The surface parking lot was a key element in the destruction of traditional public space. It fragmented the fabric of traditional cities, prompting the sprawl of twentieth century cities and suburbs. The automobile permitted the average individual, for the first time in history, to move through public space insulated in a private shell, shielded from strangers and protected from undesirables. Consequently, the role of the sidewalk dwindled. The automobile and the parking lot dominated the pedestrian and the sidewalk, whose diminished vitality further encouraged widespread automobile use. As a result, the parking lot became one of the defining features of the American city. In the United States parking lots are expected to be utilitarian, prevalent and free. Even as traditional public spaces disappeared, there was little demand for new public spaces, particularly not in the parking lot. Through investigations of parking history and public space, this thesis argues that parking lots, by virtue of their visual and physical accessibility, are contemporary public space. Although they are singular in their use, in contrast to the multifaceted street, parking lots are the settings for modern public life. This thesis further asserts that the simple landscape of the utilitarian parking lot can be transformed into complex public space, thereby enlivening the public realm.

In this thesis, we study a series of closely related multi-stage stochastic programming models in production planning, from both a modeling and an algorithmic point of view. We first consider a very simple multi-stage stochastic lot-sizing problem, involving a single item with no fixed charge and capacity constraint. Although a multi-stage stochastic integer program, this problem can be shown to have a totally unimodular constraint matrix. We develop primal and dual algorithms by exploiting the problem structure. Both algorithms are strongly polynomial, and therefore much more efficient than the Simplex method. Next, motivated by applications in semiconductor tool planning, we develop a general capacity planning problem under uncertainty. Using a scenario tree to model the evolution of the uncertainties, we present a multi-stage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multi-stage model allows for revision of the capacity expansion plan as more information regarding the uncertainties is revealed. We provide analytical bounds for the value of multi-stage stochastic programming over the two-stage approach. By exploiting the special simple stochastic lot-sizing substructure inherent in the problem, we design an efficient approximation scheme and show that the proposed scheme is asymptotically optimal. We conduct a computational study with respect to a semiconductor-tool-planning problem. Numerical results indicate that even an approximate solution to the multi-stage model is far superior to any optimal solution to the two-stage model. These results show that the value of multi-stage stochastic programming for this class of problem is extremely high. Next, we extend the simple stochastic lot-sizing model to an infinite horizon problem to study the planning horizon of this problem. We show that an optimal solution of the infinite horizon problem can be approximated by optimal solutions of a series of finite horizon problems, which implies the existence of a planning horizon. We also provide a useful upper bound for the planning horizon.

The focuses of this dissertation are problems of batch production scheduling problems for perishable products with setup times, with the main applications in answering production planning problems faced by manufacturers of perishable products, such as beers, vaccines and yoghurts. The benefits of effective production plans can help companies reduce their total costs substantially to gain the competitive advantages without reduction of the service level in a globalize economy. We develop concepts and methodologies that are applied in two fundamental problems: (i) the batch production scheduling problem for perishable products with sequence-independent setup times (BPP-SI) and (ii) the batch production scheduling problem for perishable products with sequence-dependent setup times (BPP-SD). The problem is that given a set of forecast demand for perishables products to be produced by a set of parallel machines in the single stage of batch production, with each product having fixed shelf-life times and each machine requiring setup times before producing a batch of product, find the master production schedule which minimizes total cost over a specified time horizon. We present the new models for both problems by formulating them as a Mixed Integer Program (MIP) on the discrete time. Computational studies in BPP-SI and BPP-SD for industrial problems are presented. In order to efficiently solve the large BPP-SI problems in practice, we develop the five efficient heuristics. The extensive computational results show that the developed heuristics can obtain the good solution for the very large problem size and require very short amount of computational time.

Coordinated lot sizing problems, which assume a joint setup is shared by a product family, are commonly encountered in supply chain contexts. Total system costs include a joint set-up charge each time period any item in the product family is replenished, an item set-up cost for each item replenished in each time period, and inventory holding costs. Silver (1979) and subsequent researchers note the occurrence of coordinated replenishment problems within manufacturing, procurement, and transportation contexts. Due to their mathematical complexity and importance in industry, coordinated lot-size problems are frequently studied in the operations management literature. In this research, we address both uncapacitated and capacitated variants of the problem. For each variant we propose new problem formulations, one or more construction heuristics, and a simulated annealing metaheuristic (SAM). We first propose new tight mathematical formulations for the uncapacitated problem and document their improved computational efficiency over earlier models. We then develop two forward-pass heuristics, a two-phase heuristic, and SAM to solve the uncapacitated version of the problem. The two-phase and SAM find solutions with an average optimality gap of 0.56% and 0.2% respectively. The corresponding average computational requirements are less than 0.05 and 0.18 CPU seconds. Next, we propose tight mathematical formulations for the capacitated problem and evaluate their performance against existing approaches. We then extend the two-phase heuristic to solve this more general capacitated version. We further embed the six-phase heuristic in a SAM framework, which improves heuristic performance at minimal additional computational expense. The metaheuristic finds solutions with an average optimality gap of 0.43% and within an average time of 0.25 CPU seconds. This represents an improvement over those reported in the literature. Overall the heuristics provide a general approach to the dynamic demand lot-size problem that is capable of being applied as a stand-alone solver, an algorithm embedded with supply chain planning software, or as an upper-bounding procedure within an optimization based algorithm. Finally, this research investigates the performance of alternative coordinated lotsizing procedures when implemented in a rolling schedule environment. We find the perturbation metaheuristic to be the most suitable heuristic for implementation in rolling schedules.

In this thesis, we work on adisassembly lot-sizing problem for multiple products with parts commonality,i.e., general product structure. We assume that supply of discarded products is infinite. When a product (or a subassembly) is disassembled, all its immediate child items are obtained,i.e., complete disassembly case.Intermediate and leaf items obtained are demandedbyexternal suppliers or remanufacturers. The maximum possible salesfor each intermediate and leaf item are known.Sales of the intermediate and leaf items are the revenue sources. The discarded products are purchased ata unit purchasing cost. The disassembly operation incurs a fixed and a variable disassembly cost. Due to this cost structure, intermediate and leaf items can be stocked incurring an inventory holding cost. We develop an integer programming formulation to determine the time and quantity of the discarded products to be purchased / thetime and quantity of the discarded products and the intermediateitemsto be disassembled / and the time and quantity of intermediate and leaf items to be soldin order tomaximizethe total profit over a finite planning horizon. We state that ourproblem is NP-hard by refering the study of Kim et. al. (2009). We propose a heuristic solution approach that solves the problem in a reasonable computational time and generates near optimal solutions. The solution approach is based on the idea of sequentially solving a relaxed version of the problem and one-period integer programming models.In a computational study, the performance of the heuristic approach is assessed for a number ofrandomly generated problem instances.The results of the computational study show that the solutions of the heuristic approacharevery close to the optimal and the best feasible solutions obtained within the time limit.

Aufgrund ökologischer Bedenken ist die Verwendung von bleihaltigen Werkstoffen in der Elektro- und Elektronikindustrie ab 2006 verboten. Ag-Cu-Sn Legierungen gelten dabei als möglicher Ersatz für das herkömmliche Lötzinn (Pb-Sn). Ziel dieser Arbeit ist es, die Nahordnung in den Legierungsschmelzen mittels Röntgen- und Neutronendiffraktion zu untersuchen. Desweiteren wird die Oberflächenspannung im Legierungssystem gemessen und mit Modellrechnungen verglichen.

The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate goal is to coordinate production, inventory, and delivery to meet customer demand so that the corresponding costs are minimized. Achieving this goal provides the foundations for streamlining the logistics network and for integrating other operational and financial components of the system. In this paper, a model is presented that includes a single production facility, a set of customers with time varying demand, a finite planning horizon, and a fleet of vehicles for making the deliveries. Demand can be satisfied from either inventory held at the customer sites or from daily product distribution. A procedure centering on a reactive tabu search is developed for solving the full problem. After a solution is found, path relinking is applied to improve the results. A novel feature of the methodology is the use of an allocation model in the form of a mixed integer program to find good feasible solutions that serve as starting points for the tabu search. Lower bounds on the optimum are obtained by solving a modified version of the allocation model. Computational testing on a set of 90 benchmark instances with up to 200 customers and 20 time periods demonstrates the effectiveness of the approach. In all cases, improvements ranging from 10 - 20% were realized when compared to those obtained from an existing greedy randomized adaptive search procedure (GRASP). This often came at a three- to five-fold increase in runtime, however. A hybrid scheme that combines the features of reactive tabu search algorithm and branch-and-price algorithm is also developed. The combined approach takes advantage of the efficiency of the tabu search heuristic and the precision of the branch-and-price algorithm. Branching strategy that is suitable for the problem is proposed. Several advance techniques such as column generation heuristic and rounding heuristic are also implemented to improve the efficiency of the algorithm. Computational testing on standard data sets shows that a hybrid algorithm can practically solve instances with up to 50 customers and 8 time periods which is not possible by standard branch-and-price algorithm alone. / text

published_or_final_version / Mathematics / Master / Master of Philosophy

We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical study, we compare approximate dynamic programming with a global search for parameters of simple control policies. We propose two value function approximation schemes based on linear combinations of piecewise- constant functions as well as control policies that can be described by a small set of parameters. While approximate value iteration worked well for small problems with three products, it was clearly outperformed by the global policy search as soon as problem size increased. The most reliable choice in our study was a globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on optimal average cost revealed that the cost effect of product diversity was negligible. (authors' abstract)

Thomas Pynchon’s novel, The Crying of Lot 49, details Oedipa Maas’ quest to unearth a possibly centuries-old clandestine mail system, the Trystero. Oedipa is immersed in notions of sociality and she must navigate the social landscape, searching for clues as to the existence of the social system. In her quest she assumes the role of a detective who searches for meaning, as she looks for clues and questions others who might potentially be privy to the secrets of the Trystero. She necessarily performs the process of symbolic exchange with those she encounters in an attempt at ascertaining some greater meaning within the world that she thinks might lie behind the Trystero. In this, the nature of the circulation of meaning is revealed as a cultural construct.