Optimal Flow Problem Research Articles

Fireworks Explosion Algorithm for Hybrid Flow Shop Scheduling and Optimization Problem1

fireworks explosion algorithm is a relatively new population intelligent evolution algorithm, having a good performance in local mining and global search for optimal solution while little weak in searching speed. Therefore, this study focus on researching how to use the fireworks explosion algorithm(FEA) to deal with hybrid flow shop scheduling and optimization problem effectively both in accuracy and speed. The main contribution of this study is to develop a new firework individual encoding and decoding methods which could help FEA to solve HFS problem easily and effectively. Several practical instances testing proved that the FEA algorithm has better performance in dealing with HFS problem than GA and CS algorithm.

Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: A case study on China

This paper presents a novel hybridized indirect and direct encoding (HybrID) genetic algorithm for solving air traffic network flow optimization problems. A heuristic, which uses the Dijkstra algorithm for generating different types of shortest paths on a graph while controlling the weights on each arc, is proposed for selecting optimal flight routes based on current air traffic. A novel HybrID chromosome representation is employed along with the proposed heuristic and a genetic algorithm for optimization. Experiments on synthetic problems and real data of the Chinese airspace show the proposed method outperforms the direct encoding method on efficiency and efficacy metrics.

The cone of flow matrices: Approximation hierarchies and applications

Let be a directed acyclic graph with arcs, a source and a sink . We introduce the cone of flow matrices, which is a polyhedral cone generated by the matrices , where is the incidence vector of the path . We show that several hard flow (or path) optimization problems, that cannot be solved by using the standard arc‐representation of a flow, reduce to a linear optimization problem over . This cone is intractable: we prove that the membership problem associated to is NP‐complete. However, the affine hull of this cone admits a nice description, and we give an algorithm which computes in polynomial‐time the decomposition of a matrix as a linear combination of some 's. Then, we provide two convergent approximation hierarchies, one of them based on a completely positive representation of . We illustrate this approach by computing bounds for the quadratic shortest path problem, as well as a maximum flow problem with pairwise arc‐capacities.

Impact of Control System Structure and Performance of Inventory Goods Flow System with Long-Variable Delay

In this paper, we use a mathematical model of a inventory system with time-varying delivery delay and three control systems in order to compare their properties for the goods flow optimization problem in the inventory systems. Structures of the chosen control systems are based on mathematical discrete equations: a periodic inventory system with adaptive maximal inventory level and perpetual inventory system with adaptive order quantity level as well as the methods proposed by the authors in previous works based on the Smith predictor. The selection of the control systems parameters is done by solving optimization tasks for a specific scenario of time-varying market demand using a genetic algorithm in Matlab/Simulink. In this article, we mainly want to compare which of the control structures is able to achieve a high service level with maintaining of a given inventory maximal level in response to assumed consumer demand scenario.DOI: http://dx.doi.org/10.5755/j01.eie.24.1.14244

Traffic Flow Optimization Using a Quantum Annealer

Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

Simultaneous Optimization of Airspace Congestion and Flight Delay in Air Traffic Network Flow Management

Air traffic flow management (ATFM) aims to facilitate the utilization of airspace and airport resources and is critical in air transportation systems. During the past decades, several challenging problems have arisen from this domain and attracted intensive studies. This paper addresses the problem of alleviating the airspace congestion and reducing the flight delays in ATFM simultaneously. We formulate this problem as a multi-objective air traffic network flow optimization (MATNFO) problem. In this MATNFO model, comprehensive ATFM actions, for instance, ground-holding, airborne-holding, rerouting, and speed control, are considered. Meanwhile, a systematic approach, namely route and time-slot assignment (RTA) algorithm, is developed to solve the MATNFO problem. The idea of divide-and-conquer is embedded in the algorithm by sequentially applying both route searching module and time refinement module. Furthermore, for the sake of efficiency, a pre-selection operator is proposed as one heuristic strategy to identify promising solutions and reduce the search space by defining a sector equilibrium metric. Experiments on real data of the Chinese airspace show that the RTA algorithm outperforms an existing competitor and three related multi-objective evolutionary algorithms. In addition, RTA is competent for high-quality real-time air traffic network flow assignment.

Financial Resource Allocation in Higher Education

The paper considers a problem of financial resource allocation in a higher education institution. The basic financial management instruments and the multi-stage cost minimization model created are described involving financial instruments to constraints. Both societal and institutional factors that determine the costs of educating students are examined and involved into the model, too. A financial flow planning model of an education institution (e.g., university) has been created, using two-stage or four-stage stochastic programming algorithms, with easily selected education institution's accounting data. The created model has been adapted to solve the two-stage and multi-stage financial flow optimization problem of the branch of university, and the obtained results of two-stage and multi-stage tasks have been compared. A mixed integer programming algorithm, realized in the model using CPLEX Studio 126 for optimization, can be flexibly adapted for practical needs of financial planning of education institutions.

On computational complexity of the electric power flow optimization problem in market environment

Under consideration is the electric power flow optimization problem for an electric power system which typically arises in calculation of electrical power auctions in the “day-ahead” and balancing markets. It was established that the problem of finding a feasible flow in the balancing market is NP-hard in the strong sense even in case of one generator. The problem of finding an optimal flow in the day-ahead market is proved to be NP-hard even with one generator and without controlled cuts.

Fault-Aware Resource Allocation for Heterogeneous Data Sources with Multipath Routing

With the rapid development of cloud computing and big data, diverse types of traffic generated from heterogeneous data sources are delivered throughout communication networks, which consist of various node kinds such as digital sensors and smart actuators, and different applications. Due to the shared medium, communication networks are vulnerable to misbehaving nodes, and it is a crucial aspect to maintain an acceptable level of service degradation. This paper studies the fault-aware resource allocation problem by exploiting multipath routing and dynamic rate assignment for heterogeneous sources. We estimate the impacts of faults and formulate the resource allocation as a lossy network flow optimization problem based on these estimates. The traditional flow optimization solutions focus on homogeneous traffic. In our work, we model the performance of heterogeneous applications as a relaxed utility function and develop an effective utility framework of rate control for heterogeneous sources with multipath routing in presence of misbehaving nodes. We design a distributed algorithm to decide the routing strategy and obtain the rate assignments on the available paths in a lossy utility fair manner. Extensive performance evaluations corroborate the significant performance of our algorithm in effective utility and utility fairness in the presence of misbehaving nodes.

A Discrete Adjoint Formulation for Inviscid Flow Nozzle Optimization

A discrete adjoint formulation with an ad hoc flow solver has been recently developed and tested on transonic inviscid flow optimization problems. In the present paper the formulation is extended to compressible as well as incompressible flow solvers. First, the adjoint equations are coupled with an accurate in-house flow solver to test the approach on some inverse design problems involving two- and three-dimensional transonic and subsonic flows. Then, the previous design test cases are re-computed coupling the extended adjoint formulation with commercial and open source flow solvers, without noticing any relevant difference in the optimization convergence histories. Finally, incompressible design test cases are successfully computed by means of commercial solver for incompressible flows. The extended compressible adjoint formulation appears to have a wide application, insofar as it allows to perform accurate and efficient design optimization using different flow conditions, different flow solvers and even a solver for incompressible flows.

A network based dynamic air traffic flow model for en route airspace system traffic flow optimization

This study proposes a mesoscopic dynamic air traffic model based on a dynamic network for en route airspaces by characterizing the dynamics and distribution of traffic speed. Based on this model, we solve a flow optimization problem for enforcing capacity constraints with the minimum operational cost using a dual decomposition method. A case study of an en route airspace in Shanghai demonstrates the accuracy of the proposed model in successfully capturing the flow dynamics, as well as the effectiveness of the proposed optimization framework to reduce en route delays by balancing the dynamic traffic demand and airspace capacity.

A linear programming approach to optimise the management of water in dammed river systems for meeting demands and preventing floods

Abstract Water in sufficient quantity and quality is indispensable for multiple purposes like domestic and industrial use, irrigated agriculture, hydropower generation and ecosystem functioning. In many regions of the world, water availability is limited and even declining. Moreover, water availability is variable in space and time and often does not match with the spatio-temporal demand pattern. To overcome the temporal discrepancy between availability and consumption, reservoirs are constructed. Monitoring and predicting the water available in the reservoirs, the needs of the consumers and the losses throughout the river and water distribution system are necessary requirements to fairly allocate the available water to the different users, prevent floods and ensure sufficient water flow in the river. In this paper, this surface water allocation problem is considered a Network Flow Optimisation Problem (NFOP) solved by spatio-temporal optimisation using linear programming techniques.

Aggregated surrogate simulator for groundwater-surface water management via simulation-optimization modeling: Theory, development and tests

Computational requirements sometimes discourage using mathematical optimization for groundwater management. To dramatically reduce computation time, the presented hybrid response matrix method (RMM), Coefficient Generation and Use method 4 (CGU4), prepares surrogate simulators used during optimization. CGU4 reduces numerical model simulations needed to populate the surrogates (linearized convolution equations, LCEs). LCEs often represent flow and head constraints within groundwater flow optimization problems. CGU4 reduces computations for problems having: varying time period sizes; eras of sequential time periods of equal duration; and system (non)linearity. For a situation having: linear, piece-wise, and nonlinear groundwater flows; 20 periods of varying and sequentially constant durations; and optimization problems employing (non)linear objective functions and linear head and aquifer-surface seepage constraints, CGU4 requires 22–61% fewer simulations to compute optimal objective function values within 0.001–0.003% of the best alternative RMM. For hypothetical (non)linear dynamic stream-aquifer problems, CGU4 required the same or 63–89% less time than previous RMMs.

Distributed AC Optimal Power Flow using ALADIN

This paper investigates the distributed solution of non-convex ac power flow optimization problems arising in electrical grids. Specifically, we consider the application of a recently proposed Augmented Lagrangian Based Alternating Direct Inexact Newton (aladin) scheme to ac optimal power flow problems. Using standard reformulations, we show how aladin can be applied to electrical grids of generic topology. We draw upon an ieee 5-bus system to demonstrate that aladin offers the potential to outperform common admm schemes.

On the solution of differential-algebraic equations through gradient flow embedding

In this paper Gradient Flow methods are used to solve systems of differential-algebraic equations via a novel reformulation strategy, focusing on the solution of index-1 differential-algebraic equation systems. A reformulation is first effected on semi-explicit index-1 differential-algebraic equation systems, which casts them as pure ordinary differential equation systems subject to an embedded pointwise least-squares problem. This is then formulated as a gradient flow optimization problem. Rigorous proofs for this novel scheme are provided for asymptotic and epsilon convergence. The computational results validate the predictions of the effectiveness of the proposed approach, with efficient and accurate solutions obtained for the case studies considered. Beyond the theoretical and practical value for the solution of DAE systems as pure ODE ones, the methodology is expected to have an impact in similar cases where an ODE system is subjected to algebraic constraints, such as the Hamiltonian necessary conditions of optimality in optimal control problems.

Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm

An improved strength Pareto evolutionary algorithm is proposed to solve the multi-objective optimal power flow problem. The fuel cost and emission are considered as two objective functions for the optimal flow problem. In the proposed algorithm, there are three aspects of improvements in the original strength Pareto evolutionary algorithm. First, the external archive population is only composed of the variable size of non-dominated individuals in environmental selection operator. Secondly, the Euclidean distance between the elite individuals and its k-th neighboring individuals is adopted to update the external archive population. Thirdly, the local search strategy is embedded into strength Pareto evolutionary algorithm. The performance of the proposed method has been tested on the IEEE 30-bus and IEEE 57-bus systems. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective optimal power flow problem. Compared with the results obtained by other methods, the superiority of the proposed method is verified.

Dynamic Contact Plan Design in Broadband Satellite Networks With Varying Contact Capacity

Contact plan design (CPD) aims to schedule conflicting forthcoming communication opportunities (i.e., contacts). In CPD, the capacity of established contacts is indeed time-varying. Neglecting such variations in contact capacity inevitably leads to system performance deterioration. In this letter, we develop a new dynamic CPD algorithm while considering the time-varying property of contacts in broadband data relay satellite networks. The novelty lies in that we treat the flow optimization problem in a time-expanded graph as a queue-stability-related stochastic optimization problem, such that the problem can be solved independently slot by slot. Furthermore, we prove that the proposed online algorithm can approach arbitrarily close to the optimal throughput. Simulation results validate the efficiency of our proposed algorithm.

A novel disruption in biogeography-based optimization with application to optimal power flow problem

Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. Due to ease of implementation and very few user-dependent parameters, BBO gained popularity among researchers. The performance of BBO is highly dependent on its two operators, migration and mutation. The performance of BBO can be significantly improved by either modifying these operators or by introducing a new operator into it. This paper proposes a new operator, namely the disruption operator to improve the capability of exploration and exploitation in BBO. The proposed DisruptBBO (DBBO) has been tested on well-known benchmark problems and compared with various versions of BBO and other state-of-the-art metaheuristics. The experimental results and statistical analyses confirm the superior performance of the proposed DBBO in solving various nonlinear complex optimization problems. The proposed algorithm has also been applied to the optimal power flow optimization problem from the electrical engineering background.

An agent-based simulation approach to congestion management for the Colombian electricity market

Power markets are complex systems in which decisions made by independent actors must satisfy technological constraints while providing a reliable service that increases social welfare. While agents freely trade their energy, trades must satisfy physical constraints of the transmission grid. The mechanisms for organizing trade and operating networks are increasingly complex and there is a growing need for computational tools that help understand how market rules work under transmission constraints. We present a hybrid optimization and agent based simulation approach for analyzing changes in transmission pricing in Colombia. The model has a module for transmission based on optimal flow problems and a market module that simulates the market operation. We show how the approach proposed is useful for evaluating the performance of generation–transmission expansion plans.

Traffic flow optimization: A reinforcement learning approach

Traffic congestion causes important problems such as delays, increased fuel consumption and additional pollution. In this paper we propose a new method to optimize traffic flow, based on reinforcement learning. We show that a traffic flow optimization problem can be formulated as a Markov Decision Process. We use Q-learning to learn policies dictating the maximum driving speed that is allowed on a highway, such that traffic congestion is reduced. An important difference between our work and existing approaches is that we take traffic predictions into account. A series of simulation experiments shows that the resulting policies significantly reduce traffic congestion under high traffic demand, and that inclusion of traffic predictions improves the quality of the resulting policies. Additionally, the policies are sufficiently robust to deal with inaccurate speed and density measurements.