for the Welland Canal is presented. [ABSTRACT FROM AUTHOR]

The materials in trash, or municipal solid waste (MSW), may be recycled, composted, incinerated, or landfilled. Transporting these materials to waste-management facilities consumes part of the operating budget of a municipality. Many factors, the majority uncertain, may influence the planning of waste-flow allocation. MSW managers want to know (1) whether the existing pattern is close to the optimal, and (2) if not, what is the optimal? We used a gray linear-programming (GLP) model to address these questions for the regional municipality of Hamilton-Wentworth, Ontario. This approach can effectively reflect the interactive relationships between uncertain system components. It can also bring uncertain information into the optimization process and resulting solutions, permitting feasible decision alternatives to be generated. The region's waste managers have used the results to verify or adjust the existing waste-flow allocation pattern. They may also be used to formulate related local policies. [ABSTRACT FROM AUTHOR]

We discuss the development and structure of the University of Alberta BALANCE model of Canadian energy supply and demand, The model is a multi-time-period linear programming formulation of Canadian energy supply integrated with a large econometric model of price-sensitive energy demands in the Canadian economy. The model finds an equilibrium set of prices, supplies and demands over all time periods using a new dynamic version of the equilibrium-seeking algorithm developed for the Project Independence Evaluation System. The special aspects of a dynamic model and equilibration are described. An example illustrating the convergence properties of the algorithm is given. Finally, the question of whether or not to increase Canadian natural gas exports to the United States is examined using the model. [ABSTRACT FROM AUTHOR]

Examines competition in the Canadian personal finance sector for the period 1987-90 using cross-section continuous time series data. Generalized linear pricing model; Significance of firm entry in the setting of deposit and mortgage rates; Presence of incontestable market structure consistent with Cournot oligopoly; Evidence of price-making behavior.

Discusses findings of an empirical evaluation of government-subsidized manpower training schemes in Ontario. Description of the structure of a linear programming model used in the evaluation; Information on the three resource constraints used; Reasons justifying the choice of the linear programming approach.

Context: Many countries have aging populations. Thus, the need for palliative care will increase. However, the methods to estimate optimal staffing for specialist palliative care teams are rudimentary as yet.Objectives: To develop a population-need workforce planning model for community-based palliative care specialist teams and to apply the model to forecast the staff needed to care for all patients with terminal illness, organ failure, and frailty during the next 20 years, with and without the expansion of primary palliative care.Methods: We used operations research (linear programming) to model the problem. We used the framework of the Canadian Society of Palliative Care Physicians and the Nova Scotia palliative care strategy to apply the model.Results: To meet the palliative care needs for persons dying across Nova Scotia in 2019, the model generated an estimate of 70.8 nurses, 23.6 physicians, and 11.9 social workers, a total of 106.3 staff. Thereby, the model indicated that a 64% increase in specialist palliative care staff was needed immediately, and a further 13.1% increase would be needed during the next 20 years. Trained primary palliative care providers currently meet 3.7% of need, and with their expansion are expected to meet 20.3% by 2038.Conclusion: Historical, current, and projected data can be used with operations research to forecast staffing levels for specialist palliative care teams under various scenarios. The forecast can be updated as new data emerge, applied to other populations, and used to test alternative delivery models. [ABSTRACT FROM AUTHOR]

• A novel optimization model to maximize energy for short-term hydropower unit commitment is proposed. • A MILP formulation based on the efficiency curves is established. • The optimized solutions can be directly implemented in practice. • A real case study in Canada shows benefits of optimal solutions compared to historical decisions. This paper presents a linear mixed-integer formulation to solve the short-term hydropower unit commitment problem. It uses the pair of maximum efficiency points of water discharge and the power produced at the maximum storage for all possible combinations of turbines. The goal is to maximize total energy production for all periods. The objective function is calculated using the correction between the power produced at the current volume and the maximum storage and penalizes unit start-ups. Constraints on the maximum number of turbine changes are imposed to find a viable solution. Computational results are reported for 65 instances with two powerhouses of five turbines each located in the Saguenay-Lac-St-Jean region of the province of Quebec in Canada. [ABSTRACT FROM AUTHOR]

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes. • A scenario-based framework was developed for residential homes energy upgrades. • It considers occupant behavior and preference in selecting energy upgrades. • Field data is used to represent energy use of a family of four and a stay-at-home retired couple. • Energy upgrades are same under pro-economic choices for two profiles. • Energy upgrades vary under pro-environmental scenarios for two profiles. [ABSTRACT FROM AUTHOR]

• A generic model was developed for the optimum power generation pathways. • Power generation share from each pathway was determined in the optimum zone. • GHG, water, and land impacts on the cost of power generation were identified. • Impacts of constraints on the sustainability of power generation were studied. The principal objective of this paper is to develop an optimization model to integrate the available natural resources in a specific jurisdiction and to find the most sustainable power generation pathways to face the electricity demand with the lowest possible environmental and economic impacts. The sustainability was assessed in this paper quantitatively through environmental, economic, and natural resources indicators. A linear programming model was constructed with the objective function to minimize the cost of power generation and constrained by the available natural resources and greenhouse gas (GHG) emissions in a specific jurisdiction. Constraints for the model were represented by electricity demand, water consumption, water withdrawals, emitted CO 2 , and land area needed for power generation. The business-as-usual scenario was based on the existing power generation mix of Alberta Province in Canada and compared to the other four scenarios conducted through the optimization model. Cost-Effective, sustainable, stringent, and renewable penetration scenarios were conducted. Compared to the business-as-usual scenario, the cost-effective scenario resulted in a less average levelized cost of electricity (LCOE AV) by 17%, higher by 41% for the sustainable scenario, and higher by 14% for the stringent scenario. [ABSTRACT FROM AUTHOR]

We examined the feasibility of linear programming (LP) to develop diets that were economical, included traditional (cultural, non-market) foods and met the dietary reference intakes (DRI) in a Canadian Indigenous population. Diet optimisation using LP is a mathematical technique that can develop food-based dietary guidelines for healthy eating in Indigenous populations where food insecurity, availability and cost are important considerations. It is a means of developing nutritionally optimal food combinations that are based on economical and culture-specific foods. Observed food consumption data were derived using 24-h food recalls from the First Nations Food, Nutrition and Environment Study. The LP models were constructed to develop diets meeting DRI, cost and food constraints. Achieving the recommended food intake was not feasible in a model meeting all nutrient requirements. Models that met most nutrient requirements at reduced cost were designed for men and women, separately. In women, it was necessary to increase energy intake to meet most nutrient requirements. Nutrient requirements could not be met for fibre, linoleic and linolenic acids, vitamin D, Ca and K in both sexes, P in women, and Mg and vitamin A in men. Using LP to develop optimal diets for First Nations people, we found simultaneous achievement of all DRI was difficult, suggesting that supplementation might be necessary which goes against recommendations for individuals to meet their nutrient needs through healthy eating patterns. Additionally, to make diets feasible, programmes to reduce market food costs and to support First Nations people in traditional food harvesting are recommended., (© The Author(s) 2019.)

This review summarizes aspects of the 2017 Canadian Nutrition Society symposium, "Modelling diets for quality and cost: examples from Inuit and First Nations in Canada". Indigenous peoples in Canada experience a high prevalence of nutrition-related chronic disease because of the poor quality and high cost of their food supply. Since European colonization, they have transitioned from a diet of minimally processed traditional foods (game, fish, and plants) procured using pursuits such as hunting, fishing, gathering, and horticulture to a diet comprised mostly of processed market foods. This nutrition transition is the result of factors such as colonial policies and practices; climate change; environmental degradation; contaminants in traditional foods; and limited availability of, or access to, economical and healthful market foods. Presenters Malek Batal and Laurie Chan characterized the contemporary diets of First Nations and Inuit populations and demonstrated novel methods for modelling more optimal diets using 2 datasets: the First Nations Food, Nutrition, and Environment Study and the Inuit Health Survey. It was demonstrated how the NOVA classification characterized the portion of the diet consisting of processed foods. Dietary components were then manipulated to reduce ultra-processed food and drink intake to increase the Healthy Eating Index score. Linear programming was explained as a way to mathematically design theoretical diets that aim to optimize food cost, nutrition quality, and contaminant level of traditional foods. While diet-modelling methodologies have limitations, they provide a basis for engaging Indigenous peoples and governments to develop nutrition goals and policies anchored in contemporary food realities.

The authors respond to a commentary on their report "Analysis of Refinery Energy Use and CO2 Emissions in the U.S. Refining Sector, With Projections to 2025" made by G. Karras in the issue. They discuss their linear programming (LP) model for analyzing emissions from petroleum refineries in the U.S. that process Canadian bitumen crudes. They argue that their model is proprietary due to the time it took to develop. Refined product slates, specifications, and hetero-atom contents such as sulfur are discussed, as well as regional differences in Petroleum Administrative Defense Districts (PADDs) in the U.S.