Browsing by Subject "Supply chain management"

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Adaptive trading agent strategies using market experience
(2011-05) Pardoe, David Merrill; Stone, Peter, 1971-; Miikkulainen, Risto; Mooney, Raymond; Saar-Tsechansky, Maytal; Wellman, Michael
Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. Often, such agents must interact directly with other market participants, and so the behavior of these participants must be taken into account when designing agent strategies. One common approach is to build a model of the market, but this approach requires the use of historical market data, which may not always be available. This dissertation addresses such a case: that of an agent entering a new market in which it has no previous experience. While the agent could adapt by learning about the behavior of other market participants, it would need to do so in an online fashion. The agent would not necessarily have to learn from scratch, however. If the agent had previous experience in similar markets, it could use this experience to tailor its learning approach to its particular situation. This dissertation explores methods that a trading agent could use to take advantage of previous market experience when adapting to a new market. Two distinct learning settings are considered. In the first, an agent acting as an auctioneer must adapt the parameters of an auction mechanism in response to bidder behavior, and a reinforcement learning approach is used. The second setting concerns agents that must adapt to the behavior of competitors in two scenarios from the Trading Agent Competition: supply chain management and ad auctions. Here, the agents use supervised learning to model the market. In both settings, methods of adaptation can be divided into four general categories: i) identifying the most similar previously encountered market, ii) learning from the current market only, iii) learning from the current market but using previous experience to tune the learning algorithm, and iv) learning from both the current and previous markets. The first contribution of this dissertation is the introduction and experimental validation of a number of novel algorithms for market adaptation fitting these categories. The second contribution is an exploration of the degree to which the quantity and nature of market experience impact the relative performance of methods from these categories.
Issues in operations management and marketing interface research : competition, product line design, and channel coordination
(2010-05) Chen, Liwen, 1974-; Gilbert, Stephen M.; Gutierrez, Genaro J.; Balakrishnan, Anant; Feng, Qi; Xia, Yusen
This dissertation studies important issues in supply chain management and marketing interface research: competition, product line design, and channel efficiency, at the presence of vertically differentiated products. Vertical differentiation as a means of price discrimination has been well-studied in both economics and marketing literature. However, less attention has been paid on how vertical differentiation has been operationalized. In this dissertation, we focus our study on two types of vertical differentiation: the one created by a product line which is produced by the same firm, and the one created by products from different firms. We especially are interested in the so-called private label products vs. the national brand products. Specifically, this dissertation explores how vertical differentiation can affect the interactions among the members of a supply chain in several different contexts. In the first piece of work, we use a game theoretic model to explore how the ability of a retailer to introduce a private label product affects its interaction with a manufacturer of a national brand. In the second essay, we are investigating how an original equipment manufacturer (OEM) will be affected by the entry of a competitor when there are strategic suppliers of a critical component. If these suppliers behave strategically, it is not clear that the entry of other players will necessarily be harmful to the incumbent. In the last work, we pay our attention to an emerging change happening in the industry: some retailers begin to sell their private labels through their competitors. We investigate the strategic role of a retailer selling her own private label products through another retailer. In summary, this dissertation illustrates how vertical differentiation play a crucial role in firms' supply chain as well as marketing strategies. Therefore, it is important for firms to recognize these strategic issues related to vertically differentiated products while making operations/marketing decisions.
Moving horizon optimization methods, applications and tools for learning and controlling dynamical systems
(2023-05) Lejarza, Fernando; Baldea, Michael; Hanasusanto, Grani A.; Edgar, Thomas F.; Stadtherr, Mark A.
Mathematical models based on dynamical systems are crucial for understanding complex phenomena across a wide range of scientific and engineering disciplines. Optimizing these models can significantly improve the performance (e.g., in the sense of socioeconomic, environmental, and safety concerns) of various processes and systems that support our modern society, such as e.g. supply chain networks and chemical manufacturing processes. However, controlling these systems in the presence of uncertainty and for high-dimensional models is challenging. Developing robust and efficient optimization models and solution algorithms for this purpose is therefore crucial. Similarly, optimization techniques can be used to infer the governing equations for such dynamical systems from available measurement data. Learning such models is important not only for performing the aforementioned control tasks, but also for advancing our understanding of the physical laws that govern the phenomena we have so long observed but cannot quantitatively explain. Motivated by the above, this dissertation contributes novel moving horizon optimization methods, applications and tools for learning and controlling a variety of dynamical systems. The first part of this dissertation introduces the background and theory of moving horizon estimation and control methods. As a motivating example, I present a novel application of these existing methods to the optimal data-driven management of the COVID-19 pandemic in the US. The proposed approach identifies optimal social distancing and testing policies that minimize socioeconomic impact, while keeping the the number of infected individuals under a specified threshold. Subsequently, I focus on dynamical system models corresponding networks of integrators for optimal supply chain management under uncertainty. The first methodological contribution corresponds to a tube-based robust economic model predictive control framework for sparse storage systems, which I shown to have improved feasibility for supply chain management under demand disturbances. The proposed approach significantly improves computational performance relative to the available methods. Subsequently, I develop an extensive and systematic case study evaluating the performance of deterministic (feedback-based, closed-loop, or online) moving horizon optimization in comparison to stochastic and robust methods for supply chain management under increasing levels of uncertainty, forecasting errors, and recourse availability. Having demonstrated the overall robust and computationally efficient performance of deterministic moving horizon optimization techniques, the second part of the dissertation is focused on a class of multi-scale dynamical systems corresponding to supply chains of highly perishable inventory. This type of supply chains require integration of the inventory management problem with quality control by manipulating environmental conditions (e.g., temperature) during shipment and storage, which directly impact the product deterioration rate. To this end, I introduce a novel modeling approach for incorporating complex, multivariate physico-chemical product quality dynamics within the supply chain inventory balances, and provide a computationally efficient reformulation thereof. Based on this modeling approach and the results introduced in Part I of the dissertation, I develop a stabilizing closed-loop optimal supply chain production and distribution planning framework to handle uncertainties, such as random customer demand and/or random product quality spoilage. I then propose a scalable solution heuristic approach to cope with larger supply chain networks, and I present several case studies to demonstrate robustness to demand uncertainty. Lastly, I develop a simultaneous state estimation and closed-loop control approach to account for the fact that product quality may not be completely measurable in practical settings. In the third and final part of the dissertation, the focus shifts from controlling dynamical systems to learning their governing equations from data via moving horizon optimization. Here, I develop methods based on dynamic nonlinear optimization which, compared to existing efforts, demonstrate greater flexibility for handling highly nonlinear systems, for incorporating prior domain knowledge, and coping with high amounts of measurement noise in the training data. I then demonstrate the extension of this learning framework to the case of reactive dynamical system and present numerical experiments for non-isothermal continuous and batch chemical reactors. Lastly, I develop a sequential dynamic nonlinear optimization approach for discovering and performing dimensionality reduction of microkinetic reaction networks.