Real-time traffic management in railway aims to minimize delays after an unexpected event perturbs the operations. It can be formalized as the real-time railway traffic management problem, which seeks for the best tra...
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Real-time traffic management in railway aims to minimize delays after an unexpected event perturbs the operations. It can be formalized as the real-time railway traffic management problem, which seeks for the best train routing and scheduling in case of perturbation, in a given time horizon. We propose a mixed-integer linear programming formulation for tackling this problem, representing the infrastructure with fine granularity. This is seldom done in the literature, unless stringent artificial constraints are imposed for reducing the size of the search space. In a thorough experimental analysis, we assess the impact of the granularity of the representation of the infrastructure on the optimal solution. We tackle randomly generated instances representing traffic in the control area named triangle of Gagny, and instances obtained from the real timetable of the control area including the Lille-Flandres station (both in France) and we consider multiple perturbation scenarios. In these experiments, the negative impact of a rough granularity on the delay suffered by trains is remarkable and statistically significant. (C) 2013 Elsevier Ltd. All rights reserved.
Generator maintenance scheduling (GMS) determines the outage periods of generating units in a oneyear or two-year planning horizon for regular safety inspection. This paper introduces a new practical GMS for centraliz...
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Generator maintenance scheduling (GMS) determines the outage periods of generating units in a oneyear or two-year planning horizon for regular safety inspection. This paper introduces a new practical GMS for centralized electrical power systems in which, in contrary to previous studies, the outage periods are scheduled based on operational hours of units. Predefined minimum and maximum operating hours of units after a maintenance outage define the beginning and ending of their maintenance windows, respectively. In addition, unit commitment (UC) as a short-term planning is considered in the GMS with hourly time scale. Therefore, this problem becomes more complex than periodic maintenance scheduling. In this paper, a novel mixed-integer linear programming (MILP) model for the problem is developed. However, due to the problem intractability, two different solution algorithms on the basis of ant colony optimization (ACO) and simulated annealing (SA) are extended for the GMS problem. Both algorithms use some developed heuristics and feasibility rules, namely UC heuristic, for solving the UC problem. Numerical results indicate that the solution algorithms perform well compared to the exact solution of the MILP model obtained using CPLEX solver. Also, the solution algorithms as well as the UC heuristic are examined carefully. To demonstrate the performance of the proposed algorithms, two test systems containing 26 and 36 generating units are investigated. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper, we consider a variant of the many-to-many location-routing problem, where hub facilities have to be located and customers with either pickup or delivery demands have to be combined in vehicle routes. In...
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In this paper, we consider a variant of the many-to-many location-routing problem, where hub facilities have to be located and customers with either pickup or delivery demands have to be combined in vehicle routes. In addition, several commodities and inter-hub transport processes are taken into account. A practical application of the problem can be found in the timber-trade industry, where companies provide their services using hub-and-spoke networks. We present a mixed-integerlinear model for the problem and use CPLEX 12.4 to solve small-scale instances. Furthermore, a multi-start procedure based on a fix-and-optimize scheme and a genetic algorithm are introduced that efficiently construct promising solutions for medium- and large-scale instances. A computational performance analysis shows that the presented methods are suitable for practical application. (C) 2013 Elsevier B.V. All rights reserved.
This paper proposes a new separable model for the unit commitment (UC) problem and three deterministic global optimization methods for it ensuring convergence to the global optimum within a desired tolerance. By decom...
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This paper proposes a new separable model for the unit commitment (UC) problem and three deterministic global optimization methods for it ensuring convergence to the global optimum within a desired tolerance. By decomposing a multivariate function into several univariate functions, a tighter outer approximation methodology that can be used to improve the outer approximations of several classical convex programming techniques is presented. Based on the idea of the outer approximation (OA) method and the proposed separable model, an outer-inner approximation (OIA) approach is also presented to solve this new formulation of UC problem. In this OIA approach, the UC problem is decomposed into a tighter outer approximation subproblem and an inner approximation subproblem, where the former leads to a better lower bound than the OA method, and the later provides a better upper bound. The simulation results for systems of up to 100 units with 24 h are compared with those of previously published methods, which show that the OIA approach is very promising due to the excellent performance. The proposed approaches are also applied to the large-scale systems of up to 1000 units with 24 h, and systems of up to 100 units with 96 h and 168 h.
This paper presents an overview of the different methodologies and mathematical optimization models developed in the framework of the EU-funded project SiNGULAR towards the optimal exploitation and efficient short-ter...
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This paper presents an overview of the different methodologies and mathematical optimization models developed in the framework of the EU-funded project SiNGULAR towards the optimal exploitation and efficient short-term operation of RES production in insular electricity networks. Specifically, the algorithms employed for the creation of system load and RES production scenarios that capture the spatial and temporal correlations of the corresponding variables as well as the procedure followed for the creation of units' availability scenarios using Monte Carlo simulation are discussed. In addition, the advanced unit commitment and economic dispatch models, that have been developed for the short-term scheduling of the conventional and RES generating units in different short-term time-scales (day-ahead, intra-day, and real-time) are presented. Indicative test results from the implementation of all models in the pilot system of the island of Crete, Greece, are illustrated and valuable conclusions are drawn. (C) 2014 Elsevier Ltd. All rights reserved.
Although intensity modulated radiation therapy plans are optimized as a single overall treatment plan, they are delivered over 30-50 treatment sessions (fractions) and both cumulative and per-fraction dose constraints...
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Although intensity modulated radiation therapy plans are optimized as a single overall treatment plan, they are delivered over 30-50 treatment sessions (fractions) and both cumulative and per-fraction dose constraints apply. Recent advances in imaging technology provide more insight on tumour biology that has been traditionally disregarded in planning. The current practice of delivering physical dose distributions across the tumour may potentially be improved by dose distributions guided by the biological responses of the tumour points. The biological optimization models developed and tested in this paper generate treatment plans reacting to the tumour biology prior to the treatment as well as the changing tumour biology throughout the treatment while satisfying both cumulative and fraction-size dose limits. Complete computational testing of the proposed methods would require an array of clinical data sets with tumour biology information. Finding no open source ones in the literature, the authors sought proof of concept by testing on a simulated head-and-neck case adapted from a more standard one in the CERR library by blending it with available tumour biology data from a published study. The results show computed biologically optimized plans improve on tumour control obtained by traditional plans ignoring biology, and that such improvements persist under likely uncertainty in sensitivity values. Furthermore, adaptive plans using biological information improve on non-adaptive methods.
This paper considers a location-allocation problem in a supply-chain distribution network with capacitated distribution centers and customers with price-sensitive demands. The problem determines location, allocation a...
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This paper considers a location-allocation problem in a supply-chain distribution network with capacitated distribution centers and customers with price-sensitive demands. The problem determines location, allocation and price decisions in order to maximize the total profit under two supply policies. Serving all of the customers is compulsory under the first policy, but is optional under the second. The problem is formulated as a mixed-integerlinear program and solved by a Lagrangian relaxation algorithm under each policy. The numerical study indicates that the proposed algorithms are highly efficient and effective for solving large-sized instances of the problem.
This paper presents a quantitative and computational method to determine the optimal tax rate among generating units. To strike a balance between the reduction of carbon emission and the profit of energy sectors, the ...
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This paper presents a quantitative and computational method to determine the optimal tax rate among generating units. To strike a balance between the reduction of carbon emission and the profit of energy sectors, the proposed bilevel optimization model can be regarded as a Stackelberg game between the government agency and the generation companies. The upper-level, which represents the government agency, aims to limit total carbon emissions within a certain level by setting optimal tax rates among generators according to their emission performances. The lower-level, which represents decision behaviors of the grid operator, tries to minimize the total production cost under the tax rates set by the government. The bilevel optimization model is finally reformulated into a mixedintegerlinear program (MILP) which can be solved by off-the-shelf MILP solvers. Case studies on a 10-unit system as well as a provincial power grid in China demonstrate the validity of the proposed method and its capability in practical applications.
Suppressing the effects of liquid loading is a key issue for efficient utilization of mid and late-life wells in shale-gas systems. This state of the wells can be prevented by performing short shut-ins when the gas ra...
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Suppressing the effects of liquid loading is a key issue for efficient utilization of mid and late-life wells in shale-gas systems. This state of the wells can be prevented by performing short shut-ins when the gas rate falls below the minimum rate needed to avoid liquid loading. In this paper, we present a Lagrangian relaxation scheme for shut-in scheduling of distributed shale multi-well systems. The scheme optimizes shut-in times and a reference rate for each multi-well pad, such that the total produced rate tracks a given short-term gas demand for the field. By using simple, frequency-tuned well proxy models, we obtain a compact mixed-integer formulation which by Lagrangian relaxation renders a decomposable structure. A set of computational tests demonstrates the merits of the proposed scheme. This study indicates that the method is capable of solving large field-wide scheduling problems by producing good solutions in reasonable computation times. (C) 2014 Elsevier Ltd. All rights reserved.
Data Envelopment Analysis (DEA) is a nonparametric technique originally conceived for efficiency analysis of a set of units. The main characteristic of DEA based procedures is endogenous determination of weighting vec...
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Data Envelopment Analysis (DEA) is a nonparametric technique originally conceived for efficiency analysis of a set of units. The main characteristic of DEA based procedures is endogenous determination of weighting vectors, i.e., the weighting vectors are determined as variables of the model. Nevertheless, DEA's applications have vastly exceeded its original target. In this paper, a DEA based model for the selection of a subgroup of alternatives or units is proposed. Considering a set of alternatives, the procedure seeks to determine the group that maximizes overall efficiency. The proposed model is characterized by free selection of weights and allows the inclusion of additional information, such as agent's preferences in terms of relative importance of the variables under consideration or interactions between alternatives. The solution is achieved by computing a mixed-integer linear programming model. Finally, the proposed model is applied to plan the deployment of filling stations in the province of Seville (Spain). (C) 2014 Elsevier Inc. All rights reserved.
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