The purpose of this paper is to study several schemes for applying Danlzig-Wolfe decomposition (DWD) to the production-assembly-distribution system design problem (PADS). Each scheme exploits selected embedded structu...
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The purpose of this paper is to study several schemes for applying Danlzig-Wolfe decomposition (DWD) to the production-assembly-distribution system design problem (PADS). Each scheme exploits selected embedded structures. The research objective is to enhance the rate of DWD convergence in application to PADS through formulating a rationale for decomposition by analyzing potential schemes, adopting acceleration techniques, and assessing the impacts of schemes and techniques computationally. Test results provide insights that may be relevant to other applications of DWD. (c) 2007 Published by Elsevier Ltd.
In this paper, we continue to analyze optimal dispatch of generation and transmission topology to meet load as a mixedinteger program (MIP) with binary variables representing the state of the transmission element (li...
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In this paper, we continue to analyze optimal dispatch of generation and transmission topology to meet load as a mixedinteger program (MIP) with binary variables representing the state of the transmission element (line or transformer). Previous research showed a 25% savings by dispatching the IEEE 118-bus test case. This paper is an extension of that work. It presents how changing the topology affects nodal prices, load payment, generation revenues, cost, and rents, congestion rents, and flowgate prices. Results indicate that changing the topology to cut costs typically results in lower load payments and higher generation rents for this network. Computational issues are also discussed.
While the world energy demand is steadily growing, the concern for the environmental aspects of energy use and natural resource exploitation has increased. A new market has emerged for renewable energy, often referred...
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While the world energy demand is steadily growing, the concern for the environmental aspects of energy use and natural resource exploitation has increased. A new market has emerged for renewable energy, often referred to as "green energy". This paper presents an optimization model developed as part of a feasibility study on the idea of exporting renewable energy in the form of hydrogen, from Iceland to the continent of Europe. The goal is to find the most economical ways of producing hydrogen solely via electrolysis from water, using electricity from hydro and geothermal power. The mixed integer programming model presented here facilitates the search for optimal choices from the 23 potential power plants, 11 of which are based on geothermal sources, and 12 are hydropower stations. The best locations for hydrogen production are in the southwest region of Iceland and the least expensive hydrogen production is at energy demand between 2 and 4TWh/yr. At lower demand, the cost of energy is relatively higher, due to inefficient use of power plants and transfer lines. At higher demand, the cost of constructing new power transfer lines increases, leading to higher overall energy costs. (C) 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.
The regression tree (RT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the RT that was generated in the growth phase by generating a subtree that ...
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The regression tree (RT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the RT that was generated in the growth phase by generating a subtree that avoids over-fitting to the training data. Most post-pruning methods essentially address post-pruning as if it were a single objective problem (i.e., maximize validation accuracy), and address the issue of simplicity (in terms of the number of leaves) only in the case of a tie. However, it is well known that apart from accuracy there are other performance measures (e.g., stability, simplicity) that are important for evaluating DT quality. In this paper we present an integrated approach to post-pruning phase that simultaneously accommodates multiple performance measures that are important for evaluating RT quality, and obtains the optimal subtree based on user provided preference and value function information. (C) 2007 Elsevier Ltd. All rights reserved.
In this paper, we address a scheduling problem related to people's livelihood, such as road construction works that include laying pipes for gas, water and phone. The problem is a multi-processing-stage open shop ...
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In this paper, we address a scheduling problem related to people's livelihood, such as road construction works that include laying pipes for gas, water and phone. The problem is a multi-processing-stage open shop with the characteristics of movable dedicated machines and no-wait restriction, also known as no intermediate queue. The objective is to schedule the jobs such that the total occupation time for all the processing stages is minimized. Some two-phase heuristic algorithms are proposed for solving the problem. Computational results show that the heuristic is fairly effective in finding an optimal or a near-optimal solution for small-sized problems. Results of the heuristic for experiments tallied with the real-life environment demonstrate the potential of the heuristic to efficiently deal with the scheduling problems. (c) 2007 Elsevier B.V. All rights reserved.
Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sew...
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Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands-the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of unce
To mitigate the adverse environmental impact of forest roads, especially degradation of endangered salmonid habitat, many public and private land managers in the western United States are actively decommissioning road...
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To mitigate the adverse environmental impact of forest roads, especially degradation of endangered salmonid habitat, many public and private land managers in the western United States are actively decommissioning roads where practical and affordable. Road decommissioning is associated with reduced long-term environmental impact. When decommissioning a road, it may be possible to recover some aggregate (crushed rock) from the road surface. Aggregate is used on many low volume forest roads to reduce wheel stresses transferred to the subgrade, reduce erosion, reduce maintenance costs, and improve driver comfort. Previous studies have demonstrated the potential for aggregate to be recovered and used elsewhere on the road network, at a reduced cost compared to purchasing aggregate from a quarry. This article investigates the potential for aggregate recycling to provide an economic incentive to decommission additional roads by reducing transport distance and aggregate procurement costs for other actively used roads. Decommissioning additional roads may, in turn, result in improved aquatic habitat. We present real-world examples of aggregate recycling and discuss the advantages of doing so. Further, we present mixedinteger formulations to determine optimal levels of aggregate recycling under economic and environmental objectives. Tested on an example road network, incorporation of aggregate recycling demonstrates substantial cost-savings relative to a baseline scenario without recycling, increasing the likelihood of road decommissioning and reduced habitat degradation. We find that aggregate recycling can result in up to 24% in cost savings (economic objective) and up to 890% in additional length of roads decommissioned (environmental objective).
In this paper we study the 0-1 maximum probability model that consists in maximizing the probability that a certain quantity c(T)x is greater than a prescribed constant t, where c and x are n vectors. c(1),..., c(n), ...
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In this paper we study the 0-1 maximum probability model that consists in maximizing the probability that a certain quantity c(T)x is greater than a prescribed constant t, where c and x are n vectors. c(1),..., c(n), are mutually independent and normally distributed random variables and x is a vector of n binary variables such that Ax less than or equal to b, where b is an m vector and A is an m x n matrix. It is known that this problem can be formulated as a nonlinear fractional program. We show how to solve it exactly using mixed integer programming. The advantage of the approach is that it requires only standard, commercially available software. The computational results which we present show that this technique makes it possible to treat instances with up to 100 random variables in a few seconds of CPU time. (C) 2003 Elsevier B.V. All rights reserved.
Infrastructure issues pose more challenges and uncertainties for hydrogen than other alternative "fuels" such as biofuels and electricity. A key challenge of developing a future commercial hydrogen economy i...
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Infrastructure issues pose more challenges and uncertainties for hydrogen than other alternative "fuels" such as biofuels and electricity. A key challenge of developing a future commercial hydrogen economy is how the infrastructure will be best designed and operated as time progresses, given that numerous technological options exist and are still in development for hydrogen production, storage, distribution and dispensing. This paper presents a generic optimization-based model for the strategic dynamic investment planning and design of future hydrogen supply chains. The features and capabilities of the model are illustrated through a detailed case study of China. It is shown how the proposed methodology can provide policy-makers with new tools for hydrogen development strategies. (C) 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.
We study a scheduling problem, motivated by air-traffic control. When aircraft reach the final descent in the "Terminal Radar Approach CONontrol" area (TRACON), a set of disjoint time windows in which the la...
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We study a scheduling problem, motivated by air-traffic control. When aircraft reach the final descent in the "Terminal Radar Approach CONontrol" area (TRACON), a set of disjoint time windows in which the landing is possible, can be automatically assigned to each aircraft. The objective is then to determine landing times, within these time windows, which maximize the minimum time elapsed between consecutive landings. We study the complexity of the problem and describe several special cases that can be solved in polynomial time. We also provide a compact mixed integer programming formulation that allows us to solve large instances of the general problem when all time windows have the same size. Finally, we introduce a general hybrid branch and cut framework to solve the problem with arbitrary time windows. Experimental results show that our approach outperforms earlier formulation of the problem. (C) 2007 Elsevier B.V. All rights reserved.
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