Balancing Constraints Research Articles

Evaluation of propensity score methods for causal inference with high-dimensional covariates.

In recent work, researchers have paid considerable attention to the estimation of causal effects in observational studies with a large number of covariates, which makes the unconfoundedness assumption plausible. In this paper, we review propensity score (PS) methods developed in high-dimensional settings and broadly group them into model-based methods that extend models for prediction to causal inference and balance-based methods that combine covariate balancing constraints. We conducted systematic simulation experiments to evaluate these two types of methods, and studied whether the use of balancing constraints further improved estimation performance. Our comparison methods were post-double-selection (PDS), double-index PS (DiPS), outcome-adaptive LASSO (OAL), group LASSO and doubly robust estimation (GLiDeR), high-dimensional covariate balancing PS (hdCBPS), regularized calibrated estimators (RCAL) and approximate residual balancing method (balanceHD). For the four model-based methods, simulation studies showed that GLiDeR was the most stable approach, with high estimation accuracy and precision, followed by PDS, OAL and DiPS. For balance-based methods, hdCBPS performed similarly to GLiDeR in terms of accuracy, and outperformed balanceHD and RCAL. These findings imply that PS methods do not benefit appreciably from covariate balancing constraints in high-dimensional settings. In conclusion, we recommend the preferential use of GLiDeR and hdCBPS approaches for estimating causal effects in high-dimensional settings; however, further studies on the construction of valid confidence intervals are required.

Equilibrium price in intraday electricity markets

AbstractWe formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity. Forecasts are prone to idiosyncratic noise and common noise (weather). Agents production capacities are subject to independent random outages, which are each modeled by a Markov chain. The equilibrium price is defined as the price that minimizes trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following: (i) when there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price is consistent with Almgren and Chriss's model, and we identify the fundamental part and the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for Samuelson's effect to hold. We show that when heterogeneity lies only on costs, Samuelson's effect holds true. A similar result stands when heterogeneity lies only on market access quality. (ii) When there is production uncertainty only, we provide an approximation of the equilibrium for large number of players. The resulting price exhibits increasing volatility with time.

Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

Aiming at the problem that traditional heuristic algorithm is difficult to extract the empirical model in time from large sample terrain data, a multi-UAV collaborative path planning method based on attention reinforcement learning is proposed. The method draws on a combined consideration of influencing factors, such as survival probability, path length, and load balancing and endurance constraints, and works as a support system for multimachine collaborative optimizing. The attention neural network is used to generate the cooperative reconnaissance strategy of the UAV, and a large amount of simulation data is tested to optimize the attention network using the REINFORCE algorithm. Experimental results show that the proposed method is effective in solving the multi-UAV path planning issue with high real-time requirements, and the solving time is less than the traditional algorithms.

Performance analysis of clustering methods for balanced multi-robot task allocations

This paper models the Multi-Robot Task Allocation (MRTA) problem with a balance constraint to improve the utilisation (completion time) of the robots. Our balancing constraint attempts to minimise the travel distance difference among the robots as well as allocates an equal set of tasks to these robots. The clustering-based approach is employed to solve the Balanced Multi-Robot Task Allocation (BMRTA) problem for two principal reasons. That is, this approach clusters given tasks into groups using various clustering techniques for each robot and sequences the route for each robot using the travelling salesman problem (TSP) conhull algorithm. This work analyses the suitability and performance of the clustering techniques with respect to the balancing criteria using a benchmark dataset. Our findings suggest that K-means clustering is the most suitable for the solving BMRTA problem with complex topologies and it is scalable to deal with any number of tasks and robots compared with Gaussian Mixtures Models (GMM) and hierarchical clustering methods.

A three-stage layer-based heuristic to solve the 3D bin-packing problem under balancing constraint

This research investigates the Three Dimensional-Single Bin-Size Bin-Packing Problem (3D-SBSPP). The aim is to pack n boxes of different dimensions and weights into a minimum number of equally balanced bins. The boxes can be rotated according to six possible orientations. Balancing the bins, volume and weight wise, is of high importance. To achieve this goal, a three-stage heuristic was developed. In the first stage, the different possible layers are generated. Each layer is formed by boxes of same type packed according to a common given orientation. The second stage generates solution candidates using the layers formed in stage 1. In stage 3, the boxes to be shipped are placed in the bins following the solution candidates of stage 2 that give the best volumetric and/or weight utilization. The proposed heuristic was successfully validated through an exhaustive experimental study using a variety of problem instances and involving comparisons with other existing heuristics.

Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach

Seismic prediction of fluid and lithofacies distribution is of great interest to reservoir characterization, geologic model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an extreme gradient boosting-based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and [Formula: see text] ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all of the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of our workflow.

Competition in Markets for Ancillary Services? The Implications of Rising Distributed Generation

Ancillary services are electricity products which include balancing energy, frequency regulation, voltage support, constraint management and reserves. Traditionally they have been procured by system operators from large conventional power plants, as by-products of the production of energy. This paper discusses the use of markets to procure ancillary services in the face of potentially higher demand for them, caused by rising amounts of intermittent renewable generation.We discuss: the nature of markets for ancillary services; what we really mean by ancillary services; how they are impacted by the rise of distributed generation; how they are currently procured; how they relate to the rest of the electricity system; the current state of evidence on ancillary services markets; whether these markets ever be as competitive as conventional wholesale energy markets, and offer some conclusions.

A simulated annealing-based algorithm for selecting balanced samples

Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing

The number of grid-connected large-scale solar photovoltaic (PV) power plants has increased significantly in the last 10 years, which results in high PV power penetration into the grid. Especially for the wide-area spatially distributed countries, power ramp in one PV plant can be balanced with another PV power plant generation. This has been studied in the literature for short term horizons for high-frequency data. In this study, hourly simulation data are analysed by Kendall's correlation coefficient, unsupervised and rule-based machine learning algorithms for spatio-temporal operational balancing constraints. Association rules generated by using the Apriori algorithm provide power ramp direction maps for Spatio-Temporal analysis. The K-means clustering (based on the Hartigan-Wong algorithm) is used for unsupervised learning application for the spatio-temporal relations for solar PV ramp zones.The proposed model can be used as a fast and effective decision-making tool with qualitative results for the system operator with minimal expert knowledge, while they can be also integrated to optimal power flow analysis constraints. This study introduces the first successful application of association rules integrated with the K-means technique for the Solar PV spatio-temporal balancing purpose to the best of our knowledge.

Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.

Propensity score analysis methods with balancing constraints: A Monte Carlo study.

The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.

The collaborative consistent vehicle routing problem with workload balance

The rising competition in the logistics sector forces companies to be more economically efficient. One of the major sources of inefficiency is the incomplete usage of available resources, such as vehicles’ capacities. Mechanism that allow to better exploit such resources by enabling carrier collaborations are on the rise. Our study examines a centrally organized multi-period collaborative vehicle routing problem, where carriers can exchange customers who have to be serviced on a regular basis. Collaborations, where carriers serve frequent customers, are supposed to face the problem of (i) time consistency in terms of visiting time, and of (ii) service consistency. The latter ensures that customers are visited by the same collaboration partner throughout the whole planning horizon. Additionally, carriers might only be willing to enter a collaboration if a minimum market share can be guaranteed. In order to take all these issues into account, we introduce the collaborative vehicle routing problem with time and service consistency and workload balance. The mathematical model including several valid inequalities is presented. In a computational study, we solve small-sized instances to optimality. In order to tackle larger instances, we propose an efficient and effective matheuristic and an iterated local search algorithm. We show that both methods reach near optimal solutions within very short computational times. Managerial insights on the cost of imposing time and service consistency as well as workload balancing constraints are presented and discussed. We show that, according to our computational study, consistency constraints can be imposed for almost no additional cost, while workload balancing constraints do not have any negative effect on the total collaboration profit. This is a meaningful insight and might be a strong argument for carriers to enter collaborations.

An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint

The multiple travelling salesman problem (MTSP) is one of the widely studied combinatorial optimization problems with various theoretical and practical applications. However, most of the studies intended to deal with classical MTSP, very limited attention has been given to an open multiple travelling salesman problem and its variants. In this paper, an open multiple travelling salesman problem with load balancing constraint (OMTSPLB) is addressed. The OMTSPLB differs from the conventional MTSP, in which all the salesmen start from the central depot and need not come back to it after visiting the given number of cities by accomplishing the load balance constraint, which helps in fairly distributing the task among all salesmen. The problem aims to minimize the overall traversal distance/cost for operating open tours subject to the load balancing constraint. A zero-one integer linear programming (0-1 ILP) model and an efficient metaheuristic genetic algorithm (GA), is established for the OMTSPLB. Since no existing study on OMTSPLB, the proposed GA is tested on the relaxed version of the present model, comparative results are reported. The comparative results show that the proposed GA is competent over the existing algorithms. Furthermore, extensive experiments are carried out on OMTSPLB and the results show that proposed GA can find the global solution effectively.

Nodal Flexibility Requirements for Tackling Renewable Power Fluctuations

The volatility of renewable energy causes mismatch between real-time generation and demand, calling for sufficient backup to compensate such discrepancy. This paper studies the uncertainty mitigation problem at a given operating point, which entails correctively dispatching obligate generators to maintain power balancing and network power flow constraints. The redispatch problem is modeled via a convex quadratic program parameterized in the real-time renewable output, aiming at minimizing the weighted sum of square of generator incremental output. Such an objective leads to a proper allocation of unbalanced power among generators and ensures a unique optimal solution, therefore circumvents model degeneracy. Based on the first-order optimality condition, the analytical expression of the optimal redispatch policy is proven to be piecewise affine: the renewable output set is partitioned into polyhedral regions; in each region, the optimal redispatch policy of generator is an affine function in the incremental output of renewable plants. Provided with the optimal redispatch policy in the analytical form, the nodal-wise flexibility requirements on obligate generators, including the spinning reserve capacity and the ramping capability, can be easily assessed, which is useful in generation scheduling. Case studies on the PJM 5-bus system and the modified IEEE 118-bus system demonstrate the outcome and validate the effectiveness of the proposed method.

A location-allocation-improvement heuristic for districting with multiple-activity balancing constraints and p-median-based dispersion minimization

A districting problem consisting of the minimization of a dispersion function subject to multiple-activity balancing and contiguity constraints is addressed. This problem arises from a real-world application in a commercial firm responsible for distributing bottled beverage products. The objective is to find a partition of a set of city blocks into a given number of territories that minimizes a p-median problem-based dispersion function. Minimizing this measure allows for territory compactness. Districts must be connected and balanced with respect to both activities: number of customers and workload. A heuristic procedure based on a location-allocation scheme is proposed for this NP-hard problem. This technique has been successfully used for similar territory design problems with single-activity balancing constraints. The proposed method is an iterative algorithm that consists of two phases: first centroids are determined or fixed (location phase) and then units are assigned to centroids (allocation phase). The success of this technique relies on some theoretical properties that no longer hold for problems having multiple-activity balancing constraints. The novelty of our work is to design a framework for handling both connectivity and multiple-activity balancing constraints simultaneously. The solution framework is enhanced by a local search phase that attempts to improve the quality of solutions found in the allocation phase. The empirical work includes a thorough study of the different components of the algorithm, solution of very large scale instances, and comparison with existing work. Extensive empirical testing reveals the effectiveness of the proposed approach, including the impact of both the location-allocation component and its local search component. It is based on the intelligent exploitation of problem structure. The algorithm significantly outperforms one of the best-known heuristics for this problem in terms of feasibility success and solution quality. It obtains similar quality solutions than the ones obtained by the other known heuristic in less computational effort.

Exploiting relay nodes for maximizing wireless underground sensor network lifetime

A major challenge in wireless underground sensor networks is the signal attenuation originated from multi-environment transmission between underground sensor nodes and the above-ground base station. To overcome this issue, an efficient approach is deploying a set of relay nodes aboveground, thereby reducing transmission loss by shortening transmitting distance. However, this introduces several new challenges, including load balancing and transmission loss minimization. This paper tackles the problem of deploying relay nodes to reduce transmission loss under a load balancing constraint by proposing two approximation algorithms. The first algorithm is inspired by Beam Search, combined with a new selection scheme based on Boltzmann distribution. The second algorithm aims to further improve the solutions obtained by the former by reducing the transmission loss. We observe that we can find an optimal assignment between sensor nodes and a set of the chosen relay in polynomial time by reformulating the part of the problem as a bipartite matching problem with minimum cost. Experimental results indicate that the proposed methods perform better than the other existing ones in most of our test instances while reducing the execution time.

Equitable optimized airspace sectorization based on constraint programming and OWA aggregation

PurposeAirspace sectorization is an important task, which has a significant impact in the everyday work of air control services. Especially in recent years, because of the constant increase in air traffic, existing airspace sectorization techniques have difficulties to tackle the large air traffic volumes, creating imbalanced sectors and uneven workload distribution among sectors. The purpose of this paper is to propose a new approach to find optimal airspace sectorization balancing the traffic controller workload between sectors, subject to airspace requirements.Design/methodology/approachA constraint programming (CP) model called equitable airspace sectorization problem (EQASP) relies on ordered weighted averaging (OWA) multiagent optimization and the parallel portfolio architecture has been developed, which integrates the equity into an existing CP approach (Trandac et al., 2005). The EQASP was evaluated and compared with the method of Trandac et al. (2005), according to the quality of workload balancing between sectors and the resolution performance. The comparison was achieved using real air traffic low-altitude network data sets of French airspace for five flight information regions for 24 h a day and the Algerian airspace for three various periods (off peak hours, peak hours and 24 h).FindingsIt has been demonstrated that the proposed EQASP model, which is based on OWA multicriteria optimization method, significantly improved both the solving performance and the workload equity between sectors, while offering strong theoretical properties of the balancing requirement. Interestingly, when solving hard instances, our parallel sectorization tool can provide, at any time, a workable solution, which satisfies all geometric constraints of sectorization.Practical implicationsThis study can be used to design well-balanced air sectors in terms of workload between control units in the strategic phase. To fulfil the airspace users’ constraints, one can refer to this study to assess the capacity of each air sector (especially the overloaded sectors) and then adjust the sector’s shape to respond to the dynamic changes in traffic patterns.Social implicationsThis theoretical and practical approach enables the development and support of the definition of the “Air traffic management (ATM) Concept Target” through improvements in human factors specifically (balancing workload across sectors), which contributes to raising the level of capacity, safety and efficiency (SESAR Vision of ATM 2035).Originality/valueIn their approach, the authors proposed an OWA-based multiagent optimization model, ensuring the search for the best equitable solution, without requiring user-defined balancing constraints, which enforce each sector to have a workload between two user-defined bounds (Wmin, Wmax).

Distributed Charging and Discharging Strategy for Electric Vehicle Considering Three-Phase Load Balance for Incremental distribution network

The unbalanced three-phase may occur in distributed network with large-scale uncontrolled charging loads of electric vehicles plugged in, at present, most of the researches on the coordinated electric vehicles charging and discharging strategies are based on the assumption of single-phase power networks, the load balancing constraints of three-phase are not considered. In this paper, a centralized charging and discharging optimization model for electric vehicles is established aiming to achieve a valley-filling concept, it can allocate electric vehicles demand during valley hours. Based on this, a distributed charging and discharging optimization method for electric vehicles considering three-phase load balancing constraints is proposed, and then the centralized optimization problem is decomposed into multiple sub-optimization problems, and a penalty term is added to the single electric vehicle charging and discharging optimization objective function. Solve the three-phase load imbalance problem in distribution network operation, and use CPLEX solver to solve the optimization model in MATLAB. Combining with information interaction mechanism, through the method of iteratively correcting the virtual electricity price, the orderly intelligent charging and discharging of electric vehicles can be realized. The IEEE 33-node distribution system is used for simulation analysis to verify the characteristics and effectiveness of the proposed method.

Competition in Markets for Ancillary Services? The Implications of Rising Distributed Generation

Ancillary services are electricity products which include balancing energy, frequency regulation, voltage support, constraint management and reserves. Traditionally they have been procured by system operators from large conventional power plants, as by-products of the production of energy. This paper discusses the use of markets to procure ancillary services in the face of potentially higher demand for them, caused by rising amounts of intermittent renewable generation. We discuss: the nature of markets for ancillary services; what we really mean by ancillary services; how they are impacted by the rise of distributed generation; how they are currently procured; how they relate to the rest of the electricity system; the current state of evidence on ancillary services markets; whether these markets ever be as competitive as conventional wholesale energy markets, and offer some conclusions.

State-of-the-Art Auction Algorithms for Multi-depot Bus Scheduling Problem Considering Depot Workload Balancing Constraints

This paper deals with multi-depot bus scheduling (MDBS) problem. Depot workload balancing constraints are introduced. In this case, the problem can be stated as a two-objective multi-commodity flow problem with soft constraints. Two state-of-the-art heuristics are developed including schedule-based and cluster-based heuristics, both of them extend auction algorithm. Also three different approaches are proposed to satisfy depot workload balancing constraints. The convergence of each algorithm is investigated and its complexity obtained. To illustrate the main concepts and results, a small example is solved. Also, to demonstrate the performance of the proposed algorithms, some benchmark examples are considered and CPU-time and optimality gap are compared. The results show a great improvement in the CPU time and quality of the solution of the proposed algorithms. Also the extension of the proposed algorithms under ε-scaling approach is analysed.