Product line planning aims at optimal planning of product variety. In addition, the traditional product line planning problem develops new product lines based on product attributes without considering existing product lines. However, in reality, almost all new product lines evolve from existing product lines, which leads to the product line evolution problem. Product line evolution involves trade-offs between the marketing perspective and engineering perspective. The marketing concern focuses on maximizing utility for customers; the engineering concern focuses on minimizing engineering cost. Utility represents satisfaction experienced by the customers of a product. Engineering cost is the total cost involved in the process of the development of a product line. These two goals are in conflict since the high utility requires high-end product attributes which could increase the engineering cost and vice versa. Rather than aggregating both problems as one single level optimization problem, the marketing and engineering concerns entail a non-collaborative game per se. This research investigates a game-theoretic approach to the product line evolution problem. A leader-follower joint optimization model is developed to leverage conflicting goals of marketing and engineering concerns within a coherent framework of game theoretic optimization. To solve the joint optimization model efficiently, a bi-level nested genetic algorithm is developed. A case study of smart watch product line evolution is reported to illustrate the feasibility and potential of the proposed approach.

Οι τρόποι κωδικοποίησης κειμένου για Fax καθορίζονται από το πρωτόκολλο «Facsimile Coding Schemes and Coding Control Functions for Group 4 Facsimile Apparatus» ITU-T Recommendation T.6 και το στάνταρντ JBIG2. Στη διπλωματική αναλύονται όλες οι λεπτομέρειες του στάνταρντ JBIG2, πολλές από τις οποίες δεν είναι σαφώς καθορισμένες στο στάνταρντ. Περιλαμβάνονται οι μέθοδοι για κωδικοποίηση κειμένου και bi-level (binary) εικόνων. Αναφέρονται οι σχετικές μεθοδολογίες, όπως η μέθοδος αναγνώρισης προτύπων και αντικατάστασης (pattern-matching and substitution, PM&S). / The word coding methods for fax applications are defined with the use of «Facsimile Coding Schemes and Coding Control Functions for Group 4 Facsimile Apparatus» ITU-T Recommendation T.6 protocol and JBIG2 standard. All details concerning the JBIG2 standard are thoroughly analyzed throughout this thesis, many of which are not clearly defined in the standard itself. Methods for word and bi-level images coding are included. We also focus on techniques like the pattern-matching and substitution, PM&S method.

Railroad is one of the primary modes for transporting hazardous materials (hazmat). Given the dangerous nature of the hazmat, risk mitigation in the railroad transportation is the need of the hour. Hence, we explore the idea of equitable distribution of risk in the railroad network. We propose the subsidy policy to be considered by government to induce favourable routings of the hazmat shipments. The government's objective is to achieve risk equity in the network, whereas, the carrier's cost effective approach leads to increased risk in low-cost service-legs. To model this, we formulate the problem as a bi-level mixed integer program. We derive the single level mixed integer linear program (MILP) and test it on the rail infrastructure in midwest United States using state-of-the-art solver CPLEX 12.8.0. The instances with upto 25 shipments on the network are solved efficiently on a local machine. We use high performance computing resource available at Graham cluster of Compute Canada facility to solve the large instances with 50 shipments on the network. We show the effectiveness of the subsidy policy as a risk mitigation tool for the railroad hazmat transportation, and review the efficiency of the solution methodology to solve the MILP for the network. Moreover, the results demonstrate the economic feasibility for the government to allocate the budget for the subsidy. / Thesis / Master of Science (MSc)

Master of Science / Department of Statistics / Kun Chen / We study the composite group bridge penalized regression methods for conducting bilevel variable selection in high dimensional linear regression models with a diverging number of predictors. The proposed method combines the ideas of bridge regression (Huang et al., 2008a) and group bridge regression (Huang et al., 2009), to achieve variable selection consistency in both individual and group levels simultaneously, i.e., the important groups and the important individual variables within each group can both be correctly identi ed with probability approaching to one as the sample size increases to in nity. The method takes full advantage of the prior grouping information, and the established bi-level oracle properties ensure that the method is immune to possible group misidenti cation. A related adaptive group bridge estimator, which uses adaptive penalization for improving bi-level selection, is also investigated. Simulation studies show that the proposed methods have superior performance in comparison to many existing methods.

We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an -- reliability criterion. This reliability criterion is a generalization of the well-known - criterion and dictates that at least fraction of the total system demand (for ) must be met following the failure of or fewer system components. We refer to this problem as the contingency-constrained unit commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network. These studies demonstrate the effectiveness of our proposed methods relative to current state-of-the-art.

In this study, a new selective and time-window routing problem is defined for the first time in the literature, which is called the campaign routing problem (CRP). The two special cases of the CRP correspond to the two real-life problems, namely political campaign routing problem (PCRP) and the experiments on wheels routing problem (EWRP). The PCRP is based on two main decision levels. In the first level, a set of campaign regions is selected according to a given criteria subject to the special time-window constraints. In the second level, a pair of selected regions or a single region is assigned to a campaign day. In the EWRP, a single selected region (school) is assigned to a campaign day. These two problems are modeled using classical mathematical programming and bi-level programming methods, and a two-step heuristic approach is developed for the solution of the problems. Implementation of the solution methods is done using the test instances that are compiled from the real-life data. Computational results show that the solution methods developed generate good solutions in reasonable time.

In recent decades, climate change has become one of the most crucial challenges for humanity. Climate change has a direct correlation with global warming, caused mainly by the green house gas emissions (GHG). The Environmental Protection Agency in the U.S. (EPA) attributes carbon dioxide to account for approximately 82\% of the GHG emissions. Unfortunately, the energy sector is the main producer of carbon dioxide, with China and the U.S. as the highest emitters. Therefore, there is a strong (positive) correlation between energy production, global warming, and climate change. Stringent carbon emissions reduction targets have been established in order to reduce the impacts of GHG. Achieving these emissions reduction goals will require implementation of policies like as cap-and-trade and carbon taxes, together with transformation of the electricity grid into a smarter system with high green energy penetration. However, the consideration of policies solely in view of carbon emissions reduction may adversely impact other market outcomes such as electricity prices and consumption. In this dissertation, a two-layer mathematical-statistical framework is presented, that serves to develop carbon policies to reduce emissions level while minimizing the negative impacts on other market outcomes. The bottom layer of the two layer model comprises a bi-level optimization problem. The top layer comprises a statistical model and a Pareto analysis. Two related but different problems are studied under this methodology. The first problem looks into the design of cap-and-trade policies for deregulated electricity markets that satisfy the interest of different market constituents. Via the second problem, it is demonstrated how the framework can be used to obtain levels of carbon emissions reduction while minimizing the negative impact on electricity demand and maximizing green penetration from microgrids. In the aforementioned studies, forecasts for electricity prices and production cost are considered. This, this dissertation also presents anew forecast model that can be easily integrated in the two-layer framework. It is demonstrated in this dissertation that the proposed framework can be utilized by policy-makers, power companies, consumers, and market regulators in developing emissions policy decisions, bidding strategies, market regulations, and electricity dispatch strategies.

Transportation system is one of the key functioning components of the modern society and plays an important role in the circulation of commodity and growth of economy. Transportation system is not only the major influencing factor of the efficiency of large-scale complex industrial logistics, but also closely related to everyone’s daily life. The goals of an ideal transportation system are focused on improving mobility, accessibility, safety, enhancing the coordination of different transportation modals and reducing the impact on the environment, all these activities require sophisticated design and plan that consider different factors, balance tradeoffs and maintaining efficiency. Hence, the design and planning of transportation system are strongly considered to be the most critical problems in transportation research. Transportation system planning and design is a sequential procedure which generally contains two levels: strategic and operational. This dissertation conducts extensive research covering both levels, on the strategic planning level, two network design problems are studied and on the operational level, routing and scheduling problems are analyzed. The main objective of this study is utilizing operations research techniques to generate and provide managerial decision supports in designing reliable and efficient transportation system. Specifically, three practical problems in transportation system design and operations are explored. First, we collaborate with a public transit company to study the bus scheduling problem for a bus fleet with multiples types of vehicles. By considering different cost characteristics, we develop integer program and exact algorithm to efficiently solve the problem. Next, we examine the network design problem in emergency medical service and develop a novel two stage robust optimization framework to deal with uncertainty, then propose an approximate algorithm which is fast and efficient in solving practical instance. Finally, we investigate the major drawback of vehicle sharing program network design problem in previous research and provide a counterintuitive finding that could result in unrealistic solution. A new pessimistic model as well as a customized computational scheme are then introduced. We benchmark the performance of new model with existing model on several prototypical network structures. The results show that our proposed models and solution methods offer powerful decision support tools for decision makers to design, build and maintain efficient and reliable transportation systems.

With the rapid development of industrialization and urbanization, industrial development and population growth drive the expansion of urban space, urban transportation demand shows the characteristics of spatial decentralization and diversification, and transportation travelers' requirements for mobility, accessibility, and comfort of transportation travel services are enhanced. Mobility on demand (MoD) services such as DiDi and Uber are new modes of public transportation, bringing many new opportunities and challenges. MoD travel services, shared bicycles, and other complementary public transport modes are rapidly developing in the "Internet +" environment, serving the "one mile" before and after the residents' travel. MoD technologies play an important role as a feeder to the main public transportation lines, helping to increase public transportation patronage and improve the speed of travel for residents. In this context, the study aims to develop a multi-modal public transportation system network design methodology to provide better operational coordination between different modes of transportation and to provide faster travel services. In order to promote better coordination between different transportation modes and to provide theoretical and methodological support for the development of a multi-modal public transportation system network design system, a bi-level planning model for this problem is first constructed. The upper-level planning model is used to minimize the total travel time and cost of passengers and the economic cost of public transportation operators, and to decide which bus lines to operate, the structure of bus lines, and the frequency of operating bus lines; the lower-level operating model is used to assign passengers to make travel mode choices and to carry out traffic distribution of the public transportation network based on the minimum number of interchanges. Then, based on this bi-level planning model, an improved genetic algorithm is developed to solve the upper-level public transportation network planning problem, in which the algorithm for passenger flow allocation in the lower-level planning model is nested in the genetic algorithm.  Finally, the developed methodology is validated for the benchmark Mandl network design by comparing with the traditional public transportation network. The results show that the multi-modal public transportation network can effectively reduce passenger travel time compared with the traditional public transportation network at similar costs. Finally, we applied the network design method for the Barkarby area in the north of Stockholm, Sweden. The results show that it is appropriate to allocate mobility on demand vehicles in this area. The constructed model and the proposed algorithm are scientifically valid and can provide theoretical methodological reference and decision support for engineering practice.

No description available.