Archival data of unrestricted and restricted gift card transactions was compared with national level consumer spending data from both the U.S. and Canada to investigate the similarities and differences in purchase pattern. The objective for this comparison was to investigate the motivational power of unrestricted gift card compared to cash rewards, restricted gift card and other hedonic tangible incentive. Through results interpretation, the data supported the hypotheses that people perceive and use unrestricted gift card as a cash carrying medium rather than a tangible reward. A restricted gift card that is hedonic by design constrains its recipients to hedonic purchase and removes the guilt and eliminates the justification process. The results also suggested that the elimination of justification seems to be associated to not only the hedonic dimension of goods/services but also transaction size.

Communities of practice are groups of people who share a concern, a set of problems or a passion about a topic, and who deepen their knowledge and expertise in this area by interacting on an ongoing basis (Wenger et al, 2002). An understanding of why working, technical professionals participate in knowledge- based communities of practice can provide better opportunities to support individual and organizational knowledge management strategies. Online communities of practice were investigated at two global corporations: Xerox and IBM. At Xerox, Eureka is an internal network service designed to support knowledge sharing and problem solving by a community of practice for field service technicians. It allows the submission of problems from field service technicians and the retrieval of validated solutions for use and adaptation, by all members of the global Eureka community. At IBM Corporation, public network based communities of practice were investigated that focused on db2™ and Websphere™ software technology. Unlike the Xerox Eureka community of practice, knowledge contributions at IBM communities of practice are not validated prior to submission and access is open to public participation globally by IBM employees and by independent users of IBM software technology. The purpose of this case study research was to explore and to describe how and why participants became members of communities of practice – what influenced them to join and to participate. We collected survey data from participants in these communities, to examine the relationships among members’ expectations of purpose, their relationship to the community of practice, their attitudes toward information handling, the costs and benefits of membership, the size of the community of practice and the resulting participation behaviour in these knowledge-based communities of practice at Xerox Corporation and IBM Corporation. As one aspect of exploring user behaviour, we investigated the applicability of two theoretical frameworks for understanding user behaviour in these communities, based on propositions from normative and utility theory and from public goods critical mass theory. The research study provides a test for the explanatory power of public goods, utility and normative theories in a new area; namely, online knowledge-based communities of practice in workplace contexts. This analysis provided support for the applicability of utility theory and for some aspects of public goods-based theory/critical mass theory. The findings of the case study point out some differences in the two communities of practice. A majority of the IBM-based community members reported belonging to multiple communities (6-10) and using access to the community to form online social networks and to meet members outside the community at in-person meetings. They reported their participation as being self-directed and on an ad hoc basis. Most respondents were community members for less than 2 years. In contrast, a majority of Eureka members reported belonging only to the Eureka community and do not report forming online social networks in Eureka. Participation in Eureka is seamlessly integrated into prescribed, standard work practices of the company and supported by company management and with resources. Most respondents were community members for over 5 years. The analysis of members’ contributing behaviour in two online communities of practice reaffirms that the majority of members are passive participants with a core group of regular message contributors. Message composition is a careful and deliberate activity requiring communication discipline, time and effort. Members in both settings reported a strong desire to spend more time in their communities (and more time per visit). Communities of practice are dynamic complex entities that present not only a theoretical challenge but also a practical challenge. This study’s results point to the complexity of facilitating communities of practice: benefits dynamics and flow and permanence dynamics of membership can only be externally managed to a limited extent. The participants’ roles need to be conceptualized in ways that support different types of participation while at the same time highlighting the inherently cooperative nature of self-managed communities of practice.

One of the main challenges that investors in the Clean Development Mechanism (CDM) project face is the management of the volatility of the price of Certified Emission Reduction (CERs). Large scale CDM projects require a long-term investment with significant amount of costs, and this type of investment is often irreversible. Project investors should quantitatively assess the CER trigger price that justifies the initiation of a CDM investment. The traditional discounted cash flow valuation is unable to capture the option value associated with uncertain investment, and thus it tends to underestimate the trigger price which initiates the investment. Real options theory explicitly considers the option value of delayed investment and can provide a better measurement of the trigger price. This paper presents a theoretical model of the CDM investment project and derives the CER trigger prices that guide investment decisions by using historical market data. It develops a stochastic dynamic programming model for both the geometric Brownian motion process and the mean-reverting process. An analytical solution for the trigger price is derived for the former process, and the trigger price is numerically estimated for the latter. By considering various parameter values, it analyzes the effects of different market environments on the trigger price.

This thesis investigates the process of communication between a person who has in-depth and technical knowledge about certain items and a person who has very broad and non-technical knowledge. For example, a doctor has detailed knowledge about all types of diseases, whereas, an ordinary person only knows a few common type of them. People use categories to communicate; the language, which is used in communication, is categorical. In addition, knowledge can be expressed in categories, and the categories are formed based on the knowledge that the person has. If the person has a superficial knowledge about a specific subject, he then creates superficial categories; whereas, if he has in-depth knowledge, he creates detailed and technical categories. The communication process between a person with technical categories of knowledge and a person with non-technical categories can be presented by the way that they match their categories. Shared cognition is formed if a category exists or is formed that is completely understandable for both parties. Literature on communication studies have never focused on the way that people with categorical knowledge communicate; therefore, an exploratory study is designed to figure out the process of communication when people have categorical knowledge. The task that is used in this study simulates the situation that two persons with categorical knowledge are communicating. The results of this thesis introduce a new representation for the communication process between a technical and non-technical communicators and the way that shared cognition can be analyzed.

The continuous facility layout problem consists of arranging a set of facilities so that no pair overlaps and the total sum of the pairwise connection costs (proportional to the center-to-center rectilinear distance) is minimized. This thesis presents a completely mixed integer semidefinite programming (MISDP) model for the continuous facility layout problem. To begin we describe the problem in detail; discuss the conditions required for a feasible layout; and define quaternary variables. These variables are the basis of the MISDP model. We prove that the model is an exact formulation and a distinction is made between the constraints that semidefinite programming (SDP) optimization software can solve and those that must be relaxed. The latter are called exactness constraints and three possible exactness constraints are shown to be equivalent. The main contribution of this thesis is the theoretical development of a MISDP model that is based on quaternary, as oppose to binary, variables; nevertheless preliminary computational results will be presented for problems with 5 to 20 facilities. The optimal solution is found for problems with 5 and 6 facilities, confirming the validity of the model; and the potential of the model is revealed as a new upper bound is found for an 11-facility problem.

Physical distribution networks are integral parts of modern supply chains. When faced with a question of which node in a network is more important, cost immediately jumps to mind. However, in a world of uncertainty, there are other significant factors which should be considered when trying to answer such a question. The integrity of a network, as well as its robustness are factors that we consider, in making a judgement of importance. We develop algorithms to measure several properties of a class of networks. To accelerate the optimization of multiple related linear programs, we develop a modification of the revised simplex method, which exploits several key aspects to gain efficiency. We combine these algorithms and methods, to give rankings of the relative importance of nodes in networks. In order to better understand the usefulness of our method, we analyse the effect parameter changes have on the relative importance of nodes. We present a large, realistic network, whose nodes we rank in importance. We then vary the network's parameters and observe the impact of each change.

Risk management is one of the critical issues in supply chain management. Supply chain disruptions negatively impact on the performance and the business continuity of a firm, and the disruptions should be managed proactively if possible. One of the approaches for supply disruption management is to raise the level of inventory: supply disruptions can be reduced by simply increasing the safety stock level. However, inventory costs will be increased at the same time. Therefore it is assumed that having extra safety stock when and where needed is better than keeping a high safety stock all of the time. In this thesis, the concept of dynamic inventory management by supplier behavior monitoring is suggested and explored. Key to the concept is the assumption that out-of-control situations at a supplier can be causal triggers for stockouts, and that these triggers can be potentially predicted by using statistical monitoring tools. In the suggested approach, the statistical process control approach of using run tests is employed to monitor and evaluate the supplier behavior. The supplier’s yield rate is monitored as the performance measure, and the receiver’s safety stock level is increased when the supplier’s performance is detected to be potentially out-of-control (or about to reach an out-of-control situation). The simulation results under different yield rates indicate that stockouts can be reduced by monitoring the supplier behavior and dynamically adjusting inventory policy when production capacity is relatively loose and enough variability can be seen in the performance measure.

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%.