We study the effect of strategic consumer behavior on pricing, inventory decisions, and inventory release policies of a monopoly retailer selling a single product over two periods facing uncertain demand. We consider the following three-stage two-period dynamic pricing game. In the first stage the retailer sets his inventory level and inventory release policy; in the second stage the retailer faces uncertain demand that consists of both myopic and strategic consumers. The former type of consumers purchase the good if their valuations exceed the posted price, while the latter type of consumers consider future realizations of prices, and hence their future surplus, before deciding when to purchase the good; in the third stage, the retailer releases its remaining inventory according to the release policy chosen in the first stage. Game theory is employed to model strategic decisions in this setting. Each of the strategies available to the players in this setting (the consumers and the retailer) are solved backward to yield the subgame perfect Nash equilibrium, which allows us to derive the equilibrium pricing policies. This work provides three primary contributions to the fields of dynamic pricing and revenue management. First, if, in the third stage, inventory is released to clear the market, then the presence of strategic consumers may be beneficial for the retailer. Second, we find the optimal inventory release strategy when retailers have capacity limitation. Lastly, we numerically demonstrate the retailer's optimal decisions of both inventory level and the inventory release strategy. We find that market clearance mechanism and intermediate supply strategy may emerge as the retailers optimal choice.

Shipment consolidation is a logistics strategy whereby many small shipments are combined into a few larger loads. The economies of scale achieved by shipment consolidation help in reducing the transportation costs and improving the utilization of logistics resources. The fundamental questions about shipment consolidation are i) to how large a size should the consolidated loads be allowed to accumulate? And ii) when is the best time to dispatch such loads? The answers to these questions lie in the set of decision rules known as shipment consolidation policies. A number of studies have been done in an attempt to find the optimal consolidation policy. However, these studies are restricted to only a few types of consolidation policies and are constrained by the input parameters, mainly the order arrival process and the order weight distribution. Some results on the optimal policy parameters have been obtained, but they are limited to a couple of specific types of policies. No comprehensive method has yet been developed which allows the evaluation of different types of consolidation policies in general, and permits a comparison of their performance levels. Our goal in this thesis is to develop such a method and use it to evaluate a variety of instances of shipment consolidation problem and policies. In order to achieve that goal, we will venture to use matrix-analytic methods to model and solve the shipment consolidation problem. The main advantage of applying such methods is that they can help us create a more versatile and accurate model while keeping the difficulties of computational procedures in check. More specifically, we employ a discrete batch Markovian arrival process (BMAP) to model the weight-arrival process, and for some special cases, we use phase-type (PH) distributions to represent order weights. Then we model a dispatch policy by a discrete monotonic function, and construct a discrete time Markov chain for the shipment consolidation process. Borrowing an idea from matrix-analytic methods, we develop an efficient algorithm for computing the steady state distribution of the Markov chain and various performance measures such as i) the mean accumulated weight per load, ii) the average dispatch interval and iii) the average delay per order. Lastly, after specifying the cost structures, we will compute the expected long-run cost per unit time for both the private carriage and common carriage cases.

This thesis focuses on task sequencing and manpower scheduling to develop robust schedules for an aircraft manufacturer. The production of an aircraft goes through a series of multiple workstations, each consisting of a large number of interactive tasks and a limited number of working zones. The duration of each task varies from operator to operator, because most operations are performed manually. These factors limit the ability of managers to balance, optimize, and change the statement of work in each workstation. In addition, engineers spend considerable amount of time to manually develop schedules that may be incompatible with the changes in the production rate. To address the above problems, the current state of work centers are first analyzed. Then, several deterministic mathematical programming models are developed to minimize the total production labour cost for a target cycle time. The mathematical models seek to find optimal schedules by eliminating and/or considering the effect of overtime on the production cost. The resulting schedules decrease the required number of operators by 16% and reduce production cycle time of work centers by 53% to 67%. Using these models, the time needed to develop a schedule is reduced from 36 days to less than a day. To handle the stochasticity of the task durations, a two-stage stochastic programming model is developed to minimize the total production labour cost and to find the number of operators that are able to work under every scenario. The solution of the two-stage stochastic programming model finds the same number of operators as that of the deterministic models, but reduces the time to adjust production schedules by 88%.

This thesis has investigated the positive and negative analogical transfer in which we proposed three hypotheses that shed more light on the process of human behaviour in problem solving. We have found that people exhibited both positive and negative analogical transfer in the conducted study. The positive and negative transfer depends on two factor process; search space and type of transformation. This predication was tested in an experiment with four conditions by using matchsticks arithmetic problems. Results have indicated the activation of positive transfer in the problems that share the same search space and type of transformation. On the other hand, negative transfer was activated when the problem search space and type of transformation were different. Results have also indicated, in several comparisons that were made, a simultaneous activation of both positive and negative transfer.

Although project portfolio management (PPM) has been an active research area over the past 50 years, budget allocation models that consider competition are sparse. Firms faced with the project portfolio management problem must not only consider their current projections for the returns from their projects’ target markets, but must also anticipate that these returns can depend significantly on the investment decisions made by their competitors. In this thesis, we develop four Competitive PPM (CPPM) models wherein firms allocate resources between multiple projects and project returns are influenced by the actions taken by competitors. In the first two CPPM problems, we assume all-or-nothing project investment decisions where firms fully commit to either a project targeting a mature or an emerging market and the investment amount is fixed (first model) or a decision variable (second model). In the final two CPPM problems, firms have a fixed budget which they allocate in a continuous manner between two markets (third model) or multiple markets (fourth model). The returns each firm obtains from investments into these markets are assumed to follow an s-shaped curve (first model), the Inada (1963) conditions (third model), or are determined based on linear demand functions (second and fourth model). In the first model, two competing firms consider investing into two separate projects targeting a mature and an emerging market. We assume that firms have symmetric investment opportunities for each market and each firm simultaneously decides whether to invest in the mature or the emerging market. The returns from these markets are assumed to follow an s-shaped curve and depend on both firms’ investment decision. We characterize the variety of interactions that may emerge in symmetric environments (e.g., Prisoner’s Dilemma or Game of Chicken). For each game, we outline the CPPM strategy that can offer higher returns by exploiting first-mover advantages, cooperation opportunities and aggressive choices. We also discuss the market conditions that lead to these games. In the second model, a similar CPPM setting is considered where two symmetric firms face two target markets. However, we assume that demand for the emerging market is uncertain and may expand through firms’ market entry (positive diffusion effects), while demand for the mature market is known with certainty and cannot expand. Firms decide when to invest, in which market to invest, and how much to invest into this market. Our analysis reveals that the existence of multiple investment opportunities may induce firms to delay their investment even in the absence of demand uncertainty, and that high diffusion effects coupled with low demand uncertainty can drive firms to invest early even if both firms could increase returns by delaying their investment. We then study the asymmetric case where firms differ with respect to their costs and diffusion effects and show some counter-intuitive results. In the third CPPM problem, we consider continuous budget allocations and prove that while a monopoly firm bases its budget allocation decision solely on the marginal returns of the two markets, duopoly firms also account for their average returns from the two markets. This drives duopoly firms, in particular the firm with the smaller budget, to invest more heavily into the mature market. We show that as a firm’s budget increases, the share of its budget that is invested into the mature market decreases while its competitor’s investment into the mature market increases. This chapter also explores how changes to the market parameters and market uncertainty affect the resource allocation decision of firms under competition. Considering the special case of identical budgets, we prove that as the number of competing firms increases (with a fixed total budget), firms allocate an even greater share of their budget into the mature market. The fourth model considers a general case where a number of budget-constrained firms engage in production decisions for multiple markets under competition. Each firm decides how much to produce for each market, subject to its budget constraint. We prove that firms produce greater quantities for markets with higher than average base demand and that these quantities are increasing in the number of competitors (assuming identical production capacities). With asymmetric production capacities, we numerically illustrate how firms with large production capacities may, instead, increase production into lower than average base demand markets. Furthermore, we characterize the increase in return firms can expect from budget increases and conjecture that if some markets are not served by all firms, the remaining firms reduce their production into those markets where some firms are not producing.

This thesis explores three consolidation problems derived from the daily operations of major international airfreight forwarders. First, we study the freight forwarder's unsplittable shipment planning problem in an airfreight forwarding network where a set of cargo shipments have to be transported to given destinations. We provide mixed integer programming formulations that use piecewise-linear cargo rates and account for volume and weight constraints, flight departure/arrival times, as well as shipment-ready times. After exploring the solution of such models using CPLEX, we devise two solution methodologies to handle large problem sizes. The first is based on Lagrangian relaxation, where the problems decompose into a set of knapsack problems and a set of network flow problems. The second is a local branching heuristic that combines branching ideas and local search. The two approaches show promising results in providing good quality heuristic solutions within reasonable computational times, for difficult and large shipment consolidation problems. Second, we further explore the freight forwarder's shipment planning problem with a different type of discount structure - the system-wide discount. The forwarder's cost associated with one flight depends not only on the quantity of freight assigned to that flight, but also on the total freight assigned to other flights operated by the same carrier. We propose a multi-commodity flow formulation that takes shipment volume and over-declaration into account, and solve it through a Lagrangian relaxation approach. We also model the "double-discount" scheme that incorporates both the common flight-leg discount (the one used in the unsplittable shipment problem) and the system-wide discount offered by cargo airlines. Finally, we focus on palletized loading using unit loading devices (ULDs) with pivots, which is different from what we assumed in the previous two research problems. In the international air cargo business, shipments are usually consolidated into containers; those are the ULDs. A ULD is charged depending on whether the total weight exceeds a certain threshold, called the pivot weight. Shipments are charged the under-pivot rate up to the pivot weight. Additional weight is charged at the over-pivot rate. This scheme is adopted for safety reasons to avoid the ULD overloading. We propose three solution methodologies for the air-cargo consolidation problem under the pivot-weight (ACPW), namely: an exact solution approach based on branch-and-price, a best fit decreasing loading heuristic, and an extended local branching. We found superior computational performance with a combination of the multi-level variables and a relaxation-induced neighborhood search for local branching.

Despite the volume of literature examining the role of producer amenities (e.g., highways and airports) in firms’ selection of a location, almost no quantitative studies regard the impact of consumer amenities (e.g., theatres and cafes) in attracting firms, as they are hypothesized to attract residents rather than firms or companies. Since the Information and Communication Technology (ICT) sector is regarded as a significant driver and an increasingly important part of the economy in North American and European countries, this research aims to provide insight into the importance of consumer amenities in the location pattern of companies in this sector. Consumer amenities are stated to be important factors in the lifestyle of creative and talented workers such as employees of high-tech industries (Florida, 2003); therefore, this study hypothesizes that ICT firms tend to locate near consumer amenities as they are assumed to be attractive to the talented and highly educated workers that those firms want to employ. ICT firms, because of their size and use, can also be integrated into existing land use, such as downtown where there are lots of amenities. Industrial uses would be more likely to locate near highways because of their land requirements. This thesis looks at a broad pattern as an exploratory study to see if there is a location pattern between consumer amenities and ICT firms’ location. Using census data from Canadian industries, this thesis focuses on exploring a spatial pattern for distribution of ICT companies, both with regards to amenities and the location of firms in other industries. In doing so, information of 66,078 firms that operate in Vancouver and their associated data were obtained from Statistics Canada and the Canadian Business Database. A walkability index is also developed that represents the amenity variable. The findings of this study suggest that ICT firms are more likely to be found in areas with a high concentration of consumer amenities. However, the result shows that there is statistically weak relationship between location of ICT firms and existence of consumer amenities, but this relationship is generally not detected for firms in other sectors. Moreover, the most significant finding of this thesis is that there is a tendency for ICT firms to locate close to and concentrated in downtown cores. As a result, the findings demonstrate that the agglomeration factor in ICT firms’ location decision is more important than the existence of consumer amenities in the place. This study concludes by suggesting that municipalities and their local economic development specialists wanting to attract regional economic growth to better understand and focus on the determinant elements of location decision by ICT firms.

Governments of the G7 have relied primarily on two strategies to develop their respective economies, the commercialization of research using licensing models and new venture creation. Yet, they have acknowledged no specific approach to achieving commercialization success. In fact, the results of the methods used for the commercialization of results are generally viewed as not satisfactory, thus creating room for new approaches to be proposed. One of the strategies used to assist the commercialization process has been recently instituted through social actors called innovation intermediaries. Their involvement in the commercialization process has the potential not only to facilitate the process but also to diffuse knowledge and foster innovation. To date, their practices are still under development, motivating academics in various disciplines to originate research studies aimed at gaining a better understanding of them. The literature has proposed definitions and attributed functions to innovation intermediaries, but it has not arrived at a definitive description of these actors or their activities. In practice, innovation intermediaries do not have a standard operational structure, established methods, or metrics to report their results; they have yet to, establish their own practices or use evidence to inform their activities. The objective of this study is to clarify their practices and challenge their current modus operandi with a view to improvement. To explain the activities of innovation intermediaries (their practice), to expose the role of evidence, and to represent the main concerns of innovation intermediaries, a framework based on distinctive attributes of the practice was produced using insights gained from a systematic literature review, an exploratory study, and literature stressing the importance of evidence. The framework was tested using a confirmatory study in the form of an online survey with the participation of 55 innovation intermediaries from around the world. The results show that innovation intermediaries have a predisposition to focus their practice on strategic concerns, finding a fit for the venture offering in the market while neglecting to oversee the mechanisms required for developing a viable venture offering. They tend to support their decisions anecdotally, referencing their previous experiences without the support of systematic methods to corroborate their conclusions. Their prioritized goals are first, to persuade investors and sponsors to collaborate with their clients; second, to help their clients occupy a leading position in their markets, and third, to support their clients to refine the venture offering and transform it into a commercial success. The emergent framework has characterized the practice of innovation intermediaries, identified particular gaps in their activities and their use of evidence, and suggested that the current focus in the practice of innovation intermediaries may not be contributing all that it could to the commercialization process. This framework may be of significant value to advance this field of knowledge and hopefully contribute to professionalize the practice of these social actors. Ultimately, this research could form the foundation for strengthening evidence-based best practices for innovation intermediaries.

Risky intertemporal choice is a fairly new topic in the realm of behavioral economics that involves examining the interactions between individuals’ time and risk preferences. Previous research has looked at the gains and mixed domain, but little to no research has been done in the loss domain. This study aims to fill this gap by examining how people respond to risky gambles in the loss domain given real world time delays. The thesis focuses on changes in attitudes towards risk caused by temporal distance rather than how people discount risky prospects. Based on Construal Level Theory we predict that there will be a greater focus on outcomes over probabilities in delayed gambles compared to immediate ones, and hence, individuals will become more risk-averse for delayed gambles that are in the loss domain. We conducted two experiments to test this prediction. Results revealed that while subjects in the immediate resolution group were significantly more risk-seeking than future resolution groups in both experiments, the difference in risk attitudes between two delayed resolutions depend on how big the difference between two delays is.

The paper provides a model that assesses the set of complementary components of varying compatibility and its effect towards consumer adoption decisions. The smartphone market is a system good which utilizes the device and a set of compatible applications (apps). The amount of switching costs may vary depending upon the consumer’s decision to switch devices or across platforms. Analyzing the Android ecosystem, the process of custom ROMs (and rooting) and the large set of games, news, etc. apps justify the existence of device-specific and platform-specific apps. The model reinforces the findings of a survey conducted by UBS suggesting the retention rate (i.e. level of switch costs) of Apple users is higher than Android users. The retention among Android devices is much lower in comparison as well. The model observes that the product fragmentation and the interdependence of apps lead to the noticeably lower retention rates across Android devices and platforms.