The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of prior...
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The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of priority and length of usage. Canadian Forces airlift planners typically encounter such a capacity planning problem. The solution to this problem requires the constrained assignment of n variable length missions (tasks) integrating hundreds of airlift requests from several users with many priorities to m airframes (parallel machines).
We consider a setting where a team of multiple robots has to fulfill a mission in a specifically defined formation in a partially known environment. In many real environments, not all obstacles are previously known, b...
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ISBN:
(纸本)9788995003848
We consider a setting where a team of multiple robots has to fulfill a mission in a specifically defined formation in a partially known environment. In many real environments, not all obstacles are previously known, but often most of them. For the planning of the optimal trajectory along these obstacles a mixed integer programming algorithm is used. If during the mission at least one of the robots detects a previously unknown obstacle, the control on each robot switches from centralized formation control to decentralized control. In this case, each robot is able to use its own set of Fuzzy Systems for obstacle avoidance and returns to the optimal trajectory after passing the unknown obstacles. This concept allows the robots to find the optimal trajectories for the mission task in known areas using the mixed integer programming. In unknown or for the mixed integer programming too complex scenarios the robots have to use the information based on their limited onboard sensors. With the sensor information they are in most cases able to find at least a possible way through the obstacles by using a set of onboard Fuzzy Systems for robot control.
Power quality in distribution systems may deteriorate due to an increase in nonlinear loads. The single-tuned passive filter is considered as one of the most economical and efficient approaches to reduce the harmonics...
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Power quality in distribution systems may deteriorate due to an increase in nonlinear loads. The single-tuned passive filter is considered as one of the most economical and efficient approaches to reduce the harmonics. This paper presents a new method for studying passive filter planning using Simultaneous Perturbation Stochastic Approximation (SPSA). SPSA is an easily implemented and highly efficient gradient approximation that relies on two values of the objective function. The number (integer) of capacitor bank and tuned resonance frequency (real number) of the single-tuned passive filter are considered as unknowns. Simulation results from an 18-bus system with multiple nonlinear loads show the applicability of the proposed method. It can be found that the proposed method can obtain the optimal solution in a very short CPU time.
Combined heat and power (CHP) plants fired by forest wood can significantly contribute to attaining the target of increasing the share of renewable energy production. However, the spatial distribution of biomass suppl...
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Combined heat and power (CHP) plants fired by forest wood can significantly contribute to attaining the target of increasing the share of renewable energy production. However, the spatial distribution of biomass supply and of heat demand limits the potentials of CHP production. This article assesses CHP potentials using a mixed integer programming model that optimizes locations of bioenergy plants. Investment costs of district heating infrastructure are modeled as a function of heat demand densities, which can differ substantially. Gasification of biomass in a combined cycle process is assumed as production technology. Some model parameters have a broad range according to a literature review. Monte-Carlo simulations have therefore been performed to account for model parameter uncertainty in our analysis. The model is applied to assess CHP potentials in Austria. Optimal locations of plants are clustered around big cities in the east of the country. At current power prices, biomass-based CHP production allows producing around 3% of the total energy demand in Austria. Yet, the heat utilization decreases when CHP production increases due to limited heat demand that is suitable for district heating. Production potentials are most sensitive to biomass costs and power prices. Copyright (C) 2009 John Wiley & Sons, Ltd.
This paper addresses the scheduling problem involving batch processing machines, which is Mso known as parallel batching in the literature. The presented mixed integer programming formulation first provides an elegant...
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This paper addresses the scheduling problem involving batch processing machines, which is Mso known as parallel batching in the literature. The presented mixed integer programming formulation first provides an elegant model for the problem under study. Fhrthermore, it enables solutions to the problem instances beyond the capability of exact methods developed so far. In order to alleviate computational burden, the authors propose MIP-based heuristic approaches which balance solution quality and computing time.
The intermittency and volatility of wind generation (WG) would require additional upward and downward reserves, as well as enhanced ramping capabilities in power systems. This study investigates the optimal expansion ...
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The intermittency and volatility of wind generation (WG) would require additional upward and downward reserves, as well as enhanced ramping capabilities in power systems. This study investigates the optimal expansion planning of fast-response generating capacity (e.g. gas-fired units) to accommodate the uncertainty of WG. The study utilises a mixed integer programming-based security-constrained unit commitment for analysing operational and reliability issues related to the proposed optimisation problem. Numerical experiments signify the effectiveness of the proposed method.
Political economic arguments underline the focus on cellulosic ethanol production as a preferred process for meeting future renewable fuel demand. Cellulosic ethanol production requires large volumes of the biomass in...
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Political economic arguments underline the focus on cellulosic ethanol production as a preferred process for meeting future renewable fuel demand. Cellulosic ethanol production requires large volumes of the biomass input, adding logistical challenges to the feasibility of the technology. The objective of this research is to evaluate the profitability of a field-to-refinery model develped to identify optimal harvest, storage, transportation, pretreatment, and refining activities for a study area in Northeastern North Dakota. Sensitivity analysis indicates profitability of the ethanol complex is marginal under current prices and anticipated technologies. However, increases in ethanol prices and reduced conversion costs to produce ethanol from herbaceous crop residues suggest future viability of the process. Finally, development of a viable livestock feeding industry using some or all of the AFEX-pretreated crop residue increases the profitability of harvesting crop residues for further use. (C) 2009 Elsevier Ltd. All rights reserved.
In this article, we consider the multi-item capacitated lot sizing problem with setup times. Starting from an original mixed integer programming model, we apply the standard Dantzig-Wolfe decomposition (DWD) in two di...
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In this article, we consider the multi-item capacitated lot sizing problem with setup times. Starting from an original mixed integer programming model, we apply the standard Dantzig-Wolfe decomposition (DWD) in two different ways: defining the subproblems by items and defining the subproblems by periods. A third decomposition is developed in which the subproblems of both types are integrated in the same model. The linear relaxation of this last approach, which we denote as multiple DWD, provides lower bounds (equal to or) better than the bounds obtained by the other decompositions, which in turn, provide lower bounds (equal to or) better than the ones given by the original model. For solving the three decomposition models, we implemented three branch-and-price algorithms. We describe their main aspects and report on their computational results in instances from the literature.
Forest road decommissioning is a pro-active mechanism for preventing future habitat degradation and for increasing the likelihood of endangered salmonid survival in the western U.S. High implementation costs however p...
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Forest road decommissioning is a pro-active mechanism for preventing future habitat degradation and for increasing the likelihood of endangered salmonid survival in the western U.S. High implementation costs however preclude many desirable projects from being undertaken, especially on federally owned land. Previous research and real-world applications have demonstrated the cost savings potential of reusing aggregate recovered from forest roads during decommissioning. These cost savings can effectively subsidize decommissioning projects, suggesting an economic benefit associated with improving environmental benefit. We present a mixedinteger, multiple objective formulation to identify the efficient trade-off surface between conflicting economic and environmental criteria, where environmental benefit is defined as the hazard weighted length of roads decommissioned. We compare nondominated frontiers identified with and without the opportunity to recycle aggregate. Our results suggest that aggregate recycling promotes a synergistic relationship between cost savings (subsidy) and environmental performance, where subsidies generally increase with increasing environmental performance. Effective subsidy values can reach 31% of total expenditure, at the maximum level of environmental benefit. Transportation managers are therefore able to recover and reuse a nonrenewable resource, while at the same time promoting economic and environmental efficiency.
We develop a model for flexibly ranking multi-dimensional alternatives/units into preference classes via mixed integer programming. We consider a linear aggregation model, but allow the criterion weights to vary withi...
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We develop a model for flexibly ranking multi-dimensional alternatives/units into preference classes via mixed integer programming. We consider a linear aggregation model, but allow the criterion weights to vary within pre-specified ranges. This allows the individual alternatives/units to play to their strengths. We illustrate the use of the model by considering the Financial Times Global MBA Program rankings and discuss the implications. We argue that in many applications neither the data nor the weights or the aggregation model itself is precise enough to warrant a complete ranking. providing an argument for sorting or what we call flexible ranking. (C) 2009 Elsevier B.V. All rights reserved.
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