Academic Commons Search Results
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Academic Commons Search Resultsen-usEssays on Cloud Pricing and Causal Inference
http://academiccommons.columbia.edu/catalog/ac:200357
Kilcioglu, Cinarhttp://dx.doi.org/10.7916/D8R78F9QTue, 21 Jun 2016 18:32:55 +0000In this thesis, we study economics and operations of cloud computing, and we propose new matching methods in observational studies that enable us to estimate the effect of green building practices on market rents.
In the first part, we study a stylized revenue maximization problem for a provider of cloud computing services, where the service provider (SP) operates an infinite capacity system in a market with heterogeneous customers with respect to their valuation and congestion sensitivity. The SP offers two service options: one with guaranteed service availability, and one where users bid for resource availability and only the "winning" bids at any point in time get access to the service. We show that even though capacity is unlimited, in several settings, depending on the relation between valuation and congestion sensitivity, the revenue maximizing service provider will choose to make the spot service option stochastically unavailable. This form of intentional service degradation is optimal in settings where user valuation per unit time increases sub-linearly with respect to their congestion sensitivity (i.e., their disutility per unit time when the service is unavailable) -- this is a form of "damaged goods." We provide some data evidence based on the analysis of price traces from the biggest cloud service provider, Amazon Web Services.
In the second part, we study the competition on price and quality in cloud computing. The public "infrastructure as a service" cloud market possesses unique features that make it difficult to predict long-run economic behavior. On the one hand, major providers buy their hardware from the same manufacturers, operate in similar locations and offer a similar menu of products. On the other hand, the competitors use different proprietary "fabric" to manage virtualization, resource allocation and data transfer. The menus offered by each provider involve a discrete number of choices (virtual machine sizes) and allow providers to locate in different parts of the price-quality space. We document this differentiation empirically by running benchmarking tests. This allows us to calibrate a model of firm technology. Firm technology is an input into our theoretical model of price-quality competition. The monopoly case highlights the importance of competition in blocking "bad equilibrium" where performance is intentionally slowed down or options are unduly limited. In duopoly, price competition is fierce, but prices do not converge to the same level because of price-quality differentiation. The model helps explain market trends, such the healthy operating profit margin recently reported by Amazon Web Services. Our empirically calibrated model helps not only explain price cutting behavior but also how providers can manage a profit despite predictions that the market "should be" totally commoditized.
The backbone of cloud computing is datacenters, whose energy consumption is enormous. In the past years, there has been an extensive effort on making the datacenters more energy efficient. Similarly, buildings are in the process going "green" as they have a major impact on the environment through excessive use of resources. In the last part of this thesis, we revisit a previous study about the economics of environmentally sustainable buildings and estimate the effect of green building practices on market rents. For this, we use new matching methods that take advantage of the clustered structure of the buildings data. We propose a general framework for matching in observational studies and specific matching methods within this framework that simultaneously achieve three goals: (i) maximize the information content of a matched sample (and, in some cases, also minimize the variance of a difference-in-means effect estimator); (ii) form the matches using a flexible matching structure (such as a one-to-many/many-to-one structure); and (iii) directly attain covariate balance as specified ---before matching--- by the investigator. To our knowledge, existing matching methods are only able to achieve, at most, two of these goals simultaneously. Also, unlike most matching methods, the proposed methods do not require estimation of the propensity score or other dimensionality reduction techniques, although with the proposed methods these can be used as additional balancing covariates in the context of (iii). Using these matching methods, we find that green buildings have 3.3% higher rental rates per square foot than otherwise similar buildings without green ratings ---a moderately larger effect than the one previously found.Business administration, Industrial engineering, Operations researchck2560BusinessDissertationsSoft Regulation with Crowd Recommendation: Coordinating Self-Interested Agents in Sociotechnical Systems under Imperfect Information
http://academiccommons.columbia.edu/catalog/ac:197950
Luo, Yu; Iyengar, Garud N.; Venkatasubramanian, Venkathttp://dx.doi.org/10.7916/D8HM58FXWed, 27 Apr 2016 13:48:12 +0000Regulating emerging industries is challenging, even controversial at times. Under-regulation can result in safety threats to plant personnel, surrounding communities, and the environment. Over-regulation may hinder innovation, progress, and economic growth. Since one typically has limited understanding of, and experience with, the novel technology in practice, it is difficult to accomplish a properly balanced regulation. In this work, we propose a control and coordination policy called soft regulation that attempts to strike the right balance and create a collective learning environment. In soft regulation mechanism, individual agents can accept, reject, or partially accept the regulator’s recommendation. This non-intrusive coordination does not interrupt normal operations. The extent to which an agent accepts the recommendation is mediated by a confidence level (from 0 to 100%). Among all possible recommendation methods, we investigate two in particular: the best recommendation wherein the regulator is completely informed and the crowd recommendation wherein the regulator collects the crowd’s average and recommends that value. We show by analysis and simulations that soft regulation with crowd recommendation performs well. It converges to optimum, and is as good as the best recommendation for a wide range of confidence levels. This work sheds a new theoretical perspective on the concept of the wisdom of crowds.Sociology, Industrial engineering, System scienceyl2750, gi10, vv2213Chemical Engineering, Industrial Engineering and Operations ResearchArticlesApplied Inventory Management: New Approaches to Age-Old Problems
http://academiccommons.columbia.edu/catalog/ac:194202
Daniel Guetta, Charles Raphaelhttp://dx.doi.org/10.7916/D84M94B1Fri, 05 Feb 2016 15:26:26 +0000Supply chain management is one of the fundamental topics in the field of operations research, and a vast literature exists on the subject. Many recent developments in the field are rapidly narrowing the gap between the systems handled in the literature and the real-life problems companies need to solve on a day-to-day basis. However, there are certain features often observed in real-world systems that elude even these most recent developments. In this thesis, we consider a number of these features, and propose some new heuristics together with methodologies to evaluate their performance.
In Chapter 2, we consider a general two-echelon distribution system consisting of a depot and multiple sales outlets which face random demands for a given item. The replenishment process consists of two stages: the depot procures the item from an outside supplier, while the retailers' inventories are replenished by shipments from the depot. Both of the replenishment stages are associated with a given facility-specific leadtime. The depot as well as the retailers face a limited inventory capacity. We propose a heuristic for this class of dynamic programming models to obtain an upper bound on optimal costs, together with a new approach to generate lower bounds based on Lagrangian relaxation. We report on an extensive numerical study with close to 14,000 instances which evaluates the accuracy of the lower bound and the optimality gap of the various heuristic policies. Our study reveals that our policy performs exceedingly well almost across the entire parameter spectrum.
In Chapter 3, we extend the model above to deal with distribution systems involving several items. In this setting, two interdependencies can arise between items that considerably complicate the problem. First, shared storage capacity at each of the retail outlets results in a trade-off between items; ordering more of one item means less space is available for another. Second, economies of scope can occur in the order costs if several items can be ordered from a single supplier, incurring only one fixed cost. To our knowledge, our approach is the first that has been proposed to handle such complex, multi-echelon, multi-item systems. We propose a heuristic for this class of dynamic programming models, to obtain an upper bound on optimal costs, together with an approach to generate lower bounds. We report on an extensive numerical study with close to 1,200 instances that reveals our heuristic performs excellently across the entire parameter spectrum. In Chapter 4, we consider a periodic-review stochastic inventory control system consisting of a single retailer which faces random demands for a given item, and in which demand forecasts are dynamically updated (for example, new information observed in one period may affect our beliefs about demand distributions in future periods). Replenishment orders are subject to fixed and variable costs. A number of heuristics exist to deal with such systems, but to our knowledge, no general approach exists to find lower bounds on optimal costs therein. We develop a general approach for finding lower bounds on the cost of such systems using an information relaxation. We test our approach in a model with advance demand information, and obtain good lower bounds over a range of problem parameters.
Finally, in Appendix A, we begin to tackle the problem of using these methods in real supply chain systems. We were able to obtain data from a luxury goods manufacturer to inspire our study. Unfortunately, the methods we developed in earlier chapters were not directly applicable to these data. Instead, we developed some alternate heuristic methods, and we considered statistical techniques that might be used to obtain the parameters required for these heuristics from the data available.Operations research, Industrial engineering, Business administrationBusiness, Industrial Engineering and Operations ResearchDissertationsCutting Planes for Convex Objective Nonconvex Optimization
http://academiccommons.columbia.edu/catalog/ac:166569
Michalka, Alexanderhttp://hdl.handle.net/10022/AC:P:22000Thu, 17 Oct 2013 14:46:03 +0000This thesis studies methods for tightening relaxations of optimization problems with convex objective values over a nonconvex domain. A class of linear inequalities obtained by lifting easily obtained valid inequalities is introduced, and it is shown that this class of inequalities is sufficient to describe the epigraph of a convex and differentiable function over a general domain. In the special case where the objective is a positive definite quadratic function, polynomial time separation procedures using the new class of lifted inequalities are developed for the cases when the domain is the complement of the interior of a polyhedron, a union of polyhedra, or the complement of the interior of an ellipsoid. Extensions for positive semidefinite and indefinite quadratic objectives are also studied. Applications and computational considerations are discussed, and the results from a series of numerical experiments are presented.Industrial engineeringadm2148Industrial Engineering and Operations ResearchDissertationsResource Cost Aware Scheduling Problems
http://academiccommons.columbia.edu/catalog/ac:166566
Carrasco, Rodrigohttp://hdl.handle.net/10022/AC:P:21999Thu, 17 Oct 2013 14:31:58 +0000Managing the consumption of non-renewable and/or limited resources has become an important issue in many different settings. In this dissertation we explore the topic of resource cost aware scheduling. Unlike the purely scheduling problems, in the resource cost aware setting we are not only interested in a scheduling performance metric, but also the cost of the resources consumed to achieve a certain performance level. There are several ways in which the cost of non-renewal resources can be added into a scheduling problem. Throughout this dissertation we will focus in the case where the resource consumption cost is added, as part of the objective, to a scheduling performance metric such as weighted completion time and weighted tardiness among others. In our work we make several contributions to the problem of scheduling with non-renewable resources. For the specific setting in which only energy consumption is the important resource, our contributions are the following. We introduce a model that extends the previous energy cost models by allowing more general cost functions that can be job-dependent. We further generalize the problem by allowing arbitrary precedence constraints and release dates. We give approximation algorithms for minimizing an objective that is a combination of a scheduling metric, namely total weighted completion time and total weighted tardiness, and the total energy consumption cost. Our approximation algorithm is based on an interval-and-speed-indexed IP formulation. We solve the linear relaxation of this IP and we use this solution to compute a schedule. We show that these algorithms have small constant approximation ratios. Through experimental analysis we show that the empirical approximation ratios are much better than the theoretical ones and that in fact the solutions are close to optimal. We also show empirically that the algorithm can be used in additional settings not covered by the theoretical results, such as using flow time or an online setting, with good approximation and competitiveness ratios.Industrial engineering, Applied mathematicsIndustrial Engineering and Operations ResearchDissertationsRapid Advance: High Technology in China in the Global Electronic Age
http://academiccommons.columbia.edu/catalog/ac:161506
Mays, Susan Kayhttp://hdl.handle.net/10022/AC:P:20447Thu, 23 May 2013 13:43:28 +0000This study examines how a critical high technology industry in China, the semiconductor industry, advanced from being an isolated, centrally planned industry in the mid 1980s to being an important participant in the competitive global semiconductor industry after 2000. The research examines the most important trends, projects, and enterprises in China, with attention to China's global partners and China's rapidly growing role in the world economy. In the 1990s, semiconductor enterprises in China proactively made key structural changes and global linkages that set the stage for the industry's growth after 2000. The study thus provides an industry level assessment of how reforms and technological upgrading occurred in contemporary China, including the degree and character of so-called state led development. This research also shows that the development of this high technology industry had direct and positive effects on China's larger business environment and trade policies. Finally, this study compares the development of the semiconductor industry in China with its development in Japan, South Korea, and Taiwan, identifying differences in national approaches and the effects of the global information revolution.Economic history, Asian studies, Industrial engineeringsm2075History, East Asian Languages and CulturesDissertationsLeveraging the mining industry’s energy demand to improve host countries’ power infrastructure
http://academiccommons.columbia.edu/catalog/ac:156324
Toledano, Perrinehttp://hdl.handle.net/10022/AC:P:18962Thu, 07 Feb 2013 12:46:47 +0000The World Bank estimates that African investment needs in infrastructure would cost US$93 billion per year, only half of which is for the power sector. In the same time, the availability of power lies at the core of a mine’s development strategy; mining operators need to make sure that the energy demand of mining operations is met. This is especially the case in remote areas, where mining companies are developing large projects with little or no connectivity to national grids and very limited options for electricity supply.
To address these energy problems, the mining industry has adopted different solutions depending on the power situation of the country, the projects’ energy demand, and the projects’ distance from the grid: When sourcing from the grid is too expensive or when there is no grid, industry finances and builds its own power generation facilities or sources from a third-party that is
a private power generator. When sourcing from the grid is less expensive than own generation, industry either sources from the grid or finances/co-finances the upgrade of the power assets under various arrangements with the public utility. For a mining company, the goal is to maximize cost-savings. For a host country, the challenge is to maximize welfare gains by leveraging any investment in power infrastructure development for the electrification needs of the country. This could be through connecting the mine to the grid and incentivizing the company to produce extra capacity to sell to the public utility in order to increase supply and reduce the electricity cost, or by requiring that the privately-financed network is open to third-party access, so that towns and populations between the mine and the grid benefit from the privately financed distribution lines as well. Both, cost savings and welfare gains can be met simultaneously if sound regulations and efficient coordination mechanisms are in place. Without appropriate regulation, the opportunity for the country will be missed. Without appropriate coordination mechanisms within the mining industry or between the industry and the government, scale economies will be lost. Therefore to take advantage of the opportunity of the investments of the mining industry in power infrastructure, and make sure that the country benefits from those investments, an appropriate planning, regulatory and commercial framework is needed. If power assets are leveraged and designed to contribute to the development of public infrastructure at the national, regional or community levels, the incremental capital cost of building additional capacity could be reduced and the economic and social spillover effects can extend far beyond the mining sector. The purpose of this working paper is to distill good practice principles observed in power infrastructure development leveraging the mining industry’s energy demand around the world, informed by expert opinion.Economics, Industrial engineeringpt2179Vale Columbia Center on Sustainable International InvestmentWorking papersChance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
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Bienstock, Daniel; Chertkov, Michael; Harnett, Seanhttp://hdl.handle.net/10022/AC:P:18933Tue, 05 Feb 2013 10:34:34 +0000When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to re-dispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CC-OPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic re-dispatch. CC-OPF allows efficient implementation, e.g. solving a typical instance over the 2746-bus Polish network in 20 seconds on a standard laptop.Industrial engineering, Operations researchdb17, srh2144Industrial Engineering and Operations Research, Applied Physics and Applied MathematicsArticlesChance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
http://academiccommons.columbia.edu/catalog/ac:153902
Bienstock, Daniel; Chertkov, Michael; Harnett, Seanhttp://hdl.handle.net/10022/AC:P:15118Mon, 29 Oct 2012 09:19:08 +0000When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to redispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CCOPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic redispatch. CCOPF allows efficient implementation, e.g. solving a typical instance over the 2746bus Polish network in 20s on a standard laptop.Industrial engineering, Operations researchdb17Industrial Engineering and Operations Research, Applied Physics and Applied MathematicsArticlesMultiproduct Pricing Management and Design of New Service Products
http://academiccommons.columbia.edu/catalog/ac:144706
Wang, Ruxianhttp://hdl.handle.net/10022/AC:P:12603Fri, 17 Feb 2012 12:45:47 +0000In this thesis, we study price optimization and competition of multiple differentiated substitutable products under the general Nested Logit model and also consider the designing and pricing of new service products, e.g., flexible warranty and refundable warranty, under customers' strategic claim behavior. Chapter 2 considers firms that sell multiple differentiated substitutable products and customers whose purchase behavior follows the Nested Logit model, of which the Multinomial Logit model is a special case. In the Nested Logit model, customers make product selection decision sequentially: they first select a class or a nest of products and subsequently choose a product within the selected class. We consider the general Nested Logit model with product-differentiated price coefficients and general nest-heterogenous degrees. We show that the adjusted markup, which is defined as price minus cost minus the reciprocal of the price coefficient, is constant across all the products in each nest. When optimizing multiple nests of products, the adjusted nested markup is also constant within a nest. By using this result, the multi-product optimization problem can be reduced to a single-dimensional problem in a bounded interval, which is easy to solve. We also use this result to simplify the oligopolistic price competition and characterize the Nash equilibrium. Furthermore, we investigate its application to dynamic pricing and revenue management. In Chapter 3, we investigate the flexible monthly warranty, which offers flexibility to customers and allow them to cancel it at anytime without any penalty. Frequent technological innovations and price declines severely affect sales of extended warranties as product replacement upon failure becomes an increasingly attractive alternative. To increase sales and profitability, we propose offering flexible-duration extended warranties. These warranties can appeal to customers who are uncertain about how long they will keep the product as well as to customers who are uncertain about the product's reliability. Flexibility may be added to existing services in the form of monthly-billing with month-by-month commitments, or by making existing warranties easier to cancel, with pro-rated refunds. This thesis studies flexible warranties from the perspectives of both the customer and the provider. We present a model of the customer's optimal coverage decisions under the objective of minimizing expected support costs over a random planning horizon. We show that under some mild conditions the customer's optimal coverage policy has a threshold structure. We also show through an analytical study and through numerical examples how flexible warranties can result in higher profits and higher attach rates. Chapter 4 examines the designing and pricing of residual value warranty that refunds customers at the end of warranty period based on customers' claim history. Traditional extended warranties for IT products do not differentiate customers according to their usage rates or operating environment. These warranties are priced to cover the costs of high-usage customers who tend to experience more failures and are therefore more costly to support. This makes traditional warranties economically unattractive to low-usage customers. In this chapter, we introduce, design and price residual value warranties. These warranties refund a part of the upfront price to customers who have zero or few claims according to a pre-determined refund schedule. By design, the net cost of these warranties is lower for light users than for heavy users. As a result, a residual value warranty can enable the provider to price-discriminate based on usage rates or operating conditions without the need to monitor individual customers' usage. Theoretic results and numerical experiments demonstrate how residual value warranties can appeal to a broader range of customers and significantly increase the provider's profits.Operations research, Industrial engineeringrw2267Industrial Engineering and Operations ResearchDissertationsDevelopment of Construction Projects Scheduling with Evolutionary Algorithms
http://academiccommons.columbia.edu/catalog/ac:140087
Tavakolan, Mehdihttp://hdl.handle.net/10022/AC:P:11408Mon, 10 Oct 2011 12:38:40 +0000Evolutionary Algorithms (EAs) as appropriate tools to optimize multi-objective problems have been applied to optimize construction projects in the last two decades. However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in construction engineering and management domains remain poorly understood. Furthermore, hybrid algorithms such as Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) and Shuffled Frog Leaping Algorithm (SFLA) have been presented in computational optimization and water resource management domains during recent years to prevent pitfalls of the aforementioned algorithms. In this dissertation, I present three studies on hybrid algorithms to show that our proposed hybrid approaches are superior than existing optimization algorithms in finding better project schedule solutions with less total project cost, shorter total project duration, and less total resources allocation moments. In the first, I present a HGAPSO approach to solve complex, TCRO problems in construction project planning. Our proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity). In the second, I present the SFLA algorithm to solve TCRO problems using splitting allowed during activities execution. The third study involves the evaluation of the inflation impact on resources unit price during execution of construction projects. This research presents the comprehensive TCRO model by comparing two hybrid algorithms, HGAPSO and SFLA, with the three most capable algorithms -- GA, PSO and ACO -- in six different examples in terms of the structure of projects, construction assumptions and kinds of Time-Cost functions. Each of the three studies helps overcome parts of EAs problems and contributes to obtaining optimal project schedule solutions of total project duration, total project cost and total resources allocation moments of construction projects in the planning stage. The findings have significant implications in improving the scheduling of construction projects.Civil engineering, Industrial engineeringmt2568Civil Engineering and Engineering MechanicsDissertations